Welding Robot Collision-Free Path Optimization
Directory of Open Access Journals (Sweden)
Xuewu Wang
2017-02-01
Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2015-01-01
method based uncertainty and reliability analysis. The reliability of the scanning paths are established using cumulative probability distribution functions for process output criteria such as sample density, thermal homogeneity, etc. A customized genetic algorithm is used along with the simulation model...
Optimal Paths in Gliding Flight
Wolek, Artur
Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.
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...
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.
Optimization of educational paths for higher education
Tarasyev, Alexandr A.; Agarkov, Gavriil; Medvedev, Aleksandr
2017-11-01
In our research, we combine the theory of economic behavior and the methodology of increasing efficiency of the human capital to estimate the optimal educational paths. We provide an optimization model for higher education process to analyze possible educational paths for each rational individual. The preferences of each rational individual are compared to the best economically possible educational path. The main factor of the individual choice, which is formed by the formation of optimal educational path, deals with higher salaries level in the chosen economic sector after graduation. Another factor that influences on the economic profit is the reduction of educational costs or the possibility of the budget support for the student. The main outcome of this research consists in correction of the governmental policy of investment in human capital based on the results of educational paths optimal control.
Computing the optimal path in stochastic dynamical systems
International Nuclear Information System (INIS)
Bauver, Martha; Forgoston, Eric; Billings, Lora
2016-01-01
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.
Thermodynamic metrics and optimal paths.
Sivak, David A; Crooks, Gavin E
2012-05-11
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.
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 such as ...... strategy has been developed for processing the standard sample, one unit cell at a time, using genetic algorithms, with an objective of reducing thermal asymmetries. © 2013 SPIE....
Path optimization method for the sign problem
Directory of Open Access Journals (Sweden)
Ohnishi Akira
2018-01-01
Full Text Available We propose a path optimization method (POM to evade the sign problem in the Monte-Carlo calculations for complex actions. Among many approaches to the sign problem, the Lefschetz-thimble path-integral method and the complex Langevin method are promising and extensively discussed. In these methods, real field variables are complexified and the integration manifold is determined by the flow equations or stochastically sampled. When we have singular points of the action or multiple critical points near the original integral surface, however, we have a risk to encounter the residual and global sign problems or the singular drift term problem. One of the ways to avoid the singular points is to optimize the integration path which is designed not to hit the singular points of the Boltzmann weight. By specifying the one-dimensional integration-path as z = t +if(t(f ϵ R and by optimizing f(t to enhance the average phase factor, we demonstrate that we can avoid the sign problem in a one-variable toy model for which the complex Langevin method is found to fail. In this proceedings, we propose POM and discuss how we can avoid the sign problem in a toy model. We also discuss the possibility to utilize the neural network to optimize the path.
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.
Secure Path Selection under Random Fading
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Furqan Jameel
2017-05-01
Full Text Available Application-oriented Wireless Sensor Networks (WSNs promises to be one of the most useful technologies of this century. However, secure communication between nodes in WSNs is still an unresolved issue. In this context, we propose two protocols (i.e. Optimal Secure Path (OSP and Sub-optimal Secure Path (SSP to minimize the outage probability of secrecy capacity in the presence of multiple eavesdroppers. We consider dissimilar fading at the main and wiretap link and provide detailed evaluation of the impact of Nakagami-m and Rician-K factors on the secrecy performance of WSNs. Extensive simulations are performed to validate our findings. Although the optimal scheme ensures more security, yet the sub-optimal scheme proves to be a more practical approach to secure wireless links.
Energy Optimal Path Planning: Integrating Coastal Ocean Modelling with Optimal Control
Subramani, D. N.; Haley, P. J., Jr.; Lermusiaux, P. F. J.
2016-02-01
A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. To set up the energy optimization, the relative vehicle speed and headings are considered to be stochastic, and new stochastic Dynamically Orthogonal (DO) level-set equations that govern their stochastic time-optimal reachability fronts are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. The accuracy and efficiency of the DO level-set equations for solving the governing stochastic level-set reachability fronts are quantitatively assessed, including comparisons with independent semi-analytical solutions. Energy-optimal missions are studied in wind-driven barotropic quasi-geostrophic double-gyre circulations, and in realistic data-assimilative re-analyses of multiscale coastal ocean flows. The latter re-analyses are obtained from multi-resolution 2-way nested primitive-equation simulations of tidal-to-mesoscale dynamics in the Middle Atlantic Bight and Shelbreak Front region. The effects of tidal currents, strong wind events, coastal jets, and shelfbreak fronts on the energy-optimal paths are illustrated and quantified. Results showcase the opportunities for longer-duration missions that intelligently utilize the ocean environment to save energy, rigorously integrating ocean forecasting with optimal control of autonomous vehicles.
Optimal Path Determination for Flying Vehicle to Search an Object
Heru Tjahjana, R.; Heri Soelistyo U, R.; Ratnasari, L.; Irawanto, B.
2018-01-01
In this paper, a method to determine optimal path for flying vehicle to search an object is proposed. Background of the paper is controlling air vehicle to search an object. Optimal path determination is one of the most popular problem in optimization. This paper describe model of control design for a flying vehicle to search an object, and focus on the optimal path that used to search an object. In this paper, optimal control model is used to control flying vehicle to make the vehicle move in optimal path. If the vehicle move in optimal path, then the path to reach the searched object also optimal. The cost Functional is one of the most important things in optimal control design, in this paper the cost functional make the air vehicle can move as soon as possible to reach the object. The axis reference of flying vehicle uses N-E-D (North-East-Down) coordinate system. The result of this paper are the theorems which say that the cost functional make the control optimal and make the vehicle move in optimal path are proved analytically. The other result of this paper also shows the cost functional which used is convex. The convexity of the cost functional is use for guarantee the existence of optimal control. This paper also expose some simulations to show an optimal path for flying vehicle to search an object. The optimization method which used to find the optimal control and optimal path vehicle in this paper is Pontryagin Minimum Principle.
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.
Extended shortest path selection for package routing of complex networks
Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi
The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.
Determination of Optimal Flow Paths for Safety Injection According to Accident Conditions
Energy Technology Data Exchange (ETDEWEB)
Yoo, Kwae Hwan; Kim, Ju Hyun; Kim, Dong Yeong; Na, Man Gyun [Chosun Univ., Gwangju (Korea, Republic of); Hur, Seop; Kim, Changhwoi [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2014-05-15
In case severe accidents happen, major safety parameters of nuclear reactors are rapidly changed. Therefore, operators are unable to respond appropriately. This situation causes the human error of operators that led to serious accidents at Chernobyl. In this study, we aimed to develop an algorithm that can be used to select the optimal flow path for cold shutdown in serious accidents, and to recover an NPP quickly and efficiently from the severe accidents. In order to select the optimal flow path, we applied a Dijkstra algorithm. The Dijkstra algorithm is used to find the path of minimum total length between two given nodes and needs a weight (or length) matrix. In this study, the weight between nodes was calculated from frictional and minor losses inside pipes. That is, the optimal flow path is found so that the pressure drop between a starting node (water source) and a destination node (position that cooling water is injected) is minimized. In case a severe accident has happened, if we inject cooling water through the optimized flow path, then the nuclear reactor will be safely and effectively returned into the cold shutdown state. In this study, we have analyzed the optimal flow paths for safety injection as a preliminary study for developing an accident recovery system. After analyzing the optimal flow path using the Dijkstra algorithm, and the optimal flow paths were selected by calculating the head loss according to path conditions.
Toward solving the sign problem with path optimization method
Mori, Yuto; Kashiwa, Kouji; Ohnishi, Akira
2017-12-01
We propose a new approach to circumvent the sign problem in which the integration path is optimized to control the sign problem. We give a trial function specifying the integration path in the complex plane and tune it to optimize the cost function which represents the seriousness of the sign problem. We call it the path optimization method. In this method, we do not need to solve the gradient flow required in the Lefschetz-thimble method and then the construction of the integration-path contour arrives at the optimization problem where several efficient methods can be applied. In a simple model with a serious sign problem, the path optimization method is demonstrated to work well; the residual sign problem is resolved and precise results can be obtained even in the region where the global sign problem is serious.
Optimized path planning for soft tissue resection via laser vaporization
Ross, Weston; Cornwell, Neil; Tucker, Matthew; Mann, Brian; Codd, Patrick
2018-02-01
Robotic and robotic-assisted surgeries are becoming more prevalent with the promise of improving surgical outcomes through increased precision, reduced operating times, and minimally invasive procedures. The handheld laser scalpel in neurosurgery has been shown to provide a more gentle approach to tissue manipulation on or near critical structures over classical tooling, though difficulties of control have prevented large scale adoption of the tool. This paper presents a novel approach to generating a cutting path for the volumetric resection of tissue using a computer-guided laser scalpel. A soft tissue ablation simulator is developed and used in conjunction with an optimization routine to select parameters which maximize the total resection of target tissue while minimizing the damage to surrounding tissue. The simulator predicts the ablative properties of tissue from an interrogation cut for tuning and simulates the removal of a tumorous tissue embedded on the surface of healthy tissue using a laser scalpel. We demonstrate the ability to control depth and smoothness of cut using genetic algorithms to optimize the ablation parameters and cutting path. The laser power level, cutting rate and spacing between cuts are optimized over multiple surface cuts to achieve the desired resection volumes.
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.
The concept of 'optimal' path in classical mechanics
International Nuclear Information System (INIS)
Passos, E.J.V. de; Cruz, F.F. de S.
1986-01-01
The significance of the concept of 'optimal' path in the framework of classical mechanics is discussed. The derivation of the local harmonic approximation and self-consistent collective coordinate method equations of the optimal path is based on a careful study of the concepts of local maximal decoupling and global maximal decoupling respectively. This exhibits the nature of the differences between these two theories and allows one to establish the conditions under which they become equivalent. (author)
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
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.
Optimization of the Flight Path of an Unmanned Aerial Vehicle
Directory of Open Access Journals (Sweden)
Vasyl Myklukha
2017-09-01
Full Text Available The article describes the features of optimizing the flight path of an unmanned aerial vehicle. The paper analyzes the composition and designation of main equipment and payload of unmanned aerial vehicle. In particular, attention is drawn to the basic requirements that relate to the unmanned aerial vehicle today.
Optimizing learning path selection through memetic algorithms
Acampora, G.; Gaeta, M.; Loia, V.; Ritrovato, P.; Salerno, S.
2008-01-01
e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering
Time optimized path-choice in the termite hunting ant Megaponera analis.
Frank, Erik T; Hönle, Philipp O; Linsenmair, K Eduard
2018-05-10
Trail network systems among ants have received a lot of scientific attention due to their various applications in problem solving of networks. Recent studies have shown that ants select the fastest available path when facing different velocities on different substrates, rather than the shortest distance. The progress of decision-making by these ants is determined by pheromone-based maintenance of paths, which is a collective decision. However, path optimization through individual decision-making remains mostly unexplored. Here we present the first study of time-optimized path selection via individual decision-making by scout ants. Megaponera analis scouts search for termite foraging sites and lead highly organized raid columns to them. The path of the scout determines the path of the column. Through installation of artificial roads around M. analis nests we were able to influence the pathway choice of the raids. After road installation 59% of all recorded raids took place completely or partly on the road, instead of the direct, i.e. distance-optimized, path through grass from the nest to the termites. The raid velocity on the road was more than double the grass velocity, the detour thus saved 34.77±23.01% of the travel time compared to a hypothetical direct path. The pathway choice of the ants was similar to a mathematical model of least time allowing us to hypothesize the underlying mechanisms regulating the behavior. Our results highlight the importance of individual decision-making in the foraging behavior of ants and show a new procedure of pathway optimization. © 2018. Published by The Company of Biologists Ltd.
Comparison of some evolutionary algorithms for optimization of the path synthesis problem
Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna
2018-01-01
The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.
Natural selection and optimality
International Nuclear Information System (INIS)
Torres, J.L.
1989-01-01
It is assumed that Darwin's principle translates into optimal regimes of operation along metabolical pathways in an ecological system. Fitness is then defined in terms of the distance of a given individual's thermodynamic parameters from their optimal values. The method is illustrated testing maximum power as a criterion of merit satisfied in ATP synthesis. (author). 26 refs, 2 figs
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.
Maximal Sensitive Dependence and the Optimal Path to Epidemic Extinction
2010-01-01
1996; Elgart and Kamenev, 2004). Instead, in this article, we will employ an eikonal approximation to recast the problem in terms of an effective...a control strategy on the extinction rate can be determined by its effect on the optimal path (Dykman et al., 2008). Through the use of the eikonal ...the solution of Eqs. (6a)–(6b) in the eikonal form (Elgart and Kamenev, 2004; Doering et al., 2005; Kubo et al., 1973; Wentzell, 1976; Gang, 1987
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
Optimal selection of TLD chips
International Nuclear Information System (INIS)
Phung, P.; Nicoll, J.J.; Edmonds, P.; Paris, M.; Thompson, C.
1996-01-01
Large sets of TLD chips are often used to measure beam dose characteristics in radiotherapy. A sorting method is presented to allow optimal selection of chips from a chosen set. This method considers the variation
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...
Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping
Directory of Open Access Journals (Sweden)
Fronita Mona
2018-01-01
Full Text Available Traveling Salesman Problem (TSP is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.
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...
A numerical scheme for optimal transition paths of stochastic chemical kinetic systems
International Nuclear Information System (INIS)
Liu Di
2008-01-01
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
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
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.
A Method of Forming the Optimal Set of Disjoint Path in Computer Networks
Directory of Open Access Journals (Sweden)
As'ad Mahmoud As'ad ALNASER
2017-04-01
Full Text Available This work provides a short analysis of algorithms of multipath routing. The modified algorithm of formation of the maximum set of not crossed paths taking into account their metrics is offered. Optimization of paths is carried out due to their reconfiguration with adjacent deadlock path. Reconfigurations are realized within the subgraphs including only peaks of the main and an adjacent deadlock path. It allows to reduce the field of formation of an optimum path and time complexity of its formation.
Global optimal path planning of an autonomous vehicle for overtaking a moving obstacle
Directory of Open Access Journals (Sweden)
B. Mashadi
Full Text Available In this paper, the global optimal path planning of an autonomous vehicle for overtaking a moving obstacle is proposed. In this study, the autonomous vehicle overtakes a moving vehicle by performing a double lane-change maneuver after detecting it in a proper distance ahead. The optimal path of vehicle for performing the lane-change maneuver is generated by a path planning program in which the sum of lateral deviation of the vehicle from a reference path and the rate of steering angle become minimum while the lateral acceleration of vehicle does not exceed a safe limit value. A nonlinear optimal control theory with the lateral vehicle dynamics equations and inequality constraint of lateral acceleration are used to generate the path. The indirect approach for solving the optimal control problem is used by applying the calculus of variation and the Pontryagin's Minimum Principle to obtain first-order necessary conditions for optimality. The optimal path is generated as a global optimal solution and can be used as the benchmark of the path generated by the local motion planning of autonomous vehicles. A full nonlinear vehicle model in CarSim software is used for path following simulation by importing path data from the MATLAB code. The simulation results show that the generated path for the autonomous vehicle satisfies all vehicle dynamics constraints and hence is a suitable overtaking path for the following vehicle.
Algorithms for finding optimal paths in network games with p players
Directory of Open Access Journals (Sweden)
R. Boliac
1997-08-01
Full Text Available We study the problem of finding optimal paths in network games with p players. Some polynomial-time algorithms for finding optimal paths and optimal by Nash strategies of the players in network games with p players are proposed.
Optimal path planning for video-guided smart munitions via multitarget tracking
Borkowski, Jeffrey M.; Vasquez, Juan R.
2006-05-01
An advent in the development of smart munitions entails autonomously modifying target selection during flight in order to maximize the value of the target being destroyed. A unique guidance law can be constructed that exploits both attribute and kinematic data obtained from an onboard video sensor. An optimal path planning algorithm has been developed with the goals of obstacle avoidance and maximizing the value of the target impacted by the munition. Target identification and classification provides a basis for target value which is used in conjunction with multi-target tracks to determine an optimal waypoint for the munition. A dynamically feasible trajectory is computed to provide constraints on the waypoint selection. Results demonstrate the ability of the autonomous system to avoid moving obstacles and revise target selection in flight.
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.
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....... 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...
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....
Mathematical optimization for planning and design of cycle paths
Energy Technology Data Exchange (ETDEWEB)
LiÑan Ruiz, R.J.; Perez Aracil, J.; Cabrera Cañizares, V.
2016-07-01
The daily need for citizens to move for different activities, whatever its nature, has been greatly affected by the changes. The advantages resulting from the inclusion of the bicycle as a mode of transport and the proliferation of its use among citizens are numerous and extend both in the field of urban mobility and sustainable development.Currently, there are a number of programs for the implementation, promotion or increased public participation related to cycling in cities. But ultimately, each and every one of these initiatives have the same goal, to create a mesh of effective, useful and cycling trails that allow the use of bicycles in preferred routes with high guarantees of security, incorporating bicycle model intermodal urban transport.With the gradual implementation of bike lanes, many people have begun to use them to get around the city. But everything again needs a period of adaptation, and the reality is that the road network for these vehicles is full of obstacles to the rider. The current situation has led to the proposal that many kilometers of cycle paths needed to supply the demand of this mode of transport and, if implemented and planned are correct and sufficient.This paper presents a mathematical programming model for optimal design of a network for cyclists is presented. Specifically, the model determines a network of bicycle infrastructure, appropriate to the characteristics of a network of existing roads.As an application of the proposed model, the result of these experiments give a number of useful conclusions for planning and designing networks of cycle paths from a social perspective, applied to the case in the city of Malaga. (Author)
Fast exploration of an optimal path on the multidimensional free energy surface
Chen, Changjun
2017-01-01
In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules. PMID:28542475
Path analysis for selection of feijoa with greater pulp weight
Directory of Open Access Journals (Sweden)
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.
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Mahayni, Malek A.
2011-01-01
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
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.
Using step and path selection functions for estimating resistance to movement: Pumas as a case study
Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce
2015-01-01
GPS telemetry collars and their ability to acquire accurate and consistently frequent locations have increased the use of step selection functions (SSFs) and path selection functions (PathSFs) for studying animal movement and estimating resistance. However, previously published SSFs and PathSFs often do not accommodate multiple scales or multiscale modeling....
On the Selection of Optimal Index Configuration in OO Databases
Choenni, R.S.; Bertino, E.; Blanken, Henk; Chang, S.C.
An operation in object-oriented databases gives rise to the processing of a path. Several database operations may result into the same path. The authors address the problem of optimal index configuration for a single path. As it is shown an optimal index configuration for a path can be achieved by
The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method
Directory of Open Access Journals (Sweden)
Liang Shen
2017-01-01
Full Text Available There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.
Path selection and bandwidth allocation in MPLS networks: a nonlinear programming approach
Burns, J. E.; Ott, Teunis J.; de Kock, Johan M.; Krzesinski, Anthony E.
2001-07-01
Multi-protocol Label Switching extends the IPv4 destination-based routing protocols to provide new and scalable routing capabilities in connectionless networks using relatively simple packet forwarding mechanisms. MPLS networks carry traffic on virtual connections called label switched paths. This paper considers path selection and bandwidth allocation in MPLS networks in order to optimize the network quality of service. The optimization is based upon the minimization of a non-linear objective function which under light load simplifies to OSPF routing with link metrics equal to the link propagation delays. The behavior under heavy load depends on the choice of certain parameters: It can essentially be made to minimize maximal expected utilization, or to maximize minimal expected weighted slacks (both over all links). Under certain circumstances it can be made to minimize the probability that a link has an instantaneous offered load larger than its transmission capacity. We present a model of an MPLS network and an algorithm to find and capacitate optimal LSPs. The algorithm is an improvement of the well-known flow deviation non-linear programming method. The algorithm is applied to compute optimal LSPs for several test networks carrying a single traffic class.
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.
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…
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Abubeker, Jewahir Ali; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail
2011-01-01
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
Artificial pheromone for path selection by a foraging swarm of robots.
Campo, Alexandre; Gutiérrez, Alvaro; Nouyan, Shervin; Pinciroli, Carlo; Longchamp, Valentin; Garnier, Simon; Dorigo, Marco
2010-11-01
Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.
Globally Optimal Path Planning with Anisotropic Running Costs
2013-03-01
Eikonal equation and has numerous applications, for exam- ple, in path planning, computational geometry, computer vision, and image enhancement...Sethian 1999b]. Numerical methods for solving the Eikonal equation include Tsitsiklis’ control-theoretic algorithm [Tsitsiklis 1995], Fast Marching Methods...methods for Eikonal equations on triangular meshes, SIAM J. Numer. Anal. 45(1), 83—107. Rowe, M. P., Sidhu, H. S. & Mercer, G. N. (2009) Military
Tax policy can change the production path: A model of optimal oil extraction in Alaska
International Nuclear Information System (INIS)
Leighty, Wayne; Lin, C.-Y. Cynthia
2012-01-01
We model the economically optimal dynamic oil production decisions for seven production units (fields) on Alaska's North Slope. We use adjustment cost and discount rate to calibrate the model against historical production data, and use the calibrated model to simulate the impact of tax policy on production rate. We construct field-specific cost functions from average cost data and an estimated inverse production function, which incorporates engineering aspects of oil production into our economic modeling. Producers appear to have approximated dynamic optimality. Consistent with prior research, we find that changing the tax rate alone does not change the economically optimal oil production path, except for marginal fields that may cease production. Contrary to prior research, we find that the structure of tax policy can be designed to affect the economically optimal production path, but at a cost in net social benefit. - Highlights: ► We model economically optimal dynamic oil production decisions for 7 Alaska fields. ► Changing tax rate alone does not alter the economically optimal oil production path. ► But change in tax structure can affect the economically optimal oil production path. ► Tax structures that modify the optimal production path reduce net social benefit. ► Field-specific cost functions and inverse production functions are estimated
Rowe, Neil C.; Lewis, David H.
1989-01-01
Path planning is an important issue for space robotics. Finding safe and energy-efficient paths in the presence of obstacles and other constraints can be complex although important. High-level (large-scale) path planning for robotic vehicles was investigated in three-dimensional space with obstacles, accounting for: (1) energy costs proportional to path length; (2) turn costs where paths change trajectory abruptly; and (3) safety costs for the danger associated with traversing a particular path due to visibility or invisibility from a fixed set of observers. Paths optimal with respect to these cost factors are found. Autonomous or semi-autonomous vehicles were considered operating either in a space environment around satellites and space platforms, or aircraft, spacecraft, or smart missiles operating just above lunar and planetary surfaces. One class of applications concerns minimizing detection, as for example determining the best way to make complex modifications to a satellite without being observed by hostile sensors; another example is verifying there are no paths (holes) through a space defense system. Another class of applications concerns maximizing detection, as finding a good trajectory between mountain ranges of a planet while staying reasonably close to the surface, or finding paths for a flight between two locations that maximize the average number of triangulation points available at any time along the path.
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
Optimization over polynomials : Selected topics
Laurent, M.; Jang, Sun Young; Kim, Young Rock; Lee, Dae-Woong; Yie, Ikkwon
2014-01-01
Minimizing 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 (sums of
International Nuclear Information System (INIS)
Zamirian, M.; Kamyad, A.V.; Farahi, M.H.
2009-01-01
In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.
Flexible path optimization for the Cask and Plug Remote Handling System in ITER
Energy Technology Data Exchange (ETDEWEB)
Vale, Alberto, E-mail: avale@ipfn.ist.utl.pt [Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Fonte, Daniel; Valente, Filipe; Ferreira, João [Instituto de Plasmas e Fusão Nuclear, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Ribeiro, Isabel [Laboratório de Robótica e Sistemas em Engenharia e Ciência, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Gonzalez, Carmen [Fusion for Energy Agency (F4E), Torres Diagonal Litoral B3, Josep Pla 2, 08019 Barcelona (Spain)
2013-10-15
Highlights: ► Complementary approach for path optimization named free roaming that takes full advantage of the rhombic like kinematics of the Cask and Plug Remote Handling System (CPRHS). ► Possibility to find trajectories not possible in the past using the line guidance developed in a previous work, in particular when moving the Cask Transfer System (CTS) beneath the pallet or in rescue missions. ► Methodology that maximizes the common parts of different trajectories in the same level of ITER buildings. -- Abstract: The Cask and Plug Remote Handling System (CPRHS) provides the means for the remote transfer of in-vessel components and remote handling equipment between the Hot Cell Building and the Tokamak Building in ITER along pre-defined optimized trajectories. A first approach for CPRHS path optimization was previously proposed using line guidance as the navigation methodology to be adopted. This approach might not lead to feasible paths in new situations not considered during the previous work, as rescue operations. This paper addresses this problem by presenting a complementary approach for path optimization inspired in rigid body dynamics that takes full advantage of the rhombic like kinematics of the CPRHS. It also presents a methodology that maximizes the common parts of different trajectories in the same level of ITER buildings. The results gathered from 500 optimized trajectories are summarized. Conclusions and open issues are presented and discussed.
Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.
Hawary, A. F.; Razak, N. A.
2018-05-01
Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.
A path-following driver/vehicle model with optimized lateral dynamic controller
Directory of Open Access Journals (Sweden)
Behrooz Mashadi
Full Text Available Reduction in traffic congestion and overall number of accidents, especially within the last decade, can be attributed to the enormous progress in active safety. Vehicle path following control with the presence of driver commands can be regarded as one of the important issues in vehicle active safety systems development and more realistic explanation of vehicle path tracking problem. In this paper, an integrated driver/DYC control system is presented that regulates the steering angle and yaw moment, considering driver previewed path. Thus, the driver previewed distance, the heading error and the lateral deviation between the vehicle and desired path are used as inputs. Then, the controller determines and applies a corrective steering angle and a direct yaw moment to make the vehicle follow the desired path. A PID controller with optimized gains is used for the control of integrated driver/DYC system. Genetic Algorithm as an intelligent optimization method is utilized to adapt PID controller gains for various working situations. Proposed integrated driver/DYC controller is examined on lane change manuvers andthe sensitivity of the control system is investigated through the changes in the driver model and vehicle parameters. Simulation results show the pronounced effectiveness of the controller in vehicle path following and stability.
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.
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.
Planning of optimal work path for minimizing exposure dose during radiation work in radwaste storage
International Nuclear Information System (INIS)
Kim, Yoon Hyuk; Park, Won Man; Kim, Kyung Soo; Whang, Joo Ho
2005-01-01
Since the safety of nuclear power plant has been becoming a big social issue, the exposure dose of radiation for workers has been one of the important factors concerning the safety problem. The existing calculation methods of radiation dose used in the planning of radiation work assume that dose rate dose not depend on the location within a work space, thus the variation of exposure dose by different work path is not considered. In this study, a modified numerical method was presented to estimate the exposure dose during radiation work in radwaste storage considering the effects of the distance between a worker and sources. And a new numerical algorithm was suggested to search the optimal work path minimizing the exposure dose in pre-defined work space with given radiation sources. Finally, a virtual work simulation program was developed to visualize the exposure dose of radiation during radiation works in radwaste storage and provide the capability of simulation for work planning. As a numerical example, a test radiation work was simulated under given space and two radiation sources, and the suggested optimal work path was compared with three predefined work paths. The optimal work path obtained in the study could reduce the exposure dose for the given test work. Based on the results, the developed numerical method and simulation program could be useful tools in the planning of radiation work
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.
Optimization of the Critical Diameter and Average Path Length of Social Networks
Directory of Open Access Journals (Sweden)
Haifeng Du
2017-01-01
Full Text Available Optimizing average path length (APL by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL.
Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path
Directory of Open Access Journals (Sweden)
Semi Jeon
2017-02-01
Full Text Available 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.
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.
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…
Optimization of path length stretching in Monte Carlo calculations for non-leakage problems
Energy Technology Data Exchange (ETDEWEB)
Hoogenboom, J.E. [Delft Univ. of Technology (Netherlands)
2005-07-01
Path length stretching (or exponential biasing) is a well known variance reduction technique in Monte Carlo calculations. It can especially be useful in shielding problems where particles have to penetrate a lot of material before being tallied. Several authors sought for optimization of the path length stretching parameter for detection of the leakage of neutrons from a slab. Here the adjoint function behaves as a single exponential function and can well be used to determine the stretching parameter. In this paper optimization is sought for a detector embedded in the system, which changes the adjoint function in the detector drastically. From literature it is known that the combination of path length stretching and angular biasing can result in appreciable variance reduction. However, angular biasing is not generally available in general purpose Monte Carlo codes and therefore we want to restrict ourselves to the application of pure path length stretching and finding optimum parameters for that. Nonetheless, the starting point for our research is the zero-variance scheme. In order to study the solution in detail the simplified monoenergetic two-direction model is adopted, which allows analytical solutions and can still be used in a Monte Carlo simulation. Knowing the zero-variance solution analytically, it is shown how optimum path length stretching parameters can be derived from it. It results in path length shrinking in the detector. Results for the variance in the detector response are shown in comparison with other patterns for the stretching parameter. The effect of anisotropic scattering on the path length stretching parameter is taken into account. (author)
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].
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.
An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer
Directory of Open Access Journals (Sweden)
Enery Lorenzo
2015-05-01
Full Text Available 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.
Effects of Optimizing the Scan-Path on Scanning Keyboards with QWERTY-Layout for English Text.
Sandnes, Frode Eika; Medola, Fausto Orsi
2017-01-01
Scanning keyboards can be essential tools for individuals with reduced motor function. However, most research addresses layout optimization. Learning new layouts is time-consuming. This study explores the familiar QWERTY layout with alternative scanning paths intended for English text. The results show that carefully designed scan-paths can help QWERTY nearly match optimized layouts in performance.
Number crunchers : instrumentation sector treads path of selective automation
International Nuclear Information System (INIS)
Budd, G.
2006-01-01
Automation guided by adequate monitoring and control instrumentation is playing an increasingly important role in the oil and gas sector. This article presented a overview of new instruments in the Western Canada Sedimentary Basin (WCSB), where a niche market for instrumentation and automation has grown in tandem with increased drilling activities. Fluctuating prices in oil and gas have also meant that available production methods must be optimized in order to ensure bottom line profits. However, economies of production scale can exclude extensive monitoring techniques in coalbed methane (CBM) activities. Compressor stations are the site of most monitoring and control instrumentation in CBM activities. Compressor packages at the stations include an engine panel that monitors suction pressure and water temperature. Alarm points on all monitoring instrumentation can shut down operations or assist in slight adjustments to machinery to optimize production. In addition, acoustical flow meters are fitted to headers to identify drops in a station's overall volumetric flow of natural gas. Instrumentation at the stations monitors and controls boilers that heat glycol for the gas dehydration process through the use of a pneumatic control loop that communicates with the motor control centre. The system is capable of ensuring shut-downs in emergencies. The combination of automation and carbon dioxide (CO 2 ) flooding has dramatically improved production in the Weyburn oilfield in southeastern Saskatchewan, where data transfer is now completed using Ethernet communications to a SCADA system to communicate with and from 5 satellite sites to a central main plant. It was estimated that the life expectancy of the Weyburn oilfield has been extended by almost 25 years. It was concluded that when harnessed to other technologies and combined with a user-friendly interface, automation can make a huge difference in the production profile of a field. 2 figs
Feature Selection via Chaotic Antlion Optimization.
Directory of Open Access Journals (Sweden)
Hossam M Zawbaa
Full Text Available Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting while minimizing the number of features used.We propose an optimization approach for the feature selection problem that considers a "chaotic" version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.
Hoomod, Haider K.; Kareem Jebur, Tuka
2018-05-01
Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.
Optimality Theory and Lexical Interpretation and Selection
Hogeweg, L.; Legendre, G.; Putnam, M.T.; de Swart, H.; Zaroukian, E.
2016-01-01
This chapter argues for an optimization approach to the selection and interpretation of words. Several advantages of such an approach to lexical semantics are discussed. First of all, it will be argued that competition, entailing that words and interpretations are always judged in relation to other
Optimal path planning for a mobile robot using cuckoo search algorithm
Mohanty, Prases K.; Parhi, Dayal R.
2016-03-01
The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.
A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization.
Ayari, Asma; Bouamama, Sadok
2017-01-01
Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D 2 PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD pBest and LOD gBest . Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.
Czech Academy of Sciences Publication Activity Database
Cavazos-Cadena, R.; Montes-de-Oca, R.; Sladký, Karel
2015-01-01
Roč. 52, č. 2 (2015), s. 419-440 ISSN 0021-9002 Grant - others:GA AV ČR(CZ) 171396 Institutional support: RVO:67985556 Keywords : Dominated Convergence theorem for the expected average criterion * Discrepancy function * Kolmogorov inequality * Innovations * Strong sample-path optimality Subject RIV: BC - Control Systems Theory Impact factor: 0.665, year: 2015 http://library.utia.cas.cz/separaty/2015/E/sladky-0449029.pdf
An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning
Directory of Open Access Journals (Sweden)
Nizar Hadi Abbas
2016-07-01
Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper path from the start to the destination position with minimum distance and time.
Springback effects during single point incremental forming: Optimization of the tool path
Giraud-Moreau, Laurence; Belchior, Jérémy; Lafon, Pascal; Lotoing, Lionel; Cherouat, Abel; Courtielle, Eric; Guines, Dominique; Maurine, Patrick
2018-05-01
Incremental sheet forming is an emerging process to manufacture sheet metal parts. This process is more flexible than conventional one and well suited for small batch production or prototyping. During the process, the sheet metal blank is clamped by a blank-holder and a small-size smooth-end hemispherical tool moves along a user-specified path to deform the sheet incrementally. Classical three-axis CNC milling machines, dedicated structure or serial robots can be used to perform the forming operation. Whatever the considered machine, large deviations between the theoretical shape and the real shape can be observed after the part unclamping. These deviations are due to both the lack of stiffness of the machine and residual stresses in the part at the end of the forming stage. In this paper, an optimization strategy of the tool path is proposed in order to minimize the elastic springback induced by residual stresses after unclamping. A finite element model of the SPIF process allowing the shape prediction of the formed part with a good accuracy is defined. This model, based on appropriated assumptions, leads to calculation times which remain compatible with an optimization procedure. The proposed optimization method is based on an iterative correction of the tool path. The efficiency of the method is shown by an improvement of the final shape.
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.
Selecting Optimal Subset of Security Controls
Yevseyeva, I.; Basto-Fernandes, V.; Michael, Emmerich, T. M.; Moorsel, van, A.
2015-01-01
Open Access journal Choosing an optimal investment in information security is an issue most companies face these days. Which security controls to buy to protect the IT system of a company in the best way? Selecting a subset of security controls among many available ones can be seen as a resource allocation problem that should take into account conflicting objectives and constraints of the problem. In particular, the security of the system should be improved without hindering productivity, ...
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...
Energy-aware path selection metric for IEEE 802.11s wireless mesh networking
CSIR Research Space (South Africa)
Mhlanga, MM
2009-08-01
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...
Path selection rules for droplet trains in single-lane microfluidic networks
Amon, A.; Schmit, A.; Salkin, L.; Courbin, L.; Panizza, P.
2013-07-01
We investigate the transport of periodic trains of droplets through microfluidic networks having one inlet, one outlet, and nodes consisting of T junctions. Variations of the dilution of the trains, i.e., the distance between drops, reveal the existence of various hydrodynamic regimes characterized by the number of preferential paths taken by the drops. As the dilution increases, this number continuously decreases until only one path remains explored. Building on a continuous approach used to treat droplet traffic through a single asymmetric loop, we determine selection rules for the paths taken by the drops and we predict the variations of the fraction of droplets taking these paths with the parameters at play including the dilution. Our results show that as dilution decreases, the paths are selected according to the ascending order of their hydrodynamic resistance in the absence of droplets. The dynamics of these systems controlled by time-delayed feedback is complex: We observe a succession of periodic regimes separated by a wealth of bifurcations as the dilution is varied. In contrast to droplet traffic in single asymmetric loops, the dynamical behavior in networks of loops is sensitive to initial conditions because of extra degrees of freedom.
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.
Optimal Sensor Selection for Health Monitoring Systems
Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.
2005-01-01
Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.
Optimal Portfolio Selection Under Concave Price Impact
International Nuclear Information System (INIS)
Ma Jin; Song Qingshuo; Xu Jing; Zhang Jianfeng
2013-01-01
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
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.
Quantum dot laser optimization: selectively doped layers
Korenev, Vladimir V.; Konoplev, Sergey S.; Savelyev, Artem V.; Shernyakov, Yurii M.; Maximov, Mikhail V.; Zhukov, Alexey E.
2016-08-01
Edge emitting quantum dot (QD) lasers are discussed. It has been recently proposed to use modulation p-doping of the layers that are adjacent to QD layers in order to control QD's charge state. Experimentally it has been proven useful to enhance ground state lasing and suppress the onset of excited state lasing at high injection. These results have been also confirmed with numerical calculations involving solution of drift-diffusion equations. However, deep understanding of physical reasons for such behavior and laser optimization requires analytical approaches to the problem. In this paper, under a set of assumptions we provide an analytical model that explains major effects of selective p-doping. Capture rates of elections and holes can be calculated by solving Poisson equations for electrons and holes around the charged QD layer. The charge itself is ruled by capture rates and selective doping concentration. We analyzed this self-consistent set of equations and showed that it can be used to optimize QD laser performance and to explain underlying physics.
Quantum dot laser optimization: selectively doped layers
International Nuclear Information System (INIS)
Korenev, Vladimir V; Konoplev, Sergey S; Savelyev, Artem V; Shernyakov, Yurii M; Maximov, Mikhail V; Zhukov, Alexey E
2016-01-01
Edge emitting quantum dot (QD) lasers are discussed. It has been recently proposed to use modulation p-doping of the layers that are adjacent to QD layers in order to control QD's charge state. Experimentally it has been proven useful to enhance ground state lasing and suppress the onset of excited state lasing at high injection. These results have been also confirmed with numerical calculations involving solution of drift-diffusion equations. However, deep understanding of physical reasons for such behavior and laser optimization requires analytical approaches to the problem. In this paper, under a set of assumptions we provide an analytical model that explains major effects of selective p-doping. Capture rates of elections and holes can be calculated by solving Poisson equations for electrons and holes around the charged QD layer. The charge itself is ruled by capture rates and selective doping concentration. We analyzed this self-consistent set of equations and showed that it can be used to optimize QD laser performance and to explain underlying physics. (paper)
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...... by simulations. The SNR measurement based relay selection scheme proposed previously is unsuitable for use with fast moving users in e.g. vehicular scenarios due to a large signaling overhead. The proposed location based scheme is shown to work well with fast moving users due to a lower signaling overhead...... 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...
Optimization of IBF parameters based on adaptive tool-path algorithm
Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi
2018-03-01
As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.
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.
International Nuclear Information System (INIS)
Hasselmann, K.; Hasselmann, S.; Giering, R.; Ocana, V.; Storch, H. von
1997-01-01
A structurally highly simplified, globally integrated coupled climate-economic costs model SIAM (Structural Integrated Assessment Model) is used to compute optimal paths of global CO 2 emissions that minimize the net sum of climate damage and mitigation costs. It studies the sensitivity of the computed optimal emission paths. The climate module is represented by a linearized impulse-response model calibrated against a coupled ocean-atmosphere general circulation climate model and a three-dimensional global carbon-cycle model. The cost terms are presented by expressions designed with respect to input assumptions. These include the discount rates for mitigation and damage costs, the inertia of the socio-economic system, and the dependence of climate damages on the changes in temperature and the rate of change of temperature. Different assumptions regarding these parameters are believed to cause the marked divergences of existing cost-benefit analyses. The long memory of the climate system implies that very long time horizons of several hundred years need to be considered to optimize CO 2 emissions on time scales relevant for a policy of sustainable development. Cost-benefit analyses over shorter time scales of a century or two can lead to dangerous underestimates of the long term climate impact of increasing greenhouse-gas emissions. To avert a major long term global warming, CO 2 emissions need to be reduced ultimately to very low levels. This may be done slowly but should not be interpreted as providing a time cushion for inaction: the transition becomes more costly the longer the necessary mitigation policies are delayed. However, the long time horizon provides adequate flexibility for later adjustments. Short term energy conservation alone is insufficient and can be viewed only as a useful measure in support of the necessary long term transition to carbon-free energy technologies. 46 refs., 9 figs., 2 tabs
Whitfield, Clifford A.
2009-12-01
A multi-objective technique for Unmanned Air Vehicle (UAV) path and trajectory autonomy generation, through task allocation and sensor fusion has been developed. The Dual-Optimal Path-Planning (D-O.P-P.) Technique generates on-line adaptive flight paths for UAVs based on available flight windows and environmental influenced objectives. The environmental influenced optimal condition, known as the driver' determines the condition, within a downstream virtual window of possible vehicle destinations and orientation built from the UAV kinematics. The intermittent results are pursued by a dynamic optimization technique to determine the flight path. This sequential optimization technique is a multi-objective optimization procedure consisting of two goals, without requiring additional information to combine the conflicting objectives into a single-objective. An example case-study and additional applications are developed and the results are discussed; including the application to the field of Solar Regenerative (SR) High Altitude Long Endurance (HALE) UAV flight. Harnessing solar energy has recently been adapted for use on high altitude UAV platforms. An aircraft that uses solar panels and powered by the sun during the day and through the night by SR systems, in principle could sustain flight for weeks or months. The requirements and limitations of solar powered flight were determined. The SR-HALE UAV platform geometry and flight characteristics were selected from an existing aircraft that has demonstrated the capability for sustained flight through flight tests. The goals were to maintain continual Situational Awareness (SA) over a case-study selected Area of Interest (AOI) and existing UAV power and surveillance systems. This was done for still wind and constant wind conditions at altitude along with variations in latitude. The characteristics of solar flux and the dependence on the surface location and orientation were established along with fixed flight maneuvers for
Optimal path planning for single and multiple aircraft using a reduced order formulation
Twigg, Shannon S.
High-flying unmanned reconnaissance and surveillance systems are now being used extensively in the United States military. Current development programs are producing demonstrations of next-generation unmanned flight systems that are designed to perform combat missions. Their use in first-strike combat operations will dictate operations in densely cluttered environments that include unknown obstacles and threats, and will require the use of terrain for masking. The demand for autonomy of operations in such environments dictates the need for advanced trajectory optimization capabilities. In addition, the ability to coordinate the movements of more than one aircraft in the same area is an emerging challenge. This thesis examines using an analytical reduced order formulation for trajectory generation for minimum time and terrain masking cases. First, pseudo-3D constant velocity equations of motion are used for path planning for a single vehicle. In addition, the inclusion of winds, moving targets and moving threats is considered. Then, this formulation is increased to using 3D equations of motion, both with a constant velocity and with a simplified varying velocity model. Next, the constant velocity equations of motion are expanded to include the simultaneous path planning of an unspecified number of vehicles, for both aircraft avoidance situations and formation flight cases.
Directory of Open Access Journals (Sweden)
Ehsan Zakeri
Full Text Available Abstract In this research, generation of a short and smooth path in three-dimensional space with obstacles for guiding an Unmanned Underwater Vehicle (UUV without collision is investigated. This is done by utilizing spline technique, in which the spline control points positions are determined by Imperialist Competitive Algorithm (ICA in three-dimensional space such that the shortest possible path from the starting point to the target point without colliding with obstacles is achieved. Furthermore, for guiding the UUV in the generated path, an Interval Type-2 Fuzzy Logic Controller (IT2FLC, the coefficients of which are optimized by considering an objective function that includes quadratic terms of the input forces and state error of the system, is used. Selecting such objective function reduces the control error and also the force applied to the UUV, which consequently leads to reduction of energy consumption. Therefore, by using a special method, desired signals of UUV state are obtained from generated three-dimensional optimal path such that tracking these signals by the controller leads to the tracking of this path by UUV. In this paper, the dynamical model of the UUV, entitled as "mUUV-WJ-1" , is derived and its hydrodynamic coefficients are calculated by CFD in order to be used in the simulations. For simulation by the method presented in this study, three environments with different obstacles are intended in order to check the performance of the IT2FLC controller in generating optimal paths for guiding the UUV. In this article, in addition to ICA, Particle Swarm Optimization (PSO and Artificial Bee Colony (ABC are also used for generation of the paths and the results are compared with each other. The results show the appropriate performance of ICA rather than ABC and PSO. Moreover, to evaluate the performance of the IT2FLC, optimal Type-1 Fuzzy Logic Controller (T1FLC and Proportional Integrator Differentiator (PID controller are designed
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
Limitations in artificial spin ice path selectivity: the challenges beyond topological control
International Nuclear Information System (INIS)
Walton, S K; Zeissler, K; Burn, D M; Cohen, L F; Branford, W R; Ladak, S; Read, D E; Tyliszczak, T
2015-01-01
Magnetic charge is carried through nanowire networks by domain walls, and the micromagnetic structure of a domain wall provides an opportunity to manipulate its movement. We have shown previously that magnetic monopole defects exist in artificial spin ice (ASI) and result from two bar switching at a vertex. To create and manipulate monopole defects and indeed magnetic charge in general, path selectivity of the domain wall at a vertex is required. We have recently shown that in connected ASI structures, transverse wall chirality (or topology) determines wall path direction, but a mechanism known as Walker breakdown, where a wall mutates into a wall of opposite chirality partially destroys selectivity. Recently it has been claimed that in isolated Y-shaped junctions that support vortex walls, selectivity is entirely determined by chirality (or topology), the suggestion being that vortex wall chirality is robust in the Walker breakdown process. Here we demonstrate that in Y-shaped junctions, magnetic switching in the important topologically protected regime exists only for a narrow window of field and bar geometry, and that it will be challenging to access this regime in field-driven ASI. This work has implications for the wider field of magnetic charge manipulation for high density memory storage. (paper)
Optimization of fuel cells for BWR using Path Re linking and flexible strategies of solution
International Nuclear Information System (INIS)
Castillo M, J. A.; Ortiz S, J. J.; Torres V, M.; Perusquia del Cueto, R.
2009-10-01
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)
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
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.
optimal selection of hydraulic turbines for small hydro electric power
African Journals Online (AJOL)
eobe
Keywords: optimal selection, SHP turbine, flow duration curve, energy efficiency, annual capacity factor. 1. INTRODUCTION ... depleted, with adverse environmental impacts downstream ..... Technologies, Financing Cogeneration and Small -.
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.
Directory of Open Access Journals (Sweden)
Moharam Habibnejad Korayem
2012-10-01
Full Text Available In this work, a computational algorithm is developed for the smooth-jerk optimal path planning of tricycle wheeled mobile manipulators in an obstructed environment. Due to a centred orientable wheel, the tricycle mobile manipulator exhibits more steerability and manoeuvrability over traditional mobile manipulators, especially in the presence of environmental obstacles. This paper presents a general formulation based on the combination of the potential field method and optimal control theory in order to plan the smooth point-to-point path of the tricycle mobile manipulators. The nonholonomic constraints of the tricycle mobile base are taken into account in the dynamic formulation of the system and then the optimality conditions are derived considering jerk restrictions and obstacle avoidance. Furthermore, by means of the potential field method, a new formulation of a repulsive potential function is proposed for collision avoidance between any obstacle and each part of the mobile manipulator. In addition, to ensure the accurate placement of the end effector on the target point an attractive potential function is applied to the optimal control formulation. Next, a mixed analytical-numerical algorithm is proposed to generate the point-to-point optimal path. Finally, the proposed method is verified by a number of simulations on a two-link tricycle manipulator.
Palmisano, Fabrizio; Elia, Angelo
2017-10-01
One of the main difficulties, when dealing with landslide structural vulnerability, is the diagnosis of the causes of crack patterns. This is also due to the excessive complexity of models based on classical structural mechanics that makes them inappropriate especially when there is the necessity to perform a rapid vulnerability assessment at the territorial scale. This is why, a new approach, based on a ‘simple model’ (i.e. the Load Path Method, LPM), has been proposed by Palmisano and Elia for the interpretation of the behaviour of masonry buildings subjected to landslide-induced settlements. However, the LPM is very useful for rapidly finding the 'most plausible solution' instead of the exact solution. To find the solution, optimization algorithms are necessary. In this scenario, this article aims to show how the Bidirectional Evolutionary Structural Optimization method by Huang and Xie, can be very useful to optimize the strut-and-tie models obtained by using the Load Path Method.
Randomized path optimization for thevMitigated counter detection of UAVS
2017-06-01
to tracking, such as sea state, altitude, and the position of the sun, could all be used 1 in designing a program that allows for the recovery of...qgoal , an infinite set of possible paths exists in between them. In order to scale down the problem from an infinite number of paths, a polynomial path...flight to a vehicle that used 70%. With 100 potential waypoints and five different time profiles, the set of infinite paths was narrowed down to a set
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...... with path-relinking for solving the described problem. Experimental results show that this simple heuristic improves the existing results in the literature with an average improvement of 5.72% using small computing times. The proposed heuristic can contribute to the development of effective and efficient...
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.
High Altitude Balloon Flight Path Prediction and Site Selection Based On Computer Simulations
Linford, Joel
2010-10-01
Interested in the upper atmosphere, Weber State University Physics department has developed a High Altitude Reconnaissance Balloon for Outreach and Research team, also known as HARBOR. HARBOR enables Weber State University to take a variety of measurements from ground level to altitudes as high as 100,000 feet. The flight paths of these balloons can extend as long as 100 miles from the launch zone, making the choice of where and when to fly critical. To ensure the ability to recover the packages in a reasonable amount of time, days and times are carefully selected using computer simulations limiting flight tracks to approximately 40 miles from the launch zone. The computer simulations take atmospheric data collected by National Oceanic and Atmospheric Administration (NOAA) to plot what flights might have looked like in the past, and to predict future flights. Using these simulations a launch zone has been selected in Duchesne Utah, which has hosted eight successful flights over the course of the last three years, all of which have been recovered. Several secondary launch zones in western Wyoming, Southern Idaho, and Northern Utah are also being considered.
Czech Academy of Sciences Publication Activity Database
Cavazos-Cadena, R.; Montes-de-Oca, R.; Sladký, Karel
2014-01-01
Roč. 163, č. 2 (2014), s. 674-684 ISSN 0022-3239 Grant - others:PSF Organization(US) 012/300/02; CONACYT (México) and ASCR (Czech Republic)(MX) 171396 Institutional support: RVO:67985556 Keywords : Strong sample-path optimality * Lyapunov function condition * Stationary policy * Expected average reward criterion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.509, year: 2014 http://library.utia.cas.cz/separaty/2014/E/sladky-0432661.pdf
Time-optimal path planning in uncertain flow fields using ensemble method
Wang, Tong; Le Maitre, Olivier; Hoteit, Ibrahim; Knio, Omar
2016-01-01
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
Optimal materials selection for bimaterial piezoelectric microactuators
Srinivasan, P.; Spearing, S.M.
2008-01-01
Piezoelectric actuation is one of the commonly employed actuation schemes in microsystems. This paper focuses on identifying and ranking promising active material/substrate combinations for bimaterial piezoelectric (BPE) microactuators based on their performance. The mechanics of BPE structures following simple beam theory assumptions available in the literature are applied to evolve critical performance metrics which govern the materials selection process. Contours of equal performance are p...
Directory of Open Access Journals (Sweden)
Menglin Xing
2018-04-01
Full Text Available China’s resource-based cities have currently entered a period of comprehensive transformation. The differences in the economic and technical environment and significant policy orientation make it unique to some extent. This study applied value chain theory to analyze the industrial value chain of China’s resource-based cities, and three important types of optimization paths that have been applied differently by different cities were proposed. Grey relational analysis was used to compare the comprehensive value creation capacity of the three paths and its relationship with the comparative advantage of local industry. We found that a circular economy system has significant capacity to optimize economic and social value and favorable prospects for environmental value. However, this may have obvious instability in early periods of transition. This disadvantage can be remedied by cultivating related industries that have significantly comprehensive advantages in the early period. In the long term, the other two paths need to be combined with cultivating emerging industries. Finally, we found that the value creation capacity of China’s resource-based cities has roots in optimization of the industrial value chain rather than enhancement of the industrial comparative advantage. The reason for this is that value creation capacity has not yet been transformed into a source of industrial comparative advantage.
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.
Waste system optimization - can diameter selection
International Nuclear Information System (INIS)
Ashline, R.C.
1983-08-01
The purpose of the waste system optimization study is to define in terms of cost incentives the preferred waste package for HLW which has been converted to glass at a commercial reprocessing plant. The Waste Management Economic Model (WMEM) was employed to analyze the effect of varying important design parameters on the overall net present cost of waste handling. The parameters found to have the greatest effect on the calculated overall net present cost were can diameter, repository type (salt, basalt/bentonite, or welded tuff), allowable areal heat loading, and the repository availability date. The overall net present of a waste handling option is calculated over a 20-year operating period. It includes the total capital and operating costs associated with high-level and intermediate-level liquid waste storage, liquid waste solidification, hulls storage and compaction, and general process trash handling. It also includes the cask leasing and transportation costs associated with each waste type and the waste repository disposal costs. The waste repository disposal costs used in WMEM for this analysis were obtained from Battelle Pacific Northwest Laboratories and thir RECON model. 2 figures, 2 tables
Optimal set of selected uranium enrichments that minimizes blending consequences
International Nuclear Information System (INIS)
Nachlas, J.A.; Kurstedt, H.A. Jr.; Lobber, J.S. Jr.
1977-01-01
Identities, quantities, and costs associated with producing a set of selected enrichments and blending them to provide fuel for existing reactors are investigated using an optimization model constructed with appropriate constraints. Selected enrichments are required for either nuclear reactor fuel standardization or potential uranium enrichment alternatives such as the gas centrifuge. Using a mixed-integer linear program, the model minimizes present worth costs for a 39-product-enrichment reference case. For four ingredients, the marginal blending cost is only 0.18% of the total direct production cost. Natural uranium is not an optimal blending ingredient. Optimal values reappear in most sets of ingredient enrichments
An intutionistic fuzzy optimization approach to vendor selection problem
Directory of Open Access Journals (Sweden)
Prabjot Kaur
2016-09-01
Full Text Available Selecting the right vendor is an important business decision made by any organization. The decision involves multiple criteria and if the objectives vary in preference and scope, then nature of decision becomes multiobjective. In this paper, a vendor selection problem has been formulated as an intutionistic fuzzy multiobjective optimization where appropriate number of vendors is to be selected and order allocated to them. The multiobjective problem includes three objectives: minimizing the net price, maximizing the quality, and maximizing the on time deliveries subject to supplier's constraints. The objection function and the demand are treated as intutionistic fuzzy sets. An intutionistic fuzzy set has its ability to handle uncertainty with additional degrees of freedom. The Intutionistic fuzzy optimization (IFO problem is converted into a crisp linear form and solved using optimization software Tora. The advantage of IFO is that they give better results than fuzzy/crisp optimization. The proposed approach is explained by a numerical example.
Combinatorial Optimization in Project Selection Using Genetic Algorithm
Dewi, Sari; Sawaluddin
2018-01-01
This paper discusses the problem of project selection in the presence of two objective functions that maximize profit and minimize cost and the existence of some limitations is limited resources availability and time available so that there is need allocation of resources in each project. These resources are human resources, machine resources, raw material resources. This is treated as a consideration to not exceed the budget that has been determined. So that can be formulated mathematics for objective function (multi-objective) with boundaries that fulfilled. To assist the project selection process, a multi-objective combinatorial optimization approach is used to obtain an optimal solution for the selection of the right project. It then described a multi-objective method of genetic algorithm as one method of multi-objective combinatorial optimization approach to simplify the project selection process in a large scope.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
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 met...
Optimal infrastructure selection to boost regional sustainable economy
Martín Utrillas, Manuel Guzmán; Juan-Garcia, F.; Cantó Perelló, Julián; Curiel Esparza, Jorge
2015-01-01
The role of infrastructures in boosting the economic growth of the regions is widely recognized. In many cases, an infrastructure is selected by subjective reasons. Selection of the optimal infrastructure for sustainable economic development of a region should be based on objective and founded reasons, not only economical, but also environmental and social. In this paper is developed such selection through a hybrid method based on Delphi, analytical hierarchy process (AHP), and VIKOR (from Se...
Bakar, Sumarni Abu; Ibrahim, Milbah
2017-08-01
The shortest path problem is a popular problem in graph theory. It is about finding a path with minimum length between a specified pair of vertices. In any network the weight of each edge is usually represented in a form of crisp real number and subsequently the weight is used in the calculation of shortest path problem using deterministic algorithms. However, due to failure, uncertainty is always encountered in practice whereby the weight of edge of the network is uncertain and imprecise. In this paper, a modified algorithm which utilized heuristic shortest path method and fuzzy approach is proposed for solving a network with imprecise arc length. Here, interval number and triangular fuzzy number in representing arc length of the network are considered. The modified algorithm is then applied to a specific example of the Travelling Salesman Problem (TSP). Total shortest distance obtained from this algorithm is then compared with the total distance obtained from traditional nearest neighbour heuristic algorithm. The result shows that the modified algorithm can provide not only on the sequence of visited cities which shown to be similar with traditional approach but it also provides a good measurement of total shortest distance which is lesser as compared to the total shortest distance calculated using traditional approach. Hence, this research could contribute to the enrichment of methods used in solving TSP.
Cambié, D.; Zhao, F.; Hessel, V.; Debije, M.G.; Noël, T.
2017-01-01
Luminescent solar concentrator-based photomicroreactors (LSC-PMs) have been recently proposed for sustainable and energy-efficient photochemical reactions. Herein, a Monte Carlo ray tracing algorithm to simulate photon paths within LSC-PMs was developed and experimentally validated. The simulation
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.
Capacity bounds for kth best path selection over generalized fading channels
Hanif, Muhammad Fainan; Yang, Hongchuan; Alouini, Mohamed-Slim
2014-01-01
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
Selective influence of prior allocentric knowledge on the kinesthetic learning of a path.
Lafon, Matthieu; Vidal, Manuel; Berthoz, Alain
2009-04-01
Spatial cognition studies have described two main cognitive strategies involved in the memorization of traveled paths in human navigation. One of these strategies uses the action-based memory (egocentric) of the traveled route or paths, which involves kinesthetic memory, optic flow, and episodic memory, whereas the other strategy privileges a survey memory of cartographic type (allocentric). Most studies have dealt with these two strategies separately, but none has tried to show the interaction between them in spite of the fact that we commonly use a map to imagine our journey and then proceed using egocentric navigation. An interesting question is therefore: how does prior allocentric knowledge of the environment affect the egocentric, purely kinesthetic navigation processes involved in human navigation? We designed an experiment in which blindfolded subjects had first to walk and memorize a path with kinesthetic cues only. They had previously been shown a map of the path, which was either correct or distorted (consistent shrinking or growing). The latter transformations were studied in order to observe what influence a distorted prior knowledge could have on spatial mechanisms. After having completed the first learning travel along the path, they had to perform several spatial tasks during the testing phase: (1) pointing towards the origin and (2) to specific points encountered along the path, (3) a free locomotor reproduction, and (4) a drawing of the memorized path. The results showed that prior cartographic knowledge influences the paths drawn and the spatial inference capacity, whereas neither locomotor reproduction nor spatial updating was disturbed. Our results strongly support the notion that (1) there are two independent neural bases underlying these mechanisms: a map-like representation allowing allocentric spatial inferences, and a kinesthetic memory of self-motion in space; and (2) a common use of, or a switching between, these two strategies is
Optimal Bandwidth Selection for Kernel Density Functionals Estimation
Directory of Open Access Journals (Sweden)
Su Chen
2015-01-01
Full Text Available The choice of bandwidth is crucial to the kernel density estimation (KDE and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(xf2(xdx with appropriate choice of γ(x and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(xf2(xdx can be estimated nonparametrically via kernel density functionals estimation (KDFE. However, the optimal bandwidth selection for KDFE of ∫γ(xf2(xdx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(xf2(xdx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb” and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.
The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Razali Ngah
2012-06-01
Full Text Available The standard Optimized Link State Routing (OLSR introduces an interesting concept, the multipoint relays (MPRs, to mitigate message overhead during the flooding process. We propose a new algorithm for MPRs selection to enhance the performance of OLSR using Particle Swarm Optimization Sigmoid Increasing Inertia Weight (PSOSIIW. The sigmoid increasing inertia weight has significance improve the particle swarm optimization (PSO in terms of simplicity and quick convergence towards optimum solution. The new fitness function of PSO-SIIW, packet delay of each node and degree of willingness are introduced to support MPRs selection in OLSR. We examine the throughput, packet loss and end-to-end delay of the proposed method using network simulator 2 (ns2. Overall results indicate that OLSR-PSOSIIW has shown good performance compared to the standard OLSR and OLSR-PSO, particularly for the throughput and end-to-end delay. Generally the proposed OLSR-PSOSIIW shows advantage of using PSO for optimizing routing paths in the MPRs selection algorithm.
Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir M.; Guo, Yanhui; Patel, Smita; Kazerooni, Ella A.
2012-03-01
Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9+/-10.2% using the MHES method to 9.9+/-7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (pvolume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the reference standard was also evaluated by the Bland-Altman plot. This preliminary
Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States
2016-08-12
tailoring approach paths to minimize a weighted objective firnctions based on airwake disturbances, tacking performance, and power consumption . Fuhre...2.4.3.2 Outer Loop DI controller The translational motion of helicopter in horizontal plane can be approximated by the following linear state...helicopter maneuverability Trajectories are as short as possible to minimize approach time Trajectories are friendly in saving power consumption and
Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.
Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao
2015-04-01
Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these 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 discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Optimal Parameter Selection of Power System Stabilizer using Genetic Algorithm
Energy Technology Data Exchange (ETDEWEB)
Chung, Hyeng Hwan; Chung, Dong Il; Chung, Mun Kyu [Dong-AUniversity (Korea); Wang, Yong Peel [Canterbury Univeristy (New Zealand)
1999-06-01
In this paper, it is suggested that the selection method of optimal parameter of power system stabilizer (PSS) with robustness in low frequency oscillation for power system using real variable elitism genetic algorithm (RVEGA). The optimal parameters were selected in the case of power system stabilizer with one lead compensator, and two lead compensator. Also, the frequency responses characteristics of PSS, the system eigenvalues criterion and the dynamic characteristics were considered in the normal load and the heavy load, which proved usefulness of RVEGA compare with Yu's compensator design theory. (author). 20 refs., 15 figs., 8 tabs.
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…
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.
Training set optimization under population structure in genomic selection.
Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E
2015-01-01
Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.
Backes, Michael; Meiser, Sebastian; Slowik, Marcin
2015-01-01
In this paper, we present a rigorous methodology for quantifying the anonymity provided by Tor against a variety of structural attacks, i.e., adversaries that compromise Tor nodes and thereby perform eavesdropping attacks to deanonymize Tor users. First, we provide an algorithmic approach for computing the anonymity impact of such structural attacks against Tor. The algorithm is parametric in the considered path selection algorithm and is, hence, capable of reasoning about variants of Tor and...
A compensatory approach to optimal selection with mastery scores
van der Linden, Willem J.; Vos, Hendrik J.
1994-01-01
This paper presents some Bayesian theories of simultaneous optimization of decision rules for test-based decisions. Simultaneous decision making arises when an institution has to make a series of selection, placement, or mastery decisions with respect to subjects from a population. An obvious
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
Selection and optimization of extracellular lipase production using ...
African Journals Online (AJOL)
The aim of this study was to isolate and select lipase-producing microorganisms originated from different substrates, as well as to optimize the production of microbial lipase by submerged fermentation under different nutrient conditions. Of the 40 microorganisms isolated, 39 showed a halo around the colonies and 4 were ...
An opinion formation based binary optimization approach for feature selection
Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo
2018-02-01
This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.
2016-09-01
PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION by Jason A. Schwartz...PUBLIC SECTOR RESEARCH & DEVELOPMENT PORTFOLIO SELECTION PROCESS: A CASE STUDY OF QUANTITATIVE SELECTION AND OPTIMIZATION 5. FUNDING NUMBERS 6...describing how public sector organizations can implement a research and development (R&D) portfolio optimization strategy to maximize the cost
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)
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.
Optimal multigrid algorithms for the massive Gaussian model and path integrals
International Nuclear Information System (INIS)
Brandt, A.; Galun, M.
1996-01-01
Multigrid algorithms are presented which, in addition to eliminating the critical slowing down, can also eliminate the open-quotes volume factorclose quotes. The elimination of the volume factor removes the need to produce many independent fine-grid configurations for averaging out their statistical deviations, by averaging over the many samples produced on coarse grids during the multigrid cycle. Thermodynamic limits of observables can be calculated to relative accuracy var-epsilon r in just O(var-epsilon r -2 ) computer operations, where var-epsilon r is the error relative to the standard deviation of the observable. In this paper, we describe in detail the calculation of the susceptibility in the one-dimensional massive Gaussian model, which is also a simple example of path integrals. Numerical experiments show that the susceptibility can be calculated to relative accuracy var-epsilon r in about 8 var-epsilon r -2 random number generations, independent of the mass size
Optimal control of bond selectivity in unimolecular reactions
International Nuclear Information System (INIS)
Shi Shenghua; Rabitz, H.
1991-01-01
The optimal control theory approach to designing optimal fields for bond-selective unimolecular reactions is presented. A set of equations for determining the optimal fields, which will lead to the achievement of the objective of bond-selective dissociation is developed. The numerical procedure given for solving these equations requires the repeated calculation of the time propagator for the system with the time-dependent Hamiltonian. The splitting approximation combined with the fast Fourier transform algorithm is used for computing the short time propagator. As an illustrative example, a model linear triatomic molecule is treated. The model system consists of two Morse oscillators coupled via kinetic coupling. The magnitude of the dipoles of the two Morse oscillators are the same, the fundamental frequencies are almost the same, but the dissociation energies are different. The rather demanding objective under these conditions is to break the stronger bond while leaving the weaker one intact. It is encouraging that the present computational method efficiently gives rise to the optimal field, which leads to the excellent achievement of the objective of bond selective dissociation. (orig.)
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'…
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.
Dechow, C D; Rogers, G W
2018-05-01
Expectation of genetic merit in commercial dairy herds is routinely estimated using a 4-path genetic selection model that was derived for a closed population, but commercial herds using artificial insemination sires are not closed. The 4-path model also predicts a higher rate of genetic progress in elite herds that provide artificial insemination sires than in commercial herds that use such sires, which counters other theoretical assumptions and observations of realized genetic responses. The aim of this work is to clarify whether genetic merit in commercial herds is more accurately reflected under the assumptions of the 4-path genetic response formula or by a genetic lag formula. We demonstrate by tracing the transmission of genetic merit from parents to offspring that the rate of genetic progress in commercial dairy farms is expected to be the same as that in the genetic nucleus. The lag in genetic merit between the nucleus and commercial farms is a function of sire and dam generation interval, the rate of genetic progress in elite artificial insemination herds, and genetic merit of sires and dams. To predict how strategies such as the use of young versus daughter-proven sires, culling heifers following genomic testing, or selective use of sexed semen will alter genetic merit in commercial herds, genetic merit expectations for commercial herds should be modeled using genetic lag expectations. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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.
Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States
2017-04-15
Performing Organization: The Pennsylvania State University Department of Aerospace Engineering 231C Hammond Building University Park , PA 16802 Attn...Research Assistant Penn State University Co-PI: Chengjian He (408) 523-5100 he@flightlab.com Dooyong Lee Advanced Rotorcraft Technologies...Linear Systems Optimal and Robust Control,” CRC press , 2007 6. Transitions/Impact Submitted AHS Forum Paper and presented paper at AHS UAV
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......: A model for analyses of the electricity and CHP markets in the Baltic Sea Region. 〈www.Balmorel.com〉; 2001. [1
Critical path method as the criterion for optimization of business planning process
Butsenko Elena V.
2016-01-01
In today's economy the task of improving business planning is considered a necessary component of any enterprise management process and is precisely the solution drawn from that task which determines the financial policy and economic structure. The development of technologies based on the optimization of business planning is a very urgent scientific challenge. In this paper we propose to use the methods of network planning and management as a tool for economic and mathematical modeling to...
A metaheuristic optimization framework for informative gene selection
Directory of Open Access Journals (Sweden)
Kaberi Das
Full Text Available This paper presents a metaheuristic framework using Harmony Search (HS with Genetic Algorithm (GA for gene selection. The internal architecture of the proposed model broadly works in two phases, in the first phase, the model allows the hybridization of HS with GA to compute and evaluate the fitness of the randomly selected solutions of binary strings and then HS ranks the solutions in descending order of their fitness. In the second phase, the offsprings are generated using crossover and mutation operations of GA and finally, those offsprings were selected for the next generation whose fitness value is more than their parents evaluated by SVM classifier. The accuracy of the final gene subsets obtained from this model has been evaluated using SVM classifiers. The merit of this approach is analyzed by experimental results on five benchmark datasets and the results showed an impressive accuracy over existing feature selection approaches. The occurrence of gene subsets selected from this model have also been computed and the most often selected gene subsets with the probability of [0.1â0.9] have been chosen as optimal sets of informative genes. Finally, the performance of those selected informative gene subsets have been measured and established through probabilistic measures. Keywords: Gene Selection, Metaheuristic, Harmony Search Algorithm, Genetic Algorithm, SVM
Schumann, Marcel; Armen, Roger S
2013-05-30
Molecular docking of small-molecules is an important procedure for computer-aided drug design. Modeling receptor side chain flexibility is often important or even crucial, as it allows the receptor to adopt new conformations as induced by ligand binding. However, the accurate and efficient incorporation of receptor side chain flexibility has proven to be a challenge due to the huge computational complexity required to adequately address this problem. Here we describe a new docking approach with a very fast, graph-based optimization algorithm for assignment of the near-optimal set of residue rotamers. We extensively validate our approach using the 40 DUD target benchmarks commonly used to assess virtual screening performance and demonstrate a large improvement using the developed side chain optimization over rigid receptor docking (average ROC AUC of 0.693 vs. 0.623). Compared to numerous benchmarks, the overall performance is better than nearly all other commonly used procedures. Furthermore, we provide a detailed analysis of the level of receptor flexibility observed in docking results for different classes of residues and elucidate potential avenues for further improvement. Copyright © 2013 Wiley Periodicals, Inc.
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.
Optimal foraging in marine ecosystem models: selectivity, profitability and switching
DEFF Research Database (Denmark)
Visser, Andre W.; Fiksen, Ø.
2013-01-01
ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...
Pareto-Optimal Model Selection via SPRINT-Race.
Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2018-02-01
In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.
Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology
Directory of Open Access Journals (Sweden)
Rupert Faltermeier
2015-01-01
Full Text Available Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP and intracranial pressure (ICP. Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP, with the outcome of the patients represented by the Glasgow Outcome Scale (GOS. For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.
Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology.
Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander
2015-01-01
Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.
International Nuclear Information System (INIS)
Liu Yongqing; Wu Youcong; Qiu Jinhong
2011-01-01
Based on the actual situation of nuclear island civil construction in Fangjiashan nuclear power plant and combined with the arrangement of the construction schedule, the analysis on optimization of the critical path for civil construction period is conducted. The result shows that on the premise of not increasing the contract price and the total engineering cost based on the original 57-month construction period, the construction period can be shorten to 21 month through strengthening the schedule control, keeping the critical work in a reasonable duration and rearranging the link between programmers. In addition, in view of the economic analysis, if we increase the mechanical engagement and adopt special method, the 2-month construction period can be even shortened to 20 months. (authors)
Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem
V'yugin, Vladimir
2014-01-01
We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market values. Our portfolio performs asymptotically at least as well as any stationary portfolio that redistributes the investment at each round using a continuous function of side in...
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.
Research on optimal investment path of transmission corridor under the global energy Internet
Huang, Yuehui; Li, Pai; Wang, Qi; Liu, Jichun; Gao, Han
2018-02-01
Under the background of the global energy Internet, the investment planning of transmission corridor from XinJiang to Germany is studied in this article, which passes through four countries: Kazakhstan, Russia, Belarus and Poland. Taking the specific situation of different countries into account, including the length of transmission line, unit construction cost, completion time, transmission price, state tariff, inflation rate and so on, this paper constructed a power transmission investment model. Finally, the dynamic programming method is used to simulate the example, and the optimal strategies under different objective functions are obtained.
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
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...... of a route choice model. Then, objective choice sets are applied for model estimation and results are compared to the “true model estimates”. Last, predictions from the simulation of models estimated with objective choice sets are compared to the “postulated predicted routes”. Meta-analysis allows...
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.
Directory of Open Access Journals (Sweden)
Sergey Malakhov
2017-09-01
Full Text Available The analysis of the optimal consumption-leisure choice under equilibrium price dispersion discovers the methodological difference between problems of moral hazard and adverse selection. While the phenomenon of moral hazard represents the individual behavioral reaction on the marginal rate of substitution of leisure for consumption proposed by the insurance policy, the adverse selection can take place on any imperfect market under equilibrium price dispersion and it looks like a market phenomenon of a natural selection between consumers with different income and different propensity to search. The analysis of health insurance where the propensity to search takes the form of the propensity to seek healthcare demonstrates that moral hazard takes place when the insurance policy proposes a suboptimal consumption-leisure choice and the increase in consumption of medical services with the reduction of leisure time represents not an unlimited demand for “free goods” but the simple process of the consumption-leisure optimization. The path of consumerism with consumer-directed plans can solve partly the problem of moral hazard because in order to eliminate moral hazard this trend should come to the re-sale of medical services under health vouchers like it takes place in the life settlement.
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.
1999-03-01
This report documents an investigation of the flight paths of 13 selected controlled flight into terrain (CFIT) aircraft accidents that occurred between 1985 and 1997. The Operations Assessment Division (DTS-43) and the Aviation Safety Division (DTS-...
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.
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.
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.
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.
Are in vivo selections on the path to extinction?
Berenguer, José; Mencía, Mario; Hidalgo, Aurelio
2017-01-01
Droplet microfluidics will become a disruptive technology in the field of library screening and replace biological selections if the central dogma of biology and other processes are successfully implemented within microdroplets. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.
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 It is everyone’s dream to have network connectivity anywhere at all times. This dream can only be realized provided there are feasible solutions that are put in place for the next generation of wireless works. Wireless Mesh Networks (WMNs...
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.
International Nuclear Information System (INIS)
Chenel, A.; Meier, C.; Dive, G.; Desouter-Lecomte, M.
2015-01-01
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
Directory of Open Access Journals (Sweden)
Liang Chen
Full Text Available This paper presents a forecast and analysis of population, economic development, energy consumption and CO2 emissions variation in China in the short- and long-term steps before 2020 with 2007 as the base year. The widely applied IPAT model, which is the basis for calculations, projections, and scenarios of greenhouse gases (GHGs reformulated as the Kaya equation, is extended to analyze and predict the relations between human activities and the environment. Four scenarios of CO2 emissions are used including business as usual (BAU, energy efficiency improvement scenario (EEI, low carbon scenario (LC and enhanced low carbon scenario (ELC. The results show that carbon intensity will be reduced by 40-45% as scheduled and economic growth rate will be 6% in China under LC scenario by 2020. The LC scenario, as the most appropriate and the most feasible scheme for China's low-carbon development in the future, can maximize the harmonious development of economy, society, energy and environmental systems. Assuming China's development follows the LC scenario, the paper further gives four paths of low-carbon transformation in China: technological innovation, industrial structure optimization, energy structure optimization and policy guidance.
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.
Portfolio optimization for seed selection in diverse weather scenarios.
Directory of Open Access Journals (Sweden)
Oskar Marko
Full Text Available 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.
Vasconcelos, A G; Almeida, R M; Nobre, F F
2001-08-01
This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.
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.
Making the Optimal Decision in Selecting Protective Clothing
International Nuclear Information System (INIS)
Price, J. Mark
2008-01-01
Protective Clothing plays a major role in the decommissioning and operation of nuclear facilities. Literally thousands of dress-outs occur over the life of a decommissioning project and during outages at operational plants. In order to make the optimal decision on which type of protective clothing is best suited for the decommissioning or maintenance and repair work on radioactive systems, a number of interrelating factors must be considered. This article discusses these factors as well as surveys of plants regarding their level of usage of single use protective clothing and should help individuals making decisions about protective clothing as it applies to their application. Individuals considering using SUPC should not jump to conclusions. The survey conducted clearly indicates that plants have different drivers. An evaluation should be performed to understand the facility's true drivers for selecting clothing. It is recommended that an interdisciplinary team be formed including representatives from budgets and cost, safety, radwaste, health physics, and key user groups to perform the analysis. The right questions need to be asked and answered by the company providing the clothing to formulate a proper perspective and conclusion. The conclusions and recommendations need to be shared with senior management so that the drivers, expected results, and associated costs are understood and endorsed. In the end, the individual making the recommendation should ask himself/herself: 'Is my decision emotional, or logical and economical?' 'Have I reached the optimal decision for my plant?'
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)
Bruno Basso
2009-12-01
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.
Optimized bioregenerative space diet selection with crew choice
Vicens, Carrie; Wang, Carolyn; Olabi, Ammar; Jackson, Peter; Hunter, Jean
2003-01-01
Previous studies on optimization of crew diets have not accounted for choice. A diet selection model with crew choice was developed. Scenario analyses were conducted to assess the feasibility and cost of certain crew preferences, such as preferences for numerous-desserts, high-salt, and high-acceptability foods. For comparison purposes, a no-choice and a random-choice scenario were considered. The model was found to be feasible in terms of food variety and overall costs. The numerous-desserts, high-acceptability, and random-choice scenarios all resulted in feasible solutions costing between 13.2 and 17.3 kg ESM/person-day. Only the high-sodium scenario yielded an infeasible solution. This occurred when the foods highest in salt content were selected for the crew-choice portion of the diet. This infeasibility can be avoided by limiting the total sodium content in the crew-choice portion of the diet. Cost savings were found by reducing food variety in scenarios where the preference bias strongly affected nutritional content.
Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter
2018-05-01
A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the
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.
Martinello, Martina
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 heavy tail estimation – Part 1: Order selection
Directory of Open Access Journals (Sweden)
M. Mudelsee
2017-12-01
Full Text Available The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1 serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.
Making the optimal decision in selecting protective clothing
International Nuclear Information System (INIS)
Price, J. Mark
2007-01-01
Protective Clothing plays a major role in the decommissioning and operation of nuclear facilities. Literally thousands of employee dress-outs occur over the life of a decommissioning project and during outages at operational plants. In order to make the optimal decision on which type of protective clothing is best suited for the decommissioning or maintenance and repair work on radioactive systems, a number of interrelating factors must be considered, including - Protection; - Personnel Contamination; - Cost; - Radwaste; - Comfort; - Convenience; - Logistics/Rad Material Considerations; - Reject Rate of Laundered Clothing; - Durability; - Security; - Personnel Safety including Heat Stress; - Disposition of Gloves and Booties. In addition, over the last several years there has been a trend of nuclear power plants either running trials or switching to Single Use Protective Clothing (SUPC) from traditional protective clothing. In some cases, after trial usage of SUPC, plants have chosen not to switch. In other cases after switching to SUPC for a period of time, some plants have chosen to switch back to laundering. Based on these observations, this paper reviews the 'real' drivers, issues, and interrelating factors regarding the selection and use of protective clothing throughout the nuclear industry. (authors)
Shedlock, Daniel
Compton backscatter imaging (CBI) is a single-sided imaging technique that uses the penetrating power of radiation and unique interaction properties of radiation with matter to image subsurface features. CBI has a variety of applications that include non-destructive interrogation, medical imaging, security and military applications. Radiography by selective detection (RSD), lateral migration radiography (LMR) and shadow aperture backscatter radiography (SABR) are different CBI techniques that are being optimized and developed. Radiography by selective detection (RSD) is a pencil beam Compton backscatter imaging technique that falls between highly collimated and uncollimated techniques. Radiography by selective detection uses a combination of single- and multiple-scatter photons from a projected area below a collimation plane to generate an image. As a result, the image has a combination of first- and multiple-scatter components. RSD techniques offer greater subsurface resolution than uncollimated techniques, at speeds at least an order of magnitude faster than highly collimated techniques. RSD scanning systems have evolved from a prototype into near market-ready scanning devices for use in a variety of single-sided imaging applications. The design has changed to incorporate state-of-the-art detectors and electronics optimized for backscatter imaging with an emphasis on versatility, efficiency and speed. The RSD system has become more stable, about 4 times faster, and 60% lighter while maintaining or improving image quality and contrast over the past 3 years. A new snapshot backscatter radiography (SBR) CBI technique, shadow aperture backscatter radiography (SABR), has been developed from concept and proof-of-principle to a functional laboratory prototype. SABR radiography uses digital detection media and shaded aperture configurations to generate near-surface Compton backscatter images without scanning, similar to how transmission radiographs are taken. Finally, a
Ultra-fast fluence optimization for beam angle selection algorithms
Bangert, M.; Ziegenhein, P.; Oelfke, U.
2014-03-01
Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ~ 4 s. BAS runs including FOs for 1000 different beam ensembles take ~ 15-70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.
Ultra-fast fluence optimization for beam angle selection algorithms
International Nuclear Information System (INIS)
Bangert, M; Ziegenhein, P; Oelfke, U
2014-01-01
Beam angle selection (BAS) including fluence optimization (FO) is among the most extensive computational tasks in radiotherapy. Precomputed dose influence data (DID) of all considered beam orientations (up to 100 GB for complex cases) has to be handled in the main memory and repeated FOs are required for different beam ensembles. In this paper, the authors describe concepts accelerating FO for BAS algorithms using off-the-shelf multiprocessor workstations. The FO runtime is not dominated by the arithmetic load of the CPUs but by the transportation of DID from the RAM to the CPUs. On multiprocessor workstations, however, the speed of data transportation from the main memory to the CPUs is non-uniform across the RAM; every CPU has a dedicated memory location (node) with minimum access time. We apply a thread node binding strategy to ensure that CPUs only access DID from their preferred node. Ideal load balancing for arbitrary beam ensembles is guaranteed by distributing the DID of every candidate beam equally to all nodes. Furthermore we use a custom sorting scheme of the DID to minimize the overall data transportation. The framework is implemented on an AMD Opteron workstation. One FO iteration comprising dose, objective function, and gradient calculation takes between 0.010 s (9 beams, skull, 0.23 GB DID) and 0.070 s (9 beams, abdomen, 1.50 GB DID). Our overall FO time is < 1 s for small cases, larger cases take ∼ 4 s. BAS runs including FOs for 1000 different beam ensembles take ∼ 15–70 min, depending on the treatment site. This enables an efficient clinical evaluation of different BAS algorithms.
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.
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.
Optimal fringe angle selection for digital fringe projection technique.
Wang, Yajun; Zhang, Song
2013-10-10
Existing digital fringe projection (DFP) systems mainly use either horizontal or vertical fringe patterns for three-dimensional shape measurement. This paper reveals that these two fringe directions are usually not optimal where the phase change is the largest to a given depth variation. We propose a novel and efficient method to determine the optimal fringe angle by projecting a set of horizontal and vertical fringe patterns onto a step-height object and by further analyzing two resultant phase maps. Experiments demonstrate the existence of the optimal angle and the success of the proposed optimal angle determination method.
Optimal Selective Harmonic Control for Power Harmonics Mitigation
DEFF Research Database (Denmark)
Zhou, Keliang; Yang, Yongheng; Blaabjerg, Frede
2015-01-01
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...
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
Software for industrial consumers electrical energy tariff optimal selection
Simona Ardelean; A. Ceclan; L. Czumbil; D. D. Micu; E. Simion
2008-01-01
This paper briefly presents someelectrical energy management techniques andproposes a software product dedicated forautomatic choose of the optimal tariff structure forindustrial consumers. The optimal choose ofelectrical energy invoicing model proves to be anefficient way to bring quality and economies in anycompanies administration. Advanced description ofthe proposed software is also presented.
A polynomial optimization approach to constant rebalanced portfolio selection
Takano, Y.; Sotirov, R.
2012-01-01
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
SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model
International Nuclear Information System (INIS)
Zhou, Z; Folkert, M; Wang, J
2016-01-01
Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.
SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model
Energy Technology Data Exchange (ETDEWEB)
Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)
2016-06-15
Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.
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.
Optimal selection for shielding materials by fuzzy linear programming
International Nuclear Information System (INIS)
Kanai, Y.; Miura, N.; Sugasawa, S.
1996-01-01
An application of fuzzy linear programming methods to optimization of a radiation shield is presented. The main purpose of the present study is the choice of materials and the search of the ratio of mixture-component as the first stage of the methodology on optimum shielding design according to individual requirements of nuclear reactor, reprocessing facility, shipping cask installing spent fuel, ect. The characteristic values for the shield optimization may be considered their cost, spatial space, weight and some shielding qualities such as activation rate and total dose rate for neutron and gamma ray (includes secondary gamma ray). This new approach can reduce huge combination calculations for conventional two-valued logic approaches to representative single shielding calculation by group-wised optimization parameters determined in advance. Using the fuzzy linear programming method, possibilities for reducing radiation effects attainable in optimal compositions hydrated, lead- and boron-contained materials are investigated
Software for selection of optimal layouts of fast reactors
International Nuclear Information System (INIS)
Geraskin, N.I.; Kuz'min, A.M.; Morin, D.V.
1983-01-01
A complex program for the calculation and optimization of a two-dimensional cylindrical fast reactor consisting of two axial layers and having up to 10 zones of different compositions in each layer is described. Search for optimal parameters is performed by the successive linearization method based on the small perturbation theory and linear programming. The complex program is written for the BESM-6 computer in the FORTRAN language
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...
Directory of Open Access Journals (Sweden)
Sajad Ganjehi
2013-08-01
Full Text Available Introduction : Earthquakes are imminent threats to urban areas of Iran, especially Tehran. They can cause extensive destructions and lead to heavy casualties. One of the most important aspects of disaster management after earthquake is the rapid transfer of casualties to emergency shelters. To expedite emergency evacuation process the optimal safe path method should be considered. To examine the safety of road networks and to determine the most optimal route at pre-earthquake phase, a series of parameters should be taken into account. Methods : In this study, we employed a multi-criteria decision-making approach to determine and evaluate the effective safety parameters for selection of optimal routes in emergency evacuation after an earthquake. Results: The relationship between the parameters was analyzed and the effect of each parameter was listed. A process model was described and a case study was implemented in the 13th Aban neighborhood ( Tehran’s 20th municipal district . Then, an optimal path to safe places in an emergency evacuation after an earthquake in the 13th Aban neighborhood was selected. Conclusion : Analytic hierarchy process (AHP, as the main model, was employed. Each parameter of the model was described. Also, the capabilities of GIS software such as layer coverage were used. Keywords: Earthquake, emergency evacuation, Analytic Hierarchy Process (AHP, crisis management, optimization, 13th Aban neighborhood of Tehran
Directory of Open Access Journals (Sweden)
Yuan Chen
2011-09-01
Full Text Available This paper proposes a piecewise acceleration-optimal and smooth-jerk trajectory planning method of robot manipulator. The optimal objective function is given by the weighted sum of two terms having opposite effects: the maximal acceleration and the minimal jerk. Some computing techniques are proposed to determine the optimal solution. These techniques take both the time intervals between two interpolation points and the control points of B-spline function as optimal variables, redefine the kinematic constraints as the constraints of optimal variables, and reformulate the objective function in matrix form. The feasibility of the optimal method is illustrated by simulation and experimental results with pan mechanism for cooking robot.
Penningroth, Suzanna L.; Scott, Walter D.
2012-01-01
Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…
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…
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.
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 concentration of selective agents for inhibiting in vitro ...
African Journals Online (AJOL)
Alessandra
(Murashige and Skoog, 1962) supplemented with 30 g/L sucrose, 3 mg/L 2 ... was adjusted to 5.8 ± 0.1 and autoclaved for 20 min at 121 ± 1°C. Ten seeds were ..... Takamori LM, Machado Neto NB, Vieira LGE, Ribas AF (2015). Optimization of ...
Application of numerical modeling for optimization of selective hot ...
African Journals Online (AJOL)
Jane
2011-08-29
Aug 29, 2011 ... The term flavonoid is used for a class of plant chemicals known for their activity as highly potent ... In this research, a novel optimization-extraction method of taxifolin from .... tration identified and quantified by HPLC (data not.
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…
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.
Parameter selection for the SSC trade-offs and optimization
International Nuclear Information System (INIS)
Edwards, D.A.; Syphers, M.J.
1991-01-01
In November of 1988, a site was selected in the state of Texas for the SSC. In January of 1989, the SSC Laboratory was established in Texas to adapt the design of the collider to the site and to manage the construction of the project. This paper describes the evolution of the SSC design since site selection, notes the increased concentration on the injector system, and addresses the rationale for choice of parameters
Optimal design and selection of magneto-rheological brake types based on braking torque and mass
International Nuclear Information System (INIS)
Nguyen, Q H; Lang, V T; Choi, S B
2015-01-01
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. (technical note)
Iterative Selection of Unknown Weights in Direct Weight Optimization Identification
Directory of Open Access Journals (Sweden)
Xiao Xuan
2014-01-01
Full Text Available To the direct weight optimization identification of the nonlinear system, we add some linear terms about input sequences in the former linear affine function so as to approximate the nonlinear property. To choose the two classes of unknown weights in the more linear terms, this paper derives the detailed process on how to choose these unknown weights from theoretical analysis and engineering practice, respectively, and makes sure of their key roles between the unknown weights. From the theoretical analysis, the added unknown weights’ auxiliary role can be known in the whole process of approximating the nonlinear system. From the practical analysis, we learn how to transform one complex optimization problem to its corresponding common quadratic program problem. Then, the common quadratic program problem can be solved by the basic interior point method. Finally, the efficiency and possibility of the proposed strategies can be confirmed by the simulation results.
Optimizing selection of in vitro tests for diagnosing thyroid disorders
International Nuclear Information System (INIS)
Zwas, S.T.; Rosenblum, Yossef; Boruchowsky, Sabina
1987-01-01
The optimal utilization of the thyroid related radioimmunoassays T3, T4, and TSH-RIA is derived from analysing the clinical and laboratory data for 974 patients with functional thyroid disorders. A statistical computer analysis of the contribution of each of the three tests, and in combination, to the final diagnoses of hypothyroid, euthyroid, and hyper thyroid states was designed. The best contributing test for hypothyroidism and euthyroidism was TSH-RIA (98.5 and 93%, respectively). T4/T3+TSH-RIAs were the optimal dual combination for diagnosing euthyroidism (98.0%). For diagnosing hyperthyroidism T4-RIA was the best single test (82.5%) followed by T3 + T4 as an optimal dual combination (95%). Using all three tests was of no significant additional value over dual combinations. It is concluded that the work and cost of randomly performing three tests routinely is not justified without clinical basis. An algorithm is proposed to guide thyroid studies based on computer analyses of the above-mentioned single or dual-test combinations to establish accurate diagnosis at the lowest laboratory cost. (author)
Diet selection of African elephant over time shows changing optimization currency
Pretorius, Y.; Stigter, J.D.; Boer, de W.F.; Wieren, van S.E.; Jong, de C.B.; Knegt, de H.J.; Grant, R.C.; Heitkonig, I.M.A.; Knox, N.; Kohi, E.; Mwakiwa, E.; Peel, M.J.S.; Skidmore, A.K.; Slotow, R.; Waal, van der C.; Langevelde, van F.; Prins, H.H.T.
2012-01-01
Multiple factors determine diet selection of herbivores. However, in many diet studies selection of single nutrients is studied or optimization models are developed using only one currency. In this paper, we use linear programming to explain diet selection by African elephant based on plant
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. Copyright © 2013 Elsevier Ltd. All rights reserved.
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.
International Nuclear Information System (INIS)
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. (paper)
Selection of optimal conditions for preparation of emulsified fuel fluids
Ivanov, V. A.; Berg, V. I.; Frolov, M. D.
2018-05-01
The aim of the article is to derive the optimal concept of physical and chemical effects, and its application to the production of water-fuel emulsions. The authors set a research task to attempt to estimate the influence of the surfactant concentration on such indicator as the time before the beginning of emulsion breaking. The analysis, based on experimental data, showed that an increase in the concentration of sodium lauryl sulfate is expedient to a certain point, corresponding to 0.05% of the total mass fraction. The main advantage of the model is a rational combination of methods of physical and chemical treatment used in the production of emulsions.
A Novel Automatic Phase Selection Device: Design and Optimization
Zhang, Feng; Li, Haitao; Li, Na; Zhang, Nan; Lv, Wei; Cui, Xiaojiang
2018-01-01
At present, AICD completion is an effective way to slow down the bottom water cone. Effective extension of the period without water production. According on the basis of investigating the AICD both at home and abroad, this paper designed a new type of AICD, and with the help of fluid numerical simulation software, the internal flow field was analysed, and its structure is optimized. The simulation results show that the tool can restrict the flow of water well, and the flow of oil is less.
Optimization of Storage Parameters of Selected Fruits in Passive ...
African Journals Online (AJOL)
This study was carried out to determine the optimum storage parameters of selected fruit using three sets of four types of passive evaporative cooling structures made of two different materials clay and aluminium. One set consisted of four separate cooling chambers. Two cooling chambers were made with aluminium ...
Optimal Contractor Selection in Construction Industry: The Fuzzy Way
Krishna Rao, M. V.; Kumar, V. S. S.; Rathish Kumar, P.
2018-02-01
A purely price-based approach to contractor selection has been identified as the root cause for many serious project delivery problems. Therefore, the capability of the contractor to execute the project should be evaluated using a multiple set of selection criteria including reputation, past performance, performance potential, financial soundness and other project specific criteria. An industry-wide questionnaire survey was conducted with the objective of identifying the important criteria for adoption in the selection process. In this work, a fuzzy set based model was developed for contractor prequalification/evaluation, by using effective criteria obtained from the percept of construction professionals, taking subjective judgments of decision makers also into consideration. A case study consisting of four alternatives (contractors in the present case) solicited from a public works department of Pondicherry in India, is used to illustrate the effectiveness of the proposed approach. The final selection of contractor is made based on the integrated score or Overall Evaluation Score of the decision alternative in prequalification as well as bid evaluation stages.
Cardiac resynchronization therapy : advances in optimal patient selection
Bleeker, Gabe Berend
2007-01-01
Despite the impressive results of cardiac resynchronization theraphy (CRT) in recent large randomized trials a consistent number of patients fails to improve following CRT implantation when the established CRT selection criteria (NYHA class III-IV heart failure, LV ejection fraction ≤35 % and QRS
International Nuclear Information System (INIS)
Nguyen, Q H; Choi, S B; Lee, Y S; Han, M S
2013-01-01
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. (paper)
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.
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.
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.
Toward optimal feature selection using ranking methods and classification algorithms
Directory of Open Access Journals (Sweden)
Novaković Jasmina
2011-01-01
Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Mahalanobis distance and variable selection to optimize dose response
International Nuclear Information System (INIS)
Moore, D.H. II; Bennett, D.E.; Wyrobek, A.J.; Kranzler, D.
1979-01-01
A battery of statistical techniques are combined to improve detection of low-level dose response. First, Mahalanobis distances are used to classify objects as normal or abnormal. Then the proportion classified abnormal is regressed on dose. Finally, a subset of regressor variables is selected which maximizes the slope of the dose response line. Use of the techniques is illustrated by application to mouse sperm damaged by low doses of x-rays
Fast Branch & Bound algorithms for optimal feature selection
Czech Academy of Sciences Publication Activity Database
Somol, Petr; Pudil, Pavel; Kittler, J.
2004-01-01
Roč. 26, č. 7 (2004), s. 900-912 ISSN 0162-8828 R&D Projects: GA ČR GA402/02/1271; GA ČR GA402/03/1310; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : subset search * feature selection * search tree Subject RIV: BD - Theory of Information Impact factor: 4.352, year: 2004
Daetwyler, Hans D; Hayden, Matthew J; Spangenberg, German C; Hayes, Ben J
2015-08-01
Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations. Copyright © 2015 by the Genetics Society of America.
International Nuclear Information System (INIS)
Lee, Tsung-Ying; Chen, Chun-Lung
2007-01-01
This paper presents a new algorithm for solving the optimal contract capacities of a time-of-use (TOU) rates industrial customer. This algorithm is named iteration particle swarm optimization (IPSO). A new index, called iteration best is incorporated into particle swarm optimization (PSO) to improve solution quality and computation efficiency. Expanding line construction cost and contract recovery cost are considered, as well as demand contract capacity cost and penalty bill, in the selection of the optimal contract capacities. The resulting optimal contract capacity effectively reaches the minimum electricity charge of TOU rates users. A significant reduction in electricity costs is observed. The effects of expanding line construction cost and contract recovery cost on the selection of optimal contract capacities can also be estimated. The feasibility of the new algorithm is demonstrated by a numerical example, and the IPSO solution quality and computation efficiency are compared to those of other algorithms
Schwaab, Douglas G.
1991-01-01
A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.
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.
Optimal Modality Selection for Cooperative Human-Robot Task Completion.
Jacob, Mithun George; Wachs, Juan P
2016-12-01
Human-robot cooperation in complex environments must be fast, accurate, and resilient. This requires efficient communication channels where robots need to assimilate information using a plethora of verbal and nonverbal modalities such as hand gestures, speech, and gaze. However, even though hybrid human-robot communication frameworks and multimodal communication have been studied, a systematic methodology for designing multimodal interfaces does not exist. This paper addresses the gap by proposing a novel methodology to generate multimodal lexicons which maximizes multiple performance metrics over a wide range of communication modalities (i.e., lexicons). The metrics are obtained through a mixture of simulation and real-world experiments. The methodology is tested in a surgical setting where a robot cooperates with a surgeon to complete a mock abdominal incision and closure task by delivering surgical instruments. Experimental results show that predicted optimal lexicons significantly outperform predicted suboptimal lexicons (p human-robot collision) and the differences in the lexicons are analyzed.
Optimal selection of major equipment in dual purpose plants
International Nuclear Information System (INIS)
Gabbrielli, E.
1981-01-01
Simulation of different operational conditions with the aid of a computer program is one of the best ways of assisting decision-makers in the selection of the most economic mix of equipment for a dual purpose plant. Using this approach this paper deals with the economic comparison of plants consisting of MSF desalinators and combustion gas or back pressure steam turbines coupled to low capacity electric power generators. The comparison is performed on the basis of the data made available by the OPTDIS computer program and the results are given in terms of yearly cost of production as the sum of capital, manpower, maintenance, fuel and chemical costs. (orig.)
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
Multicriteria analysis in selecting the optimal variant of solar system
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Anish Pandey
2017-02-01
Full Text Available This article introduces a singleton type-1 fuzzy logic system (T1-SFLS controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.
Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry
Directory of Open Access Journals (Sweden)
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.
Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry.
Planatscher, Hannes; Supper, Jochen; Poetz, Oliver; Stoll, Dieter; Joos, Thomas; Templin, Markus F; Zell, Andreas
2010-06-25
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. 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. 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.
Optimizing weight control in diabetes: antidiabetic drug selection
Directory of Open Access Journals (Sweden)
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
Snijders, H.M.J.; Janssen, J.G.M.; Cramers, C.A.M.G.; Sandra, P; Bertsch, W.; Sandra, P.; Devos, G.
1996-01-01
In this work a method is described which allows off-line optimization of temperature programmed GC separations. Recently, we described a new numerical method to predict off-line retention times and peak widths of a mixture containing components with known identities in capillary GC. In the present
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. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Discrete Analysis of Portfolio Selection with Optimal Stopping Time
Directory of Open Access Journals (Sweden)
Jianfeng Liang
2009-01-01
Full Text Available Most of the investments in practice are carried out without certain horizons. There are many factors to drive investment to a stop. In this paper, we consider a portfolio selection policy with market-related stopping time. Particularly, we assume that the investor exits the market once his wealth reaches a given investment target or falls below a bankruptcy threshold. Our objective is to minimize the expected time when the investment target is obtained, at the same time, we guarantee the probability that bankruptcy happens is no larger than a given level. We formulate the problem as a mix integer linear programming model and make analysis of the model by using a numerical example.
On selection of optimal stochastic model for accelerated life testing
International Nuclear Information System (INIS)
Volf, P.; Timková, J.
2014-01-01
This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test
1975-06-01
Traditionally, synchronization of concurrent processes is coded in line by operations on semaphores or similar objects. Path expressions move the...discussion about a variety of synchronization primitives . An analysis of their relative power is found in [3]. Path expressions do not introduce yet...another synchronization primitive . A path expression relates to such primitives as a for- or while-statement of an ALGOL-like language relates to a JUMP
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.
Xie, Rui; Wan, Xianrong; Hong, Sheng; Yi, Jianxin
2017-06-14
The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p -center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.
Optimization of selective exposure radiography of the chest
International Nuclear Information System (INIS)
Naimuddin, S.
1986-01-01
A major technical limitation in conventional chest radiography is the mismatch of the x-ray transmission dynamic range with the useful exposure range of a radiographic film when a patient is presented with a uniform incident exposure field. The goal of this project is to develop a faster and more reliable selective exposure system to fabricate and position a compensating filter (or digital beam attenuator, DBA) for clinical use. The essential components of this system include a dose efficient test-image detector, a special purpose field grabber (image memory), a custom made fast printer, a transport channel, and a computer. The fabrication process begins with acquisition of a 64 x 64 format low-dose patient image which undergoes corrections for detector nonuniformity and scatter. The corrected data after log transformation are used to calculate thickness of filter material needed to compensate for the image dynamic range. Using this thickness information the computer controls the printer which fabricates as attenuator by overprinting multiple layers of cerium oxide on a 35 mm film substrate. Although the images are acquired in a 64 x 64 format, the attenuator is constructed in a dithered 16 x 16 format using a special algorithm. After fabrication, the attenuator is automatically conveyed through the transport channel and is positioned in the x-ray beam between the collimator and x-ray tube before the final compensated radiograph is taken
Nishijo, Miki; Ebihara, Arata; Tokita, Daisuke; Doi, Hisashi; Hanawa, Takao; Okiji, Takashi
2018-03-28
This study aimed to investigate mechanical properties related to flexibility and fracture resistance of controlled memory wiremanufactured nickel-titanium rotary glide path files [HyFlex EDM Glide Path File (EDM) and HyFlex GPF (GPF)]. Scout RaCe (RaCe) served as control. Bending loads, torsional/cyclic fatigue resistance, and screw-in forces were measured. EDM showed a significantly larger torque at fracture, a longer time to cyclic fracture in reciprocation and a larger screw-in force compared with GPF and RaCe. GPF showed significantly lower bending loads and higher angular deflection values than EDM and RaCe, and a significantly longer time to cyclic fracture than RaCe. The time to cyclic fracture was significantly longer in reciprocation compared with continuous rotation in EDM and GPF. It can be concluded that EDM and/or GPF showed higher flexibility and cyclic/torsional fatigue resistance compared with RaCe; and that reciprocation conferred better cyclic fatigue resistance to EDM and GPF.
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...
Plastic scintillation dosimetry: Optimal selection of scintillating fibers and scintillators
International Nuclear Information System (INIS)
Archambault, Louis; Arsenault, Jean; Gingras, Luc; Sam Beddar, A.; Roy, Rene; Beaulieu, Luc
2005-01-01
Scintillation dosimetry is a promising avenue for evaluating dose patterns delivered by intensity-modulated radiation therapy plans or for the small fields involved in stereotactic radiosurgery. However, the increase in signal has been the goal for many authors. In this paper, a comparison is made between plastic scintillating fibers and plastic scintillator. The collection of scintillation light was measured experimentally for four commercial models of scintillating fibers (BCF-12, BCF-60, SCSF-78, SCSF-3HF) and two models of plastic scintillators (BC-400, BC-408). The emission spectra of all six scintillators were obtained by using an optical spectrum analyzer and they were compared with theoretical behavior. For scintillation in the blue region, the signal intensity of a singly clad scintillating fiber (BCF-12) was 120% of that of the plastic scintillator (BC-400). For the multiclad fiber (SCSF-78), the signal reached 144% of that of the plastic scintillator. The intensity of the green scintillating fibers was lower than that of the plastic scintillator: 47% for the singly clad fiber (BCF-60) and 77% for the multiclad fiber (SCSF-3HF). The collected light was studied as a function of the scintillator length and radius for a cylindrical probe. We found that symmetric detectors with nearly the same spatial resolution in each direction (2 mm in diameter by 3 mm in length) could be made with a signal equivalent to those of the more commonly used asymmetric scintillators. With augmentation of the signal-to-noise ratio in consideration, this paper presents a series of comparisons that should provide insight into selection of a scintillator type and volume for development of a medical dosimeter
Directory of Open Access Journals (Sweden)
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.
Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm
International Nuclear Information System (INIS)
Rao, R.V.; More, K.C.
2017-01-01
Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.
A multiobjective optimization model for optimal supplier selection in multiple sourcing environment
Directory of Open Access Journals (Sweden)
M. K. Mehlawat
2014-06-01
Full Text Available Supplier selection is an important concern of a firm’s competitiveness, more so in the context of the imperative of supply-chain management. In this paper, we use an approach to a multiobjective supplier selection problem in which the emphasis is on building supplier portfolios. The supplier evaluation and order allocation is based upon the criteria of expected unit price, expected score of quality and expected score of delivery. A fuzzy approach is proposed that relies on nonlinear S-shape membership functions to generate different efficient supplier portfolios. Numerical experiments conducted on a data set of a multinational company are provided to demonstrate the applicability and efficiency of the proposed approach to real-world applications of supplier selection
Directory of Open Access Journals (Sweden)
R Goudarzi
2018-03-01
Full Text Available Introduction The demand of pre-determined optimal coverage paths in agricultural environments have been increased due to the growing application of field robots and autonomous field machines. Also coverage path planning problem (CPP has been extensively studied in robotics and many algorithms have been provided in many topics, but differences and limitations in agriculture lead to several different heuristic and modified adaptive methods from robotics. In this paper, a modified and enhanced version of currently used decomposition algorithm in robotics (boustrophedon cellular decomposition has been presented as a main part of path planning systems of agricultural vehicles. Developed algorithm is based on the parallelization of the edges of the polygon representing the environment to satisfy the requirements of the problem as far as possible. This idea is based on "minimum facing to the cost making condition" in turn, it is derived from encounter concept as a basis of cost making factors. Materials and Methods Generally, a line termed as a slice in boustrophedon cellular decomposition (BCD, sweeps an area in a pre-determined direction and decomposes the area only at critical points (where two segments can be extended to top and bottom of the point. Furthermore, sweep line direction does not change until the decomposition finish. To implement the BCD for parallelization method, two modifications were applied in order to provide a modified version of the boustrophedon cellular decomposition (M-BCD. In the first modification, the longest edge (base edge is targeted, and sweep line direction is set in line with the base edge direction (sweep direction is set perpendicular to the sweep line direction. Then Sweep line moves through the environment and stops at the first (nearest critical point. Next sweep direction will be the same as previous, If the length of those polygon's newly added edges, during the decomposition, are less than or equal to the
International Nuclear Information System (INIS)
Li Yongjie; Yao Dezhong; Yao, Jonathan; Chen Wufan
2005-01-01
Automatic beam angle selection is an important but challenging problem for intensity-modulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new population-based evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a head-and-neck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSO-based algorithm seems to outperform, or at least compete with, the GA-based algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated
Optimizing the allocation of resources for genomic selection in one breeding cycle.
Riedelsheimer, Christian; Melchinger, Albrecht E
2013-11-01
We developed a universally applicable planning tool for optimizing the allocation of resources for one cycle of genomic selection in a biparental population. The framework combines selection theory with constraint numerical optimization and considers genotype × environment interactions. Genomic selection (GS) is increasingly implemented in plant breeding programs to increase selection gain but little is known how to optimally allocate the resources under a given budget. We investigated this problem with model calculations by combining quantitative genetic selection theory with constraint numerical optimization. We assumed one selection cycle where both the training and prediction sets comprised double haploid (DH) lines from the same biparental population. Grain yield for testcrosses of maize DH lines was used as a model trait but all parameters can be adjusted in a freely available software implementation. An extension of the expected selection accuracy given by Daetwyler et al. (2008) was developed to correctly balance between the number of environments for phenotyping the training set and its population size in the presence of genotype × environment interactions. Under small budget, genotyping costs mainly determine whether GS is superior over phenotypic selection. With increasing budget, flexibility in resource allocation increases greatly but selection gain leveled off quickly requiring balancing the number of populations with the budget spent for each population. The use of an index combining phenotypic and GS predicted values in the training set was especially beneficial under limited resources and large genotype × environment interactions. Once a sufficiently high selection accuracy is achieved in the prediction set, further selection gain can be achieved most efficiently by massively expanding its size. Thus, with increasing budget, reducing the costs for producing a DH line becomes increasingly crucial for successfully exploiting the
Directory of Open Access Journals (Sweden)
Xiaofeng Lv
2018-01-01
Full Text Available Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR and fault isolation rate (FIR. From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.
Reaction path simulations in multicomponent materials
International Nuclear Information System (INIS)
Seifert, H.J.
1999-01-01
The CALPHAD (calculation of phase diagrams) method is used in combination with selected experimental investigations to derive reaction paths in multicomponent systems. The method is illustrated by applying computerized thermodynamic databases and suitable software to explain quantitatively the thermal degradation of precursor-derived Si-C-N ceramics and the nitridation of titanium carbide. Reaction sequences in the Si 3 N 4 -SiC-TiC x N l-x -C-N system are illustrated by graphical representation of compatibility regions and indicated reaction paths. From these results the experimentally known microstructure development of TiC reinforced Si 3 N 4 ceramics is explained and quantitative information is provided to optimize the microstructure of such materials. The concept of reaction paths for the understanding of rapid solidification processes is shown by the example of AZ type Mg casting alloys. (orig.)
International Nuclear Information System (INIS)
Gurcan, Metin N.; Sahiner, Berkman; Chan Heangping; Hadjiiski, Lubomir; Petrick, Nicholas
2001-01-01
Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are 'optimized' by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area A z under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost
2015-07-06
seen by a ten- fold increase for the first two types of instances. This phenomenon can perhaps be explained by the combinatorial nature of CKVLP, which...consists of a combination of person types t ∈ T , e.g., child , adult, or elderly. A vaccination policy v ∈ V is a delivery of vaccine to certain types of...forb(P,X1) ∩ forb(P,X2) for any partition (X1, X2) of X . Proposition 7 generalizes the main result of [12] regarding cropped cubes. Moreover, the
DEFF Research Database (Denmark)
Sørensen, Søren N.; Stolpe, Mathias
2015-01-01
rate. The capabilities of the method and the effect of active versus inactive manufacturing constraints are demonstrated on several numerical examples of limited size, involving at most 320 binary variables. Most examples are solved to guaranteed global optimality and may constitute benchmark examples...... but is, however, convex in the original mixed binary nested form. Convexity is the foremost important property of optimization problems, and the proposed method can guarantee the global or near-global optimal solution; unlike most topology optimization methods. The material selection is limited...... for popular topology optimization methods and heuristics based on solving sequences of non-convex problems. The results will among others demonstrate that the difficulty of the posed problem is highly dependent upon the composition of the constitutive properties of the material candidates....
Optimized Policies for Improving Fairness of Location-based Relay Selection
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen; Olsen, Rasmus Løvenstein; Madsen, Tatiana Kozlova
2013-01-01
For WLAN systems in which relaying is used to improve throughput performance for nodes located at the cell edge, node mobility and information collection delays can have a significant impact on the performance of a relay selection scheme. In this paper we extend our existing Markov Chain modeling...... framework for relay selection to allow for efficient calculation of relay policies given either mean throughput or kth throughput percentile as optimization criterium. In a scenario with static access point, static relay, and a mobile destination node, the kth throughput percentile optimization...
Simulation and Optimization of Control of Selected Phases of Gyroplane Flight
Directory of Open Access Journals (Sweden)
Wienczyslaw Stalewski
2018-02-01
Full Text Available Optimization methods are increasingly used to solve problems in aeronautical engineering. Typically, optimization methods are utilized in the design of an aircraft airframe or its structure. The presented study is focused on improvement of aircraft flight control procedures through numerical optimization. The optimization problems concern selected phases of flight of a light gyroplane—a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier–Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS (Broyden–Fletcher–Goldfarb–Shanno algorithm was adapted. The developed methodology was applied to optimize the flight control procedures in selected stages of gyroplane flight in direct proximity to the ground, where proper control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved the qualitative correctness of the developed methodology. The research results can be helpful in the design of easy-to-control gyroplanes and also in the training of pilots for this type of rotorcraft.
Optimized Power Allocation and Relay Location Selection in Cooperative Relay Networks
Directory of Open Access Journals (Sweden)
Jianrong Bao
2017-01-01
Full Text Available An incremental selection hybrid decode-amplify forward (ISHDAF scheme for the two-hop single relay systems and a relay selection strategy based on the hybrid decode-amplify-and-forward (HDAF scheme for the multirelay systems are proposed along with an optimized power allocation for the Internet of Thing (IoT. Given total power as the constraint and outage probability as an objective function, the proposed scheme possesses good power efficiency better than the equal power allocation. By the ISHDAF scheme and HDAF relay selection strategy, an optimized power allocation for both the source and relay nodes is obtained, as well as an effective reduction of outage probability. In addition, the optimal relay location for maximizing the gain of the proposed algorithm is also investigated and designed. Simulation results show that, in both single relay and multirelay selection systems, some outage probability gains by the proposed scheme can be obtained. In the comparison of the optimized power allocation scheme with the equal power allocation one, nearly 0.1695 gains are obtained in the ISHDAF single relay network at a total power of 2 dB, and about 0.083 gains are obtained in the HDAF relay selection system with 2 relays at a total power of 2 dB.
Cao, Yang; Liu, Chun; Huang, Yuehui; Wang, Tieqiang; Sun, Chenjun; Yuan, Yue; Zhang, Xinsong; Wu, Shuyun
2017-02-01
With the development of roof photovoltaic power (PV) generation technology and the increasingly urgent need to improve supply reliability levels in remote areas, islanded microgrid with photovoltaic and energy storage systems (IMPE) is developing rapidly. The high costs of photovoltaic panel material and energy storage battery material have become the primary factors that hinder the development of IMPE. The advantages and disadvantages of different types of photovoltaic panel materials and energy storage battery materials are analyzed in this paper, and guidance is provided on material selection for IMPE planners. The time sequential simulation method is applied to optimize material demands of the IMPE. The model is solved by parallel algorithms that are provided by a commercial solver named CPLEX. Finally, to verify the model, an actual IMPE is selected as a case system. Simulation results on the case system indicate that the optimization model and corresponding algorithm is feasible. Guidance for material selection and quantity demand for IMPEs in remote areas is provided by this 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.
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.
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.
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.
The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns
Cao, Guohua; Shan, Dan
2013-01-01
The aims of this paper are to use a birandom variable to denote the stock return selected by some recurring technical patterns and to study the effect of exit strategy on optimal portfolio selection with birandom returns. Firstly, we propose a new method to estimate the stock return and use birandom distribution to denote the final stock return which can reflect the features of technical patterns and investors' heterogeneity simultaneously; secondly, we build a birandom safety-first model and...
Selective pressures on C4 photosynthesis evolution in grasses through the lens of optimality
Akcay, Erol; Zhou, Haoran; Helliker, Brent
2016-01-01
CO2, temperature, water availability and light intensity were potential selective pressures to propel the initial evolution and global expansion of C4 photosynthesis in grasses. To tease apart the primary selective pressures along the evolutionary trajectory, we coupled photosynthesis and hydraulics models and optimized photosynthesis over stomatal resistance and leaf/fine-root allocation. We also examined the importance of nitrogen reallocation from the dark to the light reactions. Our resul...
SOCP relaxation bounds for the optimal subset selection problem applied to robust linear regression
Flores, Salvador
2015-01-01
This paper deals with the problem of finding the globally optimal subset of h elements from a larger set of n elements in d space dimensions so as to minimize a quadratic criterion, with an special emphasis on applications to computing the Least Trimmed Squares Estimator (LTSE) for robust regression. The computation of the LTSE is a challenging subset selection problem involving a nonlinear program with continuous and binary variables, linked in a highly nonlinear fashion. The selection of a ...
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.
International Nuclear Information System (INIS)
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
2017-03-01
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.
An Optimization Model For Strategy Decision Support to Select Kind of CPO’s Ship
Suaibah Nst, Siti; Nababan, Esther; Mawengkang, Herman
2018-01-01
The selection of marine transport for the distribution of crude palm oil (CPO) is one of strategy that can be considered in reducing cost of transport. The cost of CPO’s transport from one area to CPO’s factory located at the port of destination may affect the level of CPO’s prices and the number of demands. In order to maintain the availability of CPO a strategy is required to minimize the cost of transporting. In this study, the strategy used to select kind of charter ships as barge or chemical tanker. This study aims to determine an optimization model for strategy decision support in selecting kind of CPO’s ship by minimizing costs of transport. The select of ship was done randomly, so that two-stage stochastic programming model was used to select the kind of ship. Model can help decision makers to select either barge or chemical tanker to distribute CPO.
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.
International Nuclear Information System (INIS)
Ji, Aimin; Yin, Xu; Yuan, Minghai
2015-01-01
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
Adaptive feature selection using v-shaped binary particle swarm optimization.
Teng, Xuyang; 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.
Zhang, Yong; Gong, Dun-Wei; Cheng, Jian
2017-01-01
Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.
Tan, Maxine; Pu, Jiantao; Zheng, Bin
2014-08-01
Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized
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.
Optimal relay selection and power allocation for cognitive two-way relaying networks
Pandarakkottilil, Ubaidulla; Aï ssa, Sonia
2012-01-01
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
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.
CSIR Research Space (South Africa)
Debba, Pravesh
2009-08-01
Full Text Available computer intensive search technique to find the bands optimizing the value of TSAM as a function of the bands, by continually updating this function at succes- sive steps. Band selection by means of minimizing the total accumulated correlation...
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.
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
Optimal Selection of AC Cables for Large Scale Offshore Wind Farms
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Zhe
2014-01-01
The investment of large scale offshore wind farms is high in which the electrical system has a significant contribution to the total cost. As one of the key components, the cost of the connection cables affects the initial investment a lot. The development of cable manufacturing provides a vast...... and systematical way for the optimal selection of cables in large scale offshore wind farms....
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…
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.
Directory of Open Access Journals (Sweden)
Crnogorac Miroslav P.
2016-01-01
Full Text Available In the world today for the exploitation of oil reservoirs by artificial lift methods are applied different types of deep pumps (piston, centrifugal, screw, hydraulic, water jet pumps and gas lift (continuous, intermittent and plunger. Maximum values of oil production achieved by these exploitation methods are significantly different. In order to select the optimal exploitation method of oil well, the multicriteria analysis models are used. In this paper is presented an analysis of the multicriteria model's application known as VIKOR, TOPSIS, ELECTRE, AHP and PROMETHEE for selection of optimal exploitation method for typical oil well at Serbian exploration area. Ranking results of applicability of the deep piston pumps, hydraulic pumps, screw pumps, gas lift method and electric submersible centrifugal pumps, indicated that in the all above multicriteria models except in PROMETHEE, the optimal method of exploitation are deep piston pumps and gas lift.
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.
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.
DEFF Research Database (Denmark)
Madsen, Mogens Ove
Begrebet Path Dependence blev oprindelig udviklet inden for New Institutionel Economics af bl.a. David, Arthur og North. Begrebet har spredt sig vidt i samfundsvidenskaberne og undergået en udvikling. Dette paper propagerer for at der er sket så en så omfattende udvikling af begrebet, at man nu kan...... tale om 1. og 2. generation af Path Dependence begrebet. Den nyeste udvikling af begrebet har relevans for metodologi-diskusionerne i relation til Keynes...
Optimizing Tailored Bus Bridging Paths
Gu, Wei; Yu, Jie; Ji, Yuxiong; van der Gun, J.P.T.; Pel, A.J.; Zhang, H. Michael; van Arem, B.
2017-01-01
Metro disruptions due to unexpected events reduce transit system reliability, resulting in significant productivity loss and long passenger delays. Bus bridging strategy is often used to connect stations affected by metro disruptions such that passengers could continue their journey. The literature
Laser Cutting Tool Path Optimization
Dewil, Reginald; Cattrysse, Dirk; Vansteenwegen, Pieter
2011-01-01
Given a set of irregular parts nested on a metal sheet, minimize the total non- cutting time for the cutter head, cutting all the required elements and returning to the starting location. The problem is modeled as a generalized traveling sales- person problem with special precedence constraints. An initial feasible solution is generated and improved by local moves embedded in a tabu search framework. The proposed algorithm shows promising results in comparison with a commercial...
International Nuclear Information System (INIS)
Kim, Minsun; Stewart, Robert D.; Phillips, Mark H.
2015-01-01
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 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 mean ≤ 45 Gy), lungs (D mean ≤ 20 Gy), cord (D max ≤ 45 Gy), esophagus (D max ≤ 63 Gy), and unspecified tissues (D 05 ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D 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 d (3–100 days), tumor lag-time (T 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 95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T d and T k used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with T d less than 10 days, there was no
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
Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao
2014-10-07
In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.
Directory of Open Access Journals (Sweden)
Yang Sun
2018-01-01
Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
Directory of Open Access Journals (Sweden)
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.
Hegade, Ravindra Suryakant; De Beer, Maarten; Lynen, Frederic
2017-09-15
Chiral Stationary-Phase Optimized Selectivity Liquid Chromatography (SOSLC) is proposed as a tool to optimally separate mixtures of enantiomers on a set of commercially available coupled chiral columns. This approach allows for the prediction of the separation profiles on any possible combination of the chiral stationary phases based on a limited number of preliminary analyses, followed by automated selection of the optimal column combination. Both the isocratic and gradient SOSLC approach were implemented for prediction of the retention times for a mixture of 4 chiral pairs on all possible combinations of the 5 commercial chiral columns. Predictions in isocratic and gradient mode were performed with a commercially available and with an in-house developed Microsoft visual basic algorithm, respectively. Optimal predictions in the isocratic mode required the coupling of 4 columns whereby relative deviations between the predicted and experimental retention times ranged between 2 and 7%. Gradient predictions led to the coupling of 3 chiral columns allowing baseline separation of all solutes, whereby differences between predictions and experiments ranged between 0 and 12%. The methodology is a novel tool allowing optimizing the separation of mixtures of optical isomers. Copyright © 2017 Elsevier B.V. All rights reserved.
Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models
Directory of Open Access Journals (Sweden)
Hui Wang
2017-10-01
Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.
Optimal load suitability based RAT selection for HSDPA and IEEE 802.11e
DEFF Research Database (Denmark)
Prasad, Ramjee; Cabral, O.; Felez, F.J.
2009-01-01
are a premium. This paper investigates cooperation between networks based Radio Access Technology (RAT) selection algorithm that uses suitability to optimize the choice between WiFi and High Speed Downlink Packet Access (HSDPA). It has been shown that this approach has the potential to provide gain...... by allocating a user terminal to the most preferred network based on traffic type and network load. Optimal load threshold values that maximise the total QoS throughput for the given interworking scenario are 0.6 and 0.53 for HSDPA and WiFi, respectively. This corresponds to a CRRM gain on throughput of 80...
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 shortage in the available technical information. A stochastic mixed integer nonlinear programming (sMINLP) problem is formulated for determining the optimal biorefinery configurations based on a superstructure model where parameter uncertainties are modeled and included as sampled scenarios. The solution...... the accounting of uncertainty are compared with respect to different objectives. (C) 2015 Elsevier Ltd. All rights reserved....
On the non-stationarity of financial time series: impact on optimal portfolio selection
International Nuclear Information System (INIS)
Livan, Giacomo; Inoue, Jun-ichi; Scalas, Enrico
2012-01-01
We investigate the possible drawbacks of employing the standard Pearson estimator to measure correlation coefficients between financial stocks in the presence of non-stationary behavior, and we provide empirical evidence against the well-established common knowledge that using longer price time series provides better, more accurate, correlation estimates. Then, we investigate the possible consequences of instabilities in empirical correlation coefficient measurements on optimal portfolio selection. We rely on previously published works which provide a framework allowing us to take into account possible risk underestimations due to the non-optimality of the portfolio weights being used in order to distinguish such non-optimality effects from risk underestimations genuinely due to non-stationarities. We interpret such results in terms of instabilities in some spectral properties of portfolio correlation matrices. (paper)
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.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong
2015-01-01
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.
Optimal selection of LQR parameter using AIS for LFC in a multi-area power system
Directory of Open Access Journals (Sweden)
Muhammad Abdillah
2016-12-01
Full Text Available This paper proposes a method to optimize the parameter of the linear quadratic regulator (LQR using artificial immune system (AIS via clonal selection. The parameters of LQR utilized in this paper are the weighting matrices Q and R. The optimal LQR control for load frequency control (LFC is installed on each area as a decentralized control scheme. The aim of this control design is to improve the dynamic performance of LFC automatically when unexpected load change occurred on power system network. The change of load demands 0.01 p.u used as a disturbance is applied to LFC in Area 1. The proposed method guarantees the stability of the overall closed-loop system. The simulation result shows that the proposed method can reduce the overshoot of the system and compress the time response to steady-state which is better compared to trial error method (TEM and without optimal LQR control.
She, James
2009-11-01
Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.
She, James; Ho, Pin-Han; Shihada, Basem
2009-01-01
Emerging technologies in Broadband Wireless Access (BWA) networks and video coding have enabled high-quality wireless video broadcast/multicast services in metropolitan areas. Joint source-channel coded wireless transmission, especially using hierarchical/superposition coded modulation at the channel, is recognized as an effective and scalable approach to increase the system scalability while tackling the multi-user channel diversity problem. The power allocation and modulation selection problem, however, is subject to a high computational complexity due to the nonlinear formulation and huge solution space. This paper introduces a dynamic programming framework with conditioned parsing, which significantly reduces the search space. The optimized result is further verified with experiments using real video content. The proposed approach effectively serves as a generalized and practical optimization framework that can gauge and optimize a scalable wireless video broadcast/multicast based on multi-resolution modulation in any BWA network.
Directory of Open Access Journals (Sweden)
Petr Maca
2014-01-01
Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.
Jin, Haiyan; Xing, Bei; Wang, Lei; Wang, Yanyan
2015-11-01
In this paper, we put forward a novel fusion method for remote sensing images based on the contrast pyramid (CP) using the Baldwinian Clonal Selection Algorithm (BCSA), referred to as CPBCSA. Compared with classical methods based on the transform domain, the method proposed in this paper adopts an improved heuristic evolutionary algorithm, wherein the clonal selection algorithm includes Baldwinian learning. In the process of image fusion, BCSA automatically adjusts the fusion coefficients of different sub-bands decomposed by CP according to the value of the fitness function. BCSA also adaptively controls the optimal search direction of the coefficients and accelerates the convergence rate of the algorithm. Finally, the fusion images are obtained via weighted integration of the optimal fusion coefficients and CP reconstruction. Our experiments show that the proposed method outperforms existing methods in terms of both visual effect and objective evaluation criteria, and the fused images are more suitable for human visual or machine perception.
A Generalized Measure for the Optimal Portfolio Selection Problem and its Explicit Solution
Directory of Open Access Journals (Sweden)
Zinoviy Landsman
2018-03-01
Full Text Available In this paper, we offer a novel class of utility functions applied to optimal portfolio selection. This class incorporates as special cases important measures such as the mean-variance, Sharpe ratio, mean-standard deviation and others. We provide an explicit solution to the problem of optimal portfolio selection based on this class. Furthermore, we show that each measure in this class generally reduces to the efficient frontier that coincides or belongs to the classical mean-variance efficient frontier. In addition, a condition is provided for the existence of the a one-to-one correspondence between the parameter of this class of utility functions and the trade-off parameter λ in the mean-variance utility function. This correspondence essentially provides insight into the choice of this parameter. We illustrate our results by taking a portfolio of stocks from National Association of Securities Dealers Automated Quotation (NASDAQ.
Directory of Open Access Journals (Sweden)
Mu Qiao
2017-01-01
Full Text Available We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.
DEFF Research Database (Denmark)
Karnøe, Peter; Garud, Raghu
2012-01-01
This paper employs path creation as a lens to follow the emergence of the Danish wind turbine cluster. Supplier competencies, regulations, user preferences and a market for wind power did not pre-exist; all had to emerge in a tranformative manner involving multiple actors and artefacts. Competenc......This paper employs path creation as a lens to follow the emergence of the Danish wind turbine cluster. Supplier competencies, regulations, user preferences and a market for wind power did not pre-exist; all had to emerge in a tranformative manner involving multiple actors and artefacts....... Competencies emerged through processes and mechanisms such as co-creation that implicated multiple learning processes. The process was not an orderly linear one as emergent contingencies influenced the learning processes. An implication is that public policy to catalyse clusters cannot be based...
International Nuclear Information System (INIS)
Zhou, Junle; Chen, Lingen; Ding, Zemin; Sun, Fengrui
2016-01-01
Ecological performance of a single resonance ESE heat engine with heat leakage is conducted by applying finite time thermodynamics. By introducing Nielsen function and numerical calculations, expressions about power output, efficiency, entropy generation rate and ecological objective function are derived; relationships between ecological objective function and power output, between ecological objective function and efficiency as well as between power output and efficiency are demonstrated; influences of system parameters of heat leakage, boundary energy and resonance width on the optimal performances are investigated in detail; a specific range of boundary energy is given as a compromise to make ESE heat engine system work at optimal operation regions. Comparing performance characteristics with different optimization objective functions, the significance of selecting ecological objective function as the design objective is clarified specifically: when changing the design objective from maximum power output into maximum ecological objective function, the improvement of efficiency is 4.56%, while the power output drop is only 2.68%; when changing the design objective from maximum efficiency to maximum ecological objective function, the improvement of power output is 229.13%, and the efficiency drop is only 13.53%. - Highlights: • An irreversible single resonance energy selective electron heat engine is studied. • Heat leakage between two reservoirs is considered. • Power output, efficiency and ecological objective function are derived. • Optimal performance comparison for three objective functions is carried out.
A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen
2012-01-01
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.
International Nuclear Information System (INIS)
Chandra, S.; Habicht, P.; Chexal, B.; Mahini, R.; McBrine, W.; Esselman, T.; Horowitz, J.
1995-01-01
A large amount of piping in a typical nuclear power plant is susceptible to Flow-Accelerated Corrosion (FAC) wall thinning to varying degrees. A typical PAC monitoring program includes the wall thickness measurement of a select number of components in order to judge the structural integrity of entire systems. In order to appropriately allocate resources and maintain an adequate FAC program, it is necessary to optimize the selection of components for inspection by focusing on those components which provide the best indication of system susceptibility to FAC. A better understanding of system FAC predictability and the types of FAC damage encountered can provide some of the insight needed to better focus and optimize the inspection plan for an upcoming refueling outage. Laboratory examination of FAC damaged components removed from service at Northeast Utilities' (NU) nuclear power plants provides a better understanding of the damage mechanisms involved and contributing causes. Selected results of this ongoing study are presented with specific conclusions which will help NU to better focus inspections and thus optimize the ongoing FAC inspection program
Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial.
Ibrahim, Ahmed; Alfa, Attahiru
2017-08-01
This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes.
Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach
Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar
2010-10-01
To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.
[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].
Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong
2013-03-01
Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.
Escalona Galvis, Luis Waldo; Diaz-Montiel, Paulina; Venkataraman, Satchi
2017-01-01
Electrical Resistance Tomography (ERT) offers a non-destructive evaluation (NDE) technique that takes advantage of the inherent electrical properties in carbon fiber reinforced polymer (CFRP) composites for internal damage characterization. This paper investigates a method of optimum selection of sensing configurations for delamination detection in thick cross-ply laminates using ERT. Reduction in the number of sensing locations and measurements is necessary to minimize hardware and computational effort. The present work explores the use of an effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations resulting from selecting sensing electrode pairs. Singular Value Decomposition (SVD) is applied to obtain a spectral representation of the resistance measurements in the laminate for subsequent EI based reduction to take place. The electrical potential field in a CFRP laminate is calculated using finite element analysis (FEA) applied on models for two different laminate layouts considering a set of specified delamination sizes and locations with two different sensing arrangements. The effectiveness of the EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of damage using the full set and the reduced set of resistance measurements. This investigation shows that the EI measure is effective for optimally selecting the electrode pairs needed for resistance measurements in ERT based damage detection. PMID:28772485
Space-planning and structural solutions of low-rise buildings: Optimal selection methods
Gusakova, Natalya; Minaev, Nikolay; Filushina, Kristina; Dobrynina, Olga; Gusakov, Alexander
2017-11-01
The present study is devoted to elaboration of methodology used to select appropriately the space-planning and structural solutions in low-rise buildings. Objective of the study is working out the system of criteria influencing the selection of space-planning and structural solutions which are most suitable for low-rise buildings and structures. Application of the defined criteria in practice aim to enhance the efficiency of capital investments, energy and resource saving, create comfortable conditions for the population considering climatic zoning of the construction site. Developments of the project can be applied while implementing investment-construction projects of low-rise housing at different kinds of territories based on the local building materials. The system of criteria influencing the optimal selection of space-planning and structural solutions of low-rise buildings has been developed. Methodological basis has been also elaborated to assess optimal selection of space-planning and structural solutions of low-rise buildings satisfying the requirements of energy-efficiency, comfort and safety, and economical efficiency. Elaborated methodology enables to intensify the processes of low-rise construction development for different types of territories taking into account climatic zoning of the construction site. Stimulation of low-rise construction processes should be based on the system of approaches which are scientifically justified; thus it allows enhancing energy efficiency, comfort, safety and economical effectiveness of low-rise buildings.
Dortch, Richard D; Bagnato, Francesca; Gochberg, Daniel F; Gore, John C; Smith, Seth A
2018-03-24
To optimize a selective inversion recovery (SIR) sequence for macromolecular content mapping in the human brain at 3.0T. SIR is a quantitative method for measuring magnetization transfer (qMT) that uses a low-power, on-resonance inversion pulse. This results in a biexponential recovery of free water signal that can be sampled at various inversion/predelay times (t I/ t D ) to estimate a subset of qMT parameters, including the macromolecular-to-free pool-size-ratio (PSR), the R 1 of free water (R 1f ), and the rate of MT exchange (k mf ). The adoption of SIR has been limited by long acquisition times (≈4 min/slice). Here, we use Cramér-Rao lower bound theory and data reduction strategies to select optimal t I /t D combinations to reduce imaging times. The schemes were experimentally validated in phantoms, and tested in healthy volunteers (N = 4) and a multiple sclerosis patient. Two optimal sampling schemes were determined: (i) a 5-point scheme (k mf estimated) and (ii) a 4-point scheme (k mf assumed). In phantoms, the 5/4-point schemes yielded parameter estimates with similar SNRs as our previous 16-point scheme, but with 4.1/6.1-fold shorter scan times. Pair-wise comparisons between schemes did not detect significant differences for any scheme/parameter. In humans, parameter values were consistent with published values, and similar levels of precision were obtained from all schemes. Furthermore, fixing k mf reduced the sensitivity of PSR to partial-volume averaging, yielding more consistent estimates throughout the brain. qMT parameters can be robustly estimated in ≤1 min/slice (without independent measures of ΔB 0 , B1+, and T 1 ) when optimized t I -t D combinations are selected. © 2018 International Society for Magnetic Resonance in Medicine.
Sun, Yu; Tamarit, Daniel
2017-01-01
Abstract The major codon preference model suggests that codons read by tRNAs in high concentrations are preferentially utilized in highly expressed genes. However, the identity of the optimal codons differs between species although the forces driving such changes are poorly understood. We suggest that these questions can be tackled by placing codon usage studies in a phylogenetic framework and that bacterial genomes with extreme nucleotide composition biases provide informative model systems. Switches in the background substitution biases from GC to AT have occurred in Gardnerella vaginalis (GC = 32%), and from AT to GC in Lactobacillus delbrueckii (GC = 62%) and Lactobacillus fermentum (GC = 63%). We show that despite the large effects on codon usage patterns by these switches, all three species evolve under selection on synonymous sites. In G. vaginalis, the dramatic codon frequency changes coincide with shifts of optimal codons. In contrast, the optimal codons have not shifted in the two Lactobacillus genomes despite an increased fraction of GC-ending codons. We suggest that all three species are in different phases of an on-going shift of optimal codons, and attribute the difference to a stronger background substitution bias and/or longer time since the switch in G. vaginalis. We show that comparative and correlative methods for optimal codon identification yield conflicting results for genomes in flux and discuss possible reasons for the mispredictions. We conclude that switches in the direction of the background substitution biases can drive major shifts in codon preference patterns even under sustained selection on synonymous codon sites. PMID:27540085
R. Venkata Rao
2012-01-01
A paper published by Maniya and Bhatt (2011) (An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems, Computers & Industrial Engineering, 61, 542-549) proposed an alternative multiple attribute decision making method named as “Preference Selection Index (PSI) method” for selection of an optimal facility layout design. The authors had claimed that the method was logical and more appropriate and the method gives directly the o...
A concurrent optimization model for supplier selection with fuzzy quality loss
International Nuclear Information System (INIS)
Rosyidi, C.; Murtisari, R.; Jauhari, W.
2017-01-01
The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.
A concurrent optimization model for supplier selection with fuzzy quality loss
Energy Technology Data Exchange (ETDEWEB)
Rosyidi, C.; Murtisari, R.; Jauhari, W.
2017-07-01
The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.
De Novo generation of molecular structures using optimization to select graphs on a given lattice
DEFF Research Database (Denmark)
Bywater, R.P.; Poulsen, Thomas Agersten; Røgen, Peter
2004-01-01
A recurrent problem in organic chemistry is the generation of new molecular structures that conform to some predetermined set of structural constraints that are imposed in an endeavor to build certain required properties into the newly generated structure. An example of this is the pharmacophore...... model, used in medicinal chemistry to guide de novo design or selection of suitable structures from compound databases. We propose here a method that efficiently links up a selected number of required atom positions while at the same time directing the emergent molecular skeleton to avoid forbidden...... positions. The linkage process takes place on a lattice whose unit step length and overall geometry is designed to match typical architectures of organic molecules. We use an optimization method to select from the many different graphs possible. The approach is demonstrated in an example where crystal...
Zeng, Xueqiang; Luo, Gang
2017-12-01
Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
Grobys, Klaus
2011-01-01
Passive investment strategies basically aim to replicate an underlying benchmark. Thereby, the management usually selects a subset of stocks being employed in the optimization procedure. Apart from the optimization procedure, the stock selection approach determines the stock portfolios' out-of-sample performance. The empirical study here takes into account the Danish stock market from 2000-2010 and gives evidence that stock portfolios including small companies' stocks being estimated via coin...
Rosić, Miroslav; Pešić, Dalibor; Kukić, Dragoslav; Antić, Boris; Božović, Milan
2017-01-01
Concept of composite road safety index is a popular and relatively new concept among road safety experts around the world. As there is a constant need for comparison among different units (countries, municipalities, roads, etc.) there is need to choose an adequate method which will make comparison fair to all compared units. Usually comparisons using one specific indicator (parameter which describes safety or unsafety) can end up with totally different ranking of compared units which is quite complicated for decision maker to determine "real best performers". Need for composite road safety index is becoming dominant since road safety presents a complex system where more and more indicators are constantly being developed to describe it. Among wide variety of models and developed composite indexes, a decision maker can come to even bigger dilemma than choosing one adequate risk measure. As DEA and TOPSIS are well-known mathematical models and have recently been increasingly used for risk evaluation in road safety, we used efficiencies (composite indexes) obtained by different models, based on DEA and TOPSIS, to present PROMETHEE-RS model for selection of optimal method for composite index. Method for selection of optimal composite index is based on three parameters (average correlation, average rank variation and average cluster variation) inserted into a PROMETHEE MCDM method in order to choose the optimal one. The model is tested by comparing 27 police departments in Serbia. Copyright © 2016 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Aly, Mohamed F.; Hamza, Karim T.; Farag, Mahmoud M.
2014-01-01
Highlights: • Sandwich panels optimization model. • Sandwich panels design procedure. • Study of sandwich panels for automotive vehicle flooring. • Study of sandwich panels for truck cabin exterior. - Abstract: The future of automotive industry faces many challenges in meeting increasingly strict restrictions on emissions, energy usage and recyclability of components alongside the need to maintain cost competiveness. Weight reduction through innovative design of components and proper material selection can have profound impact towards attaining such goals since most of the lifecycle energy usage occurs during the operation phase of a vehicle. In electric and hybrid vehicles, weight reduction has another important effect of extending the electric mode driving range between stops or gasoline mode. This paper adopts parametric models for design optimization and material selection of sandwich panels with the objective of weight and cost minimization subject to structural integrity constraints such as strength, stiffness and buckling resistance. The proposed design procedure employs a pre-compiled library of candidate sandwich panel material combinations, for which optimization of the layered thicknesses is conducted and the best one is reported. Example demonstration studies from the automotive industry are presented for the replacement of Aluminum and Steel panels with polypropylene-filled sandwich panel alternatives
A screening method for the optimal selection of plate heat exchanger configurations
Directory of Open Access Journals (Sweden)
Pinto J.M.
2002-01-01
Full Text Available An optimization method for determining the best configuration(s of gasketed plate heat exchangers is presented. The objective is to select the configuration(s with the minimum heat transfer area that still satisfies constraints on the number of channels, the pressure drop of both fluids, the channel flow velocities and the exchanger thermal effectiveness. The configuration of the exchanger is defined by six parameters, which are as follows: the number of channels, the numbers of passes on each side, the fluid locations, the feed positions and the type of flow in the channels. The resulting configuration optimization problem is formulated as the minimization of the exchanger heat transfer area and a screening procedure is proposed for its solution. In this procedure, subsets of constraints are successively applied to eliminate infeasible and nonoptimal solutions. Examples show that the optimization method is able to successfully determine a set of optimal configurations with a minimum number of exchanger evaluations. Approximately 5 % of the pressure drop and channel velocity calculations and 1 % of the thermal simulations are required for the solution.
Directory of Open Access Journals (Sweden)
Qiang Sun
2017-01-01
Full Text Available We focus on the power consumption problem for a downlink multiuser small-cell network (SCN considering both the quality of service (QoS and power constraints. First based on a practical power consumption model taking into account both the dynamic transmit power and static circuit power, we formulate and then transform the power consumption optimization problem into a convex problem by using semidefinite relaxation (SDR technique and obtain the optimal solution by the CVX tool. We further note that the SDR-based solution becomes infeasible for realistic implementation due to its heavy backhaul burden and computational complexity. To this end, we propose an alternative suboptimal algorithm which has low implementation overhead and complexity, based on minimum mean square error (MMSE precoding. Furthermore, we propose a distributed correlation-based antenna selection (DCAS algorithm combining with our optimization algorithms to reduce the static circuit power consumption for the SCN. Finally, simulation results demonstrate that our proposed suboptimal algorithm is very effective on power consumption minimization, with significantly reduced backhaul burden and computational complexity. Moreover, we show that our optimization algorithms with DCAS have less power consumption than the other benchmark algorithms.
Xiao, Dongming; Yang, Yongqiang; Su, Xubin; Wang, Di; Sun, Jianfeng
2013-01-01
The load-bearing bone implants materials should have sufficient stiffness and large porosity, which are interacted since larger porosity causes lower mechanical properties. This paper is to seek the maximum stiffness architecture with the constraint of specific volume fraction by topology optimization approach, that is, maximum porosity can be achieved with predefine stiffness properties. The effective elastic modulus of conventional cubic and topology optimized scaffolds were calculated using finite element analysis (FEA) method; also, some specimens with different porosities of 41.1%, 50.3%, 60.2% and 70.7% respectively were fabricated by Selective Laser Melting (SLM) process and were tested by compression test. Results showed that the computational effective elastic modulus of optimized scaffolds was approximately 13% higher than cubic scaffolds, the experimental stiffness values were reduced by 76% than the computational ones. The combination of topology optimization approach and SLM process would be available for development of titanium implants materials in consideration of both porosity and mechanical stiffness.
Energy Technology Data Exchange (ETDEWEB)
Vandewouw, Marlee M., E-mail: marleev@mie.utoronto.ca; Aleman, Dionne M. [Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8 (Canada); Jaffray, David A. [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada)
2016-08-15
Purpose: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an inverse treatment planning method for Gamma Knife® Perfexion™ that continuously delivers dose along a path in the target. Methods: The authors’ method is comprised of two steps: find a path within the target, then solve a mixed integer optimization model to find the optimal collimator configurations and durations along the selected path. Robotic path-finding techniques, specifically, simultaneous localization and mapping (SLAM) using an extended Kalman filter, are used to obtain a path that travels sufficiently close to selected isocentre locations. SLAM is novelly extended to explore a 3D, discrete environment, which is the target discretized into voxels. Further novel extensions are incorporated into the steering mechanism to account for target geometry. Results: The SLAM method was tested on seven clinical cases and compared to clinical, Hamiltonian path continuous delivery, and inverse step-and-shoot treatment plans. The SLAM approach improved dose metrics compared to the clinical plans and Hamiltonian path continuous delivery plans. Beam-on times improved over clinical plans, and had mixed performance compared to Hamiltonian path continuous plans. The SLAM method is also shown to be robust to path selection inaccuracies, isocentre selection, and dose distribution. Conclusions: The SLAM method for continuous delivery provides decreased total treatment time and increased treatment quality compared to both clinical and inverse step-and-shoot plans, and outperforms existing path methods in treatment quality. It also accounts for uncertainty in treatment planning by accommodating inaccuracies.
International Nuclear Information System (INIS)
Vandewouw, Marlee M.; Aleman, Dionne M.; Jaffray, David A.
2016-01-01
Purpose: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an inverse treatment planning method for Gamma Knife® Perfexion™ that continuously delivers dose along a path in the target. Methods: The authors’ method is comprised of two steps: find a path within the target, then solve a mixed integer optimization model to find the optimal collimator configurations and durations along the selected path. Robotic path-finding techniques, specifically, simultaneous localization and mapping (SLAM) using an extended Kalman filter, are used to obtain a path that travels sufficiently close to selected isocentre locations. SLAM is novelly extended to explore a 3D, discrete environment, which is the target discretized into voxels. Further novel extensions are incorporated into the steering mechanism to account for target geometry. Results: The SLAM method was tested on seven clinical cases and compared to clinical, Hamiltonian path continuous delivery, and inverse step-and-shoot treatment plans. The SLAM approach improved dose metrics compared to the clinical plans and Hamiltonian path continuous delivery plans. Beam-on times improved over clinical plans, and had mixed performance compared to Hamiltonian path continuous plans. The SLAM method is also shown to be robust to path selection inaccuracies, isocentre selection, and dose distribution. Conclusions: The SLAM method for continuous delivery provides decreased total treatment time and increased treatment quality compared to both clinical and inverse step-and-shoot plans, and outperforms existing path methods in treatment quality. It also accounts for uncertainty in treatment planning by accommodating inaccuracies.
Directory of Open Access Journals (Sweden)
Matheswaran Saravanan
2014-01-01
Full Text Available Wireless sensor network (WSN consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST. The proposed algorithm computes the distance-based Minimum Spanning Tree (MST of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm.
SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.
Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru
2014-01-01
Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search
Directory of Open Access Journals (Sweden)
Simon Fong
2013-01-01
Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
Li, Sui-Xian
2018-05-07
Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI). However, the traditional MLI approach is suboptimal due to the need to predefine the first filter of the selected filter set to be the maximum ℓ₂ norm among all available filters. An exhaustive imaging simulation with every single filter serving as the first filter is conducted to investigate the features of the most competent filter set. From the simulation, the characteristics of the most competent filter set are discovered. Besides minimization of the condition number, the geometric features of the best-performed filter set comprise a distinct transmittance peak along the wavelength axis of the first filter, a generally uniform distribution for the peaks of the filters and substantial overlaps of the transmittance curves of the adjacent filters. Therefore, the best-performed filter sets can be recognized intuitively by simple vector analysis and just a few experimental verifications. A practical two-step framework for selecting optimal filter set is recommended, which guarantees a significant enhancement of the performance of the systems. This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors.
Directory of Open Access Journals (Sweden)
Kuan-Cheng Lin
2015-01-01
Full Text Available Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.
Directory of Open Access Journals (Sweden)
Sui-Xian Li
2018-05-01
Full Text Available Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI. However, the traditional MLI approach is suboptimal due to the need to predefine the first filter of the selected filter set to be the maximum ℓ2 norm among all available filters. An exhaustive imaging simulation with every single filter serving as the first filter is conducted to investigate the features of the most competent filter set. From the simulation, the characteristics of the most competent filter set are discovered. Besides minimization of the condition number, the geometric features of the best-performed filter set comprise a distinct transmittance peak along the wavelength axis of the first filter, a generally uniform distribution for the peaks of the filters and substantial overlaps of the transmittance curves of the adjacent filters. Therefore, the best-performed filter sets can be recognized intuitively by simple vector analysis and just a few experimental verifications. A practical two-step framework for selecting optimal filter set is recommended, which guarantees a significant enhancement of the performance of the systems. This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors.
Directory of Open Access Journals (Sweden)
Qian Wang
2016-01-01
Full Text Available Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP. The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.
Zhang, Yu; Jin, Lei; Jiang, Dandan; Zou, Xingqi; Zhao, Zhiguo; Gao, Jing; Zeng, Ming; Zhou, Wenbin; Tang, Zhaoyun; Huo, Zongliang
2018-03-01
In order to optimize program disturbance characteristics effectively, a characterization approach that measures top select transistor (TSG) leakage from bit-line is proposed to quantify TSG leakage under program inhibit condition in 3D NAND flash memory. Based on this approach, the effect of Vth modulation of two-cell TSG on leakage is evaluated. By checking the dependence of leakage and corresponding program disturbance on upper and lower TSG Vth, this approach is validated. The optimal Vth pattern with high upper TSG Vth and low lower TSG Vth has been suggested for low leakage current and high boosted channel potential. It is found that upper TSG plays dominant role in preventing drain induced barrier lowering (DIBL) leakage from boosted channel to bit-line, while lower TSG assists to further suppress TSG leakage by providing smooth potential drop from dummy WL to edge of TSG, consequently suppressing trap assisted band-to-band tunneling current (BTBT) between dummy WL and TSG.
Optimal Training for Time-Selective Wireless Fading Channels Using Cutoff Rate
Directory of Open Access Journals (Sweden)
Tong Lang
2006-01-01
Full Text Available We consider the optimal allocation of resources—power and bandwidth—between training and data transmissions for single-user time-selective Rayleigh flat-fading channels under the cutoff rate criterion. The transmitter exploits statistical channel state information (CSI in the form of the channel Doppler spectrum to embed pilot symbols into the transmission stream. At the receiver, instantaneous, though imperfect, CSI is acquired through minimum mean-square estimation of the channel based on some set of pilot observations. We compute the ergodic cutoff rate for this scenario. Assuming estimator-based interleaving and -PSK inputs, we study two special cases in-depth. First, we derive the optimal resource allocation for the Gauss-Markov correlation model. Next, we validate and refine these insights by studying resource allocation for the Jakes model.
Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days
Directory of Open Access Journals (Sweden)
Qiu Jing
2017-08-01
Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.
Directory of Open Access Journals (Sweden)
Oforegbunam Thaddeus Ebiringa
2011-03-01
Full Text Available Traditional MIS has been made more effective through the integration of organization, human andtechnology factors into a decision matrix. The study is motivated by the need to find an optimal mixof interactive factors that will optimize the result of decision to apply ICT to manufacturingprocesses. The study used Factor analysis model based on the sampled opinion of forty (40operations/production managers and two thousand (2000 production line workers of three leadingmanufacturing firms: Uniliver Plc., PZ Plc, and Nigerian Breweries Plc operating in Aba IndustrialEstate of Nigeria. The results shows that a progressive mixed factor loading matrix, based on thepreferred ordered importance of resources factors in the formulation, implementation, monitoring,control and evaluation of ICT projects of the selected firms led to an average capability improvementof 0.764 in decision efficiency. This is considered strategic for achieving balanced corporate growthand development.
PORTFOLIO SELECTION OF INFORMATION SYSTEMS PROJECTS USING PROMETHEE V WITH C-OPTIMAL CONCEPT
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Jonatas A. de Almeida
2014-05-01
Full Text Available This paper presents a multicriteria decision model for selecting a portfolio of information system (IS projects, which integrates strategic and organizational view within a multicriteria decision structure. The PROMETHEE V method, based on outranking relations is applied, considering the c-optimal concept in order to overcome some scaling problems found in the classical PROMETHEE V approach. Then, a procedure is proposed in order to make a final analysis of the c-optimal portfolios found as a result of using PROMETHEE V. Also, the organizational view is discussed, including some factors that may influence decision making on IS projects to be included in the portfolio, such as adding the company's strategic vision and technical aspects that demonstrate how IS contributes value to a company's business.
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
Institute of Scientific and Technical Information of China (English)
Zeeshan Ahmad; Meng Jun; Muhammad Abdullah; Mazhar Nadeem Ishaq; Majid Lateef; Imran Khan
2015-01-01
This paper used the modern evaluation method of DEA (Data Envelopment Analysis) to assess the comparative efficiency and then on the basis of this among multiple schemes chose the optimal scheme of agricultural production structure adjustment. Based on the results of DEA model, we dissected scale advantages of each discretionary scheme or plan. We examined scale advantages of each discretionary scheme, tested profoundly a definitive purpose behind not-DEA efficient, which elucidated the system and methodology to enhance these discretionary plans. At the end, another method had been proposed to rank and select the optimal scheme. The research was important to guide the practice if the modification of agricultural production industrial structure was carried on.
Directory of Open Access Journals (Sweden)
Zahra Pourabdollahi
2017-12-01
Full Text Available Supplier evaluation and selection problem is among the most important of logistics decisions that have been addressed extensively in supply chain management. The same logistics decision is also important in freight transportation since it identifies trade relationships between business establishments and determines commodity flows between production and consumption points. The commodity flows are then used as input to freight transportation models to determine cargo movements and their characteristics including mode choice and shipment size. Various approaches have been proposed to explore this latter problem in previous studies. Traditionally, potential suppliers are evaluated and selected using only price/cost as the influential criteria and the state-of-practice methods. This paper introduces a hybrid agent-based computational economics and optimization approach for supplier selection. The proposed model combines an agent-based multi-criteria supplier evaluation approach with a multi-objective optimization model to capture both behavioral and economical aspects of the supplier selection process. The model uses a system of ordered response models to determine importance weights of the different criteria in supplier evaluation from a buyers’ point of view. The estimated weights are then used to calculate a utility for each potential supplier in the market and rank them. The calculated utilities are then entered into a mathematical programming model in which best suppliers are selected by maximizing the total accrued utility for all buyers and minimizing total shipping costs while balancing the capacity of potential suppliers to ensure market clearing mechanisms. The proposed model, herein, was implemented under an operational agent-based supply chain and freight transportation framework for the Chicago Metropolitan Area.
2014-01-01
Background The development of ‘energycane’ varieties of sugarcane is underway, targeting the use of both sugar juice and bagasse for ethanol production. The current study evaluated a selection of such ‘energycane’ cultivars for the combined ethanol yields from juice and bagasse, by optimization of dilute acid pretreatment optimization of bagasse for sugar yields. Method A central composite design under response surface methodology was used to investigate the effects of dilute acid pretreatment parameters followed by enzymatic hydrolysis on the combined sugar yield of bagasse samples. The pressed slurry generated from optimum pretreatment conditions (maximum combined sugar yield) was used as the substrate during batch and fed-batch simultaneous saccharification and fermentation (SSF) processes at different solid loadings and enzyme dosages, aiming to reach an ethanol concentration of at least 40 g/L. Results Significant variations were observed in sugar yields (xylose, glucose and combined sugar yield) from pretreatment-hydrolysis of bagasse from different cultivars of sugarcane. Up to 33% difference in combined sugar yield between best performing varieties and industrial bagasse was observed at optimal pretreatment-hydrolysis conditions. Significant improvement in overall ethanol yield after SSF of the pretreated bagasse was also observed from the best performing varieties (84.5 to 85.6%) compared to industrial bagasse (74.5%). The ethanol concentration showed inverse correlation with lignin content and the ratio of xylose to arabinose, but it showed positive correlation with glucose yield from pretreatment-hydrolysis. The overall assessment of the cultivars showed greater improvement in the final ethanol concentration (26.9 to 33.9%) and combined ethanol yields per hectare (83 to 94%) for the best performing varieties with respect to industrial sugarcane. Conclusions These results suggest that the selection of sugarcane variety to optimize ethanol
TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.
Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald
2018-01-01
Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.
Pretreatment of wastewater: Optimal coagulant selection using Partial Order Scaling Analysis (POSA)
International Nuclear Information System (INIS)
Tzfati, Eran; Sein, Maya; Rubinov, Angelika; Raveh, Adi; Bick, Amos
2011-01-01
Jar-test is a well-known tool for chemical selection for physical-chemical wastewater treatment. Jar test results show the treatment efficiency in terms of suspended matter and organic matter removal. However, in spite of having all these results, coagulant selection is not an easy task because one coagulant can remove efficiently the suspended solids but at the same time increase the conductivity. This makes the final selection of coagulants very dependent on the relative importance assigned to each measured parameter. In this paper, the use of Partial Order Scaling Analysis (POSA) and multi-criteria decision analysis is proposed to help the selection of the coagulant and its concentration in a sequencing batch reactor (SBR). Therefore, starting from the parameters fixed by the jar-test results, these techniques will allow to weight these parameters, according to the judgments of wastewater experts, and to establish priorities among coagulants. An evaluation of two commonly used coagulation/flocculation aids (Alum and Ferric Chloride) was conducted and based on jar tests and POSA model, Ferric Chloride (100 ppm) was the best choice. The results obtained show that POSA and multi-criteria techniques are useful tools to select the optimal chemicals for the physical-technical treatment.
A multi-fidelity analysis selection method using a constrained discrete optimization formulation
Stults, Ian C.
The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method which will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results. In many conceptual design programs, only limited data is available for quantifying model uncertainty. Because of this data sparsity, traditional probabilistic means for quantifying uncertainty should be reconsidered. This research proposes to instead quantify model uncertainty using an evidence theory formulation (also referred to as Dempster-Shafer theory) in lieu of the traditional probabilistic approach. Specific weaknesses in using evidence theory for quantifying model uncertainty are identified and addressed for the purposes of the Fidelity Selection Problem. A series of experiments was conducted to address these weaknesses using n-dimensional optimization test functions. These experiments found that model
International Nuclear Information System (INIS)
Di Maria, Costanzo; Liu, Chengyu; Zheng, Dingchang; Murray, Alan; Langley, Philip
2014-01-01
This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats 2 /min 2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013. (paper)
Optimization of multi-environment trials for genomic selection based on crop models.
Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J
2017-08-01
We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.
A reliable computational workflow for the selection of optimal screening libraries.
Gilad, Yocheved; Nadassy, Katalin; Senderowitz, Hanoch
2015-01-01
The experimental screening of compound collections is a common starting point in many drug discovery projects. Successes of such screening campaigns critically depend on the quality of the screened library. Many libraries are currently available from different vendors yet the selection of the optimal screening library for a specific project is challenging. We have devised a novel workflow for the rational selection of project-specific screening libraries. The workflow accepts as input a set of virtual candidate libraries and applies the following steps to each library: (1) data curation; (2) assessment of ADME/T profile; (3) assessment of the number of promiscuous binders/frequent HTS hitters; (4) assessment of internal diversity; (5) assessment of similarity to known active compound(s) (optional); (6) assessment of similarity to in-house or otherwise accessible compound collections (optional). For ADME/T profiling, Lipinski's and Veber's rule-based filters were implemented and a new blood brain barrier permeation model was developed and validated (85 and 74 % success rate for training set and test set, respectively). Diversity and similarity descriptors which demonstrated best performances in terms of their ability to select either diverse or focused sets of compounds from three databases (Drug Bank, CMC and CHEMBL) were identified and used for diversity and similarity assessments. The workflow was used to analyze nine common screening libraries available from six vendors. The results of this analysis are reported for each library providing an assessment of its quality. Furthermore, a consensus approach was developed to combine the results of these analyses into a single score for selecting the optimal library under different scenarios. We have devised and tested a new workflow for the rational selection of screening libraries under different scenarios. The current workflow was implemented using the Pipeline Pilot software yet due to the usage of generic
Heuristic Optimization Approach to Selecting a Transport Connection in City Public Transport
Directory of Open Access Journals (Sweden)
Kul’ka Jozef
2017-02-01
Full Text Available The article presents a heuristic optimization approach to select a suitable transport connection in the framework of a city public transport. This methodology was applied on a part of the public transport in Košice, because it is the second largest city in the Slovak Republic and its network of the public transport creates a complex transport system, which consists of three different transport modes, namely from the bus transport, tram transport and trolley-bus transport. This solution focused on examining the individual transport services and their interconnection in relevant interchange points.
Optimization of Sex Ratio in a Selection Plan for Palas Prolificacy Line
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Răzvan Popa
2011-05-01
Full Text Available The aim of the paper work is to optimize the sex ratio in a selection plan, according to model developed by King (1961, which will be proposed to be applied for prolificacy improvement in Prolific Line Palas. The method used in this paper work is modeling, which exist in the most animal breeding scientifically papers. After the simulations, we observed that the most convenient variant was that which prefigure use of 13 rams on reproduction activity. This variant offer a genetic gain per generation by 0.47497 additive standard deviations.
Directory of Open Access Journals (Sweden)
Santiago Ruiz
2015-01-01
Full Text Available This paper develops a methodology based on population genetics to improve the performance of two or more variables in job shop production systems. The methodology applies a genetic algorithm with special features in the individual selection when they pass from generation to generation. In comparison with the FIFO method, the proposed methodology showed better results in the variables makespan, idle time and energy cost. When compared with NSGA II, the methodology did not showed relevant differences in makespan and idle time; however better performance was obtained in energy cost and, especially, in the number of required iterations to get the optimal makespan.
Decision-Making Approach to Selecting Optimal Platform of Service Variants
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Vladimir Modrak
2016-01-01
Full Text Available Nowadays, it is anticipated that service sector companies will be inspired to follow mass customization trends of industrial sector. However, services are more abstract than products and therefore concepts for mass customization in manufacturing domain cannot be transformed without a methodical change. This paper is focused on the development of a methodological framework to support decisions in a selection of optimal platform of service variants when compatibility problems between service options occurred. The approach is based on mutual relations between waste and constrained design space entropy. For this purpose, software for quantification of constrained and waste design space is developed. Practicability of the methodology is presented on a realistic case.
Selection and optimization of mooring cables on floating platform for special purposes
Ma, Guang-ying; Yao, Yun-long; Zhao, Chen-yao
2017-08-01
This paper studied a new type of assembled marine floating platform for special purposes. The selection and optimization of mooring cables on the floating platform are studied. By using ANSYS AQWA software, the hydrodynamic model of the platform was established to calculate the time history response of the platform motion under complex water environments, such as wind, wave, current and mooring. On this basis, motion response and cable tension were calculated with different cable mooring states under the designed environmental load. Finally, the best mooring scheme to meet the cable strength requirements was proposed, which can lower the motion amplitude of the platform effectively.
Multi-objective optimization of cellular scanning strategy in selective laser melting
DEFF Research Database (Denmark)
Ahrari, Ali; Deb, Kalyanmoy; Mohanty, Sankhya
2017-01-01
The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study is to dev......The scanning strategy for selective laser melting - an additive manufacturing process - determines the temperature fields during the manufacturing process, which in turn affects residual stresses and distortions, two of the main sources of process-induced defects. The goal of this study......, the problem is a combination of combinatorial and choice optimization, which makes the problem difficult to solve. On a process simulation domain consisting of 32 cells, our multi-objective evolutionary method is able to find a set of trade-off solutions for the defined conflicting objectives, which cannot...
Noe, Frank
To efficiently simulate and generate understanding from simulations of complex macromolecular systems, the concept of slow collective coordinates or reaction coordinates is of fundamental importance. Here we will introduce variational approaches to approximate the slow coordinates and the reaction coordinates between selected end-states given MD simulations of the macromolecular system and a (possibly large) basis set of candidate coordinates. We will then discuss how to select physically intuitive order paremeters that are good surrogates of this variationally optimal result. These result can be used in order to construct Markov state models or other models of the stationary and kinetics properties, in order to parametrize low-dimensional / coarse-grained model of the dynamics. Deutsche Forschungsgemeinschaft, European Research Council.
AN APPLICATION OF FUZZY PROMETHEE METHOD FOR SELECTING OPTIMAL CAR PROBLEM
Directory of Open Access Journals (Sweden)
SERKAN BALLI
2013-06-01
Full Text Available Most of the economical, industrial, financial or political decision problems are multi-criteria. In these multi criteria problems, optimal selection of alternatives is hard and complex process. Recently, some kinds of methods are improved to solve these problems. Promethee is one of most efficient and easiest method and solves problems that consist quantitative criteria. However, in daily life, there are criteria which are explained as linguistic and cannot modeled numerical. Hence, Promethee method is incomplete for linguistic criteria which are imprecise. To satisfy this deficiency, fuzzy set approximation can be used. Promethee method, which is extended with using fuzzy inputs, is applied to car selection for seven different cars in same class by using criteria: price, fuel, performance and security. The obtained results are appropriate and consistent.
Contrast based band selection for optimized weathered oil detection in hyperspectral images
Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier
2012-09-01
Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore
International Nuclear Information System (INIS)
Al Moussawi, Houssein; Fardoun, Farouk; Louahlia-Gualous, Hasna
2016-01-01
Highlights: • Trigeneration technologies classified and reviewed according to prime movers. • Relevant heat recovery equipment discussed with thermal energy storage. • Trigeneration evaluated based on energy, exergy, economy, environment criteria. • Design, optimization, and decision-making methods classified and presented. • System selection suggested according to user preferences. - Abstract: Electricity, heating, and cooling are the three main components constituting the tripod of energy consumption in residential, commercial, and public buildings all around the world. Their separate generation causes higher fuel consumption, at a time where energy demands and fuel costs are continuously rising. Combined cooling, heating, and power (CCHP) or trigeneration could be a solution for such challenge yielding an efficient, reliable, flexible, competitive, and less pollutant alternative. A variety of trigeneration technologies are available and their proper choice is influenced by the employed energy system conditions and preferences. In this paper, different types of trigeneration systems are classified according to the prime mover, size and energy sequence usage. A leveled selection procedure is subsequently listed in the consecutive sections. The first level contains the applied prime mover technologies which are considered to be the heart of any CCHP system. The second level comprises the heat recovery equipment (heating and cooling) of which suitable selection should be compatible with the used prime mover. The third level includes the thermal energy storage system and heat transfer fluid to be employed. For each section of the paper, a survey of conducted studies with CHP/CCHP implementation is presented. A comprehensive table of evaluation criteria for such systems based on energy, exergy, economy, and environment measures is performed, along with a survey of the methods used in their design, optimization, and decision-making. Moreover, a classification
Optimization of chemical structure of Schottky-type selection diode for crossbar resistive memory.
Kim, Gun Hwan; Lee, Jong Ho; Jeon, Woojin; Song, Seul Ji; Seok, Jun Yeong; Yoon, Jung Ho; Yoon, Kyung Jean; Park, Tae Joo; Hwang, Cheol Seong
2012-10-24
The electrical performances of Pt/TiO(2)/Ti/Pt stacked Schottky-type diode (SD) was systematically examined, and this performance is dependent on the chemical structures of the each layer and their interfaces. The Ti layers containing a tolerable amount of oxygen showed metallic electrical conduction characteristics, which was confirmed by sheet resistance measurement with elevating the temperature, transmission line measurement (TLM), and Auger electron spectroscopy (AES) analysis. However, the chemical structure of SD stack and resulting electrical properties were crucially affected by the dissolved oxygen concentration in the Ti layers. The lower oxidation potential of the Ti layer with initially higher oxygen concentration suppressed the oxygen deficiency of the overlying TiO(2) layer induced by consumption of the oxygen from TiO(2) layer. This structure results in the lower reverse current of SDs without significant degradation of forward-state current. Conductive atomic force microscopy (CAFM) analysis showed the current conduction through the local conduction paths in the presented SDs, which guarantees a sufficient forward-current density as a selection device for highly integrated crossbar array resistive memory.
An ant colony optimization based feature selection for web page classification.
Saraç, Esra; Özel, Selma Ayşe
2014-01-01
The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.
Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection
Bogacz, Rafal; Martin Moraud, Eduardo; Abdi, Azzedine; Magill, Peter J.; Baufreton, Jérôme
2016-01-01
The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions. PMID:27389780
Energy Technology Data Exchange (ETDEWEB)
Odette, G Robert; Cunningham, Nicholas J., Wu, Yuan; Etienne, Auriane; Stergar, Erich; Yamamoto, Takuya
2012-02-21
The broad objective of this NEUP was to further develop a class of 12-15Cr ferritic alloys that are dispersion strengthened and made radiation tolerant by an ultrahigh density of Y-Ti-O nanofeatures (NFs) in the size range of less than 5 nm. We call these potentially transformable materials nanostructured ferritic alloys (NFAs). NFAs are typically processed by ball milling pre-alloyed rapidly solidified powders and yttria (Y2O3) powders. Proper milling effectively dissolves the Ti, Y and O solutes that precipitate as NFs during hot consolidation. The tasks in the present study included examining alternative processing paths, characterizing and optimizing the NFs and investigating solid state joining. Alternative processing paths involved rapid solidification by gas atomization of Fe, 14% Cr, 3% W, and 0.4% Ti powders that are also pre-alloyed with 0.2% Y (14YWT), where the compositions are in wt.%. The focus is on exploring the possibility of minimizing, or even eliminating, the milling time, as well as producing alloys with more homogeneous distributions of NFs and a more uniform, fine grain size. Three atomization environments were explored: Ar, Ar plus O (Ar/O) and He. The characterization of powders and alloys occurred through each processing step: powder production by gas atomization; powder milling; and powder annealing or hot consolidation by hot isostatic pressing (HIPing) or hot extrusion. The characterization studies of the materials described here include various combinations of: a) bulk chemistry; b) electron probe microanalysis (EPMA); c) atom probe tomography (APT); d) small angle neutron scattering (SANS); e) various types of scanning and transmission electron microscopy (SEM and TEM); and f) microhardness testing. The bulk chemistry measurements show that preliminary batches of gas-atomized powders could be produced within specified composition ranges. However, EPMA and TEM showed that the Y is heterogeneously distributed and phase separated, but
Selection of optimal multispectral imaging system parameters for small joint arthritis detection
Dolenec, Rok; Laistler, Elmar; Stergar, Jost; Milanic, Matija
2018-02-01
Early detection and treatment of arthritis is essential for a successful outcome of the treatment, but it has proven to be very challenging with existing diagnostic methods. Novel methods based on the optical imaging of the affected joints are becoming an attractive alternative. A non-contact multispectral imaging (MSI) system for imaging of small joints of human hands and feet is being developed. In this work, a numerical simulation of the MSI system is presented. The purpose of the simulation is to determine the optimal design parameters. Inflamed and unaffected human joint models were constructed with a realistic geometry and tissue distributions, based on a MRI scan of a human finger with a spatial resolution of 0.2 mm. The light transport simulation is based on a weighted-photon 3D Monte Carlo method utilizing CUDA GPU acceleration. An uniform illumination of the finger within the 400-1100 nm spectral range was simulated and the photons exiting the joint were recorded using different acceptance angles. From the obtained reflectance and transmittance images the spectral and spatial features most indicative of inflammation were identified. Optimal acceptance angle and spectral bands were determined. This study demonstrates that proper selection of MSI system parameters critically affects ability of a MSI system to discriminate the unaffected and inflamed joints. The presented system design optimization approach could be applied to other pathologies.
Anaerobic digester systems (ADS) for multiple dairy farms: A GIS analysis for optimal site selection
International Nuclear Information System (INIS)
Thompson, Ethan; Wang, Qingbin; Li, Minghao
2013-01-01
While anaerobic digester systems (ADS) have been increasingly adopted by large dairy farms to generate marketable energy products, like electricity, from animal manure, there is a growing need for assessing the feasibility of regional ADS for multiple farms that are not large enough to capitalize their own ADS. Using geographical information system (GIS) software, this study first identifies potential sites in a dairy region in Vermont, based on geographical conditions, current land use types, and energy distribution infrastructure criteria, and then selects the optimal sites for a given number of ADS, based on the number of dairy farms to be served, the primary energy input to output (PEIO) ratio of ADS, and the existing transportation network. This study suggests that GIS software is a valid technical tool for identifying the potential and optimal sites for ADS. The empirical findings provide useful information for assessing the returns of alternative numbers of ADS in this region, and the research procedures can be modified easily to incorporate any changes in the criteria for this region and can be applied in other regions with different conditions and criteria. - Highlights: • This study examines the feasibility of regional ADS for multiple dairy farms. • GIS is used to identify candidate sites and optimal locations for ADS in a dairy region. • Model includes environmental, social, infrastructure, and energy return criteria. • Empirical analysis provides scenario results on 1–15 ADS in the study region. • Method could be applied to other regions with different conditions and criteria
Optimal complex exponentials BEM and channel estimation in doubly selective channel
International Nuclear Information System (INIS)
Song, Lijun; Lei, Xia; Yu, Feng; Jin, Maozhu
2016-01-01
Over doubly selective channel, the optimal complex exponentials BEM (CE-BEM) is required to characterize the transmission in transform domain in order to reducing the huge number of the estimated parameters during directly estimating the impulse response in time domain. This paper proposed an improved CE-BEM to alleviating the high frequency sampling error caused by conventional CE-BEM. On the one hand, exploiting the improved CE-BEM, we achieve the sampling point is in the Doppler spread spectrum and the maximum sampling frequency is equal to the maximum Doppler shift. On the other hand we optimize the function and dimension of basis in CE-BEM respectively ,and obtain the closed solution of the EM based channel estimation differential operator by exploiting the above optimal BEM. Finally, the numerical results and theoretic analysis show that the dimension of basis is mainly depend on the maximum Doppler shift and signal-to-noise ratio (SNR), and if fixing the number of the pilot symbol, the dimension of basis is higher, the modeling error is smaller, while the accuracy of the parameter estimation is reduced, which implies that we need to achieve a tradeoff between the modeling error and the accuracy of the parameter estimation and the basis function influences the accuracy of describing the Doppler spread spectrum after identifying the dimension of the basis.
Where and how to manage: Optimal selection of conservation actions for multiple species.
Directory of Open Access Journals (Sweden)
Astrid van Teeffelen
2008-01-01
Full Text Available Multiple alternative options are frequently available for the protection, maintenance or restoration of conservation areas. The choice of a particular management action can have large effects on the species occurring in the area, because different actions have different effects on different species. Together with the fact that conservation funds are limited and particular management actions are costly, it would be desirable to be able to identify where, and what kind of management should be applied to maximize conservation benefits. Currently available site-selection algorithms can identify the optimal set of sites for a reserve network. However, these algorithms have not been designed to answer what kind of action would be most beneficial at these sites when multiple alternative actions are available. We describe an algorithm capable of solving multi-species planning problems with multiple management options per site. The algorithm is based on benefit functions, which translate the effect of a management action on species representation levels into a value, in order to identify the most beneficial option. We test the performance of this algorithm with simulated data for different types of benefit functions and show that the algorithm’s solutions are optimal, or very near globally optimal, partially depending on the type of benefit function used. The good performance of the proposed algorithm suggests that it could be profitably used for large multi-action multi-species conservation planning problems.
Zhang, David Z.; Zhang, Peng; Zhao, Miao; Jafar, Salman
2018-01-01
Developments in selective laser melting (SLM) have enabled the fabrication of periodic cellular lattice structures characterized by suitable properties matching the bone tissue well and by fluid permeability from interconnected structures. These multifunctional performances are significantly affected by cell topology and constitutive properties of applied materials. In this respect, a diamond unit cell was designed in particular volume fractions corresponding to the host bone tissue and optimized with a smooth surface at nodes leading to fewer stress concentrations. There were 33 porous titanium samples with different volume fractions, from 1.28 to 18.6%, manufactured using SLM. All of them were performed under compressive load to determine the deformation and failure mechanisms, accompanied by an in-situ approach using digital image correlation (DIC) to reveal stress–strain evolution. The results showed that lattice structures manufactured by SLM exhibited comparable properties to those of trabecular bone, avoiding the effects of stress-shielding and increasing longevity of implants. The curvature of optimized surface can play a role in regulating the relationship between density and mechanical properties. Owing to the release of stress concentration from optimized surface, the failure mechanism of porous titanium has been changed from the pattern of bottom-up collapse by layer (or cell row) to that of the diagonal (45°) shear band, resulting in the significant enhancement of the structural strength. PMID:29510492
Liu, Fei; Zhang, David Z; Zhang, Peng; Zhao, Miao; Jafar, Salman
2018-03-03
Developments in selective laser melting (SLM) have enabled the fabrication of periodic cellular lattice structures characterized by suitable properties matching the bone tissue well and by fluid permeability from interconnected structures. These multifunctional performances are significantly affected by cell topology and constitutive properties of applied materials. In this respect, a diamond unit cell was designed in particular volume fractions corresponding to the host bone tissue and optimized with a smooth surface at nodes leading to fewer stress concentrations. There were 33 porous titanium samples with different volume fractions, from 1.28 to 18.6%, manufactured using SLM. All of them were performed under compressive load to determine the deformation and failure mechanisms, accompanied by an in-situ approach using digital image correlation (DIC) to reveal stress-strain evolution. The results showed that lattice structures manufactured by SLM exhibited comparable properties to those of trabecular bone, avoiding the effects of stress-shielding and increasing longevity of implants. The curvature of optimized surface can play a role in regulating the relationship between density and mechanical properties. Owing to the release of stress concentration from optimized surface, the failure mechanism of porous titanium has been changed from the pattern of bottom-up collapse by layer (or cell row) to that of the diagonal (45°) shear band, resulting in the significant enhancement of the structural strength.
Optimization of ISSR-PCR reaction system and selection of primers in Bryum argenteum
Directory of Open Access Journals (Sweden)
Ma Xiaoying
2017-02-01
Full Text Available In order to determine optimum ISSR-PCR reaction system for moss Bryum argenteum,the concentrations of template DNA primers,dNTPs,Mg2+ and Taq DNA polymerase were optimized in four levels by PCR orthogonal experimental method. The appropriate primers were screened from 100 primers by temperature gradient PCR,and the optimal anneal temperature of the screened primers were determined. The results showed that the optimized 20 μL ISSR-PCR reaction system was as follows:template DNA 20 ng/20 μL,primers 0.45 μmol/L,Mg2+2.65 mmol/L,Taq DNA polymerase 0.4 U/20 μL,dNTPs 0.45 mmol/L. Using this system,50 primers with clear bands,repeatability well and polymorphism highly were selected from 100 primers. The establishment of this system,the screened primers and the annealing temperature could provide a theoretical basis for further research on the genetic diversity of bryophytes using ISSR molecular markers.
Seed selection by dark-eyed juncos (Junco hyemalis): optimal foraging with nutrient constraints?
Thompson, D B; Tomback, D F; Cunningham, M A; Baker, M C
1987-11-01
Observations of the foraging behavior of six captive dark-eyed juncos (Junco hyemalis) are used to test the assumptions and predictions of optimal diet choice models (Pyke et al. 1977) that include nutrients (Pulliam 1975). The birds sequentially encountered single seeds of niger thistle (Guizotia abyssinica) and of canary grass (Phalaris canariensis) on an artificial substrate in the laboratory. Niger thistle seeds were preferred by all birds although their profitability in terms of energy intake (J/s) was less than the profitability of canary grass seeds. Of four nutritional components used to calculate profitabilities (mg/s) lipid content was the only characteristic that could explain the junco's seed preference. As predicted by optimal diet theory the probability of consuming niger thistle seeds was independent of seed abundance. However, the consumption of 71-84% rather than 100% of the seeds encountered is not consistent with the prediction of all-or-nothing selection. Canary grass seeds were consumed at a constant rate (no./s) independent of the number of seeds encountered. This consumption pattern invalidates a model that assumes strict maximization. However, it is consistent with the assumption that canary grass seeds contain a nutrient which is required in minimum amounts to meet physiological demands (Pulliam 1975). These experiments emphasize the importance of incorporating nutrients into optimal foraging models and of combining seed preference studies with studies of the metabolic requirements of consumers.
Optimization of the Dutch Matrix Test by Random Selection of Sentences From a Preselected Subset
Directory of Open Access Journals (Sweden)
Rolph Houben
2015-04-01
Full Text Available Matrix tests are available for speech recognition testing in many languages. For an accurate measurement, a steep psychometric function of the speech materials is required. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function’s steepness. The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners. Based on half of the data set, first the sentences (140 out of 311 with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB were selected. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused second half of the data set. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. The resulting subset did not allow the construction of enough balanced test lists. Therefore, the measurement procedure was changed to randomly select the sentences during testing. Random selection may interfere with a representative occurrence of phonemes. However, in our material, the median phonemic occurrence remained close to that of the original test. This finding indicates that phonemic occurrence is not a critical factor. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function.
Optimality and stability of symmetric evolutionary games with applications in genetic selection.
Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun
2015-06-01
Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.
Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider
2016-01-01
Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-01-01
Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion
Deng, Ning
2014-01-01
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317
Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion
Directory of Open Access Journals (Sweden)
Ying Chen
2014-01-01
Full Text Available In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1, demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.
Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning
2014-01-01
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan
2014-01-01
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
Optimal Selection Method of Process Patents for Technology Transfer Using Fuzzy Linguistic Computing
Directory of Open Access Journals (Sweden)
Gangfeng Wang
2014-01-01
Full Text Available Under the open innovation paradigm, technology transfer of process patents is one of the most important mechanisms for manufacturing companies to implement process innovation and enhance the competitive edge. To achieve promising technology transfers, we need to evaluate the feasibility of process patents and optimally select the most appropriate patent according to the actual manufacturing situation. Hence, this paper proposes an optimal selection method of process patents using multiple criteria decision-making and 2-tuple fuzzy linguistic computing to avoid information loss during the processes of evaluation integration. An evaluation index system for technology transfer feasibility of process patents is designed initially. Then, fuzzy linguistic computing approach is applied to aggregate the evaluations of criteria weights for each criterion and corresponding subcriteria. Furthermore, performance ratings for subcriteria and fuzzy aggregated ratings of criteria are calculated. Thus, we obtain the overall technology transfer feasibility of patent alternatives. Finally, a case study of aeroengine turbine manufacturing is presented to demonstrate the applicability of the proposed method.
Analysis Methodology for Optimal Selection of Ground Station Site in Space Missions
Nieves-Chinchilla, J.; Farjas, M.; Martínez, R.
2013-12-01
Optimization of ground station sites is especially important in complex missions that include several small satellites (clusters or constellations) such as the QB50 project, where one ground station would be able to track several spatial vehicles, even simultaneously. In this regard the design of the communication system has to carefully take into account the ground station site and relevant signal phenomena, depending on the frequency band. To propose the optimal location of the ground station, these aspects become even more relevant to establish a trusted communication link due to the ground segment site in urban areas and/or selection of low orbits for the space segment. In addition, updated cartography with high resolution data of the location and its surroundings help to develop recommendations in the design of its location for spatial vehicles tracking and hence to improve effectiveness. The objectives of this analysis methodology are: completion of cartographic information, modelling the obstacles that hinder communication between the ground and space segment and representation in the generated 3D scene of the degree of impairment in the signal/noise of the phenomena that interferes with communication. The integration of new technologies of geographic data capture, such as 3D Laser Scan, determine that increased optimization of the antenna elevation mask, in its AOS and LOS azimuths along the horizon visible, maximizes visibility time with spatial vehicles. Furthermore, from the three-dimensional cloud of points captured, specific information is selected and, using 3D modeling techniques, the 3D scene of the antenna location site and surroundings is generated. The resulting 3D model evidences nearby obstacles related to the cartographic conditions such as mountain formations and buildings, and any additional obstacles that interfere with the operational quality of the antenna (other antennas and electronic devices that emit or receive in the same bandwidth
Shang, Han Lin
2015-01-01
The Box-Cox transformation can sometimes yield noticeable improvements in model simplicity, variance homogeneity and precision of estimation, such as in modelling and forecasting age-specific fertility. Despite its importance, there have been few studies focusing on the optimal selection of Box-Cox transformation parameters in demographic forecasting. A simple method is proposed for selecting the optimal Box-Cox transformation parameter, along with an algorithm based on an in-sample forecast ...
International Nuclear Information System (INIS)
Stassi, D.; Ma, H.; Schmidt, T. G.; Dutta, S.; Soderman, A.; Pazzani, D.; Gros, E.; Okerlund, D.
2016-01-01
Purpose: Reconstructing a low-motion cardiac phase is expected to improve coronary artery visualization in coronary computed tomography angiography (CCTA) exams. This study developed an automated algorithm for selecting the optimal cardiac phase for CCTA reconstruction. The algorithm uses prospectively gated, single-beat, multiphase data made possible by wide cone-beam imaging. The proposed algorithm differs from previous approaches because the optimal phase is identified based on vessel image quality (IQ) directly, compared to previous approaches that included motion estimation and interphase processing. Because there is no processing of interphase information, the algorithm can be applied to any sampling of image phases, making it suited for prospectively gated studies where only a subset of phases are available. Methods: An automated algorithm was developed to select the optimal phase based on quantitative IQ metrics. For each reconstructed slice at each reconstructed phase, an image quality metric was calculated based on measures of circularity and edge strength of through-plane vessels. The image quality metric was aggregated across slices, while a metric of vessel-location consistency was used to ignore slices that did not contain through-plane vessels. The algorithm performance was evaluated using two observer studies. Fourteen single-beat cardiac CT exams (Revolution CT, GE Healthcare, Chalfont St. Giles, UK) reconstructed at 2% intervals were evaluated for best systolic (1), diastolic (6), or systolic and diastolic phases (7) by three readers and the algorithm. Pairwise inter-reader and reader-algorithm agreement was evaluated using the mean absolute difference (MAD) and concordance correlation coefficient (CCC) between the reader and algorithm-selected phases. A reader-consensus best phase was determined and compared to the algorithm selected phase. In cases where the algorithm and consensus best phases differed by more than 2%, IQ was scored by three
Directory of Open Access Journals (Sweden)
Mark N Read
2016-09-01
Full Text Available The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto
Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.
2010-01-01
A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624
Cooperative path planning of unmanned aerial vehicles
Tsourdos, Antonios; Shanmugavel, Madhavan
2010-01-01
An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre-planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully. Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space-borne vehicles. Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in...
Helgeson, Melvin D; Kang, Daniel G; Lehman, Ronald A; Dmitriev, Anton E; Luhmann, Scott J
2013-08-01
There is currently no reliable technique for intraoperative assessment of pedicle screw fixation strength and optimal screw size. Several studies have evaluated pedicle screw insertional torque (IT) and its direct correlation with pullout strength. However, there is limited clinical application with pedicle screw IT as it must be measured during screw placement and rarely causes the spine surgeon to change screw size. To date, no study has evaluated tapping IT, which precedes screw insertion, and its ability to predict pedicle screw pullout strength. The objective of this study was to investigate tapping IT and its ability to predict pedicle screw pullout strength and optimal screw size. In vitro human cadaveric biomechanical analysis. Twenty fresh-frozen human cadaveric thoracic vertebral levels were prepared and dual-energy radiographic absorptiometry scanned for bone mineral density (BMD). All specimens were osteoporotic with a mean BMD of 0.60 ± 0.07 g/cm(2). Five specimens (n=10) were used to perform a pilot study, as there were no previously established values for optimal tapping IT. Each pedicle during the pilot study was measured using a digital caliper as well as computed tomography measurements, and the optimal screw size was determined to be equal to or the first size smaller than the pedicle diameter. The optimal tap size was then selected as the tap diameter 1 mm smaller than the optimal screw size. During optimal tap size insertion, all peak tapping IT values were found to be between 2 in-lbs and 3 in-lbs. Therefore, the threshold tapping IT value for optimal pedicle screw and tap size was determined to be 2.5 in-lbs, and a comparison tapping IT value of 1.5 in-lbs was selected. Next, 15 test specimens (n=30) were measured with digital calipers, probed, tapped, and instrumented using a paired comparison between the two threshold tapping IT values (Group 1: 1.5 in-lbs; Group 2: 2.5 in-lbs), randomly assigned to the left or right pedicle on each
Pashaei, Elnaz; Pashaei, Elham; Aydin, Nizamettin
2018-04-14
In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmark and three clinical microarrays. The experimental results and statistical analysis confirm the better performance of the BPSO (4-2)-BBHA compared with the BBHA, the BPSO (4-2) and several state-of-the-art methods in terms of avoiding local minima, convergence rate, accuracy and number of selected genes. The results also show that the BPSO (4-2)-BBHA model can successfully identify known biologically and statistically significant genes from the clinical datasets. Copyright © 2018 Elsevier Inc. All rights reserved.
Mi, Zhibao; Novitzky, Dimitri; Collins, Joseph F; Cooper, David Kc
2015-01-01
The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy) is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA), are statistically conservative. Hsu's multiple comparisons with the best (MCB) - adapted from the Dunnett's multiple comparisons with control (MCC) - has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM) or generalized linear mixed models (GLMM), and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS), among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation.
An ILP based Algorithm for Optimal Customer Selection for Demand Response in SmartGrids
Energy Technology Data Exchange (ETDEWEB)
Kuppannagari, Sanmukh R. [Univ. of Southern California, Los Angeles, CA (United States); Kannan, Rajgopal [Louisiana State Univ., Baton Rouge, LA (United States); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)
2015-12-07
Demand Response (DR) events are initiated by utilities during peak demand periods to curtail consumption. They ensure system reliability and minimize the utility’s expenditure. Selection of the right customers and strategies is critical for a DR event. An effective DR scheduling algorithm minimizes the curtailment error which is the absolute difference between the achieved curtailment value and the target. State-of-the-art heuristics exist for customer selection, however their curtailment errors are unbounded and can be as high as 70%. In this work, we develop an Integer Linear Programming (ILP) formulation for optimally selecting customers and curtailment strategies that minimize the curtailment error during DR events in SmartGrids. We perform experiments on real world data obtained from the University of Southern California’s SmartGrid and show that our algorithm achieves near exact curtailment values with errors in the range of 10^{-7} to 10^{-5}, which are within the range of numerical errors. We compare our results against the state-of-the-art heuristic being deployed in practice in the USC SmartGrid. We show that for the same set of available customer strategy pairs our algorithm performs 103 to 107 times better in terms of the curtailment errors incurred.
International Nuclear Information System (INIS)
Biring, Shyamal Kumar; Chaudhury, Pinaki
2012-01-01
Highlights: ► Estimation of critical points in Noble-gas clusters. ► Evaluation of first order saddle point or transition states. ► Construction of reaction path for structural change in clusters. ► Use of Monte-Carlo Simulated Annealing to study structural changes. - Abstract: This paper proposes Simulated Annealing based search to locate critical points in mixed noble gas clusters where Ne and Xe are individually doped in Ar-clusters. Using Lennard–Jones (LJ) atomic interaction we try to explore the search process of transformation through Minimum Energy Path (MEP) from one minimum energy geometry to another via first order saddle point on the potential energy surface of the clusters. Here we compare the results based on diagonalization of the full Hessian all through the search and quasi-gradient only technique to search saddle points and construction of reaction path (RP) for three sizes of doped Ar-clusters, (Ar) 19 Ne/Xe,(Ar) 24 Ne/Xe and (Ar) 29 Ne/Xe.
Keenan, Martine; Alexander, Paul W; Chaplin, Jason H; Abbott, Michael J; Diao, Hugo; Wang, Zhisen; Best, Wayne M; Perez, Catherine J; Cornwall, Scott M J; Keatley, Sarah K; Thompson, R C Andrew; Charman, Susan A; White, Karen L; Ryan, Eileen; Chen, Gong; Ioset, Jean-Robert; von Geldern, Thomas W; Chatelain, Eric
2013-10-01
Inhibitors of Trypanosoma cruzi with novel mechanisms of action are urgently required to diversify the current clinical and preclinical pipelines. Increasing the number and diversity of hits available for assessment at the beginning of the discovery process will help to achieve this aim. We report the evaluation of multiple hits generated from a high-throughput screen to identify inhibitors of T. cruzi and from these studies the discovery of two novel series currently in lead optimization. Lead compounds from these series potently and selectively inhibit growth of T. cruzi in vitro and the most advanced compound is orally active in a subchronic mouse model of T. cruzi infection. High-throughput screening of novel compound collections has an important role to play in diversifying the trypanosomatid drug discovery portfolio. A new T. cruzi inhibitor series with good drug-like properties and promising in vivo efficacy has been identified through this process.
Directory of Open Access Journals (Sweden)
Zhongbo Hu
2014-01-01
Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.
Energetical optimization and parameters selection for a fixed faceted mirror concentrator
International Nuclear Information System (INIS)
Nicolas, R.O.; Duran, J.C.; Dawidowski, L.E.
1990-01-01
A method which allows to select the parameters of a cylindrical solar collector by means of an energetical optimization is presented. In particular, the energy collected by the operating fluid and the collection efficiency of a Fixed Faceted Mirror Concentrator (FFMC) are obtained and compared for different sets of parameters. To this end, the two-dimensional optical analysis for non-perfect cylindrical concentrators presented previously is used. Some graphs analyzing the variations of the yearly efficiency of the FFMC as a function of those parameters are given. Finally, the possibility of using a second concentrator in the receiver plane of the FFMC in order to improve the whole efficiency of the prototype is also analyzed. (Author)
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, J; Zhang, Qi; Fitzek, F H P
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...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...
Directory of Open Access Journals (Sweden)
Ioannis P. Panapakidis
2018-02-01
Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.
Centralized management for LA and MA waste selection and optimization of processes
International Nuclear Information System (INIS)
Medal, G.
1985-01-01
The procedure currently used for removal of process waste produced by EDF Nuclear Power Plants consists in the local embedding of the waste on each EDF site, the embedded waste is then shipped to a National Final Storage Center. The method used is a financial limitation of opportunities for amendment of containement and volume reduction techniques. The work made by the Commissariat a l'Energie Atomique and its subsidiary TECHNICATOME on behalf of the French Electricite Board (EDF) aim at the removal of waste ''in bulk'' with minimum possible pretreatment in compliance with transport regulation, treatment and conditioning taking place in a centralized waste treatment station so as to allow final storage. This method enables: optimization of the management of waste, selection of safe treatment-processes, storage volume reduction, lower investment and operating costs [fr
Directory of Open Access Journals (Sweden)
Mi Z
2014-12-01
Full Text Available Zhibao Mi,1 Dimitri Novitzky,2 Joseph F Collins,1 David KC Cooper3 1Cooperative Studies Program Coordinating Center, VA Maryland Health Care Systems, Perry Point, MD, USA; 2Department of Cardiothoracic Surgery, University of South Florida, Tampa, FL, USA; 3Thomas E Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, USA Abstract: The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA, are statistically conservative. Hsu’s multiple comparisons with the best (MCB – adapted from the Dunnett’s multiple comparisons with control (MCC – has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM or generalized linear mixed models (GLMM, and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS, among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation. Keywords: best treatment selection, brain-dead organ donors, hormonal replacement, multiple binary endpoints, organ procurement, multiple comparisons
Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization
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Iwan Syarif
2016-12-01
Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.
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Amir Ghanifar
2016-06-01
Full Text Available Introduction The concentration of substances, including urea, creatinine, and uric acid, can be used as an index to measure toxic uremic solutes in the blood during dialysis and interdialytic intervals. The on-line monitoring of toxin concentration allows for the clearance measurement of some low-molecular-weight solutes at any time during hemodialysis.The aim of this study was to determine the optimal wavelength for estimating the changes in urea, creatinine, and uric acid in dialysate, using ultraviolet (UV spectroscopy. Materials and Methods In this study, nine uremic patients were investigated, using on-line spectrophotometry. The on-line absorption measurements (UV radiation were performed with a spectrophotometer module, connected to the fluid outlet of the dialysis machine. Dialysate samples were obtained and analyzed, using standard biochemical methods. Optimal wavelengths for both creatinine and uric acid were selected by using a combination of genetic algorithms (GAs, i.e., GA-partial least squares (GA-PLS and interval partial least squares (iPLS. Results The Artifitial Neural Network (ANN sensitivity analysis determined the wavelengths of the UV band most suitable for estimating the concentration of creatinine and uric acid. The two optimal wavelengths were 242 and 252 nm for creatinine and 295 and 298 nm for uric acid. Conclusion It can be concluded that the reduction ratio of creatinine and uric acid (dialysis efficiency could be continuously monitored during hemodialysis by UV spectroscopy.Compared to the conventional method, which is particularly sensitive to the sampling technique and involves post-dialysis blood sampling, iterative measurements throughout the dialysis session can yield more reliable data.
Franklin, Oskar; Han, Wang; Dieckmann, Ulf; Cramer, Wolfgang; Brännström, Åke; Pietsch, Stephan; Rovenskaya, Elena; Prentice, Iain Colin
2017-04-01
Dynamic global vegetation models (DGVMs) are now indispensable for understanding the biosphere and for estimating the capacity of ecosystems to provide services. The models are continuously developed to include an increasing number of processes and to utilize the growing amounts of observed data becoming available. However, while the versatility of the models is increasing as new processes and variables are added, their accuracy suffers from the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behaviour. We have initiated a collaborative working group to address this problem based on a 'missing law' - adaptation and optimization principles rooted in natural selection. Even though this 'missing law' constrains relationships between traits, and therefore can vastly reduce the number of uncertain parameters in ecosystem models, it has rarely been applied to DGVMs. Our recent research have shown that optimization- and trait-based models of gross primary production can be both much simpler and more accurate than current models based on fixed functional types, and that observed plant carbon allocations and distributions of plant functional traits are predictable with eco-evolutionary models. While there are also many other examples of the usefulness of these and other theoretical principles, it is not always straight-forward to make them operational in predictive models. In particular on longer time scales, the representation of functional diversity and the dynamical interactions among individuals and species presents a formidable challenge. Here we will present recent ideas on the use of adaptation and optimization principles in vegetation models, including examples of promising developments, but also limitations of the principles and some key challenges.
Sheu, Yun Robert; Feder, Elie; Balsim, Igor; Levin, Victor F; Bleicher, Andrew G; Branstetter, Barton F
2010-06-01
Peer review is an essential process for physicians because it facilitates improved quality of patient care and continuing physician learning and improvement. However, peer review often is not well received by radiologists who note that it is time intensive, is subjective, and lacks a demonstrable impact on patient care. Current advances in peer review include the RADPEER() system, with its standardization of discrepancies and incorporation of the peer-review process into the PACS itself. The purpose of this study was to build on RADPEER and similar systems by using a mathematical model to optimally select the types of cases to be reviewed, for each radiologist undergoing review, on the basis of the past frequency of interpretive error, the likelihood of morbidity from an error, the financial cost of an error, and the time required for the reviewing radiologist to interpret the study. The investigators compiled 612,890 preliminary radiology reports authored by residents and attending radiologists at a large tertiary care medical center from 1999 to 2004. Discrepancies between preliminary and final interpretations were classified by severity and validated by repeat review of major discrepancies. A mathematical model was then used to calculate, for each author of a preliminary report, the combined morbidity and financial costs of expected errors across 3 modalities (MRI, CT, and conventional radiography) and 4 departmental divisions (neuroradiology, abdominal imaging, musculoskeletal imaging, and thoracic imaging). A customized report was generated for each on-call radiologist that determined the category (modality and body part) with the highest total cost function. A universal total cost based on probability data from all radiologists was also compiled. The use of mathematical models to guide case selection could optimize the efficiency and effectiveness of physician time spent on peer review and produce more concrete and meaningful feedback to radiologists
Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
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Gulnaz Ahmed
2017-02-01
Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.
Adaptive Spot Detection With Optimal Scale Selection in Fluorescence Microscopy Images.
Basset, Antoine; Boulanger, Jérôme; Salamero, Jean; Bouthemy, Patrick; Kervrann, Charles
2015-11-01
Accurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) automatically selects the optimal scale corresponding to the most frequent spot size in the image. Four criteria are proposed and compared to determine the optimal scale in a scale-space framework. Then, the segmentation stage amounts to thresholding the LoG of the intensity image. In contrast to other methods, the threshold is locally adapted given a probability of false alarm (PFA) specified by the user for the whole set of images to be processed. The local threshold is automatically derived from the PFA value and local image statistics estimated in a window whose size is not a critical parameter. We also propose a new data set for benchmarking, consisting of six collections of one hundred images each, which exploits backgrounds extracted from real microscopy images. We have carried out an extensive comparative evaluation on several data sets with ground-truth, which demonstrates that ATLAS outperforms existing methods. ATLAS does not need any fine parameter tuning and requires very low computation time. Convincing results are also reported on real total internal reflection fluorescence microscopy images.
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Qianqian Wang
2015-12-01
Full Text Available Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV Model, and together with GIS (Geographical Information System and AUC (Area Under Receiver Operating Characteristic Curve test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of “no data in subclass” well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.
Hanks, Brantley R.; Skelton, Robert E.
1991-01-01
Vibration in modern structural and mechanical systems can be reduced in amplitude by increasing stiffness, redistributing stiffness and mass, and/or adding damping if design techniques are available to do so. Linear Quadratic Regulator (LQR) theory in modern multivariable control design, attacks the general dissipative elastic system design problem in a global formulation. The optimal design, however, allows electronic connections and phase relations which are not physically practical or possible in passive structural-mechanical devices. The restriction of LQR solutions (to the Algebraic Riccati Equation) to design spaces which can be implemented as passive structural members and/or dampers is addressed. A general closed-form solution to the optimal free-decay control problem is presented which is tailored for structural-mechanical system. The solution includes, as subsets, special cases such as the Rayleigh Dissipation Function and total energy. Weighting matrix selection is a constrained choice among several parameters to obtain desired physical relationships. The closed-form solution is also applicable to active control design for systems where perfect, collocated actuator-sensor pairs exist.
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Seonghoon Moon
2016-01-01
Full Text Available With the proliferation of high-performance, large-screen mobile devices, users’ expectations of having access to high-resolution video content in smooth network environments are steadily growing. To guarantee such stable streaming, a high cellular network bandwidth is required; yet network providers often charge high prices for even limited data plans. Moreover, the costs of smoothly streaming high-resolution videos are not merely monetary; the device’s battery life must also be accounted for. To resolve these problems, we design an optimal multi-interface selection system for streaming video over HTTP/TCP. An optimization problem including battery life and LTE data constraints is derived and then solved using binary integer programming. Additionally, the system is designed with an adoption of split-layer scalable video coding, which provides direct adaptations of video quality and prevents out-of-order packet delivery problems. The proposed system is evaluated using a prototype application in a real, iOS-based device as well as through experiments conducted in heterogeneous mobile scenarios. Results show that the system not only guarantees the highest-possible video quality, but also prevents reckless consumption of LTE data and battery life.
junfeng, Li; zhengying, Wei
2017-11-01
Process optimization and microstructure characterization of Ti6Al4V manufactured by selective laser melting (SLM) were investigated in this article. The relative density of sampled fabricated by SLM is influenced by the main process parameters, including laser power, scan speed and hatch distance. The volume energy density (VED) was defined to account for the combined effect of the main process parameters on the relative density. The results shown that the relative density changed with the change of VED and the optimized process interval is 55˜60J/mm3. Furthermore, compared with laser power, scan speed and hatch distance by taguchi method, it was found that the scan speed had the greatest effect on the relative density. Compared with the microstructure of the cross-section of the specimen at different scanning speeds, it was found that the microstructures at different speeds had similar characteristics, all of them were needle-like martensite distributed in the β matrix, but with the increase of scanning speed, the microstructure is finer and the lower scan speed leads to coarsening of the microstructure.
Optimization of Selective Laser Melting by Evaluation Method of Multiple Quality Characteristics
Khaimovich, A. I.; Stepanenko, I. S.; Smelov, V. G.
2018-01-01
Article describes the adoption of the Taguchi method in selective laser melting process of sector of combustion chamber by numerical and natural experiments for achieving minimum temperature deformation. The aim was to produce a quality part with minimum amount of numeric experiments. For the study, the following optimization parameters (independent factors) were chosen: the laser beam power and velocity; two factors for compensating the effect of the residual thermal stresses: the scale factor of the preliminary correction of the part geometry and the number of additional reinforcing elements. We used an orthogonal plan of 9 experiments with a factor variation at three levels (L9). As quality criterias, the values of distortions for 9 zones of the combustion chamber and the maximum strength of the material of the chamber were chosen. Since the quality parameters are multidirectional, a grey relational analysis was used to solve the optimization problem for multiple quality parameters. As a result, according to the parameters obtained, the combustion chamber segments of the gas turbine engine were manufactured.
Trotter, B Wesley; Nanda, Kausik K; Burgey, Christopher S; Potteiger, Craig M; Deng, James Z; Green, Ahren I; Hartnett, John C; Kett, Nathan R; Wu, Zhicai; Henze, Darrell A; Della Penna, Kimberly; Desai, Reshma; Leitl, Michael D; Lemaire, Wei; White, Rebecca B; Yeh, Suzie; Urban, Mark O; Kane, Stefanie A; Hartman, George D; Bilodeau, Mark T
2011-04-15
A new series of imidazopyridine CB2 agonists is described. Structural optimization improved CB2/CB1 selectivity in this series and conferred physical properties that facilitated high in vivo exposure, both centrally and peripherally. Administration of a highly selective CB2 agonist in a rat model of analgesia was ineffective despite substantial CNS exposure, while administration of a moderately selective CB2/CB1 agonist exhibited significant analgesic effects. Copyright © 2011 Elsevier Ltd. All rights reserved.
Mahalakshmi; Murugesan, R.
2018-04-01
This paper regards with the minimization of total cost of Greenhouse Gas (GHG) efficiency in Automated Storage and Retrieval System (AS/RS). A mathematical model is constructed based on tax cost, penalty cost and discount cost of GHG emission of AS/RS. A two stage algorithm namely positive selection based clonal selection principle (PSBCSP) is used to find the optimal solution of the constructed model. In the first stage positive selection principle is used to reduce the search space of the optimal solution by fixing a threshold value. In the later stage clonal selection principle is used to generate best solutions. The obtained results are compared with other existing algorithms in the literature, which shows that the proposed algorithm yields a better result compared to others.
Sutrisno; Widowati; Heru Tjahjana, R.
2017-01-01
In this paper, we propose a mathematical model in the form of dynamic/multi-stage optimization to solve an integrated supplier selection problem and tracking control problem of single product inventory system with product discount. The product discount will be stated as a piece-wise linear function. We use dynamic programming to solve this proposed optimization to determine the optimal supplier and the optimal product volume that will be purchased from the optimal supplier for each time period so that the inventory level tracks a reference trajectory given by decision maker with minimal total cost. We give a numerical experiment to evaluate the proposed model. From the result, the optimal supplier was determined for each time period and the inventory level follows the given reference well.
International Nuclear Information System (INIS)
Sutrisno; Widowati; Solikhin
2016-01-01
In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well. (paper)
An Optimization Model for the Selection of Bus-Only Lanes in a City.
Chen, Qun
2015-01-01
The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.
An Optimization Model for the Selection of Bus-Only Lanes in a City.
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Qun Chen
Full Text Available The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.
The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns
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Guohua Cao
2013-01-01
Full Text Available The aims of this paper are to use a birandom variable to denote the stock return selected by some recurring technical patterns and to study the effect of exit strategy on optimal portfolio selection with birandom returns. Firstly, we propose a new method to estimate the stock return and use birandom distribution to denote the final stock return which can reflect the features of technical patterns and investors' heterogeneity simultaneously; secondly, we build a birandom safety-first model and design a hybrid intelligent algorithm to help investors make decisions; finally, we innovatively study the effect of exit strategy on the given birandom safety-first model. The results indicate that (1 the exit strategy affects the proportion of portfolio, (2 the performance of taking the exit strategy is better than when the exit strategy is not taken, if the stop-loss point and the stop-profit point are appropriately set, and (3 the investor using the exit strategy become conservative.
Demerouti, Evangelia; Bakker, Arnold B; Leiter, Michael
2014-01-01
The present study aims to explain why research thus far has found only low to moderate associations between burnout and performance. We argue that employees use adaptive strategies that help them to maintain their performance (i.e., task performance, adaptivity to change) at acceptable levels despite experiencing burnout (i.e., exhaustion, disengagement). We focus on the strategies included in the selective optimization with compensation model. Using a sample of 294 employees and their supervisors, we found that compensation is the most successful strategy in buffering the negative associations of disengagement with supervisor-rated task performance and both disengagement and exhaustion with supervisor-rated adaptivity to change. In contrast, selection exacerbates the negative relationship of exhaustion with supervisor-rated adaptivity to change. In total, 42% of the hypothesized interactions proved to be significant. Our study uncovers successful and unsuccessful strategies that people use to deal with their burnout symptoms in order to achieve satisfactory job performance. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Darmon, David
2018-03-01
In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.
Zwicker, Jeffery D; Diaz, Nicolas A; Guerra, Alfredo J; Kirchhoff, Paul D; Wen, Bo; Sun, Duxin; Carruthers, Vern B; Larsen, Scott D
2018-06-01
The neurotropic protozoan Toxoplasma gondii is the second leading cause of death due to foodborne illness in the US, and has been designated as one of five neglected parasitic infections by the Center for Disease Control and Prevention. Currently, no treatment options exist for the chronic dormant-phase Toxoplasma infection in the central nervous system (CNS). T. gondii cathepsin L (TgCPL) has recently been implicated as a novel viable target for the treatment of chronic toxoplasmosis. In this study, we report the first body of SAR work aimed at developing potent inhibitors of TgCPL with selectivity vs the human cathepsin L. Starting from a known inhibitor of human cathepsin L, and guided by structure-based design, we were able to modulate the selectivity for Toxoplasma vs human CPL by nearly 50-fold while modifying physiochemical properties to be more favorable for metabolic stability and CNS penetrance. The overall potency of our inhibitors towards TgCPL was improved from 2 μM to as low as 110 nM and we successfully demonstrated that an optimized analog 18b is capable of crossing the BBB (0.5 brain/plasma). This work is an important first step toward development of a CNS-penetrant probe to validate TgCPL as a feasible target for the treatment of chronic toxoplasmosis. Copyright © 2018 Elsevier Ltd. All rights reserved.
Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta
2016-06-01
With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.
Optimization of Cu-Zn Massive Sulphide Flotation by Selective Reagents
Soltani, F.; Koleini, S. M. J.; Abdollahy, M.
2014-10-01
Selective floatation of base metal sulphide minerals can be achieved by using selective reagents. Sequential floatation of chalcopyrite-sphalerite from Taknar (Iran) massive sulphide ore with 3.5 % Zn and 1.26 % Cu was studied. D-optimal design of response surface methodology was used. Four mixed collector types (Aer238 + SIPX, Aero3477 + SIPX, TC1000 + SIPX and X231 + SIPX), two depressant systems (CuCN-ZnSO4 and dextrin-ZnSO4), pH and ZnSO4 dosage were considered as operational factors in the first stage of flotation. Different conditions of pH, CuSO4 dosage and SIPX dosage were studied for sphalerite flotation from first stage tailings. Aero238 + SIPX induced better selectivity for chalcopyrite against pyrite and sphalerite. Dextrin-ZnSO4 was as effective as CuCN-ZnSO4 in sphalerite-pyrite depression. Under optimum conditions, Cu recovery, Zn recovery and pyrite content in Cu concentrate were 88.99, 33.49 and 1.34 % by using Aero238 + SIPX as mixed collector, CuCN-ZnSO4 as depressant system, at ZnSO4 dosage of 200 g/t and pH 10.54. When CuCN was used at the first stage, CuSO4 consumption increased and Zn recovery decreased during the second stage. Maximum Zn recovery was 72.19 % by using 343.66 g/t of CuSO4, 22.22 g/t of SIPX and pH 9.99 at the second stage.
Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu
2017-01-01
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.
Lou, Xin Yuan; Sun, Lin Fu
2017-01-01
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem. PMID:28369096
Path Creation, Path Dependence and Breaking Away from the Path
Wang, Jens; Hedman, Jonas; Tuunainen, Virpi Kristiina
2016-01-01
The explanation of how and why firms succeed or fail is a recurrent research challenge. This is particularly important in the context of technological innovations. We focus on the role of historical events and decisions in explaining such success and failure. Using a case study of Nokia, we develop and extend a multi-layer path dependence framework. We identify four layers of path dependence: technical, strategic and leadership, organizational, and external collaboration. We show how path dep...
Multi-AGV path planning with double-path constraints by using an improved genetic algorithm.
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Zengliang Han
Full Text Available This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.
Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz
2017-08-01
Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii
Reutterer, Bernd; Traxler, Lukas; Bayer, Natascha; Drauschke, Andreas
2016-04-01
Selective Laser Sintering (SLS) is considered as one of the most important additive manufacturing processes due to component stability and its broad range of usable materials. However the influence of the different process parameters on mechanical workpiece properties is still poorly studied, leading to the fact that further optimization is necessary to increase workpiece quality. In order to investigate the impact of various process parameters, laboratory experiments are implemented to improve the understanding of the SLS limitations and advantages on an educational level. Experiments are based on two different workstations, used to teach students the fundamentals of SLS. First of all a 50 W CO2 laser workstation is used to investigate the interaction of the laser beam with the used material in accordance with varied process parameters to analyze a single-layered test piece. Second of all the FORMIGA P110 laser sintering system from EOS is used to print different 3D test pieces in dependence on various process parameters. Finally quality attributes are tested including warpage, dimension accuracy or tensile strength. For dimension measurements and evaluation of the surface structure a telecentric lens in combination with a camera is used. A tensile test machine allows testing of the tensile strength and the interpreting of stress-strain curves. The developed laboratory experiments are suitable to teach students the influence of processing parameters. In this context they will be able to optimize the input parameters depending on the component which has to be manufactured and to increase the overall quality of the final workpiece.
Lathwal, Priyanka; Nehra, Kiran; Singh, Manpreet; Jamdagni, Pragati; Rana, Jogender S
2015-01-01
The enormous applications of conventional non-biodegradable plastics have led towards their increased usage and accumulation in the environment. This has become one of the major causes of global environmental concern in the present century. Polyhydroxybutyrate (PHB), a biodegradable plastic is known to have properties similar to conventional plastics, thus exhibiting a potential for replacing conventional non-degradable plastics. In the present study, a total of 303 different bacterial isolates were obtained from soil samples collected from the rhizospheric area of three crops, viz., wheat, mustard and sugarcane. All the isolates were screened for PHB (Poly-3-hydroxy butyric acid) production using Sudan Black staining method, and 194 isolates were found to be PHB positive. Based upon the amount of PHB produced, the isolates were divided into three categories: high, medium and low producers. Representative isolates from each category were selected for biochemical characterization; and for optimization of various culture parameters (carbon source, nitrogen source, C/N ratio, different pH, temperature and incubation time periods) for maximizing PHB accumulation. The highest PHB yield was obtained when the culture medium was supplemented with glucose as the carbon source, ammonium sulphate at a concentration of 1.0 g/l as the nitrogen source, and by maintaining the C/N ratio of the medium as 20:1. The physical growth parameters which supported maximum PHB accumulation included a pH of 7.0, and an incubation temperature of 30 degrees C for a period of 48 h. A few isolates exhibited high PHB accumulation under optimized conditions, thus showing a potential for their industrial exploitation.
Path Minima Queries in Dynamic Weighted Trees
DEFF Research Database (Denmark)
Davoodi, Pooya; Brodal, Gerth Stølting; Satti, Srinivasa Rao
2011-01-01
In the path minima problem on a tree, each edge is assigned a weight and a query asks for the edge with minimum weight on a path between two nodes. For the dynamic version of the problem, where the edge weights can be updated, we give data structures that achieve optimal query time\\todo{what about...
Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation
Simon, Donald L.; Garg, Sanjay
2011-01-01
An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined 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 that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation
Directory of Open Access Journals (Sweden)
Chiang Mung
2006-01-01
Full Text Available The problem of choosing the optimal multipath components to be employed at a minimum mean square error (MMSE selective Rake receiver is considered for an impulse radio ultra-wideband system. First, the optimal finger selection problem is formulated as an integer programming problem with a nonconvex objective function. Then, the objective function is approximated by a convex function and the integer programming problem is solved by means of constraint relaxation techniques. The proposed algorithms are suboptimal due to the approximate objective function and the constraint relaxation steps. However, they perform better than the conventional finger selection algorithm, which is suboptimal since it ignores the correlation between multipath components, and they can get quite close to the optimal scheme that cannot be implemented in practice due to its complexity. In addition to the convex relaxation techniques, a genetic-algorithm- (GA- based approach is proposed, which does not need any approximations or integer relaxations. This iterative algorithm is based on the direct evaluation of the objective function, and can achieve near-optimal performance with a reasonable number of iterations. Simulation results are presented to compare the performance of the proposed finger selection algorithms with that of the conventional and the optimal schemes.
Directory of Open Access Journals (Sweden)
Laura Bix
Full Text Available Effective standardization of medical device labels requires objective study of varied designs. Insufficient empirical evidence exists regarding how practitioners utilize and view labeling.Measure the effect of graphic elements (boxing information, grouping information, symbol use and color-coding to optimize a label for comparison with those typical of commercial medical devices.Participants viewed 54 trials on a computer screen. Trials were comprised of two labels that were identical with regard to graphics, but differed in one aspect of information (e.g., one had latex, the other did not. Participants were instructed to select the label along a given criteria (e.g., latex containing as quickly as possible. Dependent variables were binary (correct selection and continuous (time to correct selection.Eighty-nine healthcare professionals were recruited at Association of Surgical Technologists (AST conferences, and using a targeted e-mail of AST members.Symbol presence, color coding and grouping critical pieces of information all significantly improved selection rates and sped time to correct selection (α = 0.05. Conversely, when critical information was graphically boxed, probability of correct selection and time to selection were impaired (α = 0.05. Subsequently, responses from trials containing optimal treatments (color coded, critical information grouped with symbols were compared to two labels created based on a review of those commercially available. Optimal labels yielded a significant positive benefit regarding the probability of correct choice ((P<0.0001 LSM; UCL, LCL: 97.3%; 98.4%, 95.5%, as compared to the two labels we created based on commercial designs (92.0%; 94.7%, 87.9% and 89.8%; 93.0%, 85.3% and time to selection.Our study provides data regarding design factors, namely: color coding, symbol use and grouping of critical information that can be used to significantly enhance the performance of medical device labels.
Energy Technology Data Exchange (ETDEWEB)
Chaleil, A.; Le Flanchec, V.; Binet, A.; Nègre, J.P.; Devaux, J.F.; Jacob, V.; Millerioux, M.; Bayle, A.; Balleyguier, P. [CEA DAM DIF, F-91297 Arpajon (France); Prazeres, R. [CLIO/LCP, Bâtiment 201, Université Paris-Sud, F-91450 Orsay (France)
2016-12-21
An inverse Compton scattering source is under development at the ELSA linac of CEA, Bruyères-le-Châtel. Ultra-short X-ray pulses are produced by inverse Compton scattering of 30 ps-laser pulses by relativistic electron bunches. The source will be able to operate in single shot mode as well as in recurrent mode with 72.2 MHz pulse trains. Within this framework, an optical multipass system that multiplies the number of emitted X-ray photons in both regimes has been designed in 2014, then implemented and tested on ELSA facility in the course of 2015. The device is described from both geometrical and timing viewpoints. It is based on the idea of folding the laser optical path to pile-up laser pulses at the interaction point, thus increasing the interaction probability. The X-ray output gain measurements obtained using this system are presented and compared with calculated expectations.
Directory of Open Access Journals (Sweden)
Valérian Mannoni
2004-09-01
Full Text Available This paper deals with optimized channel coding for OFDM transmissions (COFDM over frequency-selective channels using irregular low-density parity-check (LDPC codes. Firstly, we introduce a new characterization of the LDPC code irregularity called Ã‚Â“irregularity profile.Ã‚Â” Then, using this parameterization, we derive a new criterion based on the minimization of the transmission bit error probability to design an irregular LDPC code suited to the frequency selectivity of the channel. The optimization of this criterion is done using the Gaussian approximation technique. Simulations illustrate the good performance of our approach for different transmission channels.
Miyauchi, T.; Machimura, T.
2013-12-01
In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the
Gui, Huaping; Li, Xia; Liu, Yubo; Han, Kai; Li, Xianggan
2014-07-01
An efficient mannose selection system was established for transformation of Indica cultivar IR58025B . Different selection pressures were required to achieve optimum transformation frequency for different PMI selectable marker cassettes. This study was conducted to establish an efficient transformation system for Indica rice, cultivar IR58025B. Four combinations of two promoters, rice Actin 1 and maize Ubiquitin 1, and two manA genes, native gene from E. coli (PMI-01) and synthetic maize codon-optimized gene (PMI-09) were compared under various concentrations of mannose. Different selection pressures were required for different gene cassettes to achieve corresponding optimum transformation frequency (TF). Higher TFs as 54 and 53% were obtained when 5 g/L mannose was used for selection of prActin-PMI-01 cassette and 7.5 g/L mannose used for selection of prActin-PMI-09, respectively. TFs as 67 and 56% were obtained when 7.5 and 15 g/L mannose were used for selection of prUbi-PMI-01 and prUbi-PMI-09, respectively. We conclude that higher TFs can be achieved for different gene cassettes when an optimum selection pressure is applied. By investigating the PMI expression level in transgenic calli and leaves, we found there was a significant positive correlation between the protein expression level and the optimal selection pressure. Higher optimal selection pressure is required for those constructs which confer higher expression of PMI protein. The single copy rate of those transgenic events for prActin-PMI-01 cassette is lower than that for other three cassettes. We speculate some of low copy events with low protein expression levels might not have been able to survive in the mannose selection.
Normalization method for metabolomics data using optimal selection of multiple internal standards
Directory of Open Access Journals (Sweden)
Yetukuri Laxman
2007-03-01
Full Text Available Abstract Background Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task. Results With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS. We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by l2 norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization. Conclusion Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.
Selection of optimal treatment scheme for brain metastases of non-small cell lung cancer
International Nuclear Information System (INIS)
Dong Mingxin; Zhao Tong; Huang Jingzi; Yu Shukun; Ma Yan; Tian Zhongcheng; Jin Xiangshun; Quan Jizhong; Liu Jin; Wang Dongxu
2006-01-01
Objective: To select the optimal treatment scheme for brain metastases of non-small cell lung cancers (NSCLCs). Methods: Seventy-two NSCLC cases diagnosesd by pathology with brain metastases were randomly classified into three groups, Group I, 24 cases with whole brain conventional external fractioned irradiation of D T 36-41 Gy/4-5 w, Group II, 22 eases with y-knife treatment plus whole brain conventional external fractioned irradiation, and Group III, 26 cases with γ-knife plus whole brain conventional external fractioned irradiation in combination with chemotherapy of Vm-26. The surrounding area of tumor was strictly covered with 50% para-central-dosal curve in γ-knife treatment (D T 16-25 Gy with a mean of 16 Gy). The muirleaf collimator was selected according to the volume of tumors. Chemotherapy of Vm-26 (60 mg/m 2 d1-3) was applied during the treatment with whole brain conventional external fractioned irradiation (D T 19-29 Gy/2-3 w), 21 days in a period, 2 periods in total. Results: The median survival time was estimated to be 6.0 months (ranged from 1.2 to 19.0 months) in the Group I, 9.2 months (4.4-30 months) in the Group II, and 10.8 months (5.2-42.2 months) in the Group III. The 1-year and 2-year survival rates were 34.6% and 12.6% , 62.2% and 30.2%, and 70.8% and 35.6% respectively in Group I, Group II, and Group III, respectively. Conclusion: For brain metastases of NSCLC, γ-knife plus whole brain conventional external fractioned irradiation combined with treatment of Vm-26 had a significantly beneficial influence on improvement of the local control and 1-year and 2-year survival. There was no complaint about the side-effects of the treatment. (authors)
Pereira-da-Silva, Tiago; M Soares, Rui; Papoila, Ana Luísa; Pinto, Iola; Feliciano, Joana; Almeida-Morais, Luís; Abreu, Ana; Cruz Ferreira, Rui
2018-02-01
Selecting patients for heart transplantation is challenging. We aimed to identify the most important risk predictors in heart failure and an approach to optimize the selection of candidates for heart transplantation. Ambulatory patients followed in our center with symptomatic heart failure and left ventricular ejection fraction ≤40% prospectively underwent a comprehensive baseline assessment including clinical, laboratory, electrocardiographic, echocardiographic, and cardiopulmonary exercise testing parameters. All patients were followed for 60 months. The combined endpoint was cardiac death, urgent heart transplantation or need for mechanical circulatory support, up to 36 months. In the 263 enrolled patients (75% male, age 54±12 years), 54 events occurred. The independent predictors of adverse outcome were ventilatory efficiency (VE/VCO 2 ) slope (HR 1.14, 95% CI 1.11-1.18), creatinine level (HR 2.23, 95% CI 1.14-4.36), and left ventricular ejection fraction (HR 0.96, 95% CI 0.93-0.99). VE/VCO 2 slope was the most accurate risk predictor at any follow-up time analyzed (up to 60 months). The threshold of 39.0 yielded high specificity (97%), discriminated a worse or better prognosis than that reported for post-heart transplantation, and outperformed peak oxygen consumption thresholds of 10.0 or 12.0 ml/kg/min. For low-risk patients (VE/VCO 2 slope <39.0), sodium and creatinine levels and variations in end-tidal carbon dioxide partial pressure on exercise identified those with excellent prognosis. VE/VCO 2 slope was the most accurate parameter for risk stratification in patients with heart failure and reduced ejection fraction. Those with VE/VCO 2 slope ≥39.0 may benefit from heart transplantation. Copyright © 2018 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.
Hu, Xuemin; Chen, Long; Tang, Bo; Cao, Dongpu; He, Haibo
2018-02-01
This paper presents a real-time dynamic path planning method for autonomous driving that avoids both static and moving obstacles. The proposed path planning method determines not only an optimal path, but also the appropriate acceleration and speed for a vehicle. In this method, we first construct a center line from a set of predefined waypoints, which are usually obtained from a lane-level map. A series of path candidates are generated by the arc length and offset to the center line in the s - ρ coordinate system. Then, all of these candidates are converted into Cartesian coordinates. The optimal path is selected considering the total cost of static safety, comfortability, and dynamic safety; meanwhile, the appropriate acceleration and speed for the optimal path are also identified. Various types of roads, including single-lane roads and multi-lane roads with static and moving obstacles, are designed to test the proposed method. The simulation results demonstrate the effectiveness of the proposed method, and indicate its wide practical application to autonomous driving.
Road networks as collections of minimum cost paths
Wegner, Jan Dirk; Montoya-Zegarra, Javier Alexander; Schindler, Konrad
2015-10-01
We present a probabilistic representation of network structures in images. Our target application is the extraction of urban roads from aerial images. Roads appear as thin, elongated, partially curved structures forming a loopy graph, and this complex layout requires a prior that goes beyond standard smoothness and co-occurrence assumptions. In the proposed model the network is represented as a union of 1D paths connecting distant (super-)pixels. A large set of putative candidate paths is constructed in such a way that they include the true network as much as possible, by searching for minimum cost paths in the foreground (road) likelihood. Selecting the optimal subset of candidate paths is posed as MAP inference in a higher-order conditional random field. Each path forms a higher-order clique with a type of clique potential, which attracts the member nodes of cliques with high cumulative road evidence to the foreground label. That formulation induces a robust PN -Potts model, for which a global MAP solution can be found efficiently with graph cuts. Experiments with two road data sets show that the proposed model significantly improves per-pixel accuracies as well as the overall topological network quality with respect to several baselines.
Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain
Fernando, Chrisantha; Vasas, Vera; Szathmáry, Eörs; Husbands, Phil
2011-01-01
We propose a previously unrecognized kind of informational entity in the brain that is capable of acting as the basis for unlimited hereditary variation in neuronal networks. This unit is a path of activity through a network of neurons, analogous to a path taken through a hidden Markov model. To prove in principle the capabilities of this new kind of informational substrate, we show how a population of paths can be used as the hereditary material for a neuronally implemented genetic algorithm, (the swiss-army knife of black-box optimization techniques) which we have proposed elsewhere could operate at somatic timescales in the brain. We compare this to the same genetic algorithm that uses a standard ‘genetic’ informational substrate, i.e. non-overlapping discrete genotypes, on a range of optimization problems. A path evolution algorithm (PEA) is defined as any algorithm that implements natural selection of paths in a network substrate. A PEA is a previously unrecognized type of natural selection that is well suited for implementation by biological neuronal networks with structural plasticity. The important similarities and differences between a standard genetic algorithm and a PEA are considered. Whilst most experiments are conducted on an abstract network model, at the conclusion of the paper a slightly more realistic neuronal implementation of a PEA is outlined based on Izhikevich spiking neurons. Finally, experimental predictions are made for the identification of such informational paths in the brain. PMID:21887266
Li, Zejing
2012-01-01
This dissertation is mainly devoted to the research of two problems - the continuous-time portfolio optimization in different Wishart models and the effects of discrete rebalancing on portfolio wealth distribution and optimal portfolio strategy.
Research and proposal on selective catalytic reduction reactor optimization for industrial boiler.
Yang, Yiming; Li, Jian; He, Hong
2017-08-24
The advanced computational fluid dynamics (CFD) software STAR-CCM+ was used to simulate a denitrification (De-NOx) project for a boiler in this paper, and the simulation result was verified based on a physical model. Two selective catalytic reduction (SCR) reactors were developed: reactor 1 was optimized and reactor 2 was developed based on reactor 1. Various indicators, including gas flow field, ammonia concentration distribution, temperature distribution, gas incident angle, and system pressure drop were analyzed. The analysis indicated that reactor 2 was of outstanding performance and could simplify developing greatly. Ammonia injection grid (AIG), the core component of the reactor, was studied; three AIGs were developed and their performances were compared and analyzed. The result indicated that AIG 3 was of the best performance. The technical indicators were proposed for SCR reactor based on the study. Flow filed distribution, gas incident angle, and temperature distribution are subjected to SCR reactor shape to a great extent, and reactor 2 proposed in this paper was of outstanding performance; ammonia concentration distribution is subjected to ammonia injection grid (AIG) shape, and AIG 3 could meet the technical indicator of ammonia concentration without mounting ammonia mixer. The developments above on the reactor and the AIG are both of great application value and social efficiency.
Directory of Open Access Journals (Sweden)
Rong-Chao Peng
2016-01-01
Full Text Available Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI, which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR, spectral energy ratio (SER, template matching (TM, temporal difference (TD, and gradient (GRAD. Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm and smaller limits of agreement (<3 bpm. The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.
Multicriteria Selection of Optimal Location of TCSC in a Competitive Energy Market
Alomoush, Muwaffaq I.
2010-05-01
The paper investigates selection of the best location of thyristor-controlled series compensator (TCSC) in a transmission system from many candidate locations in a competitive energy market such that the TCSC causes a net valuable impact on congestion management outcome, transmission utilization, transmission losses, voltage stability, degree of fulfillment of spot market contracts, and system security. The problem is treated as a multicriteria decision-making process such that the candidate locations of TCSC are the alternatives and the conflicting objectives are the outcomes of the dispatch process, which may have different importance weights. The paper proposes some performance indices that the dispatch decision-making entity can use to measure market dispatch outcomes of each alternative. Based on agreed-upon preferences, the measures presented may help the decision maker compare and rank dispatch scenarios to ultimately decide which location is the optimal one. To solve the multicriteria decision, we use the preference ranking organization method for enrichment evaluations (PROMETHEE), which is a multicriteria decision support method that can handle complex conflicting-objective decision-making processes.
Optimal defocus selection based on normed Fourier transform for digital fringe pattern profilometry.
Kamagara, Abel; Wang, Xiangzhao; Li, Sikun
2017-10-01
Owing to gamma-effect robustness and high-speed imaging capabilities, projector defocusing of binary-coded fringe patterns is by far the most widely used and effective technique in generating sinusoidal fringe patterns for three-dimensional optical topography measurement with digital fringe projection techniques. However, this technique is not trouble-free. It is borne with uncertainty and challenges mainly because it remains somewhat difficult to quantify and ascertain the level of defocus required for desired fidelity in sinuousness of the projected fringe pattern. Too much or too little defocusing will affect the sinuosity accuracy of fringe patterns and consequently jeopardize the quality of the measurement results. In this paper, by combining intrinsic phase spectral sensitivities and normed Fourier transform, a method to quantify the amount of defocus and subsequently select the optimal degree of sinuosity for generating digital sinusoidal fringe patterns with projector defocusing for fringe pattern optical three-dimensional profilometry is proposed. Numerical simulations plus experiments give evidence of the feasibility and validity of the proposed method in enabling an improved digital binary defocusing technique for optical phase-shift profilometry using the digital fringe projection technique.
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Fernández, J.
2001-12-01
Full Text Available The maintenance of genetic diversity is, from a genetic point of view, a key objective of conservation programmes. The selection of individuals contributing offspring and the decision of the mating scheme are the steps on which managers can control genetic diversity, specially on ‘ex situ’ programmes. Previous studies have shown that the optimal management strategy is to look for the parents’ contributions that yield minimum group coancestry (overall probability of identity by descent in the population and, then, to arrange mating couples following minimum pairwise coancestry. However, physiological constraints make it necessary to account for mating restrictions when deciding the contributions and, therefore, these should be implemented in a single step along with the mating plan. In the present paper, a single-step method is proposed to optimise the management of a conservation programme when restrictions on the mating scheme exist. The performance of the method is tested by computer simulation. The strategy turns out to be as efficient as the two-step method, regarding both the genetic diversity preserved and the fitness of the population.
Madaria, Anuj R; Yao, Maoqing; Chi, Chunyung; Huang, Ningfeng; Lin, Chenxi; Li, Ruijuan; Povinelli, Michelle L; Dapkus, P Daniel; Zhou, Chongwu
2012-06-13
Vertically aligned, catalyst-free semiconducting nanowires hold great potential for photovoltaic applications, in which achieving scalable synthesis and optimized optical absorption simultaneously is critical. Here, we report combining nanosphere lithography (NSL) and selected area metal-organic chemical vapor deposition (SA-MOCVD) for the first time for scalable synthesis of vertically aligned gallium arsenide nanowire arrays, and surprisingly, we show that such nanowire arrays with patterning defects due to NSL can be as good as highly ordered nanowire arrays in terms of optical absorption and reflection. Wafer-scale patterning for nanowire synthesis was done using a polystyrene nanosphere template as a mask. Nanowires grown from substrates patterned by NSL show similar structural features to those patterned using electron beam lithography (EBL). Reflection of photons from the NSL-patterned nanowire array was used as a measure of the effect of defects present in the structure. Experimentally, we show that GaAs nanowires as short as 130 nm show reflection of <10% over the visible range of the solar spectrum. Our results indicate that a highly ordered nanowire structure is not necessary: despite the "defects" present in NSL-patterned nanowire arrays, their optical performance is similar to "defect-free" structures patterned by more costly, time-consuming EBL methods. Our scalable approach for synthesis of vertical semiconducting nanowires can have application in high-throughput and low-cost optoelectronic devices, including solar cells.
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Alejandro Gonzalez
2014-01-01
Full Text Available Brain-machine interfaces (BMI rely on the accurate classification of event-related potentials (ERPs and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.
International Nuclear Information System (INIS)
Liu, Zhexuan; Qiu, Tong; Chen, Bingzhen
2014-01-01
Highlights: • A LCA based biofuel supply chain model considering 3E criteria was proposed. • The model was used to design a supply chain considering three conversion pathways. • An experimental biofuel supply chain for China was designed. • A Pareto-optimal solution surface of this multi-objective problem was obtained. • The designed supply chain was rather robust to price variation. - Abstract: In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a ε-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the
Directory of Open Access Journals (Sweden)
Maolong Xi
2016-01-01
Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun
2016-01-01
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363
DEFF Research Database (Denmark)
Luckow, Kasper Søe; Păsăreanu, Corina
2014-01-01
We describe Symbolic PathFinder v7 in terms of its updated design addressing the changes of Java PathFinder v7 and of its new optimization when computing path conditions. Furthermore, we describe the Symbolic Execution Tree Extension; a newly added feature that allows for outputting the symbolic...... execution tree that characterizes the execution paths covered during symbolic execution. The new extension can be tailored to the needs of subsequent analyses/processing facilities, and we demonstrate this by presenting SPF-Visualizer, which is a tool for customizable visualization of the symbolic execution...
Feynman's path integrals and Bohm's particle paths
International Nuclear Information System (INIS)
Tumulka, Roderich
2005-01-01
Both Bohmian mechanics, a version of quantum mechanics with trajectories, and Feynman's path integral formalism have something to do with particle paths in space and time. The question thus arises how the two ideas relate to each other. In short, the answer is, path integrals provide a re-formulation of Schroedinger's equation, which is half of the defining equations of Bohmian mechanics. I try to give a clear and concise description of the various aspects of the situation. (letters and comments)
Daetwyler, Hans D.; Hayden, Matthew J.; Spangenberg, German C.; Hayes, Ben J.
2015-01-01
Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selec...
Directory of Open Access Journals (Sweden)
Li-Chi Chiang
2014-03-01
Full Text Available Suites of Best Management Practices (BMPs are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA and a watershed model (Soil and Water Assessment Tool—SWAT. For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS, and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.
Path coupling and aggregate path coupling
Kovchegov, Yevgeniy
2018-01-01
This book describes and characterizes an extension to the classical path coupling method applied to statistical mechanical models, referred to as aggregate path coupling. In conjunction with large deviations estimates, the aggregate path coupling method is used to prove rapid mixing of Glauber dynamics for a large class of statistical mechanical models, including models that exhibit discontinuous phase transitions which have traditionally been more difficult to analyze rigorously. The book shows how the parameter regions for rapid mixing for several classes of statistical mechanical models are derived using the aggregate path coupling method.
International Nuclear Information System (INIS)
Ferreira, Jose C.; Gaspar-Cunha, Antonio; Fonseca, Carlos M.
2007-01-01
Most of the real world optimization problems involve multiple, usually conflicting, optimization criteria. Generating Pareto optimal solutions plays an important role in multi-objective optimization, and the problem is considered to be solved when the Pareto optimal set is found, i.e., the set of non-dominated solutions. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to produce the complete set of non-dominated solutions. However, this is not allays enough since the aim is not only to know the Pareto set but, also, to obtain one solution from this Pareto set. Thus, the definition of a methodology able to select a single solution from the set of non-dominated solutions (or a region of the Pareto frontier), and taking into account the preferences of a Decision Maker (DM), is necessary. A different method, based on a weighted stress function, is proposed. It is able to integrate the user's preferences in order to find the best region of the Pareto frontier accordingly with these preferences. This method was tested on some benchmark test problems, with two and three criteria, and on a polymer extrusion problem. This methodology is able to select efficiently the best Pareto-frontier region for the specified relative importance of the criteria
You, Yu-cong; Yi, Lu-xia
2017-11-01
From the perspective of energy supply-side reform, this paper, by conducting an empirical study on foreign trade enterprises, conducts a research on the cross-role effect of optimizing the resources allocation and enhancing the energy efficiency. Methodologically, this paper creatively introduces the HILE's probabilistic structured property into Granger causality test analysis, forming an HILE-Granger (H-G) model, so as to empirically estimate both the short-term and long-term causal relationship effects between the energy efficiency and resources allocation. Conclusion is drawn that optimization of resources allocation is positively proportional with the energy efficiency enhancement. This paper is to provide a decision-making reference for the supply side reform strategy of foreign trade enterprises under the background of green energy economy.
Schwann, Thomas A; Gaudino, Mario; Baldawi, Mustafa; Tranbaugh, Robert; Schwann, Alexandra N; Habib, Robert H
2018-05-01
The current literature on radial artery grafting is reviewed focusing on the optimal deployment of radial artery grafts in coronary artery bypass surgery with specific attention to the selection of patients and target vessels for radial artery grafting. © 2018 Wiley Periodicals, Inc.
Weigl, Matthias; Mueller, Andreas; Hornung, Severin; Zacher, Hannes; Angerer, Peter
Work ability describes employees' capability to carry out their work with respect to physical and psychological job demands. This study investigated direct and interactive effects of age, job control, and the use of successful aging strategies called selection, optimization, and compensation (SOC)
International Nuclear Information System (INIS)
Alpman, Emre
2014-01-01
The effect of selecting the twist angle and chord length distributions on the wind turbine blade design was investigated by performing aerodynamic optimization of a two-bladed stall regulated horizontal axis wind turbine. Twist angle and chord length distributions were defined using Bezier curve using 3, 5, 7 and 9 control points uniformly distributed along the span. Optimizations performed using a micro-genetic algorithm with populations composed of 5, 10, 15, 20 individuals showed that, the number of control points clearly affected the outcome of the process; however the effects were different for different population sizes. The results also showed the superiority of micro-genetic algorithm over a standard genetic algorithm, for the selected population sizes. Optimizations were also performed using a macroevolutionary algorithm and the resulting best blade design was compared with that yielded by micro-genetic algorithm