Tree-based shortest-path routing algorithm
NASA Astrophysics Data System (ADS)
Long, Y. H.; Ho, T. K.; Rad, A. B.; Lam, S. P. S.
1998-12-01
A tree-based shortest path routing algorithm is introduced in this paper. With this algorithm, every network node can maintain a shortest path routing tree topology of the network with itself as the root. In this algorithm, every node constructs its own routing tree based upon its neighbors' routing trees. Initially, the routing tree at each node has the root only, the node itself. As information exchanges, every node's routing tree will evolve until a complete tree is obtained. This algorithm is a trade-off between distance vector algorithm and link state algorithm. Loops are automatically deleted, so there is no count-to- infinity effect. A simple routing tree information storage approach and a protocol data until format to transmit the tree information are given. Some special issues, such as adaptation to topology change, implementation of the algorithm on LAN, convergence and computation overhead etc., are also discussed in the paper.
An improved Physarum polycephalum algorithm for the shortest path problem.
Zhang, Xiaoge; Wang, Qing; Adamatzky, Andrew; Chan, Felix T S; Mahadevan, Sankaran; Deng, Yong
2014-01-01
Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm. PMID:24982960
An Improved Physarum polycephalum Algorithm for the Shortest Path Problem
Wang, Qing; Adamatzky, Andrew; Chan, Felix T. S.; Mahadevan, Sankaran
2014-01-01
Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm. PMID:24982960
Multiple object tracking using the shortest path faster association algorithm.
Xi, Zhenghao; Liu, Heping; Liu, Huaping; Yang, Bin
2014-01-01
To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.
A Hybrid Shortest Path Algorithm for Navigation System
NASA Astrophysics Data System (ADS)
Cho, Hsun-Jung; Lan, Chien-Lun
2007-12-01
Combined with Geographic Information System (GIS) and Global Positioning System (GPS), the vehicle navigation system had become a quite popular product in daily life. A key component of the navigation system is the Shortest Path Algorithm. Navigation in real world must face a network consists of tens of thousands nodes and links, and even more. Under the limited computation capability of vehicle navigation equipment, it is difficult to satisfy the realtime response requirement that user expected. Hence, this study focused on shortest path algorithm that enhances the computation speed with less memory requirement. Several well-known algorithms such as Dijkstra, A* and hierarchical concepts were integrated to build hybrid algorithms that reduce searching space and improve searching speed. Numerical examples were conducted on Taiwan highway network that consists of more than four hundred thousands of links and nearly three hundred thousands of nodes. This real network was divided into two connected sub-networks (layers). The upper layer is constructed by freeways and expressways; the lower layer is constructed by local networks. Test origin-destination pairs were chosen randomly and divided into three distance categories; short, medium and long distances. The evaluation of outcome is judged by actual length and travel time. The numerical example reveals that the hybrid algorithm proposed by this research might be tens of thousands times faster than traditional Dijkstra algorithm; the memory requirement of the hybrid algorithm is also much smaller than the tradition algorithm. This outcome shows that this proposed algorithm would have an advantage over vehicle navigation system.
Algorithm for shortest path search in Geographic Information Systems by using reduced graphs.
Rodríguez-Puente, Rafael; Lazo-Cortés, Manuel S
2013-01-01
The use of Geographic Information Systems has increased considerably since the eighties and nineties. As one of their most demanding applications we can mention shortest paths search. Several studies about shortest path search show the feasibility of using graphs for this purpose. Dijkstra's algorithm is one of the classic shortest path search algorithms. This algorithm is not well suited for shortest path search in large graphs. This is the reason why various modifications to Dijkstra's algorithm have been proposed by several authors using heuristics to reduce the run time of shortest path search. One of the most used heuristic algorithms is the A* algorithm, the main goal is to reduce the run time by reducing the search space. This article proposes a modification of Dijkstra's shortest path search algorithm in reduced graphs. It shows that the cost of the path found in this work, is equal to the cost of the path found using Dijkstra's algorithm in the original graph. The results of finding the shortest path, applying the proposed algorithm, Dijkstra's algorithm and A* algorithm, are compared. This comparison shows that, by applying the approach proposed, it is possible to obtain the optimal path in a similar or even in less time than when using heuristic algorithms.
A shortest path algorithm for satellite time-varying topological network
NASA Astrophysics Data System (ADS)
Zhang, Tao; Liu, Zhongkan; Zhuang, Jun
2005-11-01
Mobile satellite network is a special time-varying network. It is different from the classical fixed network and other time-dependent networks which have been studied. Therefore some classical network theories, such as the shortest path algorithm, can not be applied to it availably. However, no study about its shortest path problem has been done. In this paper, based on the proposed model of satellite time-varying topological network, the classical shortest path algorithm of fixed network, such as the Dijkstra algorithm, is proved to be restrictive when it is applied in satellite network. Here, a novel shortest path algorithm for satellite time-varying topological network is given and optimized. Correlative simulation indicates that this algorithm can be effectively applied to the satellite time-varying topological network.
An Evaluation of Potentials of Genetic Algorithm in Shortest Path Problem
NASA Astrophysics Data System (ADS)
Hassany Pazooky, S.; Rahmatollahi Namin, Sh; Soleymani, A.; Samadzadegan, F.
2009-04-01
One of the most typical issues considered in combinatorial systems in transportation networks, is the shortest path problem. In such networks, routing has a significant impact on the network's performance. Due to natural complexity in transportation networks and strong impact of routing in different fields of decision making, such as traffic management and vehicle routing problem (VRP), appropriate solutions to solve this problem are crucial to be determined. During last years, in order to solve the shortest path problem, different solutions are proposed. These techniques are divided into two categories of classic and evolutionary approaches. Two well-known classic algorithms are Dijkstra and A*. Dijkstra is known as a robust, but time consuming algorithm in finding the shortest path problem. A* is also another algorithm very similar to Dijkstra, less robust but with a higher performance. On the other hand, Genetic algorithms are introduced as most applicable evolutionary algorithms. Genetic Algorithm uses a parallel search method in several parts of the domain and is not trapped in local optimums. In this paper, the potentiality of Genetic algorithm for finding the shortest path is evaluated by making a comparison between this algorithm and classic algorithms (Dijkstra and A*). Evaluation of the potential of these techniques on a transportation network in an urban area shows that due to the problem of classic methods in their small search space, GA had a better performance in finding the shortest path.
Finding splitting lines for touching cell nuclei with a shortest path algorithm.
Bai, Xiangzhi; Wang, Peng; Sun, Changming; Zhang, Yu; Zhou, Fugen; Meng, Cai
2015-08-01
A shortest path-based algorithm is proposed in this paper to find splitting lines for touching cell nuclei. First, an initial splitting line is obtained through the distance transform of a marker image and the watershed algorithm. The initial splitting line is then separated into different line segments as necessary, and the endpoint positions of these line segments are adjusted to the concave points on the contour. Finally, a shortest path algorithm is used to find the accurate splitting line between the starting-point and the end-point, and the final split can be achieved by the contour of the touching cell nuclei and the splitting lines. Comparisons of experimental results show that the proposed algorithm is effective for segmentation of different types of touching cell nuclei.
Genetic Algorithm for Solving Fuzzy Shortest Path Problem in a Network with mixed fuzzy arc lengths
NASA Astrophysics Data System (ADS)
Mahdavi, Iraj; Tajdin, Ali; Hassanzadeh, Reza; Mahdavi-Amiri, Nezam; Shafieian, Hosna
2011-06-01
We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α -cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers. we propose a new approach to solve the fuzzy APSPP using of genetic algorithm. Examples are worked out to illustrate the applicability of the proposed model.
Performance of Thorup's Shortest Path Algorithm for Large-Scale Network Simulation
NASA Astrophysics Data System (ADS)
Sakumoto, Yusuke; Ohsaki, Hiroyuki; Imase, Makoto
In this paper, we investigate the performance of Thorup's algorithm by comparing it to Dijkstra's algorithm for large-scale network simulations. One of the challenges toward the realization of large-scale network simulations is the efficient execution to find shortest paths in a graph with N vertices and M edges. The time complexity for solving a single-source shortest path (SSSP) problem with Dijkstra's algorithm with a binary heap (DIJKSTRA-BH) is O((M+N)log N). An sophisticated algorithm called Thorup's algorithm has been proposed. The original version of Thorup's algorithm (THORUP-FR) has the time complexity of O(M+N). A simplified version of Thorup's algorithm (THORUP-KL) has the time complexity of O(Mα(N)+N) where α(N) is the functional inverse of the Ackerman function. In this paper, we compare the performances (i.e., execution time and memory consumption) of THORUP-KL and DIJKSTRA-BH since it is known that THORUP-FR is at least ten times slower than Dijkstra's algorithm with a Fibonaccii heap. We find that (1) THORUP-KL is almost always faster than DIJKSTRA-BH for large-scale network simulations, and (2) the performances of THORUP-KL and DIJKSTRA-BH deviate from their time complexities due to the presence of the memory cache in the microprocessor.
a Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks
NASA Astrophysics Data System (ADS)
Heidari, A. A.; Delavar, M. R.
2016-06-01
In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.
NASA Astrophysics Data System (ADS)
Schafer, Sebastian; Singh, Vikas; Hoffmann, Kenneth R.; Noël, Peter B.; Xu, Jinhui
2007-03-01
Endovascular interventional procedures are being used more frequently in cardiovascular surgery. Unfortunately, procedural failure, e.g., vessel dissection, may occur and is often related to improper guidewire and/or device selection. To support the surgeon's decision process and because of the importance of the guidewire in positioning devices, we propose a method to determine the guidewire path prior to insertion using a model of its elastic potential energy coupled with a representative graph construction. The 3D vessel centerline and sizes are determined for a specified vessel. Points in planes perpendicular to the vessel centerline are generated. For each pair of consecutive planes, a vector set is generated which joins all points in these planes. We construct a graph representing these vector sets as nodes. The nodes representing adjacent vector sets are joined by edges with weights calculated as a function of the angle between the corresponding vectors (nodes). The optimal path through this weighted directed graph is then determined using shortest path algorithms, such as topological sort based shortest path algorithm or Dijkstra's algorithm. Volumetric data of an internal carotid artery phantom (Ø 3.5mm) were acquired. Several independent guidewire (Ø 0.4mm) placements were performed, and the 3D paths were determined using rotational angiography. The average RMS distance between the actual and the average simulated guidewire path was 0.7mm; the computation time to determine the path was 3 seconds. The ability to predict the guidewire path inside vessels may facilitate calculation of vessel-branch access and force estimation on devices and the vessel wall.
An improved real-time endovascular guidewire position simulation using shortest path algorithm.
Qiu, Jianpeng; Qu, Zhiyi; Qiu, Haiquan; Zhang, Xiaomin
2016-09-01
In this study, we propose a new graph-theoretical method to simulate guidewire paths inside the carotid artery. The minimum energy guidewire path can be obtained by applying the shortest path algorithm, such as Dijkstra's algorithm for graphs, based on the principle of the minimal total energy. Compared to previous results, experiments of three phantoms were validated, revealing that the first and second phantoms overlap completely between simulated and real guidewires. In addition, 95 % of the third phantom overlaps completely, and the remaining 5 % closely coincides. The results demonstrate that our method achieves 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively. Furthermore, 91 % improvements were obtained for the second phantom under the condition with reduced graph construction complexity.
An improved real-time endovascular guidewire position simulation using shortest path algorithm.
Qiu, Jianpeng; Qu, Zhiyi; Qiu, Haiquan; Zhang, Xiaomin
2016-09-01
In this study, we propose a new graph-theoretical method to simulate guidewire paths inside the carotid artery. The minimum energy guidewire path can be obtained by applying the shortest path algorithm, such as Dijkstra's algorithm for graphs, based on the principle of the minimal total energy. Compared to previous results, experiments of three phantoms were validated, revealing that the first and second phantoms overlap completely between simulated and real guidewires. In addition, 95 % of the third phantom overlaps completely, and the remaining 5 % closely coincides. The results demonstrate that our method achieves 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively. Furthermore, 91 % improvements were obtained for the second phantom under the condition with reduced graph construction complexity. PMID:26467345
Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.
Jiang, Yang; Zhang, Peiwei; Li, Li-Peng; He, Yi-Chun; Gao, Ru-jian; Gao, Yu-Fei
2015-01-01
Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.
Shi, Ying; Li, Li-Peng; Ren, Hui
2014-01-01
Gastric cancer, as one of the leading causes of cancer related deaths worldwide, causes about 800,000 deaths per year. Up to now, the mechanism underlying this disease is still not totally uncovered. Identification of related genes of this disease is an important step which can help to understand the mechanism underlying this disease, thereby designing effective treatments. In this study, some novel gastric cancer related genes were discovered based on the knowledge of known gastric cancer related ones. These genes were searched by applying the shortest path algorithm in protein-protein interaction network. The analysis results suggest that some of them are indeed involved in the biological process of gastric cancer, which indicates that they are the actual gastric cancer related genes with high probability. It is hopeful that the findings in this study may help promote the study of this disease and the methods can provide new insights to study various diseases. PMID:24729971
NASA Astrophysics Data System (ADS)
Meyer, Ulrich; Negoescu, Andrei; Weichert, Volker
Despite disillusioning worst-case behavior, classic algorithms for single-source shortest-paths (SSSP) like Bellman-Ford are still being used in practice, especially due to their simple data structures. However, surprisingly little is known about the average-case complexity of these approaches. We provide new theoretical and experimental results for the performance of classic label-correcting SSSP algorithms on graph classes with non-negative random edge weights. In particular, we prove a tight lower bound of Ω(n 2) for the running times of Bellman-Ford on a class of sparse graphs with O(n) nodes and edges; the best previous bound was Ω(n 4/3 - ɛ ). The same improvements are shown for Pallottino's algorithm. We also lift a lower bound for the approximate bucket implementation of Dijkstra's algorithm from Ω(n logn / loglogn) to Ω(n 1.2 - ɛ ). Furthermore, we provide an experimental evaluation of our new graph classes in comparison with previously used test inputs.
Shortest path and Schramm-Loewner Evolution
Posé, N.; Schrenk, K. J.; Araújo, N. A. M.; Herrmann, H. J.
2014-01-01
We numerically show that the statistical properties of the shortest path on critical percolation clusters are consistent with the ones predicted for Schramm-Loewner evolution (SLE) curves for κ = 1.04 ± 0.02. The shortest path results from a global optimization process. To identify it, one needs to explore an entire area. Establishing a relation with SLE permits to generate curves statistically equivalent to the shortest path from a Brownian motion. We numerically analyze the winding angle, the left passage probability, and the driving function of the shortest path and compare them to the distributions predicted for SLE curves with the same fractal dimension. The consistency with SLE opens the possibility of using a solid theoretical framework to describe the shortest path and it raises relevant questions regarding conformal invariance and domain Markov properties, which we also discuss. PMID:24975019
Exact geodesics and shortest paths on polyhedral surfaces.
Balasubramanian, Mukund; Polimeni, Jonathan R; Schwartz, Eric L
2009-06-01
We present two algorithms for computing distances along convex and non-convex polyhedral surfaces. The first algorithm computes exact minimal-geodesic distances and the second algorithm combines these distances to compute exact shortest-path distances along the surface. Both algorithms have been extended to compute the exact minimal-geodesic paths and shortest paths. These algorithms have been implemented and validated on surfaces for which the correct solutions are known, in order to verify the accuracy and to measure the run-time performance, which is cubic or less for each algorithm. The exact-distance computations carried out by these algorithms are feasible for large-scale surfaces containing tens of thousands of vertices, and are a necessary component of near-isometric surface flattening methods that accurately transform curved manifolds into flat representations.
Approximate Shortest Path Queries Using Voronoi Duals
NASA Astrophysics Data System (ADS)
Honiden, Shinichi; Houle, Michael E.; Sommer, Christian; Wolff, Martin
We propose an approximation method to answer point-to-point shortest path queries in undirected edge-weighted graphs, based on random sampling and Voronoi duals. We compute a simplification of the graph by selecting nodes independently at random with probability p. Edges are generated as the Voronoi dual of the original graph, using the selected nodes as Voronoi sites. This overlay graph allows for fast computation of approximate shortest paths for general, undirected graphs. The time-quality tradeoff decision can be made at query time. We provide bounds on the approximation ratio of the path lengths as well as experimental results. The theoretical worst-case approximation ratio is bounded by a logarithmic factor. Experiments show that our approximation method based on Voronoi duals has extremely fast preprocessing time and efficiently computes reasonably short paths.
ON THE ACCELERATION OF SHORTEST PATH CALCULATIONS IN TRANSPORTATION NETWORKS
BAKER, ZACHARY K.; GOKHALE, MAYA B.
2007-01-08
Shortest path algorithms are a key element of many graph problems. They are used in such applications as online direction finding and navigation, as well as modeling of traffic for large scale simulations of major metropolitan areas. As the shortest path algorithms are an execution bottleneck, it is beneficial to move their execution to parallel hardware such as Field-Programmable Gate Arrays (FPGAs). Hardware implementation is accomplished through the use of a small A core replicated on the order of 20 times on an FPGA device. The objective is to maximize the use of on-board random-access memory bandwidth through the use of multi-threaded latency tolerance. Each shortest path core is responsible for one shortest path calculation, and when it is finished it outputs its result and requests the next source from a queue. One of the innovations of this approach is the use of a small bubble sort core to produce the extract-min function. While bubble sort is not usually considered an appropriate algorithm for any non-trivial usage, it is appropriate in this case as it can produce a single minimum out of the list in O(n) cycles, whwere n is the number of elements in the vertext list. The cost of this min operation does not impact the running time of the architecture, because the queue depth for fetching the next set of edges from memory is roughly equivalent to the number of cores in the system. Additionally, this work provides a collection of simulation results that model the behavior of the node queue in hardware. The results show that a hardware queue, implementing a small bubble-type minimum function, need only be on the order of 16 elements to provide both correct and optimal paths. Because the graph database size is measured in the hundreds of megabytes, the Cray SRAM memory is insufficient. In addition to the A* cores, they have developed a memory management system allowing round-robin servicing of the nodes as well as virtual memory managed over the Hypertransport
The role of convexity for solving some shortest path problems in plane without triangulation
NASA Astrophysics Data System (ADS)
An, Phan Thanh; Hai, Nguyen Ngoc; Hoai, Tran Van
2013-09-01
Solving shortest path problems inside simple polygons is a very classical problem in motion planning. To date, it has usually relied on triangulation of the polygons. The question: "Can one devise a simple O(n) time algorithm for computing the shortest path between two points in a simple polygon (with n vertices), without resorting to a (complicated) linear-time triangulation algorithm?" raised by J. S. B. Mitchell in Handbook of Computational Geometry (J. Sack and J. Urrutia, eds., Elsevier Science B.V., 2000), is still open. The aim of this paper is to show that convexity contributes to the design of efficient algorithms for solving some versions of shortest path problems (namely, computing the convex hull of a finite set of points and convex rope on rays in 2D, computing approximate shortest path between two points inside a simple polygon) without triangulation on the entire polygons. New algorithms are implemented in C and numerical examples are presented.
Optimal Design of Pipeline Based on the Shortest Path
NASA Astrophysics Data System (ADS)
Chu, Fei-xue; Chen, Shi-yi
Design and operation of long-distance pipeline are complex engineering tasks. Even small improvement in the design of a pipeline system can lead to substantial savings in capital. In this paper, graph theory was used to analyze the problem of pipeline optimal design. The candidate pump station locations were taken as the vertexes and the total cost of the pipeline system between the two vertexes corresponded to the edge weight. An algorithm recursively calling the Dijkstra algorithm was designed and analyzed to obtain N shortest paths. The optimal process program and the quasi-optimal process programs were obtained at the same time, which could be used in decision-making. The algorithm was tested by a real example. The result showed that it could meet the need of real application.
An Effective Evolutionary Approach for Bicriteria Shortest Path Routing Problems
NASA Astrophysics Data System (ADS)
Lin, Lin; Gen, Mitsuo
Routing problem is one of the important research issues in communication network fields. In this paper, we consider a bicriteria shortest path routing (bSPR) model dedicated to calculating nondominated paths for (1) the minimum total cost and (2) the minimum transmission delay. To solve this bSPR problem, we propose a new multiobjective genetic algorithm (moGA): (1) an efficient chromosome representation using the priority-based encoding method; (2) a new operator of GA parameters auto-tuning, which is adaptively regulation of exploration and exploitation based on the change of the average fitness of parents and offspring which is occurred at each generation; and (3) an interactive adaptive-weight fitness assignment mechanism is implemented that assigns weights to each objective and combines the weighted objectives into a single objective function. Numerical experiments with various scales of network design problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.
Dynamic shortest path association for multiple object tracking in video sequence
NASA Astrophysics Data System (ADS)
Xi, Zhenghao; Liu, Heping; Liu, Huaping; Zheng, Yang
2015-01-01
Persistently tracking multiple objects in cluttered environments is very challenging. We present a tracking association approach based on the shortest path faster algorithm. We first formulate the multiple object tracking as an integer programming problem of the flow network. Under this framework, the integer assumption is relaxed to a standard linear programming problem. Therefore, the global optimal solution can quickly be obtained using the fast dynamic shortest path algorithm, which highlights the dynamic programming characteristic of the shortest path, thus faster, algorithm. The proposed method avoids the difficulties of integer programming; more importantly, it has a lower worst-case complexity than competing methods but a better tracking accuracy and robustness in complex environments. Simulation results show that our proposed algorithm takes less time than other methods and can operate in real time.
NASA Astrophysics Data System (ADS)
Yu, Feng; Li, Yanjun; Wu, Tie-Jun
2010-02-01
A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.
Two betweenness centrality measures based on Randomized Shortest Paths
Kivimäki, Ilkka; Lebichot, Bertrand; Saramäki, Jari; Saerens, Marco
2016-01-01
This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP’s have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice. PMID:26838176
Shortest Path Planning for a Tethered Robot or an Anchored Cable
Xavier, P.G.
1999-02-22
We consider the problem of planning shortest paths for a tethered robot with a finite length tether in a 2D environment with polygonal obstacles. We present an algorithm that runs in time O((k{sub 1} + 1){sup 2}n{sup 4}) and finds the shortest path or correctly determines that none exists that obeys the constraints; here n is the number obstacle vertices, and k{sub 1} is the number loops in the initial configuration of the tether. The robot may cross its tether but nothing can cross obstacles, which cause the tether to bend. The algorithm applies as well for planning a shortest path for the free end of an anchored cable.
Multiple Object Tracking Using K-Shortest Paths Optimization.
Berclaz, Jérôme; Fleuret, François; Türetken, Engin; Fua, Pascal
2011-09-01
Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame but is in previous and following ones, a correct trajectory will nevertheless be produced. By contrast, a false-positive detection in a few frames will be ignored. However, when dealing with a multiple target problem, the linking step results in a difficult optimization problem in the space of all possible families of trajectories. This is usually dealt with by sampling or greedy search based on variants of Dynamic Programming which can easily miss the global optimum. In this paper, we show that reformulating that step as a constrained flow optimization results in a convex problem. We take advantage of its particular structure to solve it using the k-shortest paths algorithm, which is very fast. This new approach is far simpler formally and algorithmically than existing techniques and lets us demonstrate excellent performance in two very different contexts.
von Thienen, Wolfhard; Metzler, Dirk; Witte, Volker
2015-05-01
The emergence of self-organizing behavior in ants has been modeled in various theoretical approaches in the past decades. One model explains experimental observations in which Argentine ants (Linepithema humile) selected the shorter of two alternative paths from their nest to a food source (shortest path experiments). This model serves as an important example for the emergence of collective behavior and self-organization in biological systems. In addition, it inspired the development of computer algorithms for optimization problems called ant colony optimization (ACO). In the model, a choice function describing how ants react to different pheromone concentrations is fundamental. However, the parameters of the choice function were not deduced experimentally but freely adapted so that the model fitted the observations of the shortest path experiments. Thus, important knowledge was lacking about crucial model assumptions. A recent study on the Argentine ant provided this information by measuring the response of the ants to varying pheromone concentrations. In said study, the above mentioned choice function was fitted to the experimental data and its parameters were deduced. In addition, a psychometric function was fitted to the data and its parameters deduced. Based on these findings, it is possible to test the shortest path model by applying realistic parameter values. Here we present the results of such tests using Monte Carlo simulations of shortest path experiments with Argentine ants. We compare the choice function and the psychometric function, both with parameter values deduced from the above-mentioned experiments. Our results show that by applying the psychometric function, the shortest path experiments can be explained satisfactorily by the model. The study represents the first example of how psychophysical theory can be used to understand and model collective foraging behavior of ants based on trail pheromones. These findings may be important for other
A bio-inspired method for the constrained shortest path problem.
Wang, Hongping; Lu, Xi; Zhang, Xiaoge; Wang, Qing; Deng, Yong
2014-01-01
The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method.
A Graph Search Heuristic for Shortest Distance Paths
Chow, E
2005-03-24
This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.
Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle
Zhu, Shanjiang; Levinson, David
2015-01-01
Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. This study evaluates the widely applied shortest path assumption by evaluating routes followed by residents of the Minneapolis—St. Paul metropolitan area. Accurate Global Positioning System (GPS) and Geographic Information System (GIS) data were employed to reveal routes people used over an eight to thirteen week period. Most people did not choose the shortest path. Using three weeks of that data, we find that current route choice set generation algorithms do not reveal the majority of paths that individuals took. Findings from this study may guide future efforts in building better route choice models. PMID:26267756
Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.
Qu, Hong; Yi, Zhang; Yang, Simon X
2013-06-01
Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach. PMID:23144039
Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.
Qu, Hong; Yi, Zhang; Yang, Simon X
2013-06-01
Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.
Finding the shortest path with PesCa: a tool for network reconstruction
Scardoni, Giovanni; Tosadori, Gabriele; Pratap, Sakshi; Spoto, Fausto; Laudanna, Carlo
2016-01-01
Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects. PMID:27781081
He, Yunyue; Liu, Zhong; Shi, Jianmai; Wang, Yishan; Zhang, Jiaming; Liu, Jinyuan
2015-01-01
Emergency evacuation aims to transport people from dangerous places to safe shelters as quickly as possible. Police play an important role in the evacuation process, as they can handle traffic accidents immediately and help people move smoothly on roads. This paper investigates an evacuation routing problem that involves police resource allocation. We propose a novel k-th-shortest-path-based technique that uses explicit congestion control to optimize evacuation routing and police resource allocation. A nonlinear mixed-integer programming model is presented to formulate the problem. The model’s objective is to minimize the overall evacuation clearance time. Two algorithms are given to solve the problem. The first one linearizes the original model and solves the linearized problem with CPLEX. The second one is a heuristic algorithm that uses a police resource utilization efficiency index to directly solve the original model. This police resource utilization efficiency index significantly aids in the evaluation of road links from an evacuation throughput perspective. The proposed algorithms are tested with a number of examples based on real data from cities of different sizes. The computational results show that the police resource utilization efficiency index is very helpful in finding near-optimal solutions. Additionally, comparing the performance of the heuristic algorithm and the linearization method by using randomly generated examples indicates that the efficiency of the heuristic algorithm is superior. PMID:26226109
He, Yunyue; Liu, Zhong; Shi, Jianmai; Wang, Yishan; Zhang, Jiaming; Liu, Jinyuan
2015-01-01
Emergency evacuation aims to transport people from dangerous places to safe shelters as quickly as possible. Police play an important role in the evacuation process, as they can handle traffic accidents immediately and help people move smoothly on roads. This paper investigates an evacuation routing problem that involves police resource allocation. We propose a novel k-th-shortest-path-based technique that uses explicit congestion control to optimize evacuation routing and police resource allocation. A nonlinear mixed-integer programming model is presented to formulate the problem. The model's objective is to minimize the overall evacuation clearance time. Two algorithms are given to solve the problem. The first one linearizes the original model and solves the linearized problem with CPLEX. The second one is a heuristic algorithm that uses a police resource utilization efficiency index to directly solve the original model. This police resource utilization efficiency index significantly aids in the evaluation of road links from an evacuation throughput perspective. The proposed algorithms are tested with a number of examples based on real data from cities of different sizes. The computational results show that the police resource utilization efficiency index is very helpful in finding near-optimal solutions. Additionally, comparing the performance of the heuristic algorithm and the linearization method by using randomly generated examples indicates that the efficiency of the heuristic algorithm is superior.
He, Yunyue; Liu, Zhong; Shi, Jianmai; Wang, Yishan; Zhang, Jiaming; Liu, Jinyuan
2015-01-01
Emergency evacuation aims to transport people from dangerous places to safe shelters as quickly as possible. Police play an important role in the evacuation process, as they can handle traffic accidents immediately and help people move smoothly on roads. This paper investigates an evacuation routing problem that involves police resource allocation. We propose a novel k-th-shortest-path-based technique that uses explicit congestion control to optimize evacuation routing and police resource allocation. A nonlinear mixed-integer programming model is presented to formulate the problem. The model's objective is to minimize the overall evacuation clearance time. Two algorithms are given to solve the problem. The first one linearizes the original model and solves the linearized problem with CPLEX. The second one is a heuristic algorithm that uses a police resource utilization efficiency index to directly solve the original model. This police resource utilization efficiency index significantly aids in the evaluation of road links from an evacuation throughput perspective. The proposed algorithms are tested with a number of examples based on real data from cities of different sizes. The computational results show that the police resource utilization efficiency index is very helpful in finding near-optimal solutions. Additionally, comparing the performance of the heuristic algorithm and the linearization method by using randomly generated examples indicates that the efficiency of the heuristic algorithm is superior. PMID:26226109
NASA Astrophysics Data System (ADS)
Kröger, Martin
2005-06-01
We present an algorithm which returns a shortest path and related number of entanglements for a given configuration of a polymeric system in 2 or 3 dimensions. Rubinstein and Helfand, and later Everaers et al. introduced a concept to extract primitive paths for dense polymeric melts made of linear chains (a multiple disconnected multibead 'path'), where each primitive path is defined as a path connecting the (space-fixed) ends of a polymer under the constraint of non-interpenetration (excluded volume) between primitive paths of different chains, such that the multiple disconnected path fulfills a minimization criterion. The present algorithm uses geometrical operations and provides a—model independent—efficient approximate solution to this challenging problem. Primitive paths are treated as 'infinitely' thin (we further allow for finite thickness to model excluded volume), and tensionless lines rather than multibead chains, excluded volume is taken into account without a force law. The present implementation allows to construct a shortest multiple disconnected path (SP) for 2D systems (polymeric chain within spherical obstacles) and an optimal SP for 3D systems (collection of polymeric chains). The number of entanglements is then simply obtained from the SP as either the number of interior kinks, or from the average length of a line segment. Further, information about structure and potentially also the dynamics of entanglements is immediately available from the SP. We apply the method to study the 'concentration' dependence of the degree of entanglement in phantom chain systems. Program summaryTitle of program:Z Catalogue number:ADVG Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVG Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: Silicon Graphics (Irix), Sun (Solaris), PC (Linux) Operating systems or monitors under which the
The approach for shortest paths in fire succor based on component GIS technology
NASA Astrophysics Data System (ADS)
Han, Jie; Zhao, Yong; Dai, K. W.
2007-06-01
Fire safety is an important issue for the national economy and people's living. Efficiency and exactness of fire department succor directly relate to safety of peoples' lives and property. Many disadvantages of the traditional fire system have been emerged in practical applications. The preparation of pumpers is guided by wireless communication or wire communication, so its real-time and accurate performances are much poorer. The information about the reported fire, such as the position, disaster and map, et al., for alarm and command was processed by persons, which slows the reaction speed and delays the combat opportunity. In order to solve these disadvantages, it has an important role to construct a modern fire command center based on high technology. The construction of modern fire command center can realize the modernization and automation of fire command and management. It will play a great role in protecting safety of peoples' lives and property. The center can enhance battle ability and can reduce the direct and indirect loss of fire damage at most. With the development of science technology, Geographic Information System (GIS) has becoming a new information industry for hardware production, software development, data collection, space analysis and counseling. With the popularization of computers and the development of GIS, GIS has gained increasing broad applications for its strong functionality. Network analysis is one of the most important functions of GIS, and the most elementary and pivotal issue of network analysis is the calculation of shortest paths. The shortest paths are mostly applied to some emergent systems such as 119 fire alarms. These systems mainly require that the computation time of the optimal path should be 1-3 seconds. And during traveling, the next running path of the vehicles should be calculated in time. So the implement of the shortest paths must have a high efficiency. In this paper, the component GIS technology was applied to
NASA Astrophysics Data System (ADS)
Huang, Guo-Jiao; Bai, Chao-Ying; Greenhalgh, Stewart
2013-09-01
The traditional grid/cell-based wavefront expansion algorithms, such as the shortest path algorithm, can only find the first arrivals or multiply reflected (or mode converted) waves transmitted from subsurface interfaces, but cannot calculate the other later reflections/conversions having a minimax time path. In order to overcome the above limitations, we introduce the concept of a stationary minimax time path of Fermat's Principle into the multistage irregular shortest path method. Here we extend it from Cartesian coordinates for a flat earth model to global ray tracing of multiple phases in a 3-D complex spherical earth model. The ray tracing results for 49 different kinds of crustal, mantle and core phases show that the maximum absolute traveltime error is less than 0.12 s and the average absolute traveltime error is within 0.09 s when compared with the AK135 theoretical traveltime tables for a 1-D reference model. Numerical tests in terms of computational accuracy and CPU time consumption indicate that the new scheme is an accurate, efficient and a practical way to perform 3-D multiphase arrival tracking in regional or global traveltime tomography.
Modeling the average shortest-path length in growth of word-adjacency networks
NASA Astrophysics Data System (ADS)
Kulig, Andrzej; DroŻdŻ, Stanisław; Kwapień, Jarosław; OświÈ©cimka, Paweł
2015-03-01
We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest-path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest-path length. Then, we identify the local chainlike linear growth induced by grammar and style as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.
A shortest-path graph kernel for estimating gene product semantic similarity
2011-01-01
Background Existing methods for calculating semantic similarity between gene products using the Gene Ontology (GO) often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge. Results We present a shortest-path graph kernel (spgk) method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used. Conclusions Spgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance. PMID:21801410
Quan, Chanqin
2016-01-01
The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967
Hua, Lei; Quan, Chanqin
2016-01-01
The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967
Hua, Lei; Quan, Chanqin
2016-01-01
The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.
NASA Astrophysics Data System (ADS)
Ben Haddou, N.; Ez-zahraouy, H.; Rachadi, A.
2016-07-01
The shortest path is a basic routing model which is still used in many systems. However, due to the low exploitation of the delivery capacity of peripheral nodes, the performance achieved by this policy is very limited. Starting from the fact that changing all network routers by others more robust is not practical, we propose the improvement of the capacity of a scale-free network under the shortest path strategy by the implantation of global dynamic routers. We have studied two targeting approaches to designate specific nodes to route the packets following the global dynamic protocol; one is based on node degree and the other on its betweenness. We show that we already exceed twice the capacity under the shortest path protocol with only 4% of global dynamic routers when we target nodes with high betweenness and 10% when we target nodes with high degrees. Moreover, the average travelling time remains low while the network capacity increases.
The shortest path problem in the stochastic networks with unstable topology.
Shirdel, Gholam H; Abdolhosseinzadeh, Mohsen
2016-01-01
The stochastic shortest path length is defined as the arrival probability from a given source node to a given destination node in the stochastic networks. We consider the topological changes and their effects on the arrival probability in directed acyclic networks. There is a stable topology which shows the physical connections of nodes; however, the communication between nodes does not stable and that is defined as the unstable topology where arcs may be congested. A discrete time Markov chain with an absorbing state is established in the network according to the unstable topological changes. Then, the arrival probability to the destination node from the source node in the network is computed as the multi-step transition probability of the absorption in the final state of the established Markov chain. It is assumed to have some wait states, whenever there is a physical connection but it is not possible to communicate between nodes immediately. The proposed method is illustrated by different numerical examples, and the results can be used to anticipate the probable congestion along some critical arcs in the delay sensitive networks.
Task-parallel implementation of 3D shortest path raytracing for geophysical applications
NASA Astrophysics Data System (ADS)
Giroux, Bernard; Larouche, Benoît
2013-04-01
This paper discusses two variants of the shortest path method and their parallel implementation on a shared-memory system. One variant is designed to perform raytracing in models with stepwise distributions of interval velocity while the other is better suited for continuous velocity models. Both rely on a discretization scheme where primary nodes are located at the corners of cuboid cells and where secondary nodes are found on the edges and sides of the cells. The parallel implementations allow raytracing concurrently for different sources, providing an attractive framework for ray-based tomography. The accuracy and performance of the implementations were measured by comparison with the analytic solution for a layered model and for a vertical gradient model. Mean relative error less than 0.2% was obtained with 5 secondary nodes for the layered model and 9 secondary nodes for the gradient model. Parallel performance depends on the level of discretization refinement, on the number of threads, and on the problem size, with the most determinant variable being the level of discretization refinement (number of secondary nodes). The results indicate that a good trade-off between speed and accuracy is achieved with the number of secondary nodes equal to 5. The programs are written in C++ and rely on the Standard Template Library and OpenMP.
The shortest path problem in the stochastic networks with unstable topology.
Shirdel, Gholam H; Abdolhosseinzadeh, Mohsen
2016-01-01
The stochastic shortest path length is defined as the arrival probability from a given source node to a given destination node in the stochastic networks. We consider the topological changes and their effects on the arrival probability in directed acyclic networks. There is a stable topology which shows the physical connections of nodes; however, the communication between nodes does not stable and that is defined as the unstable topology where arcs may be congested. A discrete time Markov chain with an absorbing state is established in the network according to the unstable topological changes. Then, the arrival probability to the destination node from the source node in the network is computed as the multi-step transition probability of the absorption in the final state of the established Markov chain. It is assumed to have some wait states, whenever there is a physical connection but it is not possible to communicate between nodes immediately. The proposed method is illustrated by different numerical examples, and the results can be used to anticipate the probable congestion along some critical arcs in the delay sensitive networks. PMID:27652102
Panzacchi, Manuela; Van Moorter, Bram; Strand, Olav; Saerens, Marco; Kivimäki, Ilkka; St Clair, Colleen C; Herfindal, Ivar; Boitani, Luigi
2016-01-01
The loss, fragmentation and degradation of habitat everywhere on Earth prompts increasing attention to identifying landscape features that support animal movement (corridors) or impedes it (barriers). Most algorithms used to predict corridors assume that animals move through preferred habitat either optimally (e.g. least cost path) or as random walkers (e.g. current models), but neither extreme is realistic. We propose that corridors and barriers are two sides of the same coin and that animals experience landscapes as spatiotemporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfil specific ecological processes. Based on this conceptual framework, we propose a novel methodological approach that uses high-resolution individual-based movement data to predict corridor-barrier continua with increased realism. Our approach consists of two innovations. First, we use step selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly, we introduce to movement ecology the randomized shortest path algorithm (RSP) which operates on friction maps to predict the corridor-barrier continuum for strategic movements between functional areas. By modulating the parameter Ѳ, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Ѳ approaches infinity, RSP is equivalent to LCP) or random walk (when Ѳ → 0, RSP → current models). Using this approach, we identify migration corridors for GPS-monitored wild reindeer (Rangifer t. tarandus) in Norway. We demonstrate that reindeer movement is best predicted by an intermediate value of Ѳ, indicative of a movement trade-off between optimization and exploration. Model calibration allows identification of a corridor-barrier continuum that closely fits empirical data and demonstrates that RSP
Challenging of path planning algorithms for autonomous robot in known environment
NASA Astrophysics Data System (ADS)
Farah, R. N.; Irwan, N.; Zuraida, Raja Lailatul; Shaharum, Umairah; Hanafi@Omar, Hafiz Mohd
2014-06-01
Most of the mobile robot path planning is estimated to reach its predetermined aim through the shortest path and avoiding the obstacles. This paper is a survey on path planning algorithms of various current research and existing system of Unmanned Ground Vehicles (UGV) where their challenging issues to be intelligent autonomous robot. The focuses are some short reviews on individual papers for UGV in the known environment. Methods and algorithms in path planning for the autonomous robot had been discussed. From the reviews, we obtained that the algorithms proposed are appropriate for some cases such as single or multiple obstacles, static or movement obstacle and optimal shortest path. This paper also describes some pros and cons for every reviewed paper toward algorithms improvement for further work.
Kwon, TaeKyu; Agrawal, Kunal; Li, Yunfeng; Pizlo, Zygmunt
2016-09-01
Finding the occluding contours of objects in real 2D retinal images of natural 3D scenes is done by determining, which contour fragments are relevant, and the order in which they should be connected. We developed a model that finds the closed contour represented in the image by solving a shortest path problem that uses a log-polar representation of the image; the kind of representation known to exist in area V1 of the primate cortex. The shortest path in a log-polar representation favors the smooth, convex and closed contours in the retinal image that have the smallest number of gaps. This approach is practical because finding a globally-optimal solution to a shortest path problem is computationally easy. Our model was tested in four psychophysical experiments. In the first two experiments, the subject was presented with a fragmented convex or concave polygon target among a large number of unrelated pieces of contour (distracters). The density of these pieces of contour was uniform all over the screen to minimize spatially-local cues. The orientation of each target contour fragment was randomly perturbed by varying the levels of jitter. Subjects drew a closed contour that represented the target's contour on a screen. The subjects' performance was nearly perfect when the jitter-level was low. Their performance deteriorated as jitter-levels were increased. The performance of our model was very similar to our subjects'. In two subsequent experiments, the subject was asked to discriminate a briefly-presented egg-shaped object while maintaining fixation at several different positions relative to the closed contour of the shape. The subject's discrimination performance was affected by the fixation position in much the same way as the model's.
Kwon, TaeKyu; Agrawal, Kunal; Li, Yunfeng; Pizlo, Zygmunt
2016-09-01
Finding the occluding contours of objects in real 2D retinal images of natural 3D scenes is done by determining, which contour fragments are relevant, and the order in which they should be connected. We developed a model that finds the closed contour represented in the image by solving a shortest path problem that uses a log-polar representation of the image; the kind of representation known to exist in area V1 of the primate cortex. The shortest path in a log-polar representation favors the smooth, convex and closed contours in the retinal image that have the smallest number of gaps. This approach is practical because finding a globally-optimal solution to a shortest path problem is computationally easy. Our model was tested in four psychophysical experiments. In the first two experiments, the subject was presented with a fragmented convex or concave polygon target among a large number of unrelated pieces of contour (distracters). The density of these pieces of contour was uniform all over the screen to minimize spatially-local cues. The orientation of each target contour fragment was randomly perturbed by varying the levels of jitter. Subjects drew a closed contour that represented the target's contour on a screen. The subjects' performance was nearly perfect when the jitter-level was low. Their performance deteriorated as jitter-levels were increased. The performance of our model was very similar to our subjects'. In two subsequent experiments, the subject was asked to discriminate a briefly-presented egg-shaped object while maintaining fixation at several different positions relative to the closed contour of the shape. The subject's discrimination performance was affected by the fixation position in much the same way as the model's. PMID:26241462
Kwon, TaeKyu; Agrawal, Kunal; Li, Yunfeng; Pizlo, Zygmunt
2015-01-01
Finding the occluding contours of objects in real 2D retinal images of natural 3D scenes is done by determining, which contour fragments are relevant, and the order in which they should be connected. We developed a model that finds the closed contour represented in the image by solving a shortest path problem that uses a log-polar representation of the image; the kind of representation known to exist in area V1 of the primate cortex. The shortest path in a log-polar representation favors the smooth, convex and closed contours in the retinal image that have the smallest number of gaps. This approach is practical because finding a globally-optimal solution to a shortest path problem is computationally easy. Our model was tested in four psychophysical experiments. In the first two experiments, the subject was presented with a fragmented convex or concave polygon target among a large number of unrelated pieces of contour (distracters). The density of these pieces of contour was uniform all over the screen to minimize spatially-local cues. The orientation of each target contour fragment was randomly perturbed by varying the levels of jitter. Subjects drew a closed contour that represented the target’s contour on a screen. The subjects’ performance was nearly perfect when the jitter-level was low. Their performance deteriorated as jitter-levels were increased. The performance of our model was very similar to our subjects’. In two subsequent experiments, the subject was asked to discriminate a briefly-presented egg-shaped object while maintaining fixation at several different positions relative to the closed contour of the shape. The subject’s discrimination performance was affected by the fixation position in much the same way as the model’s. PMID:26241462
NASA Astrophysics Data System (ADS)
Shen, Yi; Ren, Gang; Liu, Yang
2016-06-01
In this paper, we propose a biased-shortest path method with minimal congestion. In the method, we use linear-prediction to estimate the queue length of nodes, and propose a dynamic accepting probability function for nodes to decide whether accept or reject the incoming packets. The dynamic accepting probability function is based on the idea of homogeneous network flow and is developed to enable nodes to coordinate their queue length to avoid congestion. A path strategy incorporated with the linear-prediction of the queue length and the dynamic accepting probability function of nodes is designed to allow packets to be automatically delivered on un-congested paths with short traveling time. Our method has the advantage of low computation cost because the optimal paths are dynamically self-organized by nodes in the delivering process of packets with local traffic information. We compare our method with the existing methods such as the efficient path method (EPS) and the optimal path method (OPS) on the BA scale-free networks and a real example. The numerical computations show that our method performs best for low network load and has minimum run time due to its low computational cost and local routing scheme.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
A Multilevel Probabilistic Beam Search Algorithm for the Shortest Common Supersequence Problem
Gallardo, José E.
2012-01-01
The shortest common supersequence problem is a classical problem with many applications in different fields such as planning, Artificial Intelligence and especially in Bioinformatics. Due to its NP-hardness, we can not expect to efficiently solve this problem using conventional exact techniques. This paper presents a heuristic to tackle this problem based on the use at different levels of a probabilistic variant of a classical heuristic known as Beam Search. The proposed algorithm is empirically analysed and compared to current approaches in the literature. Experiments show that it provides better quality solutions in a reasonable time for medium and large instances of the problem. For very large instances, our heuristic also provides better solutions, but required execution times may increase considerably. PMID:23300667
A multilevel probabilistic beam search algorithm for the shortest common supersequence problem.
Gallardo, José E
2012-01-01
The shortest common supersequence problem is a classical problem with many applications in different fields such as planning, Artificial Intelligence and especially in Bioinformatics. Due to its NP-hardness, we can not expect to efficiently solve this problem using conventional exact techniques. This paper presents a heuristic to tackle this problem based on the use at different levels of a probabilistic variant of a classical heuristic known as Beam Search. The proposed algorithm is empirically analysed and compared to current approaches in the literature. Experiments show that it provides better quality solutions in a reasonable time for medium and large instances of the problem. For very large instances, our heuristic also provides better solutions, but required execution times may increase considerably.
Analyzing the applicability of the least risk path algorithm in indoor space
NASA Astrophysics Data System (ADS)
Vanclooster, A.; Viaene, P.; Van de Weghe, N.; Fack, V.; De Maeyer, Ph.
2013-11-01
Over the last couple of years, applications that support navigation and wayfinding in indoor environments have become one of the booming industries. However, the algorithmic support for indoor navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. In outdoor space, several alternative algorithms have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-story building. Several analyses compare shortest and least risk paths in indoor and in outdoor space. The results of these analyses indicate that the current outdoor least risk path algorithm does not calculate less risky paths compared to its shortest paths. In some cases, worse routes have been suggested. Adjustments to the original algorithm are proposed to be more aligned to the specific structure of indoor environments. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments.
Yuan, Fei; Zhou, You; Wang, Meng; Yang, Jing; Wu, Kai; Lu, Changhong; Kong, Xiangyin; Cai, Yu-Dong
2015-01-01
Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486
Su, Fangchu; Chen, Lei; Huang, Tao; Cai, Yu-Dong
2016-01-01
Biologically, fruits are defined as seed-bearing reproductive structures in angiosperms that develop from the ovary. The fertilization, development and maturation of fruits are crucial for plant reproduction and are precisely regulated by intrinsic genetic regulatory factors. In this study, we used Arabidopsis thaliana as a model organism and attempted to identify novel genes related to fruit-associated biological processes. Specifically, using validated genes, we applied a shortest-path-based method to identify several novel genes in a large network constructed using the protein-protein interactions observed in Arabidopsis thaliana. The described analyses indicate that several of the discovered genes are associated with fruit fertilization, development and maturation in Arabidopsis thaliana. PMID:27434024
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1995-03-01
To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions difficult tasks. From these solutions, an evolving sparse work of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a framework in which a slow but effective planner may be improved both cost-wise and capability-wise by a faster but less effective planner coupled with experience. We analyze algorithm by formalizing the concept of improvability and deriving conditions under which a planner can be improved within the framework. The analysis is based on two stochastic models, one pessimistic (on task complexity), the other randomized (on experience utility). Using these models, we derive quantitative bounds to predict the learning behavior. We use these estimation tools to characterize the situations in which the algorithm is useful and to provide bounds on the training time. In particular, we show how to predict the maximum achievable speedup. Additionally, our analysis techniques are elementary and should be useful for studying other types of probabilistic learning as well.
A Path Algorithm for Constrained Estimation.
Zhou, Hua; Lange, Kenneth
2013-01-01
Many least-square problems involve affine equality and inequality constraints. Although there are a variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current article proposes a new path-following algorithm for quadratic programming that replaces hard constraints by what are called exact penalties. Similar penalties arise in l1 regularization in model selection. In the regularization setting, penalties encapsulate prior knowledge, and penalized parameter estimates represent a trade-off between the observed data and the prior knowledge. Classical penalty methods of optimization, such as the quadratic penalty method, solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties!are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path-following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in Lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the Lasso and generalized Lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well-chosen examples illustrate the mechanics and potential of path following. This article has supplementary materials available online.
Robot path planning using a genetic algorithm
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu
1988-01-01
Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.
Evolutionary development of path planning algorithms
Hage, M
1998-09-01
This paper describes the use of evolutionary software techniques for developing both genetic algorithms and genetic programs. Genetic algorithms are evolved to solve a specific problem within a fixed and known environment. While genetic algorithms can evolve to become very optimized for their task, they often are very specialized and perform poorly if the environment changes. Genetic programs are evolved through simultaneous training in a variety of environments to develop a more general controller behavior that operates in unknown environments. Performance of genetic programs is less optimal than a specially bred algorithm for an individual environment, but the controller performs acceptably under a wider variety of circumstances. The example problem addressed in this paper is evolutionary development of algorithms and programs for path planning in nuclear environments, such as Chernobyl.
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
NASA Astrophysics Data System (ADS)
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1993-03-01
Path planning has to be fast to support real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To alleviate this problem, we present a learning algorithm that uses past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful subgoals is learned to support faster planning. The algorithm is suitable for both stationary and incrementally-changing environments. To analyze our algorithm, we use a previously developed stochastic model that quantifies experience utility. Using this model, we characterize the situations in which the adaptive planner is useful, and provide quantitative bounds to predict its behavior. The results are demonstrated with problems in manipulator planning. Our algorithm and analysis are sufficiently general that they may also be applied to task planning or other planning domains in which experience is useful.
A load-balance path selection algorithm in automatically swiched optical network (ASON)
NASA Astrophysics Data System (ADS)
Gao, Fei; Lu, Yueming; Ji, Yuefeng
2007-11-01
In this paper, a novel load-balance algorithm is proposed to provide an approach to optimized path selection in automatically swiched optical network (ASON). By using this algorithm, improved survivability and low congestion can be achieved. The static nature of current routing algorithms, such as OSPF or IS-IS, has made the situation worse since the traffic is concentrated on the "least-cost" paths which causes the congestion for some links while leaving other links lightly loaded. So, the key is to select suitable paths to balance the network load to optimize network resource utilization and traffic performance. We present a method to provide the capability to control traffic engineering so that the carriers can define their own strategies for optimizations and apply them to path selection for dynamic load balancing. With considering load distribution and topology information, capacity utilization factor is introduced into Dijkstra (shortest path selection) for path selection to achieve balancing traffic over network. Routing simulations have been done over mesh networks to compare the two different algorithms. With the simulation results, a conclusion can be made on the performance of different algorithms.
An Adaptive Path Planning Algorithm for Cooperating Unmanned Air Vehicles
Cunningham, C.T.; Roberts, R.S.
2000-09-12
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
Adaptive path planning algorithm for cooperating unmanned air vehicles
Cunningham, C T; Roberts, R S
2001-02-08
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
Global path planning of mobile robots using a memetic algorithm
NASA Astrophysics Data System (ADS)
Zhu, Zexuan; Wang, Fangxiao; He, Shan; Sun, Yiwen
2015-08-01
In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.
Chen, Lei; Huang, Tao; Zhang, Yu-Hang; Jiang, Yang; Zheng, Mingyue; Cai, Yu-Dong
2016-01-01
Tumors are formed by the abnormal proliferation of somatic cells with disordered growth regulation under the influence of tumorigenic factors. Recently, the theory of “cancer drivers” connects tumor initiation with several specific mutations in the so-called cancer driver genes. According to the differentiation of four basic levels between tumor and adjacent normal tissues, the cancer drivers can be divided into the following: (1) Methylation level, (2) microRNA level, (3) mutation level, and (4) mRNA level. In this study, a computational method is proposed to identify novel lung adenocarcinoma drivers based on dysfunctional genes on the methylation, microRNA, mutation and mRNA levels. First, a large network was constructed using protein-protein interactions. Next, we searched all of the shortest paths connecting dysfunctional genes on different levels and extracted new candidate genes lying on these paths. Finally, the obtained candidate genes were filtered by a permutation test and an additional strict selection procedure involving a betweenness ratio and an interaction score. Several candidate genes remained, which are deemed to be related to two different levels of cancer. The analyses confirmed our assertions that some have the potential to contribute to the tumorigenesis process on multiple levels. PMID:27412431
Improved genetic algorithm for fast path planning of USV
NASA Astrophysics Data System (ADS)
Cao, Lu
2015-12-01
Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.
Huang, Tao; Cai, Yu-Dong
2014-01-01
The recently emerging Influenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the “guilt by association” rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identify Influenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies. PMID:24955349
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Lim, Wei Chen Esmonde; Kanagaraj, G.; Ponnambalam, S. G.
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198
Local path planning of a mobile robot using genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Rubo; Zhang, Guoyin; Gu, Guochang
1998-08-01
The local path planning of mobile robots can be regarded as finding a mapping from perception space to action space. Genetic algorithm is used to search optimal mapping in this paper so as to improve the obstacle avoidance ability of the robot. In this paper, the rotational angle and translation distance of the robot is divided into seven and four grades respectively. In addition, the length of the path that the robot covers before collision with obstacle is taken as fitness. The robot can learn to carry out local path planning through selection, crossover and mutation in genetic algorithm. The simulation results are given at the and of this paper.
Extracting contours of oval-shaped objects by Hough transform and minimal path algorithms
NASA Astrophysics Data System (ADS)
Tleis, Mohamed; Verbeek, Fons J.
2014-04-01
Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells, contours from our methods were compared with another solution using Pratt's figure of merit. Results indicate that our methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the segmentation and measurement results enable, in future work, to retrieve information from different developmental stages of the cell using complex features.
Mobile transporter path planning using a genetic algorithm approach
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1988-01-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Mobile Transporter Path Planning Using A Genetic Algorithm Approach
NASA Astrophysics Data System (ADS)
Baffes, Paul; Wang, Lui
1988-10-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Path querying system on mobile devices
NASA Astrophysics Data System (ADS)
Lin, Xing; Wang, Yifei; Tian, Yuan; Wu, Lun
2006-01-01
Traditional approaches to path querying problems are not efficient and convenient under most circumstances. A more convenient and reliable approach to this problem has to be found. This paper is devoted to a path querying solution on mobile devices. By using an improved Dijkstra's shortest path algorithm and a natural language translating module, this system can help people find the shortest path between two places through their cell phones or other mobile devices. The chosen path is prompted in text of natural language, as well as a map picture. This system would be useful in solving best path querying problems and have potential to be a profitable business system.
Smell Detection Agent Based Optimization Algorithm
NASA Astrophysics Data System (ADS)
Vinod Chandra, S. S.
2016-09-01
In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.
A Bat Algorithm with Mutation for UCAV Path Planning
Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi
2012-01-01
Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models. PMID:23365518
Path Planning Algorithms for the Adaptive Sensor Fleet
NASA Technical Reports Server (NTRS)
Stoneking, Eric; Hosler, Jeff
2005-01-01
The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.
Path integral hybrid Monte Carlo algorithm for correlated Bose fluids.
Miura, Shinichi; Tanaka, Junji
2004-02-01
Path integral hybrid Monte Carlo (PIHMC) algorithm for strongly correlated Bose fluids has been developed. This is an extended version of our previous method [S. Miura and S. Okazaki, Chem. Phys. Lett. 308, 115 (1999)] applied to a model system consisting of noninteracting bosons. Our PIHMC method for the correlated Bose fluids is constituted of two trial moves to sample path-variables describing system coordinates along imaginary time and a permutation of particle labels giving a boundary condition with respect to imaginary time. The path-variables for a given permutation are generated by a hybrid Monte Carlo method based on path integral molecular dynamics techniques. Equations of motion for the path-variables are formulated on the basis of a collective coordinate representation of the path, staging variables, to enhance the sampling efficiency. The permutation sampling to satisfy Bose-Einstein statistics is performed using the multilevel Metropolis method developed by Ceperley and Pollock [Phys. Rev. Lett. 56, 351 (1986)]. Our PIHMC method has successfully been applied to liquid helium-4 at a state point where the system is in a superfluid phase. Parameters determining the sampling efficiency are optimized in such a way that correlation among successive PIHMC steps is minimized. PMID:15268354
A dynamic path planning algorithm for UAV tracking
NASA Astrophysics Data System (ADS)
Chen, Hongda; Chang, K. C.; Agate, Craig S.
2009-05-01
A dynamic path-planning algorithm is proposed for UAV tracking. Based on tangent lines between two dynamic UAV turning and objective circles, analytical optimal path is derived with UAV operational constraints given a target position and the current UAV dynamic state. In this paper, we first illustrate that path planning for UAV tracking a ground target can be formulated as an optimal control problem consisting of a system dynamic, a set of boundary conditions, control constraints and a cost criterion. Then we derive close form solution to initiate dynamic tangent lines between UAV turning limit circle and an objective circle, which is a desired orbit pattern over a target. Basic tracking strategies are illustrated to find the optimal path for UAV tracking. Particle filter method is applied as a target is moving on a defined road network. Obstacle avoidance strategies are also addressed. With the help of computer simulations, we showed that the algorithm provides an efficient and effective tracking performance in various scenarios, including a target moving according to waypoints (time-based and/or speed-based) or a random kinematics model.
Multi-path planning algorithm based on fitness sharing and species evolution
NASA Astrophysics Data System (ADS)
Zhang, Jing-Juan; Li, Xue-Lian; Hao, Yan-Ling
2003-06-01
A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.
Algorithm Plans Collision-Free Path for Robotic Manipulator
NASA Technical Reports Server (NTRS)
Backes, Paul; Diaz-Calderon, Antonio
2007-01-01
An algorithm has been developed to enable a computer aboard a robot to autonomously plan the path of the manipulator arm of the robot to avoid collisions between the arm and any obstacle, which could be another part of the robot or an external object in the vicinity of the robot. In simplified terms, the algorithm generates trial path segments and tests each segment for potential collisions in an iterative process that ends when a sequence of collision-free segments reaches from the starting point to the destination. The main advantage of this algorithm, relative to prior such algorithms, is computational efficiency: the algorithm is designed to make minimal demands upon the limited computational resources available aboard a robot. This path-planning algorithm utilizes a modified version of the collision-detection method described in "Improved Collision-Detection Method for Robotic Manipulator" (NPO-30356), NASA Tech Briefs, Vol. 27, No. 3 (June 2003), page 72. The method involves utilization of mathematical models of the robot constructed prior to operation and similar models of external objects constructed automatically from sensory data acquired during operation. This method incorporates a previously developed method, known in the art as the method of oriented bounding boxes (OBBs), in which an object is represented approximately, for computational purposes, by a box that encloses its outer boundary. Because many parts of a robotic manipulator are cylindrical, the OBB method has been extended in this method to enable the approximate representation of cylindrical parts by use of octagonal or other multiple-OBB assemblies denoted oriented bounding prisms (OBPs). A multiresolution OBB/OBP representation of the robot and its manipulator arm and a multiresolution OBB representation of external objects (including terrain) are constructed and used in a process in which collisions at successively finer resolutions are detected through computational detection of overlaps
Precise flight-path control using a predictive algorithm
NASA Technical Reports Server (NTRS)
Hess, R. A.; Jung, Y. C.
1991-01-01
Generalized predictive control describes an algorithm for the control of dynamic systems in which a control input is generated that minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. A design technique is discussed for applying the single-input/single-output generalized predictive control algorithm to a problem of longitudinal/vertical terrain-following flight of a rotorcraft. By using the generalized predictive control technique to provide inputs to a classically designed stability and control augmentation system, it is demonstrated that a robust flight-path control system can be created that exhibits excellent tracking performance.
New Algorithms for Global Optimization and Reaction Path Determination.
Weber, D; Bellinger, D; Engels, B
2016-01-01
We present new schemes to improve the convergence of an important global optimization problem and to determine reaction pathways (RPs) between identified minima. Those methods have been implemented into the CAST program (Conformational Analysis and Search Tool). The first part of this chapter shows how to improve convergence of the Monte Carlo with minimization (MCM, also known as Basin Hopping) method when applied to optimize water clusters or aqueous solvation shells using a simple model. Since the random movement on the potential energy surface (PES) is an integral part of MCM, we propose to employ a hydrogen bonding-based algorithm for its improvement. We show comparisons of the results obtained for random dihedral and for the proposed random, rigid-body water molecule movement, giving evidence that a specific adaption of the distortion process greatly improves the convergence of the method. The second part is about the determination of RPs in clusters between conformational arrangements and for reactions. Besides standard approaches like the nudged elastic band method, we want to focus on a new algorithm developed especially for global reaction path search called Pathopt. We started with argon clusters, a typical benchmark system, which possess a flat PES, then stepwise increase the magnitude and directionality of interactions. Therefore, we calculated pathways for a water cluster and characterize them by frequency calculations. Within our calculations, we were able to show that beneath local pathways also additional pathways can be found which possess additional features. PMID:27497166
MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives.
Oral, Tugcem; Polat, Faruk
2016-01-01
The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other. In this paper, we introduce a novel multiobjective incremental algorithm, multiobjective D* lite (MOD* lite) built upon a well-known path planning algorithm, D* lite. A number of experiments are designed to compare the solution quality and execution time requirements of MOD* lite with the multiobjective A* algorithm, an alternative genetic algorithm we developed multiobjective genetic path planning and the strength Pareto evolutionary algorithm.
A Generic Path Algorithm for Regularized Statistical Estimation
Zhou, Hua; Wu, Yichao
2014-01-01
Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution towards prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized ℓ1 penalized regression methods. In this article we follow a recent idea by Wu (2011, 2012) and propose an exact path solver based on ordinary differential equations (EPSODE) that works for any convex loss function and can deal with generalized ℓ1 penalties as well as more complicated regularization such as inequality constraints encountered in shape-restricted regressions and nonparametric density estimation. Non-asymptotic error bounds for the equality regularized estimates are derived. In practice, the EPSODE can be coupled with AIC, BIC, Cp or cross-validation to select an optimal tuning parameter, or provides a convenient model space for performing model averaging or aggregation. Our applications to generalized ℓ1 regularized generalized linear models, shape-restricted regressions, Gaussian graphical models, and nonparametric density estimation showcase the potential of the EPSODE algorithm. PMID:25242834
A path following algorithm for the graph matching problem.
Zaslavskiy, Mikhail; Bach, Francis; Vert, Jean-Philippe
2009-12-01
We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtained by rewriting the weighted graph matching problem as a least-square problem on the set of permutation matrices and relaxing it to two different optimization problems: a quadratic convex and a quadratic concave optimization problem on the set of doubly stochastic matrices. The concave relaxation has the same global minimum as the initial graph matching problem, but the search for its global minimum is also a hard combinatorial problem. We, therefore, construct an approximation of the concave problem solution by following a solution path of a convex-concave problem obtained by linear interpolation of the convex and concave formulations, starting from the convex relaxation. This method allows to easily integrate the information on graph label similarities into the optimization problem, and therefore, perform labeled weighted graph matching. The algorithm is compared with some of the best performing graph matching methods on four data sets: simulated graphs, QAPLib, retina vessel images, and handwritten Chinese characters. In all cases, the results are competitive with the state of the art.
NASA Technical Reports Server (NTRS)
Izumi, K. H.; Thompson, J. L.; Groce, J. L.; Schwab, R. W.
1986-01-01
The design requirements for a 4D path definition algorithm are described. These requirements were developed for the NASA ATOPS as an extension of the Local Flow Management/Profile Descent algorithm. They specify the processing flow, functional and data architectures, and system input requirements, and recommended the addition of a broad path revision (reinitialization) function capability. The document also summarizes algorithm design enhancements and the implementation status of the algorithm on an in-house PDP-11/70 computer. Finally, the requirements for the pilot-computer interfaces, the lateral path processor, and guidance and steering function are described.
Chandrasekaran, Srinivas Niranj; Das, Jhuma; Dokholyan, Nikolay V.; Carter, Charles W.
2016-01-01
PATH rapidly computes a path and a transition state between crystal structures by minimizing the Onsager-Machlup action. It requires input parameters whose range of values can generate different transition-state structures that cannot be uniquely compared with those generated by other methods. We outline modifications to estimate these input parameters to circumvent these difficulties and validate the PATH transition states by showing consistency between transition-states derived by different algorithms for unrelated protein systems. Although functional protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH. PMID:26958584
Path planning for mobile robots based on visibility graphs and A* algorithm
NASA Astrophysics Data System (ADS)
Contreras, Juan D.; Martínez S., Fernando; Martínez S., Fredy H.
2015-07-01
One of most worked issues in the last years in robotics has been the study of strategies to path planning for mobile robots in static and observable conditions. This is an open problem without pre-defined rules (non-heuristic), which needs to measure the state of the environment, finds useful information, and uses an algorithm to select the best path. This paper proposes a simple and efficient geometric path planning strategy supported in digital image processing. The image of the environment is processed in order to identify obstacles, and thus the free space for navigation. Then, using visibility graphs, the possible navigation paths guided by the vertices of obstacles are produced. Finally the A* algorithm is used to find a best possible path. The alternative proposed is evaluated by simulation on a large set of test environments, showing in all cases its ability to find a free collision plausible path.
Birkholz, Adam B.; Schlegel, H. Bernhard
2015-12-28
The development of algorithms to optimize reaction pathways between reactants and products is an active area of study. Existing algorithms typically describe the path as a discrete series of images (chain of states) which are moved downhill toward the path, using various reparameterization schemes, constraints, or fictitious forces to maintain a uniform description of the reaction path. The Variational Reaction Coordinate (VRC) method is a novel approach that finds the reaction path by minimizing the variational reaction energy (VRE) of Quapp and Bofill. The VRE is the line integral of the gradient norm along a path between reactants and products and minimization of VRE has been shown to yield the steepest descent reaction path. In the VRC method, we represent the reaction path by a linear expansion in a set of continuous basis functions and find the optimized path by minimizing the VRE with respect to the linear expansion coefficients. Improved convergence is obtained by applying constraints to the spacing of the basis functions and coupling the minimization of the VRE to the minimization of one or more points along the path that correspond to intermediates and transition states. The VRC method is demonstrated by optimizing the reaction path for the Müller-Brown surface and by finding a reaction path passing through 5 transition states and 4 intermediates for a 10 atom Lennard-Jones cluster.
Birkholz, Adam B; Schlegel, H Bernhard
2015-12-28
The development of algorithms to optimize reaction pathways between reactants and products is an active area of study. Existing algorithms typically describe the path as a discrete series of images (chain of states) which are moved downhill toward the path, using various reparameterization schemes, constraints, or fictitious forces to maintain a uniform description of the reaction path. The Variational Reaction Coordinate (VRC) method is a novel approach that finds the reaction path by minimizing the variational reaction energy (VRE) of Quapp and Bofill. The VRE is the line integral of the gradient norm along a path between reactants and products and minimization of VRE has been shown to yield the steepest descent reaction path. In the VRC method, we represent the reaction path by a linear expansion in a set of continuous basis functions and find the optimized path by minimizing the VRE with respect to the linear expansion coefficients. Improved convergence is obtained by applying constraints to the spacing of the basis functions and coupling the minimization of the VRE to the minimization of one or more points along the path that correspond to intermediates and transition states. The VRC method is demonstrated by optimizing the reaction path for the Müller-Brown surface and by finding a reaction path passing through 5 transition states and 4 intermediates for a 10 atom Lennard-Jones cluster.
NASA Astrophysics Data System (ADS)
Birkholz, Adam B.; Schlegel, H. Bernhard
2015-12-01
The development of algorithms to optimize reaction pathways between reactants and products is an active area of study. Existing algorithms typically describe the path as a discrete series of images (chain of states) which are moved downhill toward the path, using various reparameterization schemes, constraints, or fictitious forces to maintain a uniform description of the reaction path. The Variational Reaction Coordinate (VRC) method is a novel approach that finds the reaction path by minimizing the variational reaction energy (VRE) of Quapp and Bofill. The VRE is the line integral of the gradient norm along a path between reactants and products and minimization of VRE has been shown to yield the steepest descent reaction path. In the VRC method, we represent the reaction path by a linear expansion in a set of continuous basis functions and find the optimized path by minimizing the VRE with respect to the linear expansion coefficients. Improved convergence is obtained by applying constraints to the spacing of the basis functions and coupling the minimization of the VRE to the minimization of one or more points along the path that correspond to intermediates and transition states. The VRC method is demonstrated by optimizing the reaction path for the Müller-Brown surface and by finding a reaction path passing through 5 transition states and 4 intermediates for a 10 atom Lennard-Jones cluster.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat.
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-14
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
NASA Astrophysics Data System (ADS)
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-01
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
The Path-of-Probability Algorithm for Steering and Feedback Control of Flexible Needles
Park, Wooram; Wang, Yunfeng; Chirikjian, Gregory S.
2010-01-01
In this paper we develop a new framework for path planning of flexible needles with bevel tips. Based on a stochastic model of needle steering, the probability density function for the needle tip pose is approximated as a Gaussian. The means and covariances are estimated using an error propagation algorithm which has second order accuracy. Then we adapt the path-of-probability (POP) algorithm to path planning of flexible needles with bevel tips. We demonstrate how our planning algorithm can be used for feedback control of flexible needles. We also derive a closed-form solution for the port placement problem for finding good insertion locations for flexible needles in the case when there are no obstacles. Furthermore, we propose a new method using reference splines with the POP algorithm to solve the path planning problem for flexible needles in more general cases that include obstacles. PMID:21151708
Ghosal, Dipak; Mueller, Stephen Ng
2005-04-01
With multipath routing in mobile ad hoc networks (MANETs), a source can establish multiple routes to a destination for routing data. In MANETs, mulitpath routing can be used to provide route resilience, smaller end-to-end delay, and better load balancing. However, when the multiple paths are close together, transmissions of different paths may interfere with each other, causing degradation in performance. Besides interference, the physical diversity of paths also improves fault tolerance. We present a purely distributed multipath protocol based on the AODV-Multipath (AODVM) protocol called AODVM with Path Diversity (AODVM/PD) that finds multiple paths with a desired degree of correlation between paths specified as an input parameter to the algorithm. We demonstrate through detailed simulation analysis that multiple paths with low degree of correlation determined by AODVM/PD provides both smaller end-to-end delay than AODVM in networks with low mobility and better route resilience in the presence of correlated node failures.
Multiple Manifold Clustering Using Curvature Constrained Path
Babaeian, Amir; Bayestehtashk, Alireza; Bandarabadi, Mojtaba
2015-01-01
The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering. PMID:26375819
ERIC Educational Resources Information Center
Boker, Steven M.; McArdle, J. J.; Neale, Michael
2002-01-01
Presents an algorithm for the production of a graphical diagram from a matrix formula in such a way that its components are logically and hierarchically arranged. The algorithm, which relies on the matrix equations of J. McArdle and R. McDonald (1984), calculates the individual path components of expected covariance between variables and…
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation.
Robust Flight Path Determination for Mars Precision Landing Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Bayard, David S.; Kohen, Hamid
1997-01-01
This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing. The robust flight path problem is defined here as the determination of the flight path which delivers a low-lift open-loop controlled vehicle to its desired final landing location while minimizing the effect of perturbations due to uncertainty in the atmospheric model and entry conditions. The genetic algorithm was capable of finding solutions which reduced the landing error from 111 km RMS radial (open-loop optimal) to 43 km RMS radial (optimized with respect to perturbations) using 200 hours of computation on an Ultra-SPARC workstation. Further reduction in the landing error is possible by going to closed-loop control which can utilize the GA optimized paths as nominal trajectories for linearization.
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901
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. PMID:23193383
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. PMID:23193383
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.
Evolutionary algorithm based offline/online path planner for UAV navigation.
Nikolos, I K; Valavanis, K P; Tsourveloudis, N C; Kostaras, A N
2003-01-01
An evolutionary algorithm based framework, a combination of modified breeder genetic algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an offline/online path planner for unmanned aerial vehicles (UAVs) autonomous navigation. The path planner calculates a curved path line with desired characteristics in a three-dimensional (3-D) rough terrain environment, represented using B-spline curves, with the coordinates of its control points being the evolutionary algorithm artificial chromosome genes. Given a 3-D rough environment and assuming flight envelope restrictions, two problems are solved: i) UAV navigation using an offline planner in a known environment, and, ii) UAV navigation using an online planner in a completely unknown environment. The offline planner produces a single B-Spline curve that connects the starting and target points with a predefined initial direction. The online planner, based on the offline one, is given on-board radar readings which gradually produces a smooth 3-D trajectory aiming at reaching a predetermined target in an unknown environment; the produced trajectory consists of smaller B-spline curves smoothly connected with each other. Both planners have been tested under different scenarios, and they have been proven effective in guiding an UAV to its final destination, providing near-optimal curved paths quickly and efficiently.
NASA Astrophysics Data System (ADS)
Liu, Wei; Ma, Shunjian; Sun, Mingwei; Yi, Haidong; Wang, Zenghui; Chen, Zengqiang
2016-08-01
Path planning plays an important role in aircraft guided systems. Multiple no-fly zones in the flight area make path planning a constrained nonlinear optimization problem. It is necessary to obtain a feasible optimal solution in real time. In this article, the flight path is specified to be composed of alternate line segments and circular arcs, in order to reformulate the problem into a static optimization one in terms of the waypoints. For the commonly used circular and polygonal no-fly zones, geometric conditions are established to determine whether or not the path intersects with them, and these can be readily programmed. Then, the original problem is transformed into a form that can be solved by the sequential quadratic programming method. The solution can be obtained quickly using the Sparse Nonlinear OPTimizer (SNOPT) package. Mathematical simulations are used to verify the effectiveness and rapidity of the proposed algorithm.
On-line reentry guidance algorithm with both path and no-fly zone constraints
NASA Astrophysics Data System (ADS)
Zhang, Da; Liu, Lei; Wang, Yongji
2015-12-01
This study proposes an on-line predictor-corrector reentry guidance algorithm that satisfies path and no-fly zone constraints for hypersonic vehicles with a high lift-to-drag ratio. The proposed guidance algorithm can generate a feasible trajectory at each guidance cycle during the entry flight. In the longitudinal profile, numerical predictor-corrector approaches are used to predict the flight capability from current flight states to expected terminal states and to generate an on-line reference drag acceleration profile. The path constraints on heat rate, aerodynamic load, and dynamic pressure are implemented as a part of the predictor-corrector algorithm. A tracking control law is then designed to track the reference drag acceleration profile. In the lateral profile, a novel guidance algorithm is presented. The velocity azimuth angle error threshold and artificial potential field method are used to reduce heading error and to avoid the no-fly zone. Simulated results for nominal and dispersed cases show that the proposed guidance algorithm not only can avoid the no-fly zone but can also steer a typical entry vehicle along a feasible 3D trajectory that satisfies both terminal and path constraints.
Speed-up hyperspheres homotopic path tracking algorithm for PWL circuits simulations.
Ramirez-Pinero, A; Vazquez-Leal, H; Jimenez-Fernandez, V M; Sedighi, H M; Rashidi, M M; Filobello-Nino, U; Castaneda-Sheissa, R; Huerta-Chua, J; Sarmiento-Reyes, L A; Laguna-Camacho, J R; Castro-Gonzalez, F
2016-01-01
In the present work, we introduce an improved version of the hyperspheres path tracking method adapted for piecewise linear (PWL) circuits. This enhanced version takes advantage of the PWL characteristics from the homotopic curve, achieving faster path tracking and improving the performance of the homotopy continuation method (HCM). Faster computing time allows the study of complex circuits with higher complexity; the proposed method also decrease, significantly, the probability of having a diverging problem when using the Newton-Raphson method because it is applied just twice per linear region on the homotopic path. Equilibrium equations of the studied circuits are obtained applying the modified nodal analysis; this method allows to propose an algorithm for nonlinear circuit analysis. Besides, a starting point criteria is proposed to obtain better performance of the HCM and a technique for avoiding the reversion phenomenon is also proposed. To prove the efficiency of the path tracking method, several cases study with bipolar (BJT) and CMOS transistors are provided. Simulation results show that the proposed approach can be up to twelve times faster than the original path tracking method and also helps to avoid several reversion cases that appears when original hyperspheres path tracking scheme was employed.
Speed-up hyperspheres homotopic path tracking algorithm for PWL circuits simulations.
Ramirez-Pinero, A; Vazquez-Leal, H; Jimenez-Fernandez, V M; Sedighi, H M; Rashidi, M M; Filobello-Nino, U; Castaneda-Sheissa, R; Huerta-Chua, J; Sarmiento-Reyes, L A; Laguna-Camacho, J R; Castro-Gonzalez, F
2016-01-01
In the present work, we introduce an improved version of the hyperspheres path tracking method adapted for piecewise linear (PWL) circuits. This enhanced version takes advantage of the PWL characteristics from the homotopic curve, achieving faster path tracking and improving the performance of the homotopy continuation method (HCM). Faster computing time allows the study of complex circuits with higher complexity; the proposed method also decrease, significantly, the probability of having a diverging problem when using the Newton-Raphson method because it is applied just twice per linear region on the homotopic path. Equilibrium equations of the studied circuits are obtained applying the modified nodal analysis; this method allows to propose an algorithm for nonlinear circuit analysis. Besides, a starting point criteria is proposed to obtain better performance of the HCM and a technique for avoiding the reversion phenomenon is also proposed. To prove the efficiency of the path tracking method, several cases study with bipolar (BJT) and CMOS transistors are provided. Simulation results show that the proposed approach can be up to twelve times faster than the original path tracking method and also helps to avoid several reversion cases that appears when original hyperspheres path tracking scheme was employed. PMID:27386338
NASA Astrophysics Data System (ADS)
Ayazi, S. M.; Mashhorroudi, M. F.; Ghorbani, M.
2014-10-01
Among the main issues in the theory of geometric grids on spatial information systems, is the problem of finding the shortest path routing between two points. In this paper tried to using the graph theory and A* algorithms in transport management, the optimal method to find the shortest path with shortest time condition to be reviewed. In order to construct a graph that consists of a network of pathways and modelling of physical and phasing area, the shortest path routes, elected with the use of the algorithm is modified A*.At of the proposed method node selection Examining angle nodes the desired destination node and the next node is done. The advantage of this method is that due to the elimination of some routes, time of route calculation is reduced.
Calibration of neural networks using genetic algorithms, with application to optimal path planning
NASA Technical Reports Server (NTRS)
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
A novel load-balanced fixed routing (LBFR) algorithm for wavelength routed optical networks
NASA Astrophysics Data System (ADS)
Shen, Gangxiang; Li, Yongcheng; Peng, Limei
2011-11-01
In the wavelength-routed optical transport networks, fixed shortest path routing is one of major lightpath service provisioning strategies, which shows simplicity in network control and operation. Specifically, once a shortest route is found for a node pair, the route is always used for any future lightpath service provisioning, which therefore does not require network control and management system to maintain any active network-wide link state database. On the other hand, the fixed shortest path routing strategy suffers from the disadvantage of unbalanced network traffic load distribution and network congestion because it keeps on employing the same fixed shortest route between each pair of nodes. To avoid the network congestion and meanwhile retain the operational simplicity, in this study we develop a Load-Balanced Fixed Routing (LBFR) algorithm. Through a training process based on a forecasted network traffic load matrix, the proposed algorithm finds a fixed (or few) route(s) for each node pair and employs the fixed route(s) for lightpath service provisioning. Different from the fixed shortest path routes between node pairs, these routes can well balance traffic load within the network when they are used for lightpath service provisioning. Compared to the traditional fixed shortest path routing algorithm, the LBFR algorithm can achieve much better lightpath blocking performance according to our simulation and analytical studies. Moreover, the performance improvement is more significant with the increase of network nodal degree.
An efficient algorithm for finding the minimum energy path for cation migration in ionic materials.
Rong, Ziqin; Kitchaev, Daniil; Canepa, Pieremanuele; Huang, Wenxuan; Ceder, Gerbrand
2016-08-21
The Nudged Elastic Band (NEB) is an established method for finding minimum-energy paths and energy barriers of ion migration in materials, but has been hampered in its general application by its significant computational expense when coupled with density functional theory (DFT) calculations. Typically, an NEB calculation is initialized from a linear interpolation of successive intermediate structures (also known as images) between known initial and final states. However, the linear interpolation introduces two problems: (1) slow convergence of the calculation, particularly in cases where the final path exhibits notable curvature; (2) divergence of the NEB calculations if any intermediate image comes too close to a non-diffusing species, causing instabilities in the ensuing calculation. In this work, we propose a new scheme to accelerate NEB calculations through an improved path initialization and associated energy estimation workflow. We demonstrate that for cation migration in an ionic framework, initializing the diffusion path as the minimum energy path through a static potential built upon the DFT charge density reproduces the true NEB path within a 0.2 Å deviation and yields up to a 25% improvement in typical NEB runtimes. Furthermore, we find that the locally relaxed energy barrier derived from this initialization yields a good approximation of the NEB barrier, with errors within 20 meV of the true NEB value, while reducing computational expense by up to a factor of 5. Finally, and of critical importance for the automation of migration path calculations in high-throughput studies, we find that the new approach significantly enhances the stability of the calculation by avoiding unphysical image initialization. Our algorithm promises to enable efficient calculations of diffusion pathways, resolving a long-standing obstacle to the computational screening of intercalation compounds for Li-ion and multivalent batteries.
An efficient algorithm for finding the minimum energy path for cation migration in ionic materials.
Rong, Ziqin; Kitchaev, Daniil; Canepa, Pieremanuele; Huang, Wenxuan; Ceder, Gerbrand
2016-08-21
The Nudged Elastic Band (NEB) is an established method for finding minimum-energy paths and energy barriers of ion migration in materials, but has been hampered in its general application by its significant computational expense when coupled with density functional theory (DFT) calculations. Typically, an NEB calculation is initialized from a linear interpolation of successive intermediate structures (also known as images) between known initial and final states. However, the linear interpolation introduces two problems: (1) slow convergence of the calculation, particularly in cases where the final path exhibits notable curvature; (2) divergence of the NEB calculations if any intermediate image comes too close to a non-diffusing species, causing instabilities in the ensuing calculation. In this work, we propose a new scheme to accelerate NEB calculations through an improved path initialization and associated energy estimation workflow. We demonstrate that for cation migration in an ionic framework, initializing the diffusion path as the minimum energy path through a static potential built upon the DFT charge density reproduces the true NEB path within a 0.2 Å deviation and yields up to a 25% improvement in typical NEB runtimes. Furthermore, we find that the locally relaxed energy barrier derived from this initialization yields a good approximation of the NEB barrier, with errors within 20 meV of the true NEB value, while reducing computational expense by up to a factor of 5. Finally, and of critical importance for the automation of migration path calculations in high-throughput studies, we find that the new approach significantly enhances the stability of the calculation by avoiding unphysical image initialization. Our algorithm promises to enable efficient calculations of diffusion pathways, resolving a long-standing obstacle to the computational screening of intercalation compounds for Li-ion and multivalent batteries. PMID:27544092
An efficient algorithm for finding the minimum energy path for cation migration in ionic materials
NASA Astrophysics Data System (ADS)
Rong, Ziqin; Kitchaev, Daniil; Canepa, Pieremanuele; Huang, Wenxuan; Ceder, Gerbrand
2016-08-01
The Nudged Elastic Band (NEB) is an established method for finding minimum-energy paths and energy barriers of ion migration in materials, but has been hampered in its general application by its significant computational expense when coupled with density functional theory (DFT) calculations. Typically, an NEB calculation is initialized from a linear interpolation of successive intermediate structures (also known as images) between known initial and final states. However, the linear interpolation introduces two problems: (1) slow convergence of the calculation, particularly in cases where the final path exhibits notable curvature; (2) divergence of the NEB calculations if any intermediate image comes too close to a non-diffusing species, causing instabilities in the ensuing calculation. In this work, we propose a new scheme to accelerate NEB calculations through an improved path initialization and associated energy estimation workflow. We demonstrate that for cation migration in an ionic framework, initializing the diffusion path as the minimum energy path through a static potential built upon the DFT charge density reproduces the true NEB path within a 0.2 Å deviation and yields up to a 25% improvement in typical NEB runtimes. Furthermore, we find that the locally relaxed energy barrier derived from this initialization yields a good approximation of the NEB barrier, with errors within 20 meV of the true NEB value, while reducing computational expense by up to a factor of 5. Finally, and of critical importance for the automation of migration path calculations in high-throughput studies, we find that the new approach significantly enhances the stability of the calculation by avoiding unphysical image initialization. Our algorithm promises to enable efficient calculations of diffusion pathways, resolving a long-standing obstacle to the computational screening of intercalation compounds for Li-ion and multivalent batteries.
Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles
NASA Astrophysics Data System (ADS)
Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi
2012-09-01
In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.
Optimization of the transition path of the head hardening with using the genetic algorithms
NASA Astrophysics Data System (ADS)
Wróbel, Joanna; Kulawik, Adam
2016-06-01
An automated method of choice of the transition path of the head hardening in heat treatment process for the plane steel element is proposed in this communication. This method determines the points on the path of moving heat source using the genetic algorithms. The fitness function of the used algorithm is determined on the basis of effective stresses and yield point depending on the phase composition. The path of the hardening tool and also the area of the heat affected zone is determined on the basis of obtained points. A numerical model of thermal phenomena, phase transformations in the solid state and mechanical phenomena for the hardening process is implemented in order to verify the presented method. A finite element method (FEM) was used for solving the heat transfer equation and getting required temperature fields. The moving heat source is modeled with a Gaussian distribution and the water cooling is also included. The macroscopic model based on the analysis of the CCT and CHT diagrams of the medium-carbon steel is used to determine the phase transformations in the solid state. A finite element method is also used for solving the equilibrium equations giving us the stress field. The thermal and structural strains are taken into account in the constitutive relations.
Shortest recurrence periods of novae
Kato, Mariko; Saio, Hideyuki; Hachisu, Izumi; Nomoto, Ken'ichi
2014-10-01
Stimulated by the recent discovery of the 1 yr recurrence period nova M31N 2008-12a, we examined the shortest recurrence periods of hydrogen shell flashes on mass-accreting white dwarfs (WDs). We discuss the mechanism that yields a finite minimum recurrence period for a given WD mass. Calculating the unstable flashes for various WD masses and mass accretion rates, we identified a shortest recurrence period of about two months for a non-rotating 1.38 M {sub ☉} WD with a mass accretion rate of 3.6 × 10{sup –7} M {sub ☉} yr{sup –1}. A 1 yr recurrence period is realized for very massive (≳ 1.3 M {sub ☉}) WDs with very high accretion rates (≳ 1.5 × 10{sup –7} M {sub ☉} yr{sup –1}). We revised our stability limit of hydrogen shell burning, which will be useful for binary evolution calculations toward Type Ia supernovae.
NASA Astrophysics Data System (ADS)
Liu, Bing-Yi; Wang, Jun-Yang; Liu, Zhi-Shen
2014-11-01
Spaceborne integrated path differential absorption (IPDA) lidar is an active-detection system which is able to perform global CO2 measurement with high accuracy of 1ppmv at day and night over ground and clouds. To evaluate the detection performance of the system, simulation of the ground return signal and retrieval algorithm for CO2 concentration are presented in this paper. Ground return signals of spaceborne IPDA lidar under various ground surface reflectivity and atmospheric aerosol optical depths are simulated using given system parameters, standard atmosphere profiles and HITRAN database, which can be used as reference for determining system parameters. The simulated signals are further applied to the research on retrieval algorithm for CO2 concentration. The column-weighted dry air mixing ratio of CO2 denoted by XCO2 is obtained. As the deviations of XCO2 between the initial values for simulation and the results from retrieval algorithm are within the expected error ranges, it is proved that the simulation and retrieval algorithm are reliable.
Xie, XianMing; Li, YingHui
2014-06-20
This paper presents an enhanced phase unwrapping algorithm by combining an unscented Kalman filter, an enhanced local phase gradient estimator based on an amended matrix pencil model, and a path-following strategy. This technology is able to accurately unwrap seriously noisy wrapped phase images by applying the unscented Kalman filter to simultaneously perform noise suppression and phase unwrapping along the path from the high-quality region to the low-quality region of the wrapped phase images. Results obtained with synthetic data and real data validate the effectiveness of the proposed method and show improved performance of this new algorithm with respect to some of the most used algorithms.
In search of preferential flow paths in structured porous media using a simple genetic algorithm
NASA Astrophysics Data System (ADS)
Gwo, Jin-Ping
2001-06-01
Fracture network and preferential flow path images from exposed outcrops of geological formations, exposed soil pedon faces, and extracted soil columns and rock cores are often used to conceptualize and construct models to predict the fate and transport of subsurface contaminants. Both the scale resolutions inherent in these observations and the upscaling methods used to obtain macroscopic flow and transport parameters may result in uncertainties in the prediction. We present a mechanistic-based approach that utilizes a discrete fracture flow and transport model, a distributed and high performance computational architecture, and a genetic-based search algorithm to invert scale- representative, equivalent fracture networks or the preferential flow paths. Synthetic breakthrough curves (BTCs) and exposed structural information from known fracture networks in hypothetical soil columns are presented to the search algorithm. Using three genetic operators, a simple genetic algorithm (SGA) is able to invert the correct pictures of simple but not complex fracture networks. Solute transport experiments using soil columns often assume that the structure of soil columns is laterally uniform with respect to the macroscopic transport direction and the transport process is longitudinally one- dimensional. This assumption and the one BTC thus collected for each injection of solutes, even with flow interruptions, are not sufficient to guide the search algorithm toward the global optimum. Additional information (e.g., multiple solute BTCs along a cross section of the soil column) is necessary for the SGA to invert the correct fracture network. Three SGA population statistics, fracture network uncertainty, informatic entropy, and matrix-fracture contact area, are proposed to measure the uncertainty of SGA near optima. A positive correlation between the reduction of these statistics and the level of relevant information to better confine the SGA search space was found. The SGA search
Gao, Ming-ke; Chen, Yi-min; Liu, Quan; Huang, Chen; Li, Ze-yu; Zhang, Dian-hua
2015-11-01
Preoperative path planning plays a critical role in vascular access surgery. Vascular access surgery has superior difficulties and requires long training periods as well as precise operation. Yet doctors are on different leves, thus bulky size of blood vessels is usually chosen to undergo surgery and other possible optimal path is not considered. Moreover, patients and surgeons will suffer from X-ray radiation during the surgical procedure. The study proposed an improved ant colony algorithm to plan a vascular optimal three-dimensional path with overall consideration of factors such as catheter diameter, vascular length, diameter as well as the curvature and torsion. To protect the doctor and patient from exposing to X-ray long-term, the paper adopted augmented reality technology to register the reconstructed vascular model and physical model meanwhile, locate catheter by the electromagnetic tracking system and used Head Mounted Display to show the planning path in real time and monitor catheter push procedure. The experiment manifests reasonableness of preoperative path planning and proves the reliability of the algorithm. The augmented reality experiment real time and accurately displays the vascular phantom model, planning path and the catheter trajectory and proves the feasibility of this method. The paper presented a useful and feasible surgical scheme which was based on the improved ant colony algorithm to plan vascular three-dimensional path in augmented reality. The study possessed practical guiding significance in preoperative path planning, intraoperative catheter guiding and surgical training, which provided a theoretical method of path planning for vascular access surgery. It was a safe and reliable path planning approach and possessed practical reference value.
A conflict-free, path-level parallelization approach for sequential simulation algorithms
NASA Astrophysics Data System (ADS)
Rasera, Luiz Gustavo; Machado, Péricles Lopes; Costa, João Felipe C. L.
2015-07-01
Pixel-based simulation algorithms are the most widely used geostatistical technique for characterizing the spatial distribution of natural resources. However, sequential simulation does not scale well for stochastic simulation on very large grids, which are now commonly found in many petroleum, mining, and environmental studies. With the availability of multiple-processor computers, there is an opportunity to develop parallelization schemes for these algorithms to increase their performance and efficiency. Here we present a conflict-free, path-level parallelization strategy for sequential simulation. The method consists of partitioning the simulation grid into a set of groups of nodes and delegating all available processors for simulation of multiple groups of nodes concurrently. An automated classification procedure determines which groups are simulated in parallel according to their spatial arrangement in the simulation grid. The major advantage of this approach is that it does not require conflict resolution operations, and thus allows exact reproduction of results. Besides offering a large performance gain when compared to the traditional serial implementation, the method provides efficient use of computational resources and is generic enough to be adapted to several sequential algorithms.
Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms. PMID:24790555
Calculating Least Risk Paths in 3d Indoor Space
NASA Astrophysics Data System (ADS)
Vanclooster, A.; De Maeyer, Ph.; Fack, V.; Van de Weghe, N.
2013-08-01
Over the last couple of years, research on indoor environments has gained a fresh impetus; more specifically applications that support navigation and wayfinding have become one of the booming industries. Indoor navigation research currently covers the technological aspect of indoor positioning and the modelling of indoor space. The algorithmic development to support navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. However, alternative algorithms for outdoor navigation have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behaviour (e.g. simplest paths, least risk paths). These algorithms are currently restricted to outdoor applications. The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). As such, the clarity and easiness of route instructions is of paramount importance when distributing indoor routes. A shortest or fastest path indoors not necessarily aligns with the cognitive mapping of the building. Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-storey building. The results of several least risk path calculations are compared to the shortest paths in indoor environments in terms of total length, improvement in route description complexity and number of turns. Several scenarios are tested in this comparison: paths covering a single floor, paths crossing several building wings and/or floors. Adjustments to the algorithm are proposed to be more aligned to the
Herráez, Miguel Arevallilo; Burton, David R; Lalor, Michael J; Gdeisat, Munther A
2002-12-10
We describe what is to our knowledge a novel technique for phase unwrapping. Several algorithms based on unwrapping the most-reliable pixels first have been proposed. These were restricted to continuous paths and were subject to difficulties in defining a starting pixel. The technique described here uses a different type of reliability function and does not follow a continuous path to perform the unwrapping operation. The technique is explained in detail and illustrated with a number of examples. PMID:12502301
Herráez, Miguel Arevallilo; Burton, David R; Lalor, Michael J; Gdeisat, Munther A
2002-12-10
We describe what is to our knowledge a novel technique for phase unwrapping. Several algorithms based on unwrapping the most-reliable pixels first have been proposed. These were restricted to continuous paths and were subject to difficulties in defining a starting pixel. The technique described here uses a different type of reliability function and does not follow a continuous path to perform the unwrapping operation. The technique is explained in detail and illustrated with a number of examples.
NASA Technical Reports Server (NTRS)
Longendorfer, B. A.
1976-01-01
The construction of an autonomous roving vehicle requires the development of complex data-acquisition and processing systems, which determine the path along which the vehicle travels. Thus, a vehicle must possess algorithms which can (1) reliably detect obstacles by processing sensor data, (2) maintain a constantly updated model of its surroundings, and (3) direct its immediate actions to further a long range plan. The first function consisted of obstacle recognition. Obstacles may be identified by the use of edge detection techniques. Therefore, the Kalman Filter was implemented as part of a large scale computer simulation of the Mars Rover. The second function consisted of modeling the environment. The obstacle must be reconstructed from its edges, and the vast amount of data must be organized in a readily retrievable form. Therefore, a Terrain Modeller was developed which assembled and maintained a rectangular grid map of the planet. The third function consisted of directing the vehicle's actions.
WORM ALGORITHM PATH INTEGRAL MONTE CARLO APPLIED TO THE 3He-4He II SANDWICH SYSTEM
NASA Astrophysics Data System (ADS)
Al-Oqali, Amer; Sakhel, Asaad R.; Ghassib, Humam B.; Sakhel, Roger R.
2012-12-01
We present a numerical investigation of the thermal and structural properties of the 3He-4He sandwich system adsorbed on a graphite substrate using the worm algorithm path integral Monte Carlo (WAPIMC) method [M. Boninsegni, N. Prokof'ev and B. Svistunov, Phys. Rev. E74, 036701 (2006)]. For this purpose, we have modified a previously written WAPIMC code originally adapted for 4He on graphite, by including the second 3He-component. To describe the fermions, a temperature-dependent statistical potential has been used. This has proven very effective. The WAPIMC calculations have been conducted in the millikelvin temperature regime. However, because of the heavy computations involved, only 30, 40 and 50 mK have been considered for the time being. The pair correlations, Matsubara Green's function, structure factor, and density profiles have been explored at these temperatures.
NASA Astrophysics Data System (ADS)
González, Javier; Giménez, Xavier; Bofill, Josep Maria
2009-08-01
A derivation of a quantum reaction path Hamiltonian is proposed, which is based on a reformulation of the classical version of González et al. [J. Phys. Chem. A 105, 5022 (2001)], and the resulting equations are solved by means of a discrete variable representation approach, leading to a well-suited algorithm for the calculation of quantum dynamics of chemical reactions involving polyatomic molecules. General expressions for any type of reaction path are presented with special interest in the intrinsic reaction coordinate, which have been used to study selected cases, including a one-dimensional Eckart barrier, for which results are shown to be exact, two bidimensional systems, namely, a Müller-Brown potential energy surface, which is characteristic of polyatomic isomerization processes, and the collinear H+H2 chemical reaction, and finally the tridimensional, J =0, F+H2 reaction. Results for the specific chemical systems are shown to be in quite good agreement with exact two- and three-dimensional quantum calculations concerning autocorrelation functions as well as transmission factors as a function of total energy.
Research on optimal path planning algorithm of task-oriented optical remote sensing satellites
NASA Astrophysics Data System (ADS)
Liu, Yunhe; Xu, Shengli; Liu, Fengjing; Yuan, Jingpeng
2015-08-01
GEO task-oriented optical remote sensing satellite, is very suitable for long-term continuous monitoring and quick access to imaging. With the development of high resolution optical payload technology and satellite attitude control technology, GEO optical remote sensing satellites will become an important developing trend for aerospace remote sensing satellite in the near future. In the paper, we focused on GEO optical remote sensing satellite plane array stare imaging characteristics and real-time leading mission of earth observation mode, targeted on satisfying needs of the user with the minimum cost of maneuver, and put forward the optimal path planning algorithm centered on transformation from geographic coordinate space to Field of plane, and finally reduced the burden of the control system. In this algorithm, bounded irregular closed area on the ground would be transformed based on coordinate transformation relations in to the reference plane for field of the satellite payload, and then using the branch and bound method to search for feasible solutions, cutting off the non-feasible solution in the solution space based on pruning strategy; and finally trimming some suboptimal feasible solutions based on the optimization index until a feasible solution for the global optimum. Simulation and visualization presentation software testing results verified the feasibility and effectiveness of the strategy.
Trajectory Generation and Path Planning for Autonomous Aerobots
NASA Technical Reports Server (NTRS)
Sharma, Shivanjli; Kulczycki, Eric A.; Elfes, Alberto
2007-01-01
This paper presents global path planning algorithms for the Titan aerobot based on user defined waypoints in 2D and 3D space. The algorithms were implemented using information obtained through a planner user interface. The trajectory planning algorithms were designed to accurately represent the aerobot's characteristics, such as minimum turning radius. Additionally, trajectory planning techniques were implemented to allow for surveying of a planar area based solely on camera fields of view, airship altitude, and the location of the planar area's perimeter. The developed paths allow for planar navigation and three-dimensional path planning. These calculated trajectories are optimized to produce the shortest possible path while still remaining within realistic bounds of airship dynamics.
Bowen, J.; Dozier, G.
1996-12-31
This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves (Constraint Satisfaction Problems (CSPs)). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method, on a test suite of 750 randomly generated CSPs.
Ullah, Ehsan; Walker, Mark; Lee, Kyongbum; Hassoun, Soha
2015-01-01
Pathway analysis is a powerful approach to enable rational design or redesign of biochemical networks for optimizing metabolic engineering and synthetic biology objectives such as production of desired chemicals or biomolecules from specific nutrients. While experimental methods can be quite successful, computational approaches can enhance discovery and guide experimentation by efficiently exploring very large design spaces. We present a computational algorithm, Predictably Profitable Path (PreProPath), to identify target pathways best suited for engineering modifications. The algorithm utilizes uncertainties about the metabolic networks operating state inherent in the underdetermined linear equations representing the stoichiometric model. Flux Variability Analysis is used to determine the operational flux range. PreProPath identifies a path that is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The algorithm is computationally efficient because it does not require enumeration of pathways. The results of case studies show that PreProPath can efficiently analyze variances in metabolic states and model uncertainties to suggest pathway engineering strategies that have been previously supported by experimental data.
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality
NASA Astrophysics Data System (ADS)
Newman, M. E.
2001-07-01
Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.
Improvement of phase diversity algorithm for non-common path calibration in extreme AO context
NASA Astrophysics Data System (ADS)
Robert, Clélia; Fusco, Thierry; Sauvage, Jean-François; Mugnier, Laurent
2008-07-01
Exoplanet direct imaging with a ground-based telescope needs a very high performance adaptive optics (AO) system, so-called eXtreme AO (XAO), a coronagraph device, and a smart imaging process. One limitation of AO system in operation remains the Non Common Path Aberrations (NCPA). To achieve the ultimate XAO performance, these aberrations have to be measured with a dedicated wavefront sensor placed in the imaging camera focal plane, and then pre-compensated using the AO closed loop process. In any events, the pre-compensation should minimize the aberrations at the coronagraph focal plane mask. An efficient way for the NCPA measurement is the phase diversity technique. A pixel-wise approach is well-suited to estimate NCPA on large pupils and subsequent projection on the deformable mirror with Cartesian geometry. However it calls for a careful regularization for optimal results. The weight of the regularization is written in close-form for un-supervised tuning. The accuracy of NCPA pre-compensation is below 8 nm for a wide range of conditions. Point-by-point phase estimation improves the accuracy of the Phase Diversity method. The algorithm is validated in simulation and experimentally. It will be implemented in SAXO, the XAO system of the second generation VLT instrument: SPHERE.
Spreading paths in partially observed social networks
NASA Astrophysics Data System (ADS)
Onnela, Jukka-Pekka; Christakis, Nicholas A.
2012-03-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.
A computational study of routing algorithms for realistic transportation networks
Jacob, R.; Marathe, M.V.; Nagel, K.
1998-12-01
The authors carry out an experimental analysis of a number of shortest path (routing) algorithms investigated in the context of the TRANSIMS (Transportation Analysis and Simulation System) project. The main focus of the paper is to study how various heuristic and exact solutions, associated data structures affected the computational performance of the software developed especially for realistic transportation networks. For this purpose the authors have used Dallas Fort-Worth road network with very high degree of resolution. The following general results are obtained: (1) they discuss and experimentally analyze various one-one shortest path algorithms, which include classical exact algorithms studied in the literature as well as heuristic solutions that are designed to take into account the geometric structure of the input instances; (2) they describe a number of extensions to the basic shortest path algorithm. These extensions were primarily motivated by practical problems arising in TRANSIMS and ITS (Intelligent Transportation Systems) related technologies. Extensions discussed include--(i) time dependent networks, (ii) multi-modal networks, (iii) networks with public transportation and associated schedules. Computational results are provided to empirically compare the efficiency of various algorithms. The studies indicate that a modified Dijkstra`s algorithm is computationally fast and an excellent candidate for use in various transportation planning applications as well as ITS related technologies.
Using an expert system with genetic algorithm for effective multilink manipulator path planning
NASA Astrophysics Data System (ADS)
Yussupova, Nafissa I.; Kussimov, Salavat T.; Woern, Heinz; Shakhmametova, Gouzel; Nikiforov, Dmitri
2001-10-01
The genetic approach as the basis for the path search method for a multilink manipulator in complex environment has been discussed. To get over the major downfalls of the genetic approach the developed expert system (ES) to provide a specific purpose orientation at the stages of initial population formation and path crossing is proposed. We use the ES built of two modules: for forming recommendations for the initial population generation and for path crossing. The result for more than 4,000 experiments using five test workcells with various obstacles show that using the expert system increases the point attainability by 20 percent on the average.
Adaptive Routing Algorithm in Wireless Communication Networks Using Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Yan, Xuesong; Wu, Qinghua; Cai, Zhihua
At present, mobile communications traffic routing designs are complicated because there are more systems inter-connecting to one another. For example, Mobile Communication in the wireless communication networks has two routing design conditions to consider, i.e. the circuit switching and the packet switching. The problem in the Packet Switching routing design is its use of high-speed transmission link and its dynamic routing nature. In this paper, Evolutionary Algorithms is used to determine the best solution and the shortest communication paths. We developed a Genetic Optimization Process that can help network planners solving the best solutions or the best paths of routing table in wireless communication networks are easily and quickly. From the experiment results can be noted that the evolutionary algorithm not only gets good solutions, but also a more predictable running time when compared to sequential genetic algorithm.
Optimization of transport protocols with path-length constraints in complex networks
NASA Astrophysics Data System (ADS)
Ramasco, José J.; de La Lama, Marta S.; López, Eduardo; Boettcher, Stefan
2010-09-01
We propose a protocol optimization technique that is applicable to both weighted and unweighted graphs. Our aim is to explore by how much a small variation around the shortest-path or optimal-path protocols can enhance protocol performance. Such an optimization strategy can be necessary because even though some protocols can achieve very high traffic tolerance levels, this is commonly done by enlarging the path lengths, which may jeopardize scalability. We use ideas borrowed from extremal optimization to guide our algorithm, which proves to be an effective technique. Our method exploits the degeneracy of the paths or their close-weight alternatives, which significantly improves the scalability of the protocols in comparison to shortest-path or optimal-path protocols, keeping at the same time almost intact the length or weight of the paths. This characteristic ensures that the optimized routing protocols are composed of paths that are quick to traverse, avoiding negative effects in data communication due to path-length increases that can become specially relevant when information losses are present.
An algorithm to find minimum free-energy paths using umbrella integration
NASA Astrophysics Data System (ADS)
Bohner, Matthias U.; Kästner, Johannes
2012-07-01
The calculation of free-energy barriers by umbrella sampling and many other methods is hampered by the necessity for an a priori choice of the reaction coordinate along which to sample. We avoid this problem by providing a method to search for saddle points on the free-energy surface in many coordinates. The necessary gradients and Hessians of the free energy are obtained by multidimensional umbrella integration. We construct the minimum free-energy path by following the gradient down to minima on the free-energy surface. The change of free energy along the path is obtained by integrating out all coordinates orthogonal to the path. While we expect the method to be applicable to large systems, we test it on the alanine dipeptide in vacuum. The minima, transition states, and free-energy barriers agree well with those obtained previously with other methods.
ERIC Educational Resources Information Center
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…
Research on Taxiway Path Optimization Based on Conflict Detection
Zhou, Hang; Jiang, Xinxin
2015-01-01
Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency. PMID:26226485
Research on Taxiway Path Optimization Based on Conflict Detection.
Zhou, Hang; Jiang, Xinxin
2015-01-01
Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency.
Research on Taxiway Path Optimization Based on Conflict Detection.
Zhou, Hang; Jiang, Xinxin
2015-01-01
Taxiway path planning is one of the effective measures to make full use of the airport resources, and the optimized paths can ensure the safety of the aircraft during the sliding process. In this paper, the taxiway path planning based on conflict detection is considered. Specific steps are shown as follows: firstly, make an improvement on A * algorithm, the conflict detection strategy is added to search for the shortest and safe path in the static taxiway network. Then, according to the sliding speed of aircraft, a time table for each node is determined and the safety interval is treated as the constraint to judge whether there is a conflict or not. The intelligent initial path planning model is established based on the results. Finally, make an example in an airport simulation environment, detect and relieve the conflict to ensure the safety. The results indicate that the model established in this paper is effective and feasible. Meanwhile, make comparison with the improved A*algorithm and other intelligent algorithms, conclude that the improved A*algorithm has great advantages. It could not only optimize taxiway path, but also ensure the safety of the sliding process and improve the operational efficiency. PMID:26226485
A path-following interior-point algorithm for linear and quadratic problems
Wright, S.J.
1993-12-01
We describe an algorithm for the monotone linear complementarity problem that converges for many positive, not necessarily feasible, starting point and exhibits polynomial complexity if some additional assumptions are made on the starting point. If the problem has a strictly complementary solution, the method converges subquadratically. We show that the algorithm and its convergence extend readily to the mixed monotone linear complementarity problem and, hence, to all the usual formulations of the linear programming and convex quadratic programming problems.
Efficient algorithms for semiclassical instanton calculations based on discretized path integrals
Kawatsu, Tsutomu E-mail: smiura@mail.kanazawa-u.ac.jp; Miura, Shinichi E-mail: smiura@mail.kanazawa-u.ac.jp
2014-07-14
Path integral instanton method is a promising way to calculate the tunneling splitting of energies for degenerated two state systems. In order to calculate the tunneling splitting, we need to take the zero temperature limit, or the limit of infinite imaginary time duration. In the method developed by Richardson and Althorpe [J. Chem. Phys. 134, 054109 (2011)], the limit is simply replaced by the sufficiently long imaginary time. In the present study, we have developed a new formula of the tunneling splitting based on the discretized path integrals to take the limit analytically. We have applied our new formula to model systems, and found that this approach can significantly reduce the computational cost and gain the numerical accuracy. We then developed the method combined with the electronic structure calculations to obtain the accurate interatomic potential on the fly. We present an application of our ab initio instanton method to the ammonia umbrella flip motion.
PathGrid: The Transfer of Astronomical Image Algorithms to the Analysis of Medical Microscopy Data
NASA Astrophysics Data System (ADS)
Walton, N. A.; Brenton, J. D.; Caldas, C.; Irwin, M. J.; Akram, A.; Gonzalez-Solares, E.; Lewis, J. R.; MacCullum, P.; Morris, L. J.; Rixon, G. T.
2009-09-01
We describe our pilot `PathGrid' study which applies astronomical image processing and data handling techniques to the challenges involved in analysing Tissue Micro Array (TMA) image data. Image analysis has been applied to the input TMA data using open source solutions developed for an astronomical context. The resulting data products are in turn interfaced to the clinical trials systems in use at the Cambridge Research Institute (Cancer Research-UK).
Van Wart, Adam T; Durrant, Jacob; Votapka, Lane; Amaro, Rommie E
2014-02-11
Allostery can occur by way of subtle cooperation among protein residues (e.g., amino acids) even in the absence of large conformational shifts. Dynamical network analysis has been used to model this cooperation, helping to computationally explain how binding to an allosteric site can impact the behavior of a primary site many ångstroms away. Traditionally, computational efforts have focused on the most optimal path of correlated motions leading from the allosteric to the primary active site. We present a program called Weighted Implementation of Suboptimal Paths (WISP) capable of rapidly identifying additional suboptimal pathways that may also play important roles in the transmission of allosteric signals. Aside from providing signal redundancy, suboptimal paths traverse residues that, if disrupted through pharmacological or mutational means, could modulate the allosteric regulation of important drug targets. To demonstrate the utility of our program, we present a case study describing the allostery of HisH-HisF, an amidotransferase from T. maritima thermotiga. WISP and its VMD-based graphical user interface (GUI) can be downloaded from http://nbcr.ucsd.edu/wisp.
Novel theory and algorithm for fuzzy distance transform and its applications
NASA Astrophysics Data System (ADS)
Saha, Punam K.; Gomberg, Bryon R.; Wehrli, Felix W.
2002-05-01
This paper describes the theory and algorithms of fuzzy distance transform (FDT). Fuzzy distance is defined as the length of the shortest path between two points. The length of a path in a fuzzy subset is defined as the integration of fuzzy membership values of the points along the path. The shortest path between two points is the one with the minimum length among all (infinitely many) paths between the two points. It is demonstrated that, unlike in the binary case, the shortest path in a fuzzy subset is not necessarily a straight-line segment. The support of a fuzzy subset is the set of points with nonzero membership values. It is shown that, for any fuzzy subset, fuzzy distance is a metric for the interior of its support. FDT is defined as the process on a fuzzy subset that assigns at each point the smallest fuzzy distance from the boundary of the support. The theoretical framework of FDT in continuous space is extended to digital spaces and a dynamic programming-based algorithm is presented for its computation. Several potential medical imaging applications are presented including the quantification of blood vessels and trabecular bone thickness in the regime of limited special resolution.
Van Nguyen; Javaid, Abdul Q; Weitnauer, Mary Ann
2014-01-01
We introduce the Spectrum-averaged Harmonic Path (SHAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. Periodic movement of human torso caused by respiration and heart beat induces fundamental frequencies and their harmonics at the respiration and heart rates. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods of identifying the fundamental component either in frequency or time domain to estimate the HR and/or RR lead to significant error if the fundamental is distorted or cancelled by interference. The SHAPA algorithm (1) takes advantage of the HR harmonics, where there is less interference, and (2) exploits the information in previous spectra to achieve more reliable and robust estimation of the fundamental frequency in the spectrum under consideration. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference and produces 16% to 60% more valid estimates.
Multi-Path Relay Selection Algorithm Based on the Broadcast TV
NASA Astrophysics Data System (ADS)
Zhang, Chaoyi; Luan, Linlin; Wu, Muqing
This paper presents a relay selection method for Broadcast TV services. This method get through the node's time-delay and power information, obtain the value of the system interrupt as to be a decision threshold, then chose the relay node. At the same time this paper proposes an optimal relay selection strategy which can minimize the system interrupt probability combination with power distribution--Multi-Path Relay Routing Protocol. This protocol can dynamically change the appropriate route according to the shifty network. Simulation results show that the protocol can extend the coverage area, reducing time-delay and increase system throughput, improve system spectral efficiency, and enhance the Qos of the Broadcast TV service.
Xie, Xian Ming; Zeng, Qing Ning
2015-11-01
This paper presents an efficient and robust phase unwrapping algorithm which combines an unscented Kalman filter (UKF) with a strategy of quantizing a paths-guided map and a pixel classification strategy based on phase quality information. The advantages of the proposed method depend on the following contributions: (1) the strategy of quantizing the paths-guided map can accelerate the process of searching unwrapping paths and greatly reducing time consumption on the unwrapping procedure; (2) the pixel classification strategy proposed by this paper can reduce the error propagation effect by decreasing the amounts of pixels with equal quantized paths-guided value in the process of unwrapping; and (3) the unscented Kalman filter enables simultaneous filtering and unwrapping without the information loss caused by linearization of a nonlinear model. In addition, a new paths-guided map derived from a phase quality map is inserted into the strategy of quantizing the paths-guided map to provide a more robust path of unwrapping, and then ensures better unwrapping results. Results obtained from synthetic data and real data show that the proposed method can efficiently obtain better solutions with respect to some of the most used algorithms.
Information Spread of Emergency Events: Path Searching on Social Networks
Hu, Hongzhi; Wu, Tunan
2014-01-01
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323
Information spread of emergency events: path searching on social networks.
Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui
2014-01-01
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.
Methodology for Augmenting Existing Paths with Additional Parallel Transects
Wilson, John E.
2013-09-30
Visual Sample Plan (VSP) is sample planning software that is used, among other purposes, to plan transect sampling paths to detect areas that were potentially used for munition training. This module was developed for application on a large site where existing roads and trails were to be used as primary sampling paths. Gap areas between these primary paths needed to found and covered with parallel transect paths. These gap areas represent areas on the site that are more than a specified distance from a primary path. These added parallel paths needed to optionally be connected together into a single path—the shortest path possible. The paths also needed to optionally be attached to existing primary paths, again with the shortest possible path. Finally, the process must be repeatable and predictable so that the same inputs (primary paths, specified distance, and path options) will result in the same set of new paths every time. This methodology was developed to meet those specifications.
A labeling algorithm for the navigation of automated guided vehicles
Huang, J.; Palekar, U.S.; Kapoor, S.G. . Dept. of Mechanical and Industrial Engineering)
1993-08-01
Material handling is an important component of most automated manufacturing systems. AGVs are commonly employed for this function. Efficient use of the AGV system requires proper routing and scheduling of vehicular traffic. This problem is modeled as a shortest path problem with multiple time windows on arcs and at nodes of a network. A polynomial-time labeling algorithm has been developed. The algorithm has complexity O (D[sup 2]log[sub d]D), where D is the total number of time windows in the problem. The data required for the model is easy to maintain.
A priori least expected time paths in fuzzy, time-variant transportation networks
NASA Astrophysics Data System (ADS)
Wang, Li; Gao, Ziyou; Yang, Lixing
2016-02-01
Dynamics and fuzziness are two significant characteristics of real-world transportation networks. To capture these two features theoretically, this article proposes the concept of a fuzzy, time-variant network characterized by a series of time-dependent fuzzy link travel times. To find an effective route guidance for travelers, the expected travel time is specifically adopted as an evaluation criterion to assess the route generation process. Then the shortest path problem is formulated as a multi-objective 0-1 optimization model for finding the least expected time path over the considered time horizon. Different from the shortest path problem in dynamic and random networks, an efficient method is proposed in this article to calculate the fuzzy expected travel time for each given path. A tabu search algorithm is designed for the problem to generate the best solution under the framework of linear weighted methods. Finally, two numerical experiments are performed to verify the effectiveness and efficiency of the model and algorithm.
Benefit of adaptive FEC in shared backup path protected elastic optical network.
Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang
2015-07-27
We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm. PMID:26367673
Benefit of adaptive FEC in shared backup path protected elastic optical network.
Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang
2015-07-27
We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm.
NASA Technical Reports Server (NTRS)
Hueschen, Richard M.
1988-01-01
This report contains results of flight tests for three path update algorithms designed to provide smooth transition for an aircraft guidance system from DME, VORTAC, and barometric navaids to the more precise MLS by modifying the desired 3-D flight path. The first algorithm, called Zero Cross Track, eliminates the discontinuity in cross-track and altitude error at transition by designating the first valid MLS aircraft position as the desired first waypoint, while retaining all subsequent waypoints. The discontinuity in track angle is left unaltered. The second, called Tangent Path, also eliminates the discontinuity in cross-track and altitude errors and chooses a new desired heading to be tangent to the next oncoming circular arc turn. The third, called Continued Track, eliminates the discontinuity in cross-track, altitude, and track angle errors by accepting the current MLS position and track angle as the desired ones and recomputes the location of the next waypoint. The flight tests were conducted on the Transportation Systems Research Vehicle, a small twin-jet transport aircraft modified for research under the Advanced Transport Operating Systems program at Langley Research Center. The flight tests showed that the algorithms provided a smooth transition to MLS.
Lord, Etienne; Le Cam, Margaux; Bapteste, Éric; Méheust, Raphaël; Makarenkov, Vladimir; Lapointe, François-Joseph
2016-01-01
Various types of genome and gene similarity networks along with their characteristics have been increasingly used for retracing different kinds of evolutionary and ecological relationships. Here, we present a new polynomial time algorithm and the corresponding software (BRIDES) to provide characterization of different types of paths existing in evolving (or augmented) similarity networks under the constraint that such paths contain at least one node that was not present in the original network. These different paths are denoted as Breakthroughs, Roadblocks, Impasses, Detours, Equal paths, and Shortcuts. The analysis of their distribution can allow discriminating among different evolutionary hypotheses concerning genomes or genes at hand. Our approach is based on an original application of the popular shortest path Dijkstra’s and Yen’s algorithms. The C++ and R versions of the BRIDES program are freely available at: https://github.com/etiennelord/BRIDES. PMID:27580188
Lord, Etienne; Le Cam, Margaux; Bapteste, Éric; Méheust, Raphaël; Makarenkov, Vladimir; Lapointe, François-Joseph
2016-01-01
Various types of genome and gene similarity networks along with their characteristics have been increasingly used for retracing different kinds of evolutionary and ecological relationships. Here, we present a new polynomial time algorithm and the corresponding software (BRIDES) to provide characterization of different types of paths existing in evolving (or augmented) similarity networks under the constraint that such paths contain at least one node that was not present in the original network. These different paths are denoted as Breakthroughs, Roadblocks, Impasses, Detours, Equal paths, and Shortcuts. The analysis of their distribution can allow discriminating among different evolutionary hypotheses concerning genomes or genes at hand. Our approach is based on an original application of the popular shortest path Dijkstra's and Yen's algorithms. The C++ and R versions of the BRIDES program are freely available at: https://github.com/etiennelord/BRIDES. PMID:27580188
Adaptive DNA Computing Algorithm by Using PCR and Restriction Enzyme
NASA Astrophysics Data System (ADS)
Kon, Yuji; Yabe, Kaoru; Rajaee, Nordiana; Ono, Osamu
In this paper, we introduce an adaptive DNA computing algorithm by using polymerase chain reaction (PCR) and restriction enzyme. The adaptive algorithm is designed based on Adleman-Lipton paradigm[3] of DNA computing. In this work, however, unlike the Adleman- Lipton architecture a cutting operation has been introduced to the algorithm and the mechanism in which the molecules used by computation were feedback to the next cycle devised. Moreover, the amplification by PCR is performed in the molecule used by feedback and the difference concentration arisen in the base sequence can be used again. By this operation the molecules which serve as a solution candidate can be reduced down and the optimal solution is carried out in the shortest path problem. The validity of the proposed adaptive algorithm is considered with the logical simulation and finally we go on to propose applying adaptive algorithm to the chemical experiment which used the actual DNA molecules for solving an optimal network problem.
NASA Astrophysics Data System (ADS)
Gilliam, Kyle L.
As part of former and current sea-surface altimetry missions, brightness temperatures measured by nadir-viewing 18-34 GHz microwave radiometers are used to determine apparent path delay due to variations in index of refraction caused by changes in the humidity of the troposphere. This tropospheric wet-path delay can be retrieved from these measurements with sufficient accuracy over open oceans. However, in coastal zones and over inland water the highly variable radiometric emission from land surfaces at microwave frequencies has prevented accurate retrieval of wet-path delay using conventional algorithms. To extend wet path delay corrections into the coastal zone (within 25 km of land) and to inland water bodies, a new method is proposed to correct for tropospheric wet-path delay by using higher-frequency radiometer channels from approximately 50-170 GHz to provide sufficiently small fields of view on the surface. A new approach is introduced based on the variability of observations in several millimeter-wave radiometer channels on small spatial scales due to surface emissivity in contrast to the larger-scale variability in atmospheric absorption. The new technique is based on the measurement of deflection ratios among several radiometric bands to estimate the transmissivity of the atmosphere due to water vapor. To this end, the Brightness Temperature Deflection Ratio (BTDR) method is developed starting from a radiative transfer model for a downward-looking microwave radiometer, and is extended to pairs of frequency channels to retrieve the wet path delay. Then a mapping between the wet transmissivity and wet-path delay is performed using atmospheric absorption models. A frequency selection study is presented to determine the suitability of frequency sets for accurate retrieval of tropospheric wet-path delay, and comparisons are made to frequency sets based on currently-available microwave radiometers. Statistical noise analysis results are presented for a number
Computing the Length of the Shortest Telomere in the Nucleus
NASA Astrophysics Data System (ADS)
Dao Duc, K.; Holcman, D.
2013-11-01
The telomere length can either be shortened or elongated by an enzyme called telomerase after each cell division. Interestingly, the shortest telomere is involved in controlling the ability of a cell to divide. Yet, its dynamics remains elusive. We present here a stochastic approach where we model this dynamics using a Markov jump process. We solve the forward Fokker-Planck equation to obtain the steady state distribution and the statistical moments of telomere lengths. We focus specifically on the shortest one and we estimate its length difference with the second shortest telomere. After extracting key parameters such as elongation and shortening dynamics from experimental data, we compute the length of telomeres in yeast and obtain as a possible prediction the minimum concentration of telomerase required to ensure a proper cell division.
Link prediction based on path entropy
NASA Astrophysics Data System (ADS)
Xu, Zhongqi; Pu, Cunlai; Yang, Jian
2016-08-01
Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, first we study the information entropy or uncertainty of a path using the information theory. After that, we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream of link predictors.
NASA Astrophysics Data System (ADS)
Xing, Jianchao; Zhang, Jie; Zhao, Yongli; Cao, Xuping; Wang, Dajiang; Gu, Wanyi
2011-12-01
The traditional approach for inter-domain Traffic Engineering Label Switching Path (TE-LSP) computation like BRPC could provide a shortest inter-domain constrained TE-LSP, but under wavelength continuity constraint, it couldn't guarantee the success of the resources reservation for the shortest path. In this paper, a Collision-aware Backward Recursive PCE-based Computation Algorithm (CA-BRPC) in multi-domain optical networks under wavelength continuity constraint is proposed, which is implemented based on Hierarchical PCE (H-PCE) architecture, could provide an optimal inter-domain TE-LSP and avoid resources reservation conflict. Numeric results show that the CA-BRPC could reduce the blocking probability of entire network.
Nonlinear multi-agent path search method based on OFDM communication
NASA Astrophysics Data System (ADS)
Sato, Masatoshi; Igarashi, Yusuke; Tanaka, Mamoru
This paper presents novel shortest paths searching system based on analog circuit analysis which is called sequential local current comparison method on alternating-current (AC) circuit (AC-SLCC). Local current comparison (LCC) method is a path searching method where path is selected in the direction of the maximum current in a direct-current (DC) resistive circuit. Since a plurality of shortest paths searching by LCC method can be done by solving the current distribution on the resistive circuit analysis, the shortest path problem can be solved at supersonic speed. AC-SLCC method is a novel LCC method with orthogonal frequency division multiplexing (OFDM) communication on AC circuit. It is able to send data with the shortest path and without major data loss, and this suggest the possibility of application to various things (especially OFDM communication techniques).
NASA Astrophysics Data System (ADS)
Miura, Shinichi; Okazaki, Susumu
2001-09-01
In this paper, the path integral molecular dynamics (PIMD) method has been extended to employ an efficient approximation of the path action referred to as the pair density matrix approximation. Configurations of the isomorphic classical systems were dynamically sampled by introducing fictitious momenta as in the PIMD based on the standard primitive approximation. The indistinguishability of the particles was handled by a pseudopotential of particle permutation that is an extension of our previous one [J. Chem. Phys. 112, 10 116 (2000)]. As a test of our methodology for Boltzmann statistics, calculations have been performed for liquid helium-4 at 4 K. We found that the PIMD with the pair density matrix approximation dramatically reduced the computational cost to obtain the structural as well as dynamical (using the centroid molecular dynamics approximation) properties at the same level of accuracy as that with the primitive approximation. With respect to the identical particles, we performed the calculation of a bosonic triatomic cluster. Unlike the primitive approximation, the pseudopotential scheme based on the pair density matrix approximation described well the bosonic correlation among the interacting atoms. Convergence with a small number of discretization of the path achieved by this approximation enables us to construct a method of avoiding the problem of the vanishing pseudopotential encountered in the calculations by the primitive approximation.
Path similarity skeleton graph matching.
Bai, Xiang; Latecki, Longin Jan
2008-07-01
This paper presents a novel framework to for shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the shortest paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we completely ignore the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of shortest paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and occlusion.
Poulsen, Jens Aage; Nyman, Gunnar; Rossky, Peter J
2006-11-01
The Feynman-Kleinert Linearized Path Integral (FK-LPI) representation of quantum correlation functions is extended in applications and algorithms. Diffusion including quantum effects for a flexible simple point charge model of liquid water is explored, including new tests of internal consistency. An ab initio quantum correction factor (QCF) is also obtained to correct the far-infrared spectrum of water. After correction, a spectrum based on a classical simulation is in good agreement with the experiment. The FK-LPI QCF is shown to be superior to the so-called harmonic QCF. New computational algorithms are introduced so that the quantum Boltzmann Wigner phase-space density, the central object in the implementation, can be obtained for arbitrary potentials. One scheme requires only that the standard classical force routine be replaced when turning from one molecular problem to another. The new algorithms are applied to the calculation of the Van Hove spectrum of liquid He(4) at 27 K. The spectrum moments are in very good agreement with the experiment. These observations indicate that the FK-LPI approach can be broadly effective for molecular problems involving the dynamics of light nuclei.
NASA Astrophysics Data System (ADS)
Riihimaki, Laura D.; Comstock, Jennifer M.; Anderson, Kevin K.; Holmes, Aimee; Luke, Edward
2016-06-01
Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.
Riihimaki, Laura D.; Comstock, Jennifer M.; Anderson, Kevin K.; Holmes, Aimee; Luke, Edward
2016-06-10
Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty informationmore » on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.« less
A Dynamic Programming Approach to Identifying the Shortest Path in Virtual Learning Environments
ERIC Educational Resources Information Center
Fazlollahtabar, Hamed
2008-01-01
E-learning has been widely adopted as a promising solution by many organizations to offer learning-on-demand opportunities to individual employees (learners) in order to reduce training time and cost. While successful information systems models have received much attention among researchers, little research has been conducted to assess the success…
The graph-theoretic minimum energy path problem for ionic conduction
NASA Astrophysics Data System (ADS)
Kishida, Ippei
2015-10-01
A new computational method was developed to analyze the ionic conduction mechanism in crystals through graph theory. The graph was organized into nodes, which represent the crystal structures modeled by ionic site occupation, and edges, which represent structure transitions via ionic jumps. We proposed a minimum energy path problem, which is similar to the shortest path problem. An effective algorithm to solve the problem was established. Since our method does not use randomized algorithm and time parameters, the computational cost to analyze conduction paths and a migration energy is very low. The power of the method was verified by applying it to α-AgI and the ionic conduction mechanism in α-AgI was revealed. The analysis using single point calculations found the minimum energy path for long-distance ionic conduction, which consists of 12 steps of ionic jumps in a unit cell. From the results, the detailed theoretical migration energy was calculated as 0.11 eV by geometry optimization and nudged elastic band method. Our method can refine candidates for possible jumps in crystals and it can be adapted to other computational methods, such as the nudged elastic band method. We expect that our method will be a powerful tool for analyzing ionic conduction mechanisms, even for large complex crystals.
Mobile transporter path planning
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1990-01-01
The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.
A Decision Processing Algorithm for CDC Location Under Minimum Cost SCM Network
NASA Astrophysics Data System (ADS)
Park, N. K.; Kim, J. Y.; Choi, W. Y.; Tian, Z. M.; Kim, D. J.
Location of CDC in the matter of network on Supply Chain is becoming on the high concern these days. Present status of methods on CDC has been mainly based on the calculation manually by the spread sheet to achieve the goal of minimum logistics cost. This study is focused on the development of new processing algorithm to overcome the limit of present methods, and examination of the propriety of this algorithm by case study. The algorithm suggested by this study is based on the principle of optimization on the directive GRAPH of SCM model and suggest the algorithm utilizing the traditionally introduced MST, shortest paths finding methods, etc. By the aftermath of this study, it helps to assess suitability of the present on-going SCM network and could be the criterion on the decision-making process for the optimal SCM network building-up for the demand prospect in the future.
Efficient mapping algorithms for survivable GMPLS networks
NASA Astrophysics Data System (ADS)
Laborczi, Peter
2003-10-01
With the advent of intelligent IP over optical networks, like GMPLS, connections can be protected against failures effectively; however, to capitalize the advantages, novel sophisticated methods are needed. This paper addresses the task of finding efficient mapping in a survivable multilayer network in order to ensure high availability for connections. Known methods (like running a shortest path algorithm) do not consider finding physically disjoint paths in the upper layer and thus cause failure propagation. Besides formulating the problem, we propose a randomized heuristic method to solve it. The quality of the solution is evaluated (1) by the number of node-pairs for which physically-disjoint path-pair can be found in the upper layer, or (2) by the number of spans used by both working and protection paths (i.e., failure propagation effect). It is shown with numerous simulations that our proposed method finds solution for significantly more node pairs (86% instead of 45% in the 35-node network) than traditional methods. Furthermore, it yields connection availabilities near to the optimum.
Algorithmic Strategies in Combinatorial Chemistry
GOLDMAN,DEBORAH; ISTRAIL,SORIN; LANCIA,GIUSEPPE; PICCOLBONI,ANTONIO; WALENZ,BRIAN
2000-08-01
Combinatorial Chemistry is a powerful new technology in drug design and molecular recognition. It is a wet-laboratory methodology aimed at ``massively parallel'' screening of chemical compounds for the discovery of compounds that have a certain biological activity. The power of the method comes from the interaction between experimental design and computational modeling. Principles of ``rational'' drug design are used in the construction of combinatorial libraries to speed up the discovery of lead compounds with the desired biological activity. This paper presents algorithms, software development and computational complexity analysis for problems arising in the design of combinatorial libraries for drug discovery. The authors provide exact polynomial time algorithms and intractability results for several Inverse Problems-formulated as (chemical) graph reconstruction problems-related to the design of combinatorial libraries. These are the first rigorous algorithmic results in the literature. The authors also present results provided by the combinatorial chemistry software package OCOTILLO for combinatorial peptide design using real data libraries. The package provides exact solutions for general inverse problems based on shortest-path topological indices. The results are superior both in accuracy and computing time to the best software reports published in the literature. For 5-peptoid design, the computation is rigorously reduced to an exhaustive search of about 2% of the search space; the exact solutions are found in a few minutes.
Liu, Xiangan; Jiang, Wen; Jakana, Joanita; Chiu, Wah
2007-01-01
Accurately determining a cryoEM particle’s alignment parameters is crucial to high resolution single particle 3-D reconstruction. We developed Multi-Path Simulated Annealing, a Monte Carlo type of optimization algorithm, for globally aligning the center and orientation of a particle simultaneously. A consistency criterion was developed to ensure the alignment parameters are correct and to remove some bad particles from a large pool of images of icosahedral particles. Without using any a priori model, this procedure is able to reconstruct a structure from a random initial model. Combining the procedure above with a new empirical double threshold particle selection method, we are able to pick tens of best quality particles to reconstruct a subnanometer resolution map from scratch. Using the best 62 particles of rice dwarf virus, the reconstruction reached 9.6Å resolution at which 4 helices of the P3A subunit of RDV are resolved. Furthermore, with the 284 best particles, the reconstruction is improved to 7.9Å resolution, and 21 of 22 helices and 6 of 7 beta sheets are resolved. PMID:17698370
An Almost Linear Time Algorithm for Field Splitting in Radiation Therapy.
Wu, Xiaodong; Dou, Xin; Bayouth, John E; Buatti, John M
2013-08-01
In this paper, we study an interesting geometric partition problem, called optimal field splitting, which arises in Intensity-Modulated Radiation Therapy (IMRT). In current clinical practice, a multi-leaf collimator (MLC) with a maximum leaf spread constraint is used to deliver the prescribed intensity maps (IMs). However, the maximum leaf spread of an MLC may require to split a large intensity map into several overlapping sub-IMs with each being delivered separately. We develop a close-to-linear time algorithm for solving the field splitting problem while minimizing the total complexity of the resulting sub-IMs, thus improving the treatment delivery efficiency. Meanwhile, our algorithm strives to minimize the maximum beam-on time of those sub-IMs. Our basic idea is to formulate the field splitting problem as computing a shortest path in a directed acyclic graph, which expresses a special "layered" structure. The edge weights of the graph satisfy the Monge property, which enables us to solve this shortest path problem by examining only a small portion of the graph, yielding a close-to-linear time algorithm. To minimize the maximum beam-on time of the resulting sub-IMs, we consider an interesting min-max slope path problem in a monotone polygon which is solvable in linear time. The min-max slope path problem may be of interest in its own right. Experimental results based on real medical data and computer generated IMs showed that our new algorithm runs fast and produces high quality field splitting results. PMID:24999294
Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples.
Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Di Paolo, Ezequiel A; Liu, Hao
2016-01-01
Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.
A Flexible Reservation Algorithm for Advance Network Provisioning
Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex
2010-04-12
Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n2r2) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.
Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms.
Kuhn, Tobias; Fonseca, Carlos M; Paquete, Luís; Ruzika, Stefan; Duarte, Miguel M; Figueira, José Rui
2016-01-01
The hypervolume subset selection problem consists of finding a subset, with a given cardinality k, of a set of nondominated points that maximizes the hypervolume indicator. This problem arises in selection procedures of evolutionary algorithms for multiobjective optimization, for which practically efficient algorithms are required. In this article, two new formulations are provided for the two-dimensional variant of this problem. The first is a (linear) integer programming formulation that can be solved by solving its linear programming relaxation. The second formulation is a k-link shortest path formulation on a special digraph with the Monge property that can be solved by dynamic programming in [Formula: see text] time. This improves upon the result of [Formula: see text] in Bader ( 2009 ), and slightly improves upon the result of [Formula: see text] in Bringmann et al. ( 2014b ), which was developed independently from this work using different techniques. Numerical results are shown for several values of n and k.
Rigdon, J. Brian; Smith, Marcus Daniel; Mulder, Samuel A
2014-01-07
PathFinder is a graph search program, traversing a directed cyclic graph to find pathways between labeled nodes. Searches for paths through ordered sequences of labels are termed signatures. Determining the presence of signatures within one or more graphs is the primary function of Path Finder. Path Finder can work in either batch mode or interactively with an analyst. Results are limited to Path Finder whether or not a given signature is present in the graph(s).
NASA Astrophysics Data System (ADS)
Luangpaiboon, P.
2009-10-01
Many entrepreneurs face to extreme conditions for instances; costs, quality, sales and services. Moreover, technology has always been intertwined with our demands. Then almost manufacturers or assembling lines adopt it and come out with more complicated process inevitably. At this stage, products and service improvement need to be shifted from competitors with sustainability. So, a simulated process optimisation is an alternative way for solving huge and complex problems. Metaheuristics are sequential processes that perform exploration and exploitation in the solution space aiming to efficiently find near optimal solutions with natural intelligence as a source of inspiration. One of the most well-known metaheuristics is called Ant Colony Optimisation, ACO. This paper is conducted to give an aid in complicatedness of using ACO in terms of its parameters: number of iterations, ants and moves. Proper levels of these parameters are analysed on eight noisy continuous non-linear continuous response surfaces. Considering the solution space in a specified region, some surfaces contain global optimum and multiple local optimums and some are with a curved ridge. ACO parameters are determined through hybridisations of Modified Simplex and Simulated Annealing methods on the path of Steepest Ascent, SAM. SAM was introduced to recommend preferable levels of ACO parameters via statistically significant regression analysis and Taguchi's signal to noise ratio. Other performance achievements include minimax and mean squared error measures. A series of computational experiments using each algorithm were conducted. Experimental results were analysed in terms of mean, design points and best so far solutions. It was found that results obtained from a hybridisation with stochastic procedures of Simulated Annealing method were better than that using Modified Simplex algorithm. However, the average execution time of experimental runs and number of design points using hybridisations were
Sampling diffusive transition paths
F. Miller III, Thomas; Predescu, Cristian
2006-10-12
We address the problem of sampling double-ended diffusive paths. The ensemble of paths is expressed using a symmetric version of the Onsager-Machlup formula, which only requires evaluation of the force field and which, upon direct time discretization, gives rise to a symmetric integrator that is accurate to second order. Efficiently sampling this ensemble requires avoiding the well-known stiffness problem associated with sampling infinitesimal Brownian increments of the path, as well as a different type of stiffness associated with sampling the coarse features of long paths. The fine-features sampling stiffness is eliminated with the use of the fast sampling algorithm (FSA), and the coarse-feature sampling stiffness is avoided by introducing the sliding and sampling (S&S) algorithm. A key feature of the S&S algorithm is that it enables massively parallel computers to sample diffusive trajectories that are long in time. We use the algorithm to sample the transition path ensemble for the structural interconversion of the 38-atom Lennard-Jones cluster at low temperature.
Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations
NASA Technical Reports Server (NTRS)
Bloem, Michael; Drew, Michael; Lai, Chok Fung; Bilimoria, Karl D.
2012-01-01
Air traffic controller supervisors configure available sector, operating position, and work-station resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes.
Integrated flight path planning system and flight control system for unmanned helicopters.
Jan, Shau Shiun; Lin, Yu Hsiang
2011-01-01
This paper focuses on the design of an integrated navigation and guidance system for unmanned helicopters. The integrated navigation system comprises two systems: the Flight Path Planning System (FPPS) and the Flight Control System (FCS). The FPPS finds the shortest flight path by the A-Star (A*) algorithm in an adaptive manner for different flight conditions, and the FPPS can add a forbidden zone to stop the unmanned helicopter from crossing over into dangerous areas. In this paper, the FPPS computation time is reduced by the multi-resolution scheme, and the flight path quality is improved by the path smoothing methods. Meanwhile, the FCS includes the fuzzy inference systems (FISs) based on the fuzzy logic. By using expert knowledge and experience to train the FIS, the controller can operate the unmanned helicopter without dynamic models. The integrated system of the FPPS and the FCS is aimed at providing navigation and guidance to the mission destination and it is implemented by coupling the flight simulation software, X-Plane, and the computing software, MATLAB. Simulations are performed and shown in real time three-dimensional animations. Finally, the integrated system is demonstrated to work successfully in controlling the unmanned helicopter to operate in various terrains of a digital elevation model (DEM).
Integrated Flight Path Planning System and Flight Control System for Unmanned Helicopters
Jan, Shau Shiun; Lin, Yu Hsiang
2011-01-01
This paper focuses on the design of an integrated navigation and guidance system for unmanned helicopters. The integrated navigation system comprises two systems: the Flight Path Planning System (FPPS) and the Flight Control System (FCS). The FPPS finds the shortest flight path by the A-Star (A*) algorithm in an adaptive manner for different flight conditions, and the FPPS can add a forbidden zone to stop the unmanned helicopter from crossing over into dangerous areas. In this paper, the FPPS computation time is reduced by the multi-resolution scheme, and the flight path quality is improved by the path smoothing methods. Meanwhile, the FCS includes the fuzzy inference systems (FISs) based on the fuzzy logic. By using expert knowledge and experience to train the FIS, the controller can operate the unmanned helicopter without dynamic models. The integrated system of the FPPS and the FCS is aimed at providing navigation and guidance to the mission destination and it is implemented by coupling the flight simulation software, X-Plane, and the computing software, MATLAB. Simulations are performed and shown in real time three-dimensional animations. Finally, the integrated system is demonstrated to work successfully in controlling the unmanned helicopter to operate in various terrains of a digital elevation model (DEM). PMID:22164029
An efficient QoS-aware routing algorithm for LEO polar constellations
NASA Astrophysics Data System (ADS)
Tian, Xin; Pham, Khanh; Blasch, Erik; Tian, Zhi; Shen, Dan; Chen, Genshe
2013-05-01
In this work, a Quality of Service (QoS)-aware routing (QAR) algorithm is developed for Low-Earth Orbit (LEO) polar constellations. LEO polar orbits are the only type of satellite constellations where inter-plane inter-satellite links (ISLs) are implemented in real world. The QAR algorithm exploits features of the topology of the LEO satellite constellation, which makes it more efficient than general shortest path routing algorithms such as Dijkstra's or extended Bellman-Ford algorithms. Traffic density, priority, and error QoS requirements on communication delays can be easily incorporated into the QAR algorithm through satellite distances. The QAR algorithm also supports efficient load balancing in the satellite network by utilizing the multiple paths from the source satellite to the destination satellite, and effectively lowers the rate of network congestion. The QAR algorithm supports a novel robust routing scheme in LEO polar constellation, which is able to significantly reduce the impact of inter-satellite link (ISL) congestions on QoS in terms of communication delay and jitter.
NASA Astrophysics Data System (ADS)
Moreno Oliva, Víctor Iván; Castañeda Mendoza, Álvaro; Campos García, Manuel; Díaz Uribe, Rufino
2011-09-01
The null screen is a geometric method that allows the testing of fast aspherical surfaces, this method measured the local slope at the surface and by numerical integration the shape of the surface is measured. The usual technique for the numerical evaluation of the surface is the trapezoidal rule, is well-known fact that the truncation error increases with the second power of the spacing between spots of the integration path. Those paths are constructed following spots reflected on the surface and starting in an initial select spot. To reduce the numerical errors in this work we propose the use of the Dijkstra algorithm.1 This algorithm can find the shortest path from one spot (or vertex) to another spot in a weighted connex graph. Using a modification of the algorithm it is possible to find the minimal path from one select spot to all others ones. This automates and simplifies the integration process in the test with null screens. In this work is shown the efficient proposed evaluating a previously surface with a traditional process.
An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds
2012-01-01
Background Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored. Results We formulated pathway prediction between a start compound and a goal compound as the shortest path search problem in terms of the number of enzyme reactions applied. We propose an efficient search method based on A* algorithm and heuristic techniques utilizing Linear Programming (LP) solution for estimation of the distance to the goal. First, a chemical compound is represented by a feature vector which counts frequencies of substructure occurrences in the structural formula. Second, an enzyme reaction is represented as an operator vector by detecting the structural changes to compounds before and after the reaction. By defining compound vectors as nodes and operator vectors as edges, prediction of the reaction pathway is reduced to the shortest path search problem in the vector space. In experiments on the DDT degradation pathway, we verify that the shortest paths predicted by our method are biologically correct pathways registered in the KEGG database. The results also demonstrate that the LP heuristics can achieve significant reduction in computation time. Furthermore, we apply our method to a secondary metabolite pathway of plant origin, and successfully find a novel biochemical pathway which cannot be predicted by the existing method. For the reconstruction of a known
Curved paths in raptor flight: Deterministic models.
Lorimer, John W
2006-10-21
Two deterministic models for flight of Peregrine Falcons and possibly other raptors as they approach their prey are examined mathematically. Both models make two assumptions. The first, applicable to both models, is that the angle of sight between falcon and prey is constant, consistent with observations that the falcon keeps its head straight during flight and keeps on course by use of the deep foveal region in its eye which allows maximum acuity at an angle of sight of about 45 degrees . The second assumption for the first model (conical spiral), is that the initial direction of flight determines the overall path. For the second model (flight constrained to a tilted plane), a parameter that fixes the orientation of the plane is required. A variational calculation also shows that the tilted plane flight path is the shortest total path, and, consequently, the conical spiral is another shortest total path. Numerical calculations indicate that the flight paths for the two models are very similar for the experimental conditions under which observations have been made. However, the angles of flight and bank differ significantly. More observations are needed to investigate the applicability of the two models.
Scaling up multiphoton neural scanning: the SSA algorithm.
Schuck, Renaud; Annecchino, Luca A; Schultz, Simon R
2014-01-01
In order to reverse-engineer the information processing capabilities of the cortical circuit, we need to densely sample neural circuit; it may be necessary to sample the activity of thousands of neurons simultaneously. Frame scanning techniques do not scale well in this regard, due to the time "wasted" scanning extracellular space. For scanners in which inertia can be neglected, path length minimization strategies enable large populations to be imaged at relatively high sampling rates. However, in a standard multiphoton microscope, the scanners responsible for beam deflection are inertial, indicating that an optimal solution should take rotor and mirror momentum into account. We therefore characterized the galvanometric scanners of a commercial multiphoton microscope, in order to develop and validate a MATLAB model of microscope scanning dynamics. We tested the model by simulating scan paths across pseudo-randomly positioned neuronal populations of differing neuronal density and field of view. This model motivated the development of a novel scanning algorithm, Adaptive Spiral Scanning (SSA), in which the radius of a circular trajectory is constantly updated such that it follows a spiral trajectory scanning all the cells. Due to the kinematic efficiency of near-circular trajectories, this algorithm achieves higher sampling rates than shortest path approaches, while retaining a relatively efficient coverage fraction in comparison to raster or resonance based frame-scanning approaches. PMID:25570582
NASA Technical Reports Server (NTRS)
Ng, Hok K.; Grabbe, Shon; Mukherjee, Avijit
2010-01-01
The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.
Blind Alley Aware ACO Routing Algorithm
NASA Astrophysics Data System (ADS)
Yoshikawa, Masaya; Otani, Kazuo
2010-10-01
The routing problem is applied to various engineering fields. Many researchers study this problem. In this paper, we propose a new routing algorithm which is based on Ant Colony Optimization. The proposed algorithm introduces the tabu search mechanism to escape the blind alley. Thus, the proposed algorithm enables to find the shortest route, even if the map data contains the blind alley. Experiments using map data prove the effectiveness in comparison with Dijkstra algorithm which is the most popular conventional routing algorithm.
NASA Technical Reports Server (NTRS)
Janich, Karl W.
2005-01-01
The At-Least version of the Generalized Minimum Spanning Tree Problem (L-GMST) is a problem in which the optimal solution connects all defined clusters of nodes in a given network at a minimum cost. The L-GMST is NPHard; therefore, metaheuristic algorithms have been used to find reasonable solutions to the problem as opposed to computationally feasible exact algorithms, which many believe do not exist for such a problem. One such metaheuristic uses a swarm-intelligent Ant Colony System (ACS) algorithm, in which agents converge on a solution through the weighing of local heuristics, such as the shortest available path and the number of agents that recently used a given path. However, in a network using a solution derived from the ACS algorithm, some nodes may move around to different clusters and cause small changes in the network makeup. Rerunning the algorithm from the start would be somewhat inefficient due to the significance of the changes, so a genetic algorithm based on the top few solutions found in the ACS algorithm is proposed to quickly and efficiently adapt the network to these small changes.
Ghassib, Humam B; Sakhel, Asaad R; Obeidat, Omar; Al-Oqali, Amer; Sakhel, Roger R
2012-01-01
We demonstrate the effectiveness of a statistical potential (SP) in the description of fermions in a worm-algorithm path-integral Monte Carlo simulation of a few 3He atoms floating on a 4He layer adsorbed on graphite. The SP in this work yields successful results, as manifested by the clusterization of 3He, and by the observation that the 3He atoms float on the surface of 4He. We display the positions of the particles in 3D coordinate space, which reveal clusterization of the 3He component. The correlation functions are also presented, which give further evidence for the clusterization.
2007-07-14
The PANL software determines path through an Adversary Sequence Diagram (ASD) with minimum Probability of Interruption, P(I), given the ASD information and data about site detection, delay, and response force times. To accomplish this, the software generates each path through the ASD, then applies the Estimate of Adversary Sequence Interruption (EASI) methodology for calculating P(I) to each path, and keeps track of the path with the lowest P(I). Primary use is for training purposes duringmore » courses on physical security design. During such courses PANL will be used to demonstrate to students how more complex software codes are used by the US Department of Energy to determine the most-vulnerable paths and, where security needs improvement, how such codes can help determine physical security upgrades.« less
Snell, Mark K.
2007-07-14
The PANL software determines path through an Adversary Sequence Diagram (ASD) with minimum Probability of Interruption, P(I), given the ASD information and data about site detection, delay, and response force times. To accomplish this, the software generates each path through the ASD, then applies the Estimate of Adversary Sequence Interruption (EASI) methodology for calculating P(I) to each path, and keeps track of the path with the lowest P(I). Primary use is for training purposes during courses on physical security design. During such courses PANL will be used to demonstrate to students how more complex software codes are used by the US Department of Energy to determine the most-vulnerable paths and, where security needs improvement, how such codes can help determine physical security upgrades.
Open-path Fourier transform infrared (OP/FTIR) spectrometry was used to measure the concentrations of ammonia, methane, and other atmospheric gases at an integrated swine production facility. The concentration-pathlength products of the target gases at this site often exceeded th...
A critical examination of stoichiometric and path-finding approaches to metabolic pathways.
Planes, Francisco J; Beasley, John E
2008-09-01
Advances in the field of genomics have enabled computational analysis of metabolic pathways at the genome scale. Singular attention has been devoted in the literature to stoichiometric approaches, and path-finding approaches, to metabolic pathways. Stoichiometric approaches make use of reaction stoichiometry when trying to determine metabolic pathways. Stoichiometric approaches involve elementary flux modes and extreme pathways. In contrast, path-finding approaches propose an alternative view based on graph theory in which reaction stoichiometry is not considered. Path-finding approaches use shortest path and k-shortest path concepts. In this article we give a critical overview of the theory, applications and key research challenges of stoichiometric and path-finding approaches to metabolic pathways. PMID:18436574
Performance Analysis of Path Planning Modeling
NASA Astrophysics Data System (ADS)
Wang, Zhirui; Li, Shuanghong; Zhang, Ying; Du, Qiaoling
Ant colony system (ACS) algorithm was applied to the path planning for the robot. In the same working environment, path planning based on MAKLINK graph theory and Voronoi diagram were simulated and compared. MAKLINK graph theory is appropriate to apply to precise searching in small-scale district, and Voronoi diagram is suitable for fast path planning in a large area.
Inter-Domain Redundancy Path Computation Methods Based on PCE
NASA Astrophysics Data System (ADS)
Hayashi, Rie; Oki, Eiji; Shiomoto, Kohei
This paper evaluates three inter-domain redundancy path computation methods based on PCE (Path Computation Element). Some inter-domain paths carry traffic that must be assured of high quality and high reliability transfer such as telephony over IP and premium virtual private networks (VPNs). It is, therefore, important to set inter-domain redundancy paths, i. e. primary and secondary paths. The first scheme utilizes an existing protocol and the basic PCE implementation. It does not need any extension or modification. In the second scheme, PCEs make a virtual shortest path tree (VSPT) considering the candidates of primary paths that have corresponding secondary paths. The goal is to reduce blocking probability; corresponding secondary paths may be found more often after a primary path is decided; no protocol extension is necessary. In the third scheme, PCEs make a VSPT considering all candidates of primary and secondary paths. Blocking probability is further decreased since all possible candidates are located, and the sum of primary and secondary path cost is reduced by choosing the pair with minimum cost among all path pairs. Numerical evaluations show that the second and third schemes offer only a few percent reduction in blocking probability and path pair total cost, while the overheads imposed by protocol revision and increase of the amount of calculation and information to be exchanged are large. This suggests that the first scheme, the most basic and simple one, is the best choice.
Chang, Lantian; Weiss, Nicolás; van Leeuwen, Ton G; Pollnau, Markus; de Ridder, René M; Wörhoff, Kerstin; Subramaniam, Vinod; Kanger, Johannes S
2016-06-13
We demonstrate an integrated optical probe including an on-chip microlens for a common-path swept-source optical coherence tomography system. This common-path design uses the end facet of the silicon oxynitride waveguide as the reference plane, thus eliminating the need of a space-consuming and dispersive on-chip loop reference arm, thereby obviating the need for dispersion compensation. The on-chip micro-ball lens eliminates the need of external optical elements for coupling the light between the chip and the sample. The use of this lens leads to a signal enhancement up to 37 dB compared to the chip without a lens. The light source, the common-path arm and the detector are connected by a symmetric Y junction having a wavelength independent splitting ratio (50/50) over a much larger bandwidth than can be obtained with a directional coupler. The signal-to-noise ratio of the system was measured to be 71 dB with 2.6 mW of power on a mirror sample at a distance of 0.3 mm from the waveguide end facet. Cross-sectional OCT images of a layered optical phantom sample are demonstrated with our system. A method, based on an extended Fourier-domain OCT model, for suppressing ghost images caused by additional parasitic reference planes is experimentally demonstrated. PMID:27410285
Tracing path-guided apparent motion in human primary visual cortex V1.
Akselrod, Michel; Herzog, Michael H; Öğmen, Haluk
2014-08-14
Vision is a constructive process. For example, a square, flashed at two distinct locations one after the other, appears to move smoothly between the two locations rather than as two separate flashes (apparent motion). Apparent motion is usually perceived along the shortest path between locations. Previous studies have shown that retinotopic activity in V1 correlates well with the subjective filling-in in apparent motion. If V1 activity truly reflects illusory motion, it should flexibly reflect filling-in of any path, subjectively perceived. Here, we used a path-guided apparent motion paradigm in which a faint cue, presented in addition to the squares, leads to a curved illusory motion path. We found retinotopic activity in V1 to reflect the illusory filling-in of the curved path, similarly to filling-in with linear, shortest paths. Moreover, our results show that activity along the linear path was less selective to stimulus conditions than the activity along the curved path. This finding may be interpreted as V1 activity representing a small subset of infinitely many possible solutions to ambiguous stimuli, whilst giving more weight to the shortest path/energy solution.
Transmembrane Helix Assembly by Max-Min Ant System Algorithm.
Sujaree, Kanon; Kitjaruwankul, Sunan; Boonamnaj, Panisak; Supunyabut, Chirayut; Sompornpisut, Pornthep
2015-12-01
Because of the rapid progress in biochemical and structural studies of membrane proteins, considerable attention has been given on developing efficient computational methods for solving low-to-medium resolution structures using sparse structural data. In this study, we demonstrate a novel algorithm, max-min ant system (MMAS), designed to find an assembly of α-helical transmembrane proteins using a rigid helix arrangement guided by distance constraints. The new algorithm generates a large variety with finite number of orientations of transmembrane helix bundle and finds the solution that is matched with the provided distance constraints based on the behavior of ants to search for the shortest possible path between their nest and the food source. To demonstrate the efficiency of the novel search algorithm, MMAS is applied to determine the transmembrane packing of KcsA and MscL ion channels from a limited distance information extracted from the crystal structures, and the packing of KvAP voltage sensor domain using a set of 10 experimentally determined constraints, and the results are compared with those of two popular used stochastic methods, simulated annealing Monte Carlo method and genetic algorithm. PMID:26058409
Route choices of transport bicyclists: a comparison of actually used and shortest routes
2014-01-01
Background Despite evidence that environmental features are related to physical activity, the association between the built environment and bicycling for transportation remains a poorly investigated subject. The aim of the study was to improve our understanding of the environmental determinants of bicycling as a means of transportation in urban European settings by comparing the spatial differences between the routes actually used by bicyclists and the shortest possible routes. Methods In the present study we examined differences in the currently used and the shortest possible bicycling routes, with respect to distance, type of street, and environmental characteristics, in the city of Graz, Austria. The objective measurement methods of a Global Positioning System (GPS) and a Geographic Information System (GIS) were used. Results Bicycling routes actually used were significantly longer than the shortest possible routes. Furthermore, the following attributes were also significantly different between the used route compared to the shortest possible route: Bicyclists often used bicycle lanes and pathways, flat and green areas, and they rarely used main roads and crossings. Conclusion The results of the study support our hypothesis that bicyclists prefer bicycle pathways and lanes instead of the shortest possible routes. This underlines the importance of a well-developed bicycling infrastructure in urban communities. PMID:24597725
Metabolic PathFinding: inferring relevant pathways in biochemical networks.
Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques
2005-07-01
Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).
NASA Astrophysics Data System (ADS)
Harrison, F. W.; Lin, B.; Ismail, S.; Nehrir, A. R.; Dobler, J. T.; Browell, E. V.; Kooi, S. A.; Campbell, J. F.; Obland, M. D.; Yang, M. M.; Meadows, B.
2014-12-01
This paper presents an overview of the methods for the retrieval of carbon dioxide (CO2) and oxygen (O2) column amounts and their associated path lengths measured by the Multi-Functional Fiber Laser Lidar (MFLL) and the ASCENDS CarbonHawk Experiment Simulator (ACES). MFLL and ACES are multi-frequency, Intensity-Modulated, Continuous-Wave (IM-CW) Lidar systems developed as proof-of-concept demonstrators for NASA's Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission. The National Research Council identified ASCENDS in 2007 as an important mid-term decadal survey mission to provide measurements critical to improved projections of the Earth's future climate. The ASCENDS measurement requirements have evolved significantly since first proposed by the NRC as has our understanding of the IM-CW measurement technique we propose for use by ASCENDS. To meet these requirements, both MFLL and ACES transmit wavelengths near 1.57 and 1.26 μm modulated with range-encoded signals to minimize bias from thin clouds in the CO2 and O2 column measurements while simultaneously measuring the path length to the surface and to intervening cloud layers. In preparation for the ASCENDS mission, the MFLL has been deployed on 13 airborne field campaigns since 2005, including the latest series of flights in August 2014. NASA also flew the ACES instrument as a technology demonstrator in 2014. In this paper we describe the current ASCENDS retrieval technique and present the accuracy and precision of the measurements obtained using this technique. We also present a reanalysis of the 2011 MFLL measurements and compare the results previously reported to the reanalysis. Reanalysis yields range precisions of less that one meter from an altitude of 12 kilometers from the CO2 offline channel with 1.6 watts of transmitted power.
Path optimization with limited sensing ability
Kang, Sung Ha Kim, Seong Jun Zhou, Haomin
2015-10-15
We propose a computational strategy to find the optimal path for a mobile sensor with limited coverage to traverse a cluttered region. The goal is to find one of the shortest feasible paths to achieve the complete scan of the environment. We pose the problem in the level set framework, and first consider a related question of placing multiple stationary sensors to obtain the full surveillance of the environment. By connecting the stationary locations using the nearest neighbor strategy, we form the initial guess for the path planning problem of the mobile sensor. Then the path is optimized by reducing its length, via solving a system of ordinary differential equations (ODEs), while maintaining the complete scan of the environment. Furthermore, we use intermittent diffusion, which converts the ODEs into stochastic differential equations (SDEs), to find an optimal path whose length is globally minimal. To improve the computation efficiency, we introduce two techniques, one to remove redundant connecting points to reduce the dimension of the system, and the other to deal with the entangled path so the solution can escape the local traps. Numerical examples are shown to illustrate the effectiveness of the proposed method.
Path planning in a two-dimensional environment
NASA Astrophysics Data System (ADS)
Fox, Richard K.; Garcia, Antonio, Jr.; Nelson, Michael L.
1999-07-01
This paper presents a path planning algorithm that is part of the STESCA control architecture for autonomous vehicles. The path planning algorithm models an autonomous vehicle's path as a series of line segments in Cartesian space and compares each line segment to a list of known obstacles and hazardous areas to determine if any collisions or hindrances exist. In the event of a detected collision, the algorithm selects a point outside the obstacle or hazardous area, generates two new path segments that avoid the obstruction and recursively checks the new paths for other collisions. Once underway, if the autonomous vehicle encounters previously unknown obstacles or hazardous areas, the path planner operates in a run-time mode that decides how to re-route the path around the obstacle or abort. This paper describes the path planner along with examples of path planning in a two-dimensional environment with a wheeled land-based robotic vehicle.
Minimum-Risk Path Finding by an Adaptive Amoebal Network
NASA Astrophysics Data System (ADS)
Nakagaki, Toshiyuki; Iima, Makoto; Ueda, Tetsuo; Nishiura, Yasumasa; Saigusa, Tetsu; Tero, Atsushi; Kobayashi, Ryo; Showalter, Kenneth
2007-08-01
When two food sources are presented to the slime mold Physarum in the dark, a thick tube for absorbing nutrients is formed that connects the food sources through the shortest route. When the light-avoiding organism is partially illuminated, however, the tube connecting the food sources follows a different route. Defining risk as the experimentally measurable rate of light-avoiding movement, the minimum-risk path is exhibited by the organism, determined by integrating along the path. A model for an adaptive-tube network is presented that is in good agreement with the experimental observations.
Going against the flow: finding the optimal path
NASA Astrophysics Data System (ADS)
Talbot, Julian
2010-01-01
We consider the problem of finding the optimum path of a boat traversing a straight in a current. The path of the shortest time is found using the calculus of variations with the constraint that the boat must land directly opposite to its starting point. We compare the optimal trajectory with that where the boat's local orientation is always directed to the arrival point. When analytical solutions cannot be found we use numerical methods. The level of the exposition is suitable for advanced undergraduate students, graduate students and general physicists.
A novel track reconstruction algorithm for photoelectric X-ray polarimetry
NASA Astrophysics Data System (ADS)
Li, Tenglin; Li, Hong; Feng, Hua; Zeng, Ming
2016-07-01
The key to the photoelectric X-ray polarimetry is the determination of the emission direction of photoelectrons. Due to the low mass of an electron, the ionization track is not straight and the useful information is stored only in its initial part where less energy is deposited. We present a new algorithm in order to reconstruct the electron track from a 2D track image that is blurred due to diffusion during drift in the gas chamber. The algorithm is based on the shortest path problem in graph theory, and a spatial energy filter is implemented as an improvement. Tested with simulated data, which approximate the real measurement with the gas pixel detector, we find that the new algorithm is able to trace the initial part of the track more closely and produce a higher degree of modulation than past algorithms, especially for long tracks created by high energy X-rays, in which cases the past algorithms may fail due to complicated track patterns.
NASA Astrophysics Data System (ADS)
Nakamura, T.; Sekimoto, Y.; Usui, T.; Shibasaki, R.
2012-07-01
Nowadays, for the estimation of traffic demand or people flow, modelling route choice activity in road networks is an important task and many algorithms have been developed to generate route choice sets. However, developing an algorithm based on a small amount of data that can be applied generally within a metropolitan area is difficult. This is because the characteristics of road networks vary widely. On the other hand, recently, the collection of people movement data has lately become much easier, especially through mobile phones. Lately, most mobile phones include GPS functionality. Given this background, we propose a data-oriented algorithm to generate route choice sets using mobile phone GPS data. GPS data contain a number of measurement errors; hence, they must be adjusted to account for these errors before use in advanced people movement analysis. However, this is time-consuming and expensive, because an enormous amount of daily data can be obtained. Hence, the objective of this study is to develop an algorithm that can easily manage GPS data. Specifically, at first movement data from all GPS data are selected by calculating the speed. Next, the nearest roads in the road network are selected from the GPS location and count such data for each road. Then An algorithm based on the GSP (Gateway Shortest Path) algorithm is proposed, which searches the shortest path through a given gateway. In the proposed algorithm, the road for which the utilization volume calculated by GPS data is large is selected as the gateway. Thus, route choice sets that are based on trends in real GPS data are generated. To evaluate the proposed method, GPS data from 0.7 million people a year in Japan and DRM (Digital Road Map) as the road network are used. DRM is one of the most detailed road networks in Japan. Route choice sets using the proposed algorithm are generated and the cover rate of the utilization volume of each road under evaluation is calculated. As a result, the proposed
NASA Astrophysics Data System (ADS)
Pan, Zhong-Liang; Chen, Ling; Zhang, Guang-Zhao
2016-04-01
The hybrid CMOS molecular (CMOL) circuit, which combines complementary metal-oxide-semiconductor (CMOS) components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circuits, the nanodevices, which are called cells, should be placed appropriately and are connected by nanowires. The cells should be connected such that they follow the shortest path. This paper presents an efficient method of cell allocation in CMOL circuits with the hybrid CMOS/nanodevice structure; the method is based on a cultural algorithm with chaotic behavior. The optimal model of cell allocation is derived, and the coding of an individual representing a cell allocation is described. Then the cultural algorithm with chaotic behavior is designed to solve the optimal model. The cultural algorithm consists of a population space, a belief space, and a protocol that describes how knowledge is exchanged between the population and belief spaces. In this paper, the evolutionary processes of the population space employ a genetic algorithm in which three populations undergo parallel evolution. The evolutionary processes of the belief space use a chaotic ant colony algorithm. Extensive experiments on cell allocation in benchmark circuits showed that a low area usage can be obtained using the proposed method, and the computation time can be reduced greatly compared to that of a conventional genetic algorithm.
Path planning by querying persistent stores of trajectory segments
NASA Technical Reports Server (NTRS)
Grossman, Robert L.; Mehta, S.; Qin, Xiao
1993-01-01
We introduce an algorithm for path planning (long duration) paths of dynamical systems, given a persistent object store containing suitable collections of short duration trajectory segments. We also describe experimental results from a proof-of-concept implementation of the algorithm. The basic idea is to interpret a path planning algorithm as a suitable query on a persistent object store consisting of short duration trajectory segments. The query returns a concatenation of short duration trajectory segments which is close to the desired path. The needed short duration segments are computed by using a divide and conquer algorithm to break up the original path into shorter paths; each shorter path is then matched to a nearby trajectory segment which is part of the persistent object store by using a suitable index function.
Analyzing methods for path mining with applications in metabolomics.
Tagore, Somnath; Chowdhury, Nirmalya; De, Rajat K
2014-01-25
Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need to be chosen for their better understanding and interpretation. One of the useful strategies in this regard is using path mining strategies and graph-theoretical approaches that help in building hypothetical models and perform quantitative analysis. Furthermore, they also contribute to analyzing topological parameters in metabolome networks. Path mining techniques can be based on grammars, keys, patterns and indexing. Moreover, they can also be used for modeling metabolome networks, finding structural similarities between metabolites, in-silico metabolic engineering, shortest path estimation and for various graph-based analysis. In this manuscript, we have highlighted some core and applied areas of path-mining for modeling and analysis of metabolic networks. PMID:24230973
A Comparison of Two Path Planners for Planetary Rovers
NASA Astrophysics Data System (ADS)
Tarokh, M.; Shiller, Z.; Hayati, S.
1999-01-01
The paper presents two path planners suitable for planetary rovers. The first is based on fuzzy description of the terrain, and genetic algorithm to find a traversable path in a rugged terrain. The second planner uses a global optimization method with a cost function that is the path distance divided by the velocity limit obtained from the consideration of the rover static and dynamic stability. A description of both methods is provided, and the results of paths produced are given which show the effectiveness of the path planners in finding near optimal paths. The features of the methods and their suitability and application for rover path planning are compared
Automated flight path planning for virtual endoscopy.
Paik, D S; Beaulieu, C F; Jeffrey, R B; Rubin, G D; Napel, S
1998-05-01
In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images. PMID:9608471
Planning Flight Paths of Autonomous Aerobots
NASA Technical Reports Server (NTRS)
Kulczycki, Eric; Elfes, Alberto; Sharma, Shivanjli
2009-01-01
Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions.
Efficient algorithms for wildland fire simulation
NASA Astrophysics Data System (ADS)
Kondratenko, Volodymyr Y.
In this dissertation, we develop the multiple-source shortest path algorithms and examine their application importance in real world problems, such as wildfire modeling. The theoretical basis and its implementation in the Weather Research Forecasting (WRF) model coupled with the fire spread code SFIRE (WRF-SFIRE model) are described. We present a data assimilation method that gives the fire spread model the ability to start the fire simulation from an observed fire perimeter instead of an ignition point. While the model is running, the fire state in the model changes in accordance with the new arriving data by data assimilation. As the fire state changes, the atmospheric state (which is strongly effected by heat flux) does not stay consistent with the fire state. The main difficulty of this methodology occurs in coupled fire-atmosphere models, because once the fire state is modified to match a given starting perimeter, the atmospheric circulation is no longer in sync with it. One of the possible solutions to this problem is a formation of the artificial time of ignition history from an earlier fire state, which is later used to replay the fire progression to the new perimeter with the proper heat fluxes fed into the atmosphere, so that the fire induced circulation is established. In this work, we develop efficient algorithms that start from the fire arrival times given at the set of points (called a perimeter) and create the artificial fire time of ignition and fire spread rate history. Different algorithms were developed in order to suit possible demands of the user, such as implementation in parallel programming, minimization of the required amount of iterations and memory use, and use of the rate of spread as a time dependent variable. For the algorithms that deal with the homogeneous rate of spread, it was proven that the values of fire arrival times they produce are optimal. It was also shown that starting from arbitrary initial state the algorithms have
Constrained motion control on a hemispherical surface: path planning.
Berman, Sigal; Liebermann, Dario G; McIntyre, Joseph
2014-03-01
Surface-constrained motion, i.e., motion constraint by a rigid surface, is commonly found in daily activities. The current work investigates the choice of hand paths constrained to a concave hemispherical surface. To gain insight regarding paths and their relationship with task dynamics, we simulated various control policies. The simulations demonstrated that following a geodesic path (the shortest path between 2 points on a sphere) is advantageous not only in terms of path length but also in terms of motor planning and sensitivity to motor command errors. These stem from the fact that the applied forces lie in a single plane (that of the geodesic path). To test whether human subjects indeed follow the geodesic, and to see how such motion compares to other paths, we recorded movements in a virtual haptic-visual environment from 11 healthy subjects. The task comprised point-to-point motion between targets at two elevations (30° and 60°). Three typical choices of paths were observed from a frontal plane projection of the paths: circular arcs, straight lines, and arcs close to the geodesic path for each elevation. Based on the measured hand paths, we applied k-means blind separation to divide the subjects into three groups and compared performance indicators. The analysis confirmed that subjects who followed paths closest to the geodesic produced faster and smoother movements compared with the others. The "better" performance reflects the dynamical advantages of following the geodesic path and may also reflect invariant features of control policies used to produce such a surface-constrained motion.
Evolutionary software for autonomous path planning
Couture, S; Hage, M
1999-02-10
This research project demonstrated the effectiveness of using evolutionary software techniques in the development of path-planning algorithms and control programs for mobile vehicles in radioactive environments. The goal was to take maximum advantage of the programmer's intelligence by tasking the programmer with encoding the measures of success for a path-planning algorithm, rather than developing the path-planning algorithms themselves. Evolutionary software development techniques could then be used to develop algorithms most suitable to the particular environments of interest. The measures of path-planning success were encoded in the form of a fitness function for an evolutionary software development engine. The task for the evolutionary software development engine was to evaluate the performance of individual algorithms, select the best performers for the population based on the fitness function, and breed them to evolve the next generation of algorithms. The process continued for a set number of generations or until the algorithm converged to an optimal solution. The task environment was the navigation of a rover from an initial location to a goal, then to a processing point, in an environment containing physical and radioactive obstacles. Genetic algorithms were developed for a variety of environmental configurations. Algorithms were simple and non-robust strings of behaviors, but they could be evolved to be nearly optimal for a given environment. In addition, a genetic program was evolved in the form of a control algorithm that operates at every motion of the robot. Programs were more complex than algorithms and less optimal in a given environment. However, after training in a variety of different environments, they were more robust and could perform acceptably in environments they were not trained in. This paper describes the evolutionary software development engine and the performance of algorithms and programs evolved by it for the chosen task.
BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.
Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter
2013-02-01
Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of
Inferring propagation paths for sparsely observed perturbations on complex networks
Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán-Debón, Raúl; Joven, Jorge; Sales-Pardo, Marta; Guimerà, Roger
2016-01-01
In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.
Cooperative organic mine avoidance path planning
NASA Astrophysics Data System (ADS)
McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David
2005-06-01
The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.
Egocentric path integration models and their application to desert arthropods.
Merkle, Tobias; Rost, Martin; Alt, Wolfgang
2006-06-01
Path integration enables desert arthropods to find back to their nest on the shortest track from any position. To perform path integration successfully, speeds and turning angles along the preceding outbound path have to be measured continuously and combined to determine an internal global vector leading back home at any time. A number of experiments have given an idea how arthropods might use allothetic or idiothetic signals to perceive their orientation and moving speed. We systematically review the four possible model descriptions of mathematically precise path integration, whereby we favour and elaborate the hitherto not used variant of egocentric cartesian coordinates. Its simple and intuitive structure is demonstrated in comparison to the other models. Measuring two speeds, the forward moving speed and the angular turning rate, and implementing them into a linear system of differential equations provides the necessary information during outbound route, reorientation process and return path. In addition, we propose several possible types of systematic errors that can cause deviations from the correct homeward course. Deviations have been observed for several species of desert arthropods in different experiments, but their origin is still under debate. Using our egocentric path integration model we propose simple error indices depending on path geometry that will allow future experiments to rule out or corroborate certain error types.
Learning to improve path planning performance
Chen, Pang C.
1995-04-01
In robotics, path planning refers to finding a short. collision-free path from an initial robot configuration to a desired configuratioin. It has to be fast to support real-time task-level robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To remedy this situation, we present and analyze a learning algorithm that uses past experience to increase future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a speedup-learning framework in which a slow but capable planner may be improved both cost-wise and capability-wise by a faster but less capable planner coupled with experience. The basic algorithm is suitable for stationary environments, and can be extended to accommodate changing environments with on-demand experience repair and object-attached experience abstraction. To analyze the algorithm, we characterize the situations in which the adaptive planner is useful, provide quantitative bounds to predict its behavior, and confirm our theoretical results with experiments in path planning of manipulators. Our algorithm and analysis are sufficiently, general that they may also be applied to other planning domains in which experience is useful.
An Energy Efficient Stable Election-Based Routing Algorithm for Wireless Sensor Networks
Wang, Jin; Zhang, Zhongqi; Xia, Feng; Yuan, Weiwei; Lee, Sungyoung
2013-01-01
Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs). However, sensor networks with one fixed sink node often suffer from a hot spots problem since nodes near sinks have more traffic burden to forward during a multi-hop transmission process. The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP), which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP. PMID:24284767
Robot navigation in unknown terrains: Introductory survey of non-heuristic algorithms
Rao, N.S.V.; Kareti, S.; Shi, Weimin; Iyengar, S.S.
1993-07-01
A formal framework for navigating a robot in a geometric terrain by an unknown set of obstacles is considered. Here the terrain model is not a priori known, but the robot is equipped with a sensor system (vision or touch) employed for the purpose of navigation. The focus is restricted to the non-heuristic algorithms which can be theoretically shown to be correct within a given framework of models for the robot, terrain and sensor system. These formulations, although abstract and simplified compared to real-life scenarios, provide foundations for practical systems by highlighting the underlying critical issues. First, the authors consider the algorithms that are shown to navigate correctly without much consideration given to the performance parameters such as distance traversed, etc. Second, they consider non-heuristic algorithms that guarantee bounds on the distance traversed or the ratio of the distance traversed to the shortest path length (computed if the terrain model is known). Then they consider the navigation of robots with very limited computational capabilities such as finite automata, etc.
Spaces of paths and the path topology
NASA Astrophysics Data System (ADS)
Low, Robert J.
2016-09-01
The natural topology on the space of causal paths of a space-time depends on the topology chosen on the space-time itself. Here we consider the effect of using the path topology on space-time instead of the manifold topology, and its consequences for how properties of space-time are reflected in the structure of the space of causal paths.
The prediction of radio-path characteristics
NASA Astrophysics Data System (ADS)
Gitina, G. M.; Kalinin, Iu. K.
The paper examines algorithms for the long-term prediction of radio-path characteristics in the ionosphere, the main characteristic being the MUF at a given distance. The proposed approach is based on long-term memories called DATA BANKS. Attention is given to the characteritics of the various banks, including the BANK OF CITIES, the BANK OF RADIO PATHS, the REFERENCE DATA BANK, and the OUTPUT DATA BANK.
Mao, Yuxin; Zhu, Ping
2013-01-01
The unreliability and dynamics of mobile wireless sensor networks make it hard to perform end-to-end communications. This paper presents a novel source-initiated on-demand routing mechanism for efficient data transmission in mobile wireless sensor networks. It explores the Thorup-Zwick theory to achieve source-initiated on-demand routing with time efficiency. It is able to find out shortest routing path between source and target in a network and transfer data in linear time. The algorithm is easy to be implemented and performed in resource-constrained mobile wireless sensor networks. We also evaluate the approach by analyzing its cost in detail. It can be seen that the approach is efficient to support data transmission in mobile wireless sensor networks.
Algorithms and Algorithmic Languages.
ERIC Educational Resources Information Center
Veselov, V. M.; Koprov, V. M.
This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…
VLBI Observations of the Shortest Orbital Period Black Hole X-Ray Binary
NASA Astrophysics Data System (ADS)
Paragi, Zsolt; Belloni, Tomaso M.; van der Horst, Alexander J.; Miller-Jones, James
The X-ray transient MAXI J1659-152 was discovered by Swift/BAT and it was initially identified as a GRB. Soon its Galactic origin and binary nature were established. There exists a wealth of multi-wavelength monitoring data for this source, providing a great coverage of the full X-ray transition in this candidate black hole binary system. We obtained two epochs of EVN/e-VLBI and four epochs of VLBA data of MAXI J1659-152 which show evidence for some extended emission in the early phases but -against expectations- no major collimated ejecta during the accretion disk state transition. This might be related to the fact that, with a red dwarf donor star, MAXI J1659-152 is the shortest orbital period black hole X-ray binary system.
Discrete Coherent State Path Integrals
NASA Astrophysics Data System (ADS)
Marchioro, Thomas L., II
1990-01-01
The quantum theory provides a fundamental understanding of the physical world; however, as the number of degrees of freedom rises, the information required to specify quantum wavefunctions grows geometrically. Because basis set expansions mirror this geometric growth, a strict practical limit on quantum mechanics as a numerical tool arises, specifically, three degrees of freedom or fewer. Recent progress has been made utilizing Feynman's Path Integral formalism to bypass this geometric growth and instead calculate time -dependent correlation functions directly. The solution of the Schrodinger equation is converted into a large dimensional (formally infinite) integration, which can then be attacked with Monte Carlo techniques. To date, work in this area has concentrated on developing sophisticated mathematical algorithms for evaluating the highly oscillatory integrands occurring in Feynman Path Integrals. In an alternative approach, this work demonstrates two formulations of quantum dynamics for which the number of mathematical operations does not scale geometrically. Both methods utilize the Coherent State basis of quantum mechanics. First, a localized coherent state basis set expansion and an approximate short time propagator are developed. Iterations of the short time propagator lead to the full quantum dynamics if the coherent state basis is sufficiently dense along the classical phase space path of the system. Second, the coherent state path integral is examined in detail. For a common class of Hamiltonians, H = p^2/2 + V( x) the path integral is reformulated from a phase space-like expression into one depending on (q,dot q). It is demonstrated that this new path integral expression contains localized damping terms which can serve as a statistical weight for Monte Carlo evaluation of the integral--a process which scales approximately linearly with the number of degrees of freedom. Corrections to the traditional coherent state path integral, inspired by a
Practical path planning among movable obstacles
Chen, Pang C.; Hwang, Yong K.
1990-09-05
Path planning among movable obstacles is a practical problem that is in need of a solution. In this paper an efficient heuristic algorithm that uses a generate-and-test paradigm: a good'' candidate path is hypothesized by a global planner and subsequently verified by a local planner. In the process of formalizing the problem, we also present a technique for modeling object interactions through contact. Our algorithm has been tested on a variety of examples, and was able to generate solutions within 10 seconds. 5 figs., 27 refs.
Computational path planner for product assembly in complex environments
NASA Astrophysics Data System (ADS)
Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi
2013-03-01
Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.
Path optimization for oil probe
NASA Astrophysics Data System (ADS)
Smith, O'Neil; Rahmes, Mark; Blue, Mark; Peter, Adrian
2014-05-01
We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion. Our solution may be able to help increase the energy extracted vs. input energy.
Collision-free path planning in the Belousov-Zhabotinsky medium assisted by a cellular automaton
NASA Astrophysics Data System (ADS)
Adamatzky, Andrew; de Lacy Costello, Benjamin
2002-09-01
We offer a new approach to computing a shortest collision-free path in a space containing obstacles, using an experimental chemical processor, based on the Belousov-Zhabotinsky (BZ) reaction. The chemical processor was then coupled via optical links with a two-dimensional cellular automaton (CA) processor. In the BZ chemical processor obstacles are represented by sites of local stimulation generated by an array of silver wires. Circular excitation waves are generated which travel through the medium and approximate a scalar distance-to-obstacle field. The field is taken as the initial configuration of the CA processor, which calculates a tree of 'many-sources-one-destination' shortest paths using wave spreading in a discrete excitable medium. We describe a hybrid (experimental chemical and software based) parallel processor (with parallel inputs and outputs) which uses the principles of wave-based computing in both the physical and computational levels of its architecture.
Collision-free path planning in the Belousov-Zhabotinsky medium assisted by a cellular automaton.
Adamatzky, Andrew; de Lacy Costello, Benjamin
2002-10-01
We offer a new approach to computing a shortest collision-free path in a space containing obstacles, using an experimental chemical processor, based on the Belousov-Zhabotinsky (BZ) reaction. The chemical processor was then coupled via optical links with a two-dimensional cellular automaton (CA) processor. In the BZ chemical processor obstacles are represented by sites of local stimulation generated by an array of silver wires. Circular excitation waves are generated which travel through the medium and approximate a scalar distance-to-obstacle field. The field is taken as the initial configuration of the CA processor, which calculates a tree of 'many-sources-one-destination' shortest paths using wave spreading in a discrete excitable medium. We describe a hybrid (experimental chemical and software based) parallel processor (with parallel inputs and outputs) which uses the principles of wave-based computing in both the physical and computational levels of its architecture.
2012-05-11
The ap command traveres all symlinks in a given file, directory, or executable name to identify the final absolute path. It can print just the final path, each intermediate link along with the symlink chan, and the permissions and ownership of each directory component in the final path. It has functionality similar to "which", except that it shows the final path instead of the first path. It is also similar to "pwd", but it canmore » provide the absolute path to a relative directory from the current working directory.« less
Moody, A.
2012-05-11
The ap command traveres all symlinks in a given file, directory, or executable name to identify the final absolute path. It can print just the final path, each intermediate link along with the symlink chan, and the permissions and ownership of each directory component in the final path. It has functionality similar to "which", except that it shows the final path instead of the first path. It is also similar to "pwd", but it can provide the absolute path to a relative directory from the current working directory.
Adaptive path planning for flexible manufacturing
Chen, Pang C.
1994-08-01
Path planning needs to be fast to facilitate real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To overcome this difficulty, we present an adaptive algorithm that uses past experience to speed up future performance. It is a learning algorithm suitable for automating flexible manufacturing in incrementally-changing environments. The algorithm allows the robot to adapt to its environment by having two experience manipulation schemes: For minor environmental change, we use an object-attached experience abstraction scheme to increase the flexibility of the learned experience; for major environmental change, we use an on-demand experience repair scheme to retain those experiences that remain valid and useful. Using this algorithm, we can effectively reduce the overall robot planning time by re-using the computation result for one task to plan a path for another.
An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective.
Faigl, Jan
2016-01-01
In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning. PMID:27340395
An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective
Faigl, Jan
2016-01-01
In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning. PMID:27340395
Automatic selection of switching paths
NASA Astrophysics Data System (ADS)
Meyer, B. A.
A unique solution is presented to the problem of switching path selection through an analog switch complex. Known as the ROUTER, the software package performs a dynamic allocation of switching paths at the time that an analog signal connection is required. The ROUTER also chooses the type of relay that is appropriate to the signal being transmitted. Different types of switches are furnished for small signal, RF, video, power, or logic signals. Devices using a multiple number of leads, such as synchros or resolvers, are switched as a single unit. The algorithm used by the ROUTER is based on a tree search and connection technique. The interconnections of the hardware switches and devices are described by a connection matrix as a set of data structures. Each node of the switching complex is described in terms of its connectivity and attributes.
Reconfigurable data path processor
NASA Technical Reports Server (NTRS)
Donohoe, Gregory (Inventor)
2005-01-01
A reconfigurable data path processor comprises a plurality of independent processing elements. Each of the processing elements advantageously comprising an identical architecture. Each processing element comprises a plurality of data processing means for generating a potential output. Each processor is also capable of through-putting an input as a potential output with little or no processing. Each processing element comprises a conditional multiplexer having a first conditional multiplexer input, a second conditional multiplexer input and a conditional multiplexer output. A first potential output value is transmitted to the first conditional multiplexer input, and a second potential output value is transmitted to the second conditional multiplexer output. The conditional multiplexer couples either the first conditional multiplexer input or the second conditional multiplexer input to the conditional multiplexer output, according to an output control command. The output control command is generated by processing a set of arithmetic status-bits through a logical mask. The conditional multiplexer output is coupled to a first processing element output. A first set of arithmetic bits are generated according to the processing of the first processable value. A second set of arithmetic bits may be generated from a second processing operation. The selection of the arithmetic status-bits is performed by an arithmetic-status bit multiplexer selects the desired set of arithmetic status bits from among the first and second set of arithmetic status bits. The conditional multiplexer evaluates the select arithmetic status bits according to logical mask defining an algorithm for evaluating the arithmetic status bits.
Vervet monkeys use paths consistent with context-specific spatial movement heuristics.
Teichroeb, Julie A
2015-10-01
Animal foraging routes are analogous to the computationally demanding "traveling salesman problem" (TSP), where individuals must find the shortest path among several locations before returning to the start. Humans approximate solutions to TSPs using simple heuristics or "rules of thumb," but our knowledge of how other animals solve multidestination routing problems is incomplete. Most nonhuman primate species have shown limited ability to route plan. However, captive vervets were shown to solve a TSP for six sites. These results were consistent with either planning three steps ahead or a risk-avoidance strategy. I investigated how wild vervet monkeys (Chlorocebus pygerythrus) solved a path problem with six, equally rewarding food sites; where site arrangement allowed assessment of whether vervets found the shortest route and/or used paths consistent with one of three simple heuristics to navigate. Single vervets took the shortest possible path in fewer than half of the trials, usually in ways consistent with the most efficient heuristic (the convex hull). When in competition, vervets' paths were consistent with different, more efficient heuristics dependent on their dominance rank (a cluster strategy for dominants and the nearest neighbor rule for subordinates). These results suggest that, like humans, vervets may solve multidestination routing problems by applying simple, adaptive, context-specific "rules of thumb." The heuristics that were consistent with vervet paths in this study are the same as some of those asserted to be used by humans. These spatial movement strategies may have common evolutionary roots and be part of a universal mental navigational toolkit. Alternatively, they may have emerged through convergent evolution as the optimal way to solve multidestination routing problems. PMID:26668734
Understanding disordered systems through numerical simulation and algorithm development
NASA Astrophysics Data System (ADS)
Sweeney, Sean Michael
ferromagnet is studied, which is especially useful since it serves as a prototype for more complicated disordered systems such as the random field Ising model and spin glasses. We investigate the effect that changing boundary spins has on the locations of domain walls in the interior of the random ferromagnet system. We provide an analytic proof that ground state domain walls in the two dimensional system are decomposable, and we map these domain walls to a shortest paths problem. By implementing a multiple-source shortest paths algorithm developed by Philip Klein, we are able to efficiently probe domain wall locations for all possible configurations of boundary spins. We consider lattices with uncorrelated dis- order, as well as disorder that is spatially correlated according to a power law. We present numerical results for the scaling exponent governing the probability that a domain wall can be induced that passes through a particular location in the system's interior, and we compare these results to previous results on the directed polymer problem.
2013-07-29
The OpenEIS Algorithm package seeks to provide a low-risk path for building owners, service providers and managers to explore analytical methods for improving building control and operational efficiency. Users of this software can analyze building data, and learn how commercial implementations would provide long-term value. The code also serves as a reference implementation for developers who wish to adapt the algorithms for use in commercial tools or service offerings.
Multiobjective Evolutionary Path Planning via Sugeno-Based Tournament Selection
NASA Technical Reports Server (NTRS)
Dozier, Gerry; McCullough, Shaun; Homaifar, Abdollah; Esterline, Albert
1998-01-01
This paper introduces a new tournament selection algorithm that can be used for evolutionary path planning systems. The fuzzy (Sugeno) tournament selection algorithm (STSA) described in this paper selects candidate paths (CPs) to be parents and undergo reproduction based on: (1) path feasibility, (2) the euclidean distance of a path from the origin to its destination, and (3) the average change in the slope of a path. In this paper, we provide a detailed description of the fuzzy inference system used in the STSA as well as some examples of its usefulness. We then use 12 instances of our STSA to rank a population of CPs based on the above criteria. We also show how the STSA can obviate the need for the development of an explicit (lexicographic multiobjective) evaluation function and use it to develop multiobjective motion paths.
A global path planning approach for redundant manipulators
NASA Technical Reports Server (NTRS)
Seereeram, Sanjeev; Wen, J.
1993-01-01
A new approach for global path planning of redundant manipulators is proposed. It poses the path planning problem as a finite time nonlinear control problem. The solution is found by a Newton-Raphson type algorithm. This technique is capable of handling various goal task descriptions as well as incorporating both joint and task space constraints. The algorithm has shown promising preliminary results in planning joint path sequences for 3R and 4R planar robots to meet Cartesian tip tracking and goal endpoint planning. It is robust with respect to local path planning problems such as singularity considerations and local minimum problems. Repetitive joint path solutions for cyclic end-effector tasks are also generated. Eventual goals of this work include implementation on full spatial robots, as well as provision of an interface for supervisory input to aid in path planning for more complex problems.
Spatial self-reference systems and shortest-route behavior in toddlers.
Rieser, J J; Heiman, M L
1982-04-01
2 experiments were conducted concerning the development of spatial orientation during the second year of life. Both experiments were focused on oriented search for a hidden target object in the absence of landmarks, which can be accomplished by relating one's movements to knowledge of a target's location. In experiment 1, 18-month-olds were tested to examine the precision with which they use information for the direction and magnitude of self-movement to keep track of the target location. Although the toddlers' search behavior was imprecise, the results showed that they appropriately modulated their search behavior according to the directions and magnitudes of their previous movements away from the hidden target. Experiment 2 was designed to determine whether toddlers can go beyond the information directly experienced in previous routes of travel to infer the shortest route to a hidden target. The results indicated that the 18-month-old and the highly selected 14-month-old subjects can perform spatial inferences of this type. The mechanisms through which these spatial abilities develop are discussed.
Heuristic optimization of the scanning path of particle therapy beams
Pardo, J.; Donetti, M.; Bourhaleb, F.; Ansarinejad, A.; Attili, A.; Cirio, R.; Garella, M. A.; Giordanengo, S.; Givehchi, N.; La Rosa, A.; Marchetto, F.; Monaco, V.; Pecka, A.; Peroni, C.; Russo, G.; Sacchi, R.
2009-06-15
Quasidiscrete scanning is a delivery strategy for proton and ion beam therapy in which the beam is turned off when a slice is finished and a new energy must be set but not during the scanning between consecutive spots. Different scanning paths lead to different dose distributions due to the contribution of the unintended transit dose between spots. In this work an algorithm to optimize the scanning path for quasidiscrete scanned beams is presented. The classical simulated annealing algorithm is used. It is a heuristic algorithm frequently used in combinatorial optimization problems, which allows us to obtain nearly optimal solutions in acceptable running times. A study focused on the best choice of operational parameters on which the algorithm performance depends is presented. The convergence properties of the algorithm have been further improved by using the next-neighbor algorithm to generate the starting paths. Scanning paths for two clinical treatments have been optimized. The optimized paths are found to be shorter than the back-and-forth, top-to-bottom (zigzag) paths generally provided by the treatment planning systems. The gamma method has been applied to quantify the improvement achieved on the dose distribution. Results show a reduction of the transit dose when the optimized paths are used. The benefit is clear especially when the fluence per spot is low, as in the case of repainting. The minimization of the transit dose can potentially allow the use of higher beam intensities, thus decreasing the treatment time. The algorithm implemented for this work can optimize efficiently the scanning path of quasidiscrete scanned particle beams. Optimized scanning paths decrease the transit dose and lead to better dose distributions.
NASA Astrophysics Data System (ADS)
Lloyd, Seth; Dreyer, Olaf
2016-02-01
Path integrals calculate probabilities by summing over classical configurations of variables such as fields, assigning each configuration a phase equal to the action of that configuration. This paper defines a universal path integral, which sums over all computable structures. This path integral contains as sub-integrals all possible computable path integrals, including those of field theory, the standard model of elementary particles, discrete models of quantum gravity, string theory, etc. The universal path integral possesses a well-defined measure that guarantees its finiteness. The probabilities for events corresponding to sub-integrals can be calculated using the method of decoherent histories. The universal path integral supports a quantum theory of the universe in which the world that we see around us arises out of the interference between all computable structures.
Attention trees and semantic paths
NASA Astrophysics Data System (ADS)
Giusti, Christian; Pieroni, Goffredo G.; Pieroni, Laura
2007-02-01
In the last few decades several techniques for image content extraction, often based on segmentation, have been proposed. It has been suggested that under the assumption of very general image content, segmentation becomes unstable and classification becomes unreliable. According to recent psychological theories, certain image regions attract the attention of human observers more than others and, generally, the image main meaning appears concentrated in those regions. Initially, regions attracting our attention are perceived as a whole and hypotheses on their content are formulated; successively the components of those regions are carefully analyzed and a more precise interpretation is reached. It is interesting to observe that an image decomposition process performed according to these psychological visual attention theories might present advantages with respect to a traditional segmentation approach. In this paper we propose an automatic procedure generating image decomposition based on the detection of visual attention regions. A new clustering algorithm taking advantage of the Delaunay- Voronoi diagrams for achieving the decomposition target is proposed. By applying that algorithm recursively, starting from the whole image, a transformation of the image into a tree of related meaningful regions is obtained (Attention Tree). Successively, a semantic interpretation of the leaf nodes is carried out by using a structure of Neural Networks (Neural Tree) assisted by a knowledge base (Ontology Net). Starting from leaf nodes, paths toward the root node across the Attention Tree are attempted. The task of the path consists in relating the semantics of each child-parent node pair and, consequently, in merging the corresponding image regions. The relationship detected in this way between two tree nodes generates, as a result, the extension of the interpreted image area through each step of the path. The construction of several Attention Trees has been performed and partial
Vulnerability of complex networks under path-based attacks
NASA Astrophysics Data System (ADS)
Pu, Cun-Lai; Cui, Wei
2015-02-01
We investigate vulnerability of complex networks including model networks and real-world networks subject to path-based attacks. Specifically, we remove approximately the longest simple path from a network iteratively until there are no paths left in the network. We propose two algorithms, the random augmenting approach (RPA) and the Hamilton-path based approach (HPA), for finding the approximately longest simple path in a network. Results demonstrate that steps of longest-path attacks increase with network density linearly for random networks, while exponentially increasing for scale-free networks. The more homogeneous the degree distribution is, the more fragile the network, which is different from the previous results of node or edge attacks. HPA is generally more efficient than RPA in the longest-path attacks of complex networks. These findings further help us understand the vulnerability of complex systems, better protect complex systems, and design more tolerant complex systems.
NLTT 5306: the shortest period detached white dwarf+brown dwarf binary
NASA Astrophysics Data System (ADS)
Steele, P. R.; Saglia, R. P.; Burleigh, M. R.; Marsh, T. R.; Gänsicke, B. T.; Lawrie, K.; Cappetta, M.; Girven, J.; Napiwotzki, R.
2013-03-01
We have spectroscopically confirmed a brown dwarf mass companion to the hydrogen atmosphere white dwarf NLTT 5306. The white dwarf's atmospheric parameters were measured using the Sloan Digital Sky Survey and X-shooter spectroscopy as Teff = 7756 ± 35 K and log(g) = 7.68 ± 0.08, giving a mass for the primary of MWD = 0.44 ± 0.04 M⊙ at a distance of 71 ± 4 pc with a cooling age of 710 ± 50 Myr. The existence of the brown dwarf secondary was confirmed through the near-infrared arm of the X-shooter data and a spectral type of dL4-dL7 was estimated using standard spectral indices. Combined radial velocity measurements from the Sloan Digital Sky Survey, X-shooter and the Hobby-Eberly Telescope's High Resolution Spectrograph of the white dwarf give a minimum mass of 56 ± 3 MJup for the secondary, confirming the substellar nature. The period of the binary was measured as 101.88 ± 0.02 min using both the radial velocity data and i'-band variability detected with the Isaac Newton Telescope. This variability indicates `day' side heating of the brown dwarf companion. We also observe Hα emission in our higher resolution data in phase with the white dwarf radial velocity, indicating that this system is in a low level of accretion, most likely via a stellar wind. This system represents the shortest period white dwarf+brown dwarf binary and the secondary has survived a stage of common envelope evolution, much like its longer period counterpart, WD 0137-349. Both systems likely represent bona fide progenitors of cataclysmic variables with a low-mass white dwarf and a brown dwarf donor.
AH Cam: A metal-rich RR Lyrae star with the shortest known Blazhko period
NASA Technical Reports Server (NTRS)
Smith, Horace A.; Matthews, Jaymie M.; Lee, Kevin M.; Williams, Jeffrey; Silbermann, N. A.; Bolte, Michael
1994-01-01
Analysis of 746 new V-band observations of the RR Lyrae star AH Cam obtained during 1989 - 1992 clearly show that its light curve cannot be described by a single period. In fact, at first glance, the Fourier spectrum of the photometry resembles that of a double-mode pulsator, with peaks at a fundamental period of 0.3686 d and an apparent secondary period of 0.2628 d. Nevertheless, the dual-mode solution is a poor fit to the data. Rather, we believe that AH Cam is a single-mode RR Lyrae star undergoing the Blazhko effect: periodic modulation of the amplitude and shape of its light curve. What was originally taken to be the period of the second mode is instead the 1-cycle/d alias of a modulation sidelobe in the Fourier spectrum. The data are well described by a modulation period of just under 11 d, which is the shortest Blazhko period reported to date in the literature and confirms the earlier suggestion by Goranskii. A low-resolution spectrum of AH Cam indicates that it is relatively metal rich, with delta-S less than or = 2. Its high metallicity and short modulation period may provide a critical test of at least one theory for the Blazhko effect. Moskalik's internal resonance model makes specific predictions of the growth rate of the fundamental model vs fundamental period. AH Cam falls outside the regime of other known Blazhko variables and resonance model predictions, but these are appropriate for metal-poor RR Lyrae stars. If the theory matches the behavior of AH Cam for a metal-rich stellar model, this would bolster the resonance hypothesis.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630
Path matching and graph matching in biological networks.
Yang, Qingwu; Sze, Sing-Hoi
2007-01-01
We develop algorithms for the following path matching and graph matching problems: (i) given a query path p and a graph G, find a path p' that is most similar to p in G; (ii) given a query graph G (0) and a graph G, find a graph G (0)' that is most similar to G (0) in G. In these problems, p and G (0) represent a given substructure of interest to a biologist, and G represents a large network in which the biologist desires to find a related substructure. These algorithms allow the study of common substructures in biological networks in order to understand how these networks evolve both within and between organisms. We reduce the path matching problem to finding a longest weighted path in a directed acyclic graph and show that the problem of finding top k suboptimal paths can be solved in polynomial time. This is in contrast with most previous approaches that used exponential time algorithms to find simple paths which are practical only when the paths are short. We reduce the graph matching problem to finding highest scoring subgraphs in a graph and give an exact algorithm to solve the problem when the query graph G (0) is of moderate size. This eliminates the need for less accurate heuristic or randomized algorithms. We show that our algorithms are able to extract biologically meaningful pathways from protein interaction networks in the DIP database and metabolic networks in the KEGG database. Software programs implementing these techniques (PathMatch and GraphMatch) are available at http://faculty.cs.tamu.edu/shsze/pathmatch and http://faculty.cs.tamu.edu/shsze/graphmatch.
An optimization approach for mapping and measuring the divergence and correspondence between paths.
Mueller, Shane T; Perelman, Brandon S; Veinott, Elizabeth S
2016-03-01
Many domains of empirical research produce or analyze spatial paths as a measure of behavior. Previously, approaches for measuring the similarity or deviation between two paths have either required timing information or have used ad hoc or manual coding schemes. In this paper, we describe an optimization approach for robustly measuring the area-based deviation between two paths we call ALCAMP (Algorithm for finding the Least-Cost Areal Mapping between Paths). ALCAMP measures the deviation between two paths and produces a mapping between corresponding points on the two paths. The method is robust to a number of aspects in real path data, such as crossovers, self-intersections, differences in path segmentation, and partial or incomplete paths. Unlike similar algorithms that produce distance metrics between trajectories (i.e., paths that include timing information), this algorithm uses only the order of observed path segments to determine the mapping. We describe the algorithm and show its results on a number of sample problems and data sets, and demonstrate its effectiveness for assessing human memory for paths. We also describe available software code written in the R statistical computing language that implements the algorithm to enable data analysis.
Following control for a UUV using temporary path generation guidance
NASA Astrophysics Data System (ADS)
Yan, Zheping; Chi, Dongnan; Zhou, Jiajia; Zhao, Yufei
2012-06-01
A path following control algorithm for an unmanned underwater vehicle (UUV) using temporary path generation guidance was proposed in this paper. Owing to different initial states of the vehicle, such as position and orientation, the path following control in the horizontal plane may yield a poor performance. To deal with the negative effect induced by initial states, a temporary path generation was presented based on the relationship between the original reference path and the vehicle's initial states. With different relative positions between the vehicle and reference path, including out of straight lines, as well as inside and outside a circle, the related temporary paths guiding the vehicle to the reference path were able to be generated in real time. The vehicle was guided to steer along the temporary path until it reached the tangent point at the reference path, where the controller was designed using the input-output feedback linearization method. Simulation results demonstrated that the proposed algorithm is effective under the three different situations mentioned above.
NASA Astrophysics Data System (ADS)
Yun, KiHyun
2016-07-01
We consider a gradient estimate for a conductivity problem whose inclusions are two neighboring insulators in three dimensions. When inclusions with an extreme conductivity (insulators or perfect conductors) are closely located, the gradient can be concentrated in between inclusions and then becomes arbitrarily large as the distance between inclusions approaches zero. The gradient estimate in between insulators in three dimensions has been regarded as a challenging problem, while the optimal blow-up rates in terms of the distance were successfully obtained for the other extreme conductivity problems in two and three dimensions, and are attained on the shortest line segment between inclusions. In this paper, we establish upper and lower bounds of gradients on the shortest line segment between two insulating unit spheres in three dimensions. These bounds present the optimal blow-up rate of gradient on the line segment which is substantially different from the rates in the other problems.
Optical tomography with discretized path integral
Yuan, Bingzhi; Tamaki, Toru; Kushida, Takahiro; Mukaigawa, Yasuhiro; Kubo, Hiroyuki; Raytchev, Bisser; Kaneda, Kazufumi
2015-01-01
Abstract. We present a framework for optical tomography based on a path integral. Instead of directly solving the radiative transport equations, which have been widely used in optical tomography, we use a path integral that has been developed for rendering participating media based on the volume rendering equation in computer graphics. For a discretized two-dimensional layered grid, we develop an algorithm to estimate the extinction coefficients of each voxel with an interior point method. Numerical simulation results are shown to demonstrate that the proposed method works well. PMID:26839903
Zhu, Daqi; Huang, Huan; Yang, S X
2013-04-01
For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper. PMID:22949070
Zhu, Daqi; Huang, Huan; Yang, S X
2013-04-01
For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.
Enzymatic reaction paths as determined by transition path sampling
NASA Astrophysics Data System (ADS)
Masterson, Jean Emily
, we observed changes in the reaction mechanism and altered contributions of the mutated residues to the enzymatic reaction coordinate, but we did not detect a substantial change in the time of barrier crossing. These results confirm the importance of maintaining the dynamics and structural scaffolding of the hhLDH PV in order to facilitate facile barrier passage. We also utilized TPS to investigate the possible role of fast protein dynamics in the enzymatic reaction coordinate of human dihydrofolate reductase (hsDHFR). We found that sub-picosecond dynamics of hsDHFR do contribute to the reaction coordinate, whereas this is not the case in the E. coli version of the enzyme. This result indicates a shift in the DHFR family to a more dynamic version of catalysis. The second inquiry we addressed in this thesis regarding enzymatic barrier passage concerns the variability of paths through reactive phase space for a given enzymatic reaction. We further investigated the hhLDH-catalyzed reaction using a high-perturbation TPS algorithm. Though we saw that alternate reaction paths were possible, the dominant reaction path we observed corresponded to that previously elucidated in prior hhLDH TPS studies. Since the additional reaction paths we observed were likely high-energy, these results indicate that only the dominant reaction path contributes significantly to the overall reaction rate. In conclusion, we show that the enzymes hhLDH and hsDHFR exhibit paths through reactive phase space where fast protein motions are involved in the enzymatic reaction coordinate and exhibit a non-negligible contribution to chemical barrier crossing.
MAXI J1659-152: the shortest orbital period black-hole transient in outburst
NASA Astrophysics Data System (ADS)
Kuulkers, E.; Kouveliotou, C.; Belloni, T.; Cadolle Bel, M.; Chenevez, J.; Díaz Trigo, M.; Homan, J.; Ibarra, A.; Kennea, J. A.; Muñoz-Darias, T.; Ness, J.-U.; Parmar, A. N.; Pollock, A. M. T.; van den Heuvel, E. P. J.; van der Horst, A. J.
2013-04-01
MAXI J1659-152 is a bright X-ray transient black-hole candidate binary system discovered in September 2010. We report here on MAXI, RXTE, Swift, and XMM-Newton observations during its 2010/2011 outburst. We find that during the first one and a half week of the outburst the X-ray light curves display drops in intensity at regular intervals, which we interpret as absorption dips. About three weeks into the outbursts, again drops in intensity are seen. These dips have, however, a spectral behaviour opposite to that of the absorption dips, and are related to fast spectral state changes (hence referred to as transition dips). The absorption dips recur with a period of 2.414 ± 0.005 h, which we interpret as the orbital period of the system. This implies that MAXI J1659-152 is the shortest period black-hole candidate binary known to date. The inclination of the accretion disk with respect to the line of sight is estimated to be 65-80°. We propose the companion to the black-hole candidate to be close to an M5 dwarf star, with a mass and radius of about 0.15-0.25 M⊙ and 0.2-0.25 R⊙, respectively. We derive that the companion had an initial mass of about 1.5 M⊙, which evolved to its current mass in about 5-6 billion years. The system is rather compact (orbital separation of ≳1.33 R⊙), and is located at a distance of 8.6 ± 3.7 kpc, with a height above the Galactic plane of 2.4 ± 1.0 kpc. The characteristics of short orbital period and high Galactic scale height are shared with two other transient black-hole candidate X-ray binaries, i.e., XTE J1118+480 and Swift J1735.5-0127. We suggest that all three are kicked out of the Galactic plane into the halo, rather than being formed in a globular cluster. Table 1 is available in electronic form at http://www.aanda.org
Improving the algorithm of temporal relation propagation
NASA Astrophysics Data System (ADS)
Shen, Jifeng; Xu, Dan; Liu, Tongming
2005-03-01
In the military Multi Agent System, every agent needs to analyze the temporal relationships among the tasks or combat behaviors, and it"s very important to reflect the battlefield situation in time. The temporal relation among agents is usually very complex, and we model it with interval algebra (IA) network. Therefore an efficient temporal reasoning algorithm is vital in battle MAS model. The core of temporal reasoning is path consistency algorithm, an efficient path consistency algorithm is necessary. In this paper we used the Interval Matrix Calculus (IMC) method to represent the temporal relation, and optimized the path consistency algorithm by improving the efficiency of propagation of temporal relation based on the Allen's path consistency algorithm.
Path integral learning of multidimensional movement trajectories
NASA Astrophysics Data System (ADS)
André, João; Santos, Cristina; Costa, Lino
2013-10-01
This paper explores the use of Path Integral Methods, particularly several variants of the recent Path Integral Policy Improvement (PI2) algorithm in multidimensional movement parametrized policy learning. We rely on Dynamic Movement Primitives (DMPs) to codify discrete and rhythmic trajectories, and apply the PI2-CMA and PIBB methods in the learning of optimal policy parameters, according to different cost functions that inherently encode movement objectives. Additionally we merge both of these variants and propose the PIBB-CMA algorithm, comparing all of them with the vanilla version of PI2. From the obtained results we conclude that PIBB-CMA surpasses all other methods in terms of convergence speed and iterative final cost, which leads to an increased interest in its application to more complex robotic problems.
Tortuous path chemical preconcentrator
Manginell, Ronald P.; Lewis, Patrick R.; Adkins, Douglas R.; Wheeler, David R.; Simonson, Robert J.
2010-09-21
A non-planar, tortuous path chemical preconcentrator has a high internal surface area having a heatable sorptive coating that can be used to selectively collect and concentrate one or more chemical species of interest from a fluid stream that can be rapidly released as a concentrated plug into an analytical or microanalytical chain for separation and detection. The non-planar chemical preconcentrator comprises a sorptive support structure having a tortuous flow path. The tortuosity provides repeated twists, turns, and bends to the flow, thereby increasing the interfacial contact between sample fluid stream and the sorptive material. The tortuous path also provides more opportunities for desorption and readsorption of volatile species. Further, the thermal efficiency of the tortuous path chemical preconcentrator is comparable or superior to the prior non-planar chemical preconcentrator. Finally, the tortuosity can be varied in different directions to optimize flow rates during the adsorption and desorption phases of operation of the preconcentrator.
ERIC Educational Resources Information Center
Stegemoller, William; Stegemoller, Rebecca
2004-01-01
The path taken and the turns made as a turtle traces a polygon are examined to discover an important theorem in geometry. A unique tool, the Angle Adder, is implemented in the investigation. (Contains 9 figures.)
Algorithms for effective querying of compound graph-based pathway databases
2009-01-01
Background Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. Results Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases. Conclusion The PATIKA Project Web site is http
Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm
Chen, Juan; Yang, Hai-Tao; Li, Zhu; Xu, Ning; Yu, Bo; Xu, Jun-Ping; Zhao, Pei-Ge; Wang, Yan; Zhang, Xiu-Juan; Lin, Dian-Jie
2016-01-01
Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to
The lawnmower problem and other geometric path covering problems
Fekete, S.; Arkin, E.; Mitchell, J.
1994-12-31
We discuss the Lawnmower Problem: Given a polygonal region, find the shortest closed path along which we have to move a given object (typically a square or a circle), such that any point of the region will be covered by the object for some position of it movement. In another version of the problem, known as the Milling Problem, the object has to stay within the region at all times. Practical motivations for considering the Lawnmower Problem come from manufacturing (spray painting, quality control), geography (aerial surveys), optimization (tour planning for a large number of clients with limited mobility), and gardening. The Milling Problem has gained attention by its importance for NC pocket machining. We show that both problems are NP-hard and discuss approximation methods for various versions of the problem.
Adaptive path planning in changing environments
Chen, Pang C.
1993-10-01
Path planning needs to be fast to facilitate real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To overcome this difficulty, we present an adaptive algorithm that uses previous experience to speed up future performance. It is a learning algorithm suitable for incrementally-changing environments such as those encountered in manufacturing of evolving products and waste-site remediation. The algorithm extends our previous work for stationary environments in two directions: For minor environmental change, an object-attached experience abstraction scheme is introduced to increase the flexibility of the learned experience; for major environmental change, an on-demand experience repair scheme is also introduced to retain those experiences that remain valid and useful. In addition to presenting this algorithm, we identify three other variants with different repair strategies. To compare these algorithms, we develop an analytic model to compare the costs and benefits of the corresponding repair processes. Using this model, we formalize the concept of incremental change, and prove the optimality of our proposed algorithm under such change. Empirically, we also characterize the performance curve of each variant, confirm our theoretical optimality results, and demonstrate the practicality of our algorithm.
Path efficiency of ant foraging trails in an artificial network.
Vittori, Karla; Talbot, Grégoire; Gautrais, Jacques; Fourcassié, Vincent; Araújo, Aluizio F R; Theraulaz, Guy
2006-04-21
In this paper we present an individual-based model describing the foraging behavior of ants moving in an artificial network of tunnels in which several interconnected paths can be used to reach a single food source. Ants lay a trail pheromone while moving in the network and this pheromone acts as a system of mass recruitment that attracts other ants in the network. The rules implemented in the model are based on measures of the decisions taken by ants at tunnel bifurcations during real experiments. The collective choice of the ants is estimated by measuring their probability to take a given path in the network. Overall, we found a good agreement between the results of the simulations and those of the experiments, showing that simple behavioral rules can lead ants to find the shortest paths in the network. The match between the experiments and the model, however, was better for nestbound than for outbound ants. A sensitivity study of the model suggests that the bias observed in the choice of the ants at asymmetrical bifurcations is a key behavior to reproduce the collective choice observed in the experiments. PMID:16199059
Sullivan, Blair D; Seymour, Dr. Paul Douglas
2010-01-01
Say a digraph is k-free if it has no directed cycles of length at most k, for k {element_of} Z{sup +}. Thomasse conjectured that the number of induced 3-vertex directed paths in a simple 2-free digraph on n vertices is at most (n-1)n(n+1)/15. We present an unpublished result of Bondy proving there are at most 2n{sup 3}/25 such paths, and prove that for the class of circular interval digraphs, an upper bound of n{sup 3}/16 holds. We also study the problem of bounding the number of (non-induced) 4-vertex paths in 3-free digraphs. We show an upper bound of 4n{sup 4}/75 using Bondy's result for Thomasse's conjecture.
Adaptive robot path planning in changing environments
Chen, P.C.
1994-08-01
Path planning needs to be fast to facilitate real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To overcome this difficulty, we present an adaptive algorithm that uses past experience to speed up future performance. It is a learning algorithm suitable for incrementally-changing environments such as those encountered in manufacturing of evolving products and waste-site remediation. The algorithm allows the robot to adapt to its environment by having two experience manipulation schemes: For minor environmental change, we use an object-attached experience abstraction scheme to increase the flexibility of the learned experience; for major environmental change, we use an on-demand experience repair scheme to retain those experiences that remain valid and useful. Using this algorithm, we can effectively reduce the overall robot planning time by re-using the computation result for one task to plan a path for another.
NASA Astrophysics Data System (ADS)
Li, Qingquan; Zeng, Zhe; Zhang, Tong; Li, Jonathan; Wu, Zhongheng
2011-02-01
Optimal paths computed by conventional path-planning algorithms are usually not "optimal" since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.
Coherence-path duality relations for N paths
NASA Astrophysics Data System (ADS)
Hillery, Mark; Bagan, Emilio; Bergou, Janos; Cottrell, Seth
2016-05-01
For an interferometer with two paths, there is a relation between the information about which path the particle took and the visibility of the interference pattern at the output. The more path information we have, the smaller the visibility, and vice versa. We generalize this relation to a multi-path interferometer, and we substitute two recently defined measures of quantum coherence for the visibility, which results in two duality relations. The path information is provided by attaching a detector to each path. In the first relation, which uses an l1 measure of coherence, the path information is obtained by applying the minimum-error state discrimination procedure to the detector states. In the second, which employs an entropic measure of coherence, the path information is the mutual information between the detector states and the result of measuring them. Both approaches are quantitative versions of complementarity for N-path interferometers. Support provided by the John Templeton Foundation.
Finding reaction paths using the potential energy as reaction coordinate.
Aguilar-Mogas, Antoni; Giménez, Xavier; Bofill, Josep Maria
2008-03-14
The intrinsic reaction coordinate curve (IRC), normally proposed as a representation of a reaction path, is parametrized as a function of the potential energy rather than the arc-length. This change in the parametrization of the curve implies that the values of the energy of the potential energy surface points, where the IRC curve is located, play the role of reaction coordinate. We use Caratheodory's relation to derive in a rigorous manner the proposed parametrization of the IRC path. Since this Caratheodory's relation is the basis of the theory of calculus of variations, then this fact permits to reformulate the IRC model from this mathematical theory. In this mathematical theory, the character of the variational solution (either maximum or minimum) is given through the Weierstrass E-function. As proposed by Crehuet and Bofill [J. Chem. Phys. 122, 234105 (2005)], we use the minimization of the Weierstrass E-function, as a function of the potential energy, to locate an IRC path between two minima from an arbitrary curve on the potential energy surface, and then join these two minima. We also prove, from the analysis of the Weierstrass E-function, the mathematical bases for the algorithms proposed to locate the IRC path. The proposed algorithm is applied to a set of examples. Finally, the algorithm is used to locate a discontinuous, or broken, IRC path, namely, when the path connects two first order saddle points through a valley-ridged inflection point. PMID:18345872
Finding reaction paths using the potential energy as reaction coordinate
NASA Astrophysics Data System (ADS)
Aguilar-Mogas, Antoni; Giménez, Xavier; Bofill, Josep Maria
2008-03-01
The intrinsic reaction coordinate curve (IRC), normally proposed as a representation of a reaction path, is parametrized as a function of the potential energy rather than the arc-length. This change in the parametrization of the curve implies that the values of the energy of the potential energy surface points, where the IRC curve is located, play the role of reaction coordinate. We use Carathéodory's relation to derive in a rigorous manner the proposed parametrization of the IRC path. Since this Carathéodory's relation is the basis of the theory of calculus of variations, then this fact permits to reformulate the IRC model from this mathematical theory. In this mathematical theory, the character of the variational solution (either maximum or minimum) is given through the Weierstrass E-function. As proposed by Crehuet and Bofill [J. Chem. Phys. 122, 234105 (2005)], we use the minimization of the Weierstrass E-function, as a function of the potential energy, to locate an IRC path between two minima from an arbitrary curve on the potential energy surface, and then join these two minima. We also prove, from the analysis of the Weierstrass E-function, the mathematical bases for the algorithms proposed to locate the IRC path. The proposed algorithm is applied to a set of examples. Finally, the algorithm is used to locate a discontinuous, or broken, IRC path, namely, when the path connects two first order saddle points through a valley-ridged inflection point.
NASA Technical Reports Server (NTRS)
Bill, R. C.; Johnson, R. D. (Inventor)
1979-01-01
A gas path seal suitable for use with a turbine engine or compressor is described. A shroud wearable or abradable by the abrasion of the rotor blades of the turbine or compressor shrouds the rotor bades. A compliant backing surrounds the shroud. The backing is a yieldingly deformable porous material covered with a thin ductile layer. A mounting fixture surrounds the backing.
ERIC Educational Resources Information Center
McGarvey, Lynn M.; Sterenberg, Gladys Y.; Long, Julie S.
2013-01-01
The authors elucidate what they saw as three important challenges to overcome along the path to becoming elementary school mathematics teacher leaders: marginal interest in math, low self-confidence, and teaching in isolation. To illustrate how these challenges were mitigated, they focus on the stories of two elementary school teachers--Laura and…
A neural network approach to complete coverage path planning.
Yang, Simon X; Luo, Chaomin
2004-02-01
Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.
Path planning for machine vision assisted, teleoperated pavement crack sealer
Kim, Y.S.; Haas, C.T.; Greer, R.
1998-03-01
During the last few years, several teleoperated and machine-vision-assisted systems have been developed in construction and maintenance areas such as pavement crack sealing, sewer pipe rehabilitation, excavation, surface finishing, and materials handling. This paper presents a path-planning algorithm used for a machine-vision-assisted automatic pavement crack sealing system. In general, path planning is an important task for optimal motion of a robot whether its environment is structured or unstructured. Manual path planning is not always possible or desirable. A simple greedy path algorithm is utilized for optimal motion of the automated pavement crack sealer. Some unique and broadly applicable computational tools and data structures are required to implement the algorithm in a digital image domain. These components are described, then the performance of the algorithm is compared with the implicit manual path plans of system operators. The comparison is based on computational cost versus overall gains in crack-sealing-process efficiency. Applications of this work in teleoperation, graphical control, and other infrastructure maintenance areas are also suggested.
Automatic tool path generation for finish machining
Kwok, Kwan S.; Loucks, C.S.; Driessen, B.J.
1997-03-01
A system for automatic tool path generation was developed at Sandia National Laboratories for finish machining operations. The system consists of a commercially available 5-axis milling machine controlled by Sandia developed software. This system was used to remove overspray on cast turbine blades. A laser-based, structured-light sensor, mounted on a tool holder, is used to collect 3D data points around the surface of the turbine blade. Using the digitized model of the blade, a tool path is generated which will drive a 0.375 inch diameter CBN grinding pin around the tip of the blade. A fuzzified digital filter was developed to properly eliminate false sensor readings caused by burrs, holes and overspray. The digital filter was found to successfully generate the correct tool path for a blade with intentionally scanned holes and defects. The fuzzified filter improved the computation efficiency by a factor of 25. For application to general parts, an adaptive scanning algorithm was developed and presented with simulation results. A right pyramid and an ellipsoid were scanned successfully with the adaptive algorithm.
Quad-rotor flight path energy optimization
NASA Astrophysics Data System (ADS)
Kemper, Edward
Quad-Rotor unmanned areal vehicles (UAVs) have been a popular area of research and development in the last decade, especially with the advent of affordable microcontrollers like the MSP 430 and the Raspberry Pi. Path-Energy Optimization is an area that is well developed for linear systems. In this thesis, this idea of path-energy optimization is extended to the nonlinear model of the Quad-rotor UAV. The classical optimization technique is adapted to the nonlinear model that is derived for the problem at hand, coming up with a set of partial differential equations and boundary value conditions to solve these equations. Then, different techniques to implement energy optimization algorithms are tested using simulations in Python. First, a purely nonlinear approach is used. This method is shown to be computationally intensive, with no practical solution available in a reasonable amount of time. Second, heuristic techniques to minimize the energy of the flight path are tested, using Ziegler-Nichols' proportional integral derivative (PID) controller tuning technique. Finally, a brute force look-up table based PID controller is used. Simulation results of the heuristic method show that both reliable control of the system and path-energy optimization are achieved in a reasonable amount of time.
NASA Astrophysics Data System (ADS)
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Nonadiabatic transition path sampling
NASA Astrophysics Data System (ADS)
Sherman, M. C.; Corcelli, S. A.
2016-07-01
Fewest-switches surface hopping (FSSH) is combined with transition path sampling (TPS) to produce a new method called nonadiabatic path sampling (NAPS). The NAPS method is validated on a model electron transfer system coupled to a Langevin bath. Numerically exact rate constants are computed using the reactive flux (RF) method over a broad range of solvent frictions that span from the energy diffusion (low friction) regime to the spatial diffusion (high friction) regime. The NAPS method is shown to quantitatively reproduce the RF benchmark rate constants over the full range of solvent friction. Integrating FSSH within the TPS framework expands the applicability of both approaches and creates a new method that will be helpful in determining detailed mechanisms for nonadiabatic reactions in the condensed-phase.
Studness, C.M.
1995-05-01
The financial community`s focus on utility competition has been riveted on the proceedings now in progress at state regulatory commissions. The fear that something immediately damaging will come out of these proceedings seems to have diminished in recent months, and the stock market has reacted favorably. However, regulatory developments are only one of four paths leading to competition; the others are the marketplace, the legislatures, and the courts. Each could play a critical role in the emergence of competition.
Evolving neural models of path integration.
Vickerstaff, R J; Di Paolo, E A
2005-09-01
We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animal's movements within its environment. We show that it is possible to produce a neural network without imposing a priori any particular system for the internal representation of the animal's home vector. The best evolved network obtained is analysed in detail and is found to resemble the bicomponent model of Mittelstaedt. Because of the presence of leaky integration, the model can reproduce the systematic navigation errors found in desert ants. The model also naturally mimics the searching behaviour that ants perform once they have reached their estimate of the nest location. The results support possible roles for leaky integration and cosine-shaped compass response functions in path integration.
The path decomposition expansion and multidimensional tunneling
NASA Astrophysics Data System (ADS)
Auerbach, Assa; Kivelson, S.
This paper consists of two main topics. (i) The path decomposition expansion: a new path integral technique which allows us to break configuration space into disjoint regions and express the dynamics of the full system in terms of its parts. (ii) The application of the PDX and semiclassical methods for solving quantum-mechanical tunneling problems in multidimensions. The result is a conceptually simple, computationally straightforward method for calculating tunneling effects in complicated multidimensional potentials, even in cases where the nature of the states in the classically allowed regions is nontrivial. Algorithms for computing tunneling effects in general classes of problems are obtained.In addition, we present the detailed solutions to three model problems of a tunneling coordinate coupled to a phonon. This enables us to define various well-controlled approximation schemes, which help to reduce the dimensions of complicated tunneling calculations in real physical systems.
PATHS groundwater hydrologic model
Nelson, R.W.; Schur, J.A.
1980-04-01
A preliminary evaluation capability for two-dimensional groundwater pollution problems was developed as part of the Transport Modeling Task for the Waste Isolation Safety Assessment Program (WISAP). Our approach was to use the data limitations as a guide in setting the level of modeling detail. PATHS Groundwater Hydrologic Model is the first level (simplest) idealized hybrid analytical/numerical model for two-dimensional, saturated groundwater flow and single component transport; homogeneous geology. This document consists of the description of the PATHS groundwater hydrologic model. The preliminary evaluation capability prepared for WISAP, including the enhancements that were made because of the authors' experience using the earlier capability is described. Appendixes A through D supplement the report as follows: complete derivations of the background equations are provided in Appendix A. Appendix B is a comprehensive set of instructions for users of PATHS. It is written for users who have little or no experience with computers. Appendix C is for the programmer. It contains information on how input parameters are passed between programs in the system. It also contains program listings and test case listing. Appendix D is a definition of terms.
Filtered backprojection proton CT reconstruction along most likely paths
Rit, Simon; Dedes, George; Freud, Nicolas; Sarrut, David; Letang, Jean Michel
2013-03-15
Purpose: Proton CT (pCT) has the potential to accurately measure the electron density map of tissues at low doses but the spatial resolution is prohibitive if the curved paths of protons in matter is not accounted for. The authors propose to account for an estimate of the most likely path of protons in a filtered backprojection (FBP) reconstruction algorithm. Methods: The energy loss of protons is first binned in several proton radiographs at different distances to the proton source to exploit the depth-dependency of the estimate of the most likely path. This process is named the distance-driven binning. A voxel-specific backprojection is then used to select the adequate radiograph in the distance-driven binning in order to propagate in the pCT image the best achievable spatial resolution in proton radiographs. The improvement in spatial resolution is demonstrated using Monte Carlo simulations of resolution phantoms. Results: The spatial resolution in the distance-driven binning depended on the distance of the objects from the source and was optimal in the binned radiograph corresponding to that distance. The spatial resolution in the reconstructed pCT images decreased with the depth in the scanned object but it was always better than previous FBP algorithms assuming straight line paths. In a water cylinder with 20 cm diameter, the observed range of spatial resolutions was 0.7 - 1.6 mm compared to 1.0 - 2.4 mm at best with a straight line path assumption. The improvement was strongly enhanced in shorter 200 Degree-Sign scans. Conclusions: Improved spatial resolution was obtained in pCT images with filtered backprojection reconstruction using most likely path estimates of protons. The improvement in spatial resolution combined with the practicality of FBP algorithms compared to iterative reconstruction algorithms makes this new algorithm a candidate of choice for clinical pCT.
Interferometric sensors based on sinusoidal optical path length modulation
NASA Astrophysics Data System (ADS)
Knell, Holger; Schake, Markus; Schulz, Markus; Lehmann, Peter
2014-05-01
Sinusoidal optical path length modulation of the reference or the measurement arm of an interferometer is a technique which is a fast alternative to white light or phase shifting interferometry. In this paper three different sensors using this periodical modulation are presented. In addition, signal processing algorithms based on Discrete Fourier Transform, Hilbert Transform and parameter estimation are analyzed. These algorithms are used to obtain measurement results which demonstrate the capabilities of the presented interferometric sensors.
NASA Astrophysics Data System (ADS)
Moradi, M.; Delavar, M. R.; Moradi, A.
2015-12-01
Being one of the natural disasters, earthquake can seriously damage buildings, urban facilities and cause road blockage. Post-earthquake route planning is problem that has been addressed in frequent researches. The main aim of this research is to present a route planning model for after earthquake. It is assumed in this research that no damage data is available. The presented model tries to find the optimum route based on a number of contributing factors which mainly indicate the length, width and safety of the road. The safety of the road is represented by a number of criteria such as distance to faults, percentage of non-standard buildings and percentage of high buildings around the route. An integration of genetic algorithm and ordered weighted averaging operator is employed in the model. The former searches the problem space among all alternatives, while the latter aggregates the scores of road segments to compute an overall score for each alternative. Ordered weighted averaging operator enables the users of the system to evaluate the alternative routes based on their decision strategy. Based on the proposed model, an optimistic user tries to find the shortest path between the two points, whereas a pessimistic user tends to pay more attention to safety parameters even if it enforces a longer route. The results depicts that decision strategy can considerably alter the optimum route. Moreover, post-earthquake route planning is a function of not only the length of the route but also the probability of the road blockage.
Time optimal route planning algorithm of LBS online navigation
NASA Astrophysics Data System (ADS)
Li, Yong; Bao, Shitai; Su, Kui; Fang, Qiushui; Yang, Jingfeng
2011-02-01
This paper proposes a time optimal route planning optimization algorithm in the mode of LBS online navigation based on the improved Dijkstra algorithms. Combined with the returning real-time location information by on-line users' handheld terminals, the algorithm can satisfy requirement of the optimal time in the mode of LBS online navigation. A navigation system is developed and applied in actual navigation operations. Operating results show that the algorithm could form a reasonable coordination on the basis of shortest route and fastest velocity in the requirement of optimal time. The algorithm could also store the calculated real-time route information in the cache to improve the efficiency of route planning and to reduce the planning time-consuming.
Aircraft path planning for optimal imaging using dynamic cost functions
NASA Astrophysics Data System (ADS)
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.
Kinematic path planning for space-based robotics
NASA Astrophysics Data System (ADS)
Seereeram, Sanjeev; Wen, John T.
1998-01-01
Future space robotics tasks require manipulators of significant dexterity, achievable through kinematic redundancy and modular reconfigurability, but with a corresponding complexity of motion planning. Existing research aims for full autonomy and completeness, at the expense of efficiency, generality or even user friendliness. Commercial simulators require user-taught joint paths-a significant burden for assembly tasks subject to collision avoidance, kinematic and dynamic constraints. Our research has developed a Kinematic Path Planning (KPP) algorithm which bridges the gap between research and industry to produce a powerful and useful product. KPP consists of three key components: path-space iterative search, probabilistic refinement, and an operator guidance interface. The KPP algorithm has been successfully applied to the SSRMS for PMA relocation and dual-arm truss assembly tasks. Other KPP capabilities include Cartesian path following, hybrid Cartesian endpoint/intermediate via-point planning, redundancy resolution and path optimization. KPP incorporates supervisory (operator) input at any detail to influence the solution, yielding desirable/predictable paths for multi-jointed arms, avoiding obstacles and obeying manipulator limits. This software will eventually form a marketable robotic planner suitable for commercialization in conjunction with existing robotic CAD/CAM packages.
On the importance of path for phase unwrapping in synthetic aperture radar interferometry.
Osmanoglu, Batuhan; Dixon, Timothy H; Wdowinski, Shimon; Cabral-Cano, Enrique
2011-07-01
Phase unwrapping is a key procedure in interferometric synthetic aperture radar studies, translating ambiguous phase observations to topography, and surface deformation estimates. Some unwrapping algorithms are conducted along specific paths based on different selection criteria. In this study, we analyze six unwrapping paths: line scan, maximum coherence, phase derivative variance, phase derivative variance with branch-cut, second-derivative reliability, and the Fisher distance. The latter is a new path algorithm based on Fisher information theory, which combines the phase derivative with the expected variance to get a more robust path, potentially performing better than others in the case of low image quality. In order to compare only the performance of the paths, the same unwrapping function (phase derivative integral) is used. Results indicate that the Fisher distance algorithm gives better results in most cases. PMID:21743520
On the importance of path for phase unwrapping in synthetic aperture radar interferometry.
Osmanoglu, Batuhan; Dixon, Timothy H; Wdowinski, Shimon; Cabral-Cano, Enrique
2011-07-01
Phase unwrapping is a key procedure in interferometric synthetic aperture radar studies, translating ambiguous phase observations to topography, and surface deformation estimates. Some unwrapping algorithms are conducted along specific paths based on different selection criteria. In this study, we analyze six unwrapping paths: line scan, maximum coherence, phase derivative variance, phase derivative variance with branch-cut, second-derivative reliability, and the Fisher distance. The latter is a new path algorithm based on Fisher information theory, which combines the phase derivative with the expected variance to get a more robust path, potentially performing better than others in the case of low image quality. In order to compare only the performance of the paths, the same unwrapping function (phase derivative integral) is used. Results indicate that the Fisher distance algorithm gives better results in most cases.
NASA Technical Reports Server (NTRS)
Robinson, Judith L.; Charles, John B.; Rummel, John A. (Technical Monitor)
2000-01-01
Approximately three years ago, the Agency's lead center for the human elements of spaceflight (the Johnson Space Center), along with the National Biomedical Research Institute (NSBRI) (which has the lead role in developing countermeasures) initiated an activity to identify the most critical risks confronting extended human spaceflight. Two salient factors influenced this activity: first, what information is needed to enable a "go/no go" decision to embark on extended human spaceflight missions; and second, what knowledge and capabilities are needed to address known and potential health, safety and performance risks associated with such missions. A unique approach was used to first define and assess those risks, and then to prioritize them. This activity was called the Critical Path Roadmap (CPR) and it represents an opportunity to develop and implement a focused and evolving program of research and technology designed from a "risk reduction" perspective to prevent or minimize the risks to humans exposed to the space environment. The Critical Path Roadmap provides the foundation needed to ensure that human spaceflight, now and in the future, is as safe, productive and healthy as possible (within the constraints imposed on any particular mission) regardless of mission duration or destination. As a tool, the Critical Path Roadmap enables the decision maker to select from among the demonstrated or potential risks those that are to be mitigated, and the completeness of that mitigation. The primary audience for the CPR Web Site is the members of the scientific community who are interested in the research and technology efforts required for ensuring safe and productive human spaceflight. They may already be informed about the various space life sciences research programs or they may be newcomers. Providing the CPR content to potential investigators increases the probability of their delivering effective risk mitigations. Others who will use the CPR Web Site and its
NASA Technical Reports Server (NTRS)
Robinson, Judith L.; Charles, John B.; Rummel, John A. (Technical Monitor)
2000-01-01
Approximately three years ago, the Agency's lead center for the human elements of spaceflight (the Johnson Space Center), along with the National Biomedical Research Institute (NSBRI) (which has the lead role in developing countermeasures) initiated an activity to identify the most critical risks confronting extended human spaceflight. Two salient factors influenced this activity: first, what information is needed to enable a "go/no go" decision to embark on extended human spaceflight missions; and second, what knowledge and capabilities are needed to address known and potential health, safety and performance risks associated with such missions. A unique approach was used to first define and assess those risks, and then to prioritize them. This activity was called the Critical Path Roadmap (CPR) and it represents an opportunity to develop and implement a focused and evolving program of research and technology designed from a "risk reduction" perspective to prevent or minimize the risks to humans exposed to the space environment. The Critical Path Roadmap provides the foundation needed to ensure that human spaceflight, now and in the future, is as safe, productive and healthy as possible (within the constraints imposed on any particular mission) regardless of mission duration or destination. As a tool, the Critical Path Roadmap enables the decisionmaker to select from among the demonstrated or potential risks those that are to be mitigated, and the completeness of that mitigation. The primary audience for the CPR Web Site is the members of the scientific community who are interested in the research and technology efforts required for ensuring safe and productive human spaceflight. They may already be informed about the various space life sciences research programs or they may be newcomers. Providing the CPR content to potential investigators increases the probability of their delivering effective risk mitigations. Others who will use the CPR Web Site and its content
Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
Seyler, Sean L.; Kumar, Avishek; Thorpe, M. F.; Beckstein, Oliver
2015-01-01
Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example, showed that
Energy aware path planning in complex four dimensional environments
NASA Astrophysics Data System (ADS)
Chakrabarty, Anjan
This dissertation addresses the problem of energy-aware path planning for small autonomous vehicles. While small autonomous vehicles can perform missions that are too risky (or infeasible) for larger vehicles, the missions are limited by the amount of energy that can be carried on board the vehicle. Path planning techniques that either minimize energy consumption or exploit energy available in the environment can thus increase range and endurance. Path planning is complicated by significant spatial (and potentially temporal) variations in the environment. While the main focus is on autonomous aircraft, this research also addresses autonomous ground vehicles. Range and endurance of small unmanned aerial vehicles (UAVs) can be greatly improved by utilizing energy from the atmosphere. Wind can be exploited to minimize energy consumption of a small UAV. But wind, like any other atmospheric component , is a space and time varying phenomenon. To effectively use wind for long range missions, both exploration and exploitation of wind is critical. This research presents a kinematics based tree algorithm which efficiently handles the four dimensional (three spatial and time) path planning problem. The Kinematic Tree algorithm provides a sequence of waypoints, airspeeds, heading and bank angle commands for each segment of the path. The planner is shown to be resolution complete and computationally efficient. Global optimality of the cost function cannot be claimed, as energy is gained from the atmosphere, making the cost function inadmissible. However the Kinematic Tree is shown to be optimal up to resolution if the cost function is admissible. Simulation results show the efficacy of this planning method for a glider in complex real wind data. Simulation results verify that the planner is able to extract energy from the atmosphere enabling long range missions. The Kinematic Tree planning framework, developed to minimize energy consumption of UAVs, is applied for path planning
Bleakley, Hoyt; Lin, Jeffrey
2012-01-01
We examine portage sites in the U.S. South, Mid-Atlantic, and Midwest, including those on the fall line, a geomorphological feature in the southeastern U.S. marking the final rapids on rivers before the ocean. Historically, waterborne transport of goods required portage around the falls at these points, while some falls provided water power during early industrialization. These factors attracted commerce and manufacturing. Although these original advantages have long since been made obsolete, we document the continuing importance of these portage sites over time. We interpret these results as path dependence and contrast explanations based on sunk costs interacting with decreasing versus increasing returns to scale. PMID:23935217
NASA Technical Reports Server (NTRS)
Mehhtz, Peter
2005-01-01
JPF is an explicit state software model checker for Java bytecode. Today, JPF is a swiss army knife for all sort of runtime based verification purposes. This basically means JPF is a Java virtual machine that executes your program not just once (like a normal VM), but theoretically in all possible ways, checking for property violations like deadlocks or unhandled exceptions along all potential execution paths. If it finds an error, JPF reports the whole execution that leads to it. Unlike a normal debugger, JPF keeps track of every step how it got to the defect.
Efficient transition path sampling for systems with multiple reaction pathways
NASA Astrophysics Data System (ADS)
Chen, L. Y.; Nash, P. L.; Horing, N. J. M.
2005-09-01
A new algorithm is developed for sampling transition paths and computing reaction rates. To illustrate the use of this method, we study a two-dimensional system that has two reaction pathways: one pathway is straight with a relatively high barrier and the other is roundabout with a lower barrier. The transition rate and the ratio between the numbers of the straight and roundabout transition paths are computed for a wide range of temperatures. Our study shows that the harmonic approximation for fluctuations about the steepest-descent paths is not valid even at relatively low temperatures and, furthermore, that factors related to entropy have to be determined by the global geometry of the potential-energy surface (rather than just the local curvatures alone) for complex reaction systems. It is reasonable to expect that this algorithm is also applicable to higher dimensional systems.
NASA Astrophysics Data System (ADS)
Namdari, Mohammad Hasan; Hejazi, Seyed Reza; Palhang, Maziar
2016-06-01
In this paper, modified versions of quadtree/octree, as structures used in path planning, are proposed which we call them cornered quadtree/octree. Also a new method of creating paths in quadtrees/octrees, once quadrants/octants to be passed are determined, is proposed both to improve traveled distance and path smoothness. In proposed modified versions of quadtree/octree, four corner cells of quadrants and eight corner voxels of octants are also considered as nodes of the graph to be searched for finding the shortest path. This causes better quadrant/octant selection during graph search relative to simple quadtrees and octrees. On the other hand, after that all quadrants/octants are determined, multiple gateways are nominated between each two selected nodes and path is constructed by passing through the gateway which its selection leads in shorter and smoother path. Proposed structures in this paper alongside the utilized path construction approach, creates better paths in terms of path length than those created if simple trees are used, somehow equal to the quality of the achieved paths by framed trees, meanwhile interestingly, consumed time and memory in our proposed method are closer to the used time and memory if simple trees are used.
Hierarchical path planning and control of a small fixed-wing UAV: Theory and experimental validation
NASA Astrophysics Data System (ADS)
Jung, Dongwon
2007-12-01
Recently there has been a tremendous growth of research emphasizing control of unmanned aerial vehicles (UAVs) either in isolation or in teams. As a matter of fact, UAVs increasingly find their way into military and law enforcement applications (e.g., reconnaissance, remote delivery of urgent equipment/material, resource assessment, environmental monitoring, battlefield monitoring, ordnance delivery, etc.). This trend will continue in the future, as UAVs are poised to replace the human-in-the-loop during dangerous missions. Civilian applications of UAVs are also envisioned such as crop dusting, geological surveying, search and rescue operations, etc. In this thesis we propose a new online multiresolution path planning algorithm for a small UAV with limited on-board computational resources. The proposed approach assumes that the UAV has detailed information of the environment and the obstacles only in its vicinity. Information about far-away obstacles is also available, albeit less accurately. The proposed algorithm uses the fast lifting wavelet transform (FLWT) to get a multiresolution cell decomposition of the environment, whose dimension is commensurate to the on-board computational resources. A topological graph representation of the multiresolution cell decomposition is constructed efficiently, directly from the approximation and detail wavelet coefficients. Dynamic path planning is sequentially executed for an optimal path using the A* algorithm over the resulting graph. The proposed path planning algorithm is implemented on-line on a small autopilot. Comparisons with the standard D*-lite algorithm are also presented. We also investigate the problem of generating a smooth, planar reference path from a discrete optimal path. Upon the optimal path being represented as a sequence of cells in square geometry, we derive a smooth B-spline path that is constrained inside a channel that is induced by the geometry of the cells. To this end, a constrained optimization
Real-time robot path planning based on a modified pulse-coupled neural network model.
Qu, Hong; Yang, Simon X; Willms, Allan R; Yi, Zhang
2009-11-01
This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.
Identification of limit cycles in multi-nonlinearity, multiple path systems
NASA Technical Reports Server (NTRS)
Mitchell, J. R.; Barron, O. L.
1979-01-01
A method of analysis which identifies limit cycles in autonomous systems with multiple nonlinearities and multiple forward paths is presented. The FORTRAN code for implementing the Harmonic Balance Algorithm is reported. The FORTRAN code is used to identify limit cycles in multiple path and nonlinearity systems while retaining the effects of several harmonic components.
Narendra, Ajay
2007-05-01
Highly evolved eusocial insects such as ants return from a food source to their nest by the shortest possible distance. This form of navigation, called path-integration, involves keeping track of the distance travelled and the angles steered on the outbound journey, which then aids in the computation of the shortest return distance. In featureless terrain, ants rely on the path integrator to travel the entire distance to return to the nest, whereas in landmark-rich habitats ants are guided by visual cues and in the absence of the visual cues homing ants rely on the path integrator to travel only an initial 10-60 cm of the homebound distance. The functioning of the path integrator in a habitat of intermediate landmark density is unknown. The findings reported here show that when the outward journey is on a familiar foraging area, and the inward journey is on an unfamiliar area, the Australian route-following desert ant Melophorus bagoti relies on the path integrator and consistently travels half the distance of the outward trip. However, when both the outward and inward trips are performed in plain and featureless channels, which blocks the distinct terrestrial visual cues, ants travel the entire distance accurately. A similar half-way abbreviation of the home vector occurs when the ant's outward trip is in an L-shaped channel and the homeward trip is over an open and unfamiliar region. The ecological significance of these new findings is discussed.
Reynolds, Andy M; Dutta, Tushar K; Curtis, Rosane H C; Powers, Stephen J; Gaur, Hari S; Kerry, Brian R
2011-04-01
It has long been recognized that chemotaxis is the primary means by which nematodes locate host plants. Nonetheless, chemotaxis has received scant attention. We show that chemotaxis is predicted to take nematodes to a source of a chemo-attractant via the shortest possible routes through the labyrinth of air-filled or water-filled channels within a soil through which the attractant diffuses. There are just two provisos: (i) all of the channels through which the attractant diffuses are accessible to the nematodes and (ii) nematodes can resolve all chemical gradients no matter how small. Previously, this remarkable consequence of chemotaxis had gone unnoticed. The predictions are supported by experimental studies of the movement patterns of the root-knot nematodes Meloidogyne incognita and Meloidogyne graminicola in modified Y-chamber olfactometers filled with Pluronic gel. By providing two routes to a source of the attractant, one long and one short, our experiments, the first to demonstrate the routes taken by nematodes to plant roots, serve to test our predictions. Our data show that nematodes take the most direct route to their preferred hosts (as predicted) but often take the longest route towards poor hosts. We hypothesize that a complex of repellent and attractant chemicals influences the interaction between nematodes and their hosts.
Qian, S.-B.; Zhang, J.; Wang, J.-J.; Zhu, L.-Y.; Liu, L.; Zhao, E. G.; Li, L.-J.; He, J.-J.
2013-08-15
We discovered that the O-C curve of V753 Mon shows an upward parabolic change while undergoing a cyclic variation with a period of 13.5 yr. The upward parabolic change reveals a long-term period increase at a rate of P-dot = +7.8 x 10{sup -8} days yr{sup -1}. Photometric solutions determined using the Wilson-Devinney method confirm that V753 Mon is a semi-detached binary system where the slightly less massive, hotter component star is transferring mass to the more massive one. This is in agreement with the long-term increase of the orbital period. The increase of the orbital period, the mass ratio very close to unity, and the semi-detached configuration with a less massive lobe-filling component all suggest that V753 Mon is on a key evolutionary stage just after the evolutionary stage with the shortest period during mass transfer. The results in this paper will shed light on the formation of massive contact binaries and the evolution of binary stars. The cyclic oscillation in the O-C diagram indicates that V753 Mon may be a triple system containing an extremely cool stellar companion that may play an important role for the formation and evolution in the binary system.
Detailed Broadband Study of the Shortest Orbital Period Black-hole Binary Maxi J1659-152
NASA Astrophysics Data System (ADS)
Van Der Horst, Alexander Jonathan; Kouveliotou, C.; Paragi, Z.; Linford, J. D.; Taylor, G. B.; Kuulkers, E.; Curran, P. A.; Gorosabel, J.; Guziy, S.; de Ugarte Postigo, A.; Belloni, T.; Miller-Jones, J. C. A.
2011-09-01
MAXI J1659-152 is a hard X-ray source discovered by Swift and MAXI. Optical spectroscopy showed that this source is an X-ray binary, and X-ray timing observations classified it as a black-hole candidate. Based on recurring dips in the X-ray light curves, the source was established as the shortest period black-hole binary candidate known to date, with a period of 2.4 hours. Here we present our results from the broadband follow-up campaign we initiated after the source discovery. We obtained densely sampled light curves over two orders of magnitude in radio frequencies, in the UV/optical bands, and at X- and gamma-ray energies. This enabled us to construct broadband spectral energy distributions with very good spectral coverage at many epochs, covering the various X-ray states of MAXI J1659-152 during its outburst. Very Long Baseline Interferomety observations provide constraints on the size of the radio emitting jet, which, combined with the modeling results of the broadband spectra, present a comprehensive picture of the outburst from this new X-ray binary.
Algorithms and architectures for high performance analysis of semantic graphs.
Hendrickson, Bruce Alan
2005-09-01
analysis. Since intelligence datasets can be extremely large, the focus of this work is on the use of parallel computers. We have been working to develop scalable parallel algorithms that will be at the core of a semantic graph analysis infrastructure. Our work has involved two different thrusts, corresponding to two different computer architectures. The first architecture of interest is distributed memory, message passing computers. These machines are ubiquitous and affordable, but they are challenging targets for graph algorithms. Much of our distributed-memory work to date has been collaborative with researchers at Lawrence Livermore National Laboratory and has focused on finding short paths on distributed memory parallel machines. Our implementation on 32K processors of BlueGene/Light finds shortest paths between two specified vertices in just over a second for random graphs with 4 billion vertices.
Light transport on path-space manifolds
NASA Astrophysics Data System (ADS)
Jakob, Wenzel Alban
The pervasive use of computer-generated graphics in our society has led to strict demands on their visual realism. Generally, users of rendering software want their images to look, in various ways, "real", which has been a key driving force towards methods that are based on the physics of light transport. Until recently, industrial practice has relied on a different set of methods that had comparatively little rigorous grounding in physics---but within the last decade, advances in rendering methods and computing power have come together to create a sudden and dramatic shift, in which physics-based methods that were formerly thought impractical have become the standard tool. As a consequence, considerable attention is now devoted towards making these methods as robust as possible. In this context, robustness refers to an algorithm's ability to process arbitrary input without large increases of the rendering time or degradation of the output image. One particularly challenging aspect of robustness entails simulating the precise interaction of light with all the materials that comprise the input scene. This dissertation focuses on one specific group of materials that has fundamentally been the most important source of difficulties in this process. Specular materials, such as glass windows, mirrors or smooth coatings (e.g. on finished wood), account for a significant percentage of the objects that surround us every day. It is perhaps surprising, then, that it is not well-understood how they can be accommodated within the theoretical framework that underlies some of the most sophisticated rendering methods available today. Many of these methods operate using a theoretical framework known as path space integration. But this framework makes no provisions for specular materials: to date, it is not clear how to write down a path space integral involving something as simple as a piece of glass. Although implementations can in practice still render these materials by side
NASA Technical Reports Server (NTRS)
Horton, Kent; Huffman, Mitch; Eppic, Brian; White, Harrison
2005-01-01
Path Loss Measurements were obtained on three (3) GPS equipped 757 aircraft. Systems measured were Marker Beacon, LOC, VOR, VHF (3), Glide Slope, ATC (2), DME (2), TCAS, and GPS. This data will provide the basis for assessing the EMI (Electromagnetic Interference) safety margins of comm/nav (communication and navigation) systems to portable electronic device emissions. These Portable Electronic Devices (PEDs) include all devices operated in or around the aircraft by crews, passengers, servicing personnel, as well as the general public in the airport terminals. EMI assessment capability is an important step in determining if one system-wide PED EMI policy is appropriate. This data may also be used comparatively with theoretical analysis and computer modeling data sponsored by NASA Langley Research Center and others.
Atmospheric channel for bistatic optical communication: simulation algorithms
NASA Astrophysics Data System (ADS)
Belov, V. V.; Tarasenkov, M. V.
2015-11-01
Three algorithms of statistical simulation of the impulse response (IR) for the atmospheric optical communication channel are considered, including algorithms of local estimate and double local estimate and the algorithm suggested by us. On the example of a homogeneous molecular atmosphere it is demonstrated that algorithms of double local estimate and the suggested algorithm are more efficient than the algorithm of local estimate. For small optical path length, the proposed algorithm is more efficient, and for large optical path length, the algorithm of double local estimate is more efficient. Using the proposed algorithm, the communication quality is estimated for a particular case of the atmospheric channel under conditions of intermediate turbidity. The communication quality is characterized by the maximum IR, time of maximum IR, integral IR, and bandwidth of the communication channel. Calculations of these criteria demonstrated that communication is most efficient when the point of intersection of the directions toward the source and the receiver is most close to the source point.
Interactive cutting path analysis programs
NASA Technical Reports Server (NTRS)
Weiner, J. M.; Williams, D. S.; Colley, S. R.
1975-01-01
The operation of numerically controlled machine tools is interactively simulated. Four programs were developed to graphically display the cutting paths for a Monarch lathe, Cintimatic mill, Strippit sheet metal punch, and the wiring path for a Standard wire wrap machine. These programs are run on a IMLAC PDS-ID graphic display system under the DOS-3 disk operating system. The cutting path analysis programs accept input via both paper tape and disk file.
Critical Path-Based Thread Placement for NUMA Systems
Su, C Y; Li, D; Nikolopoulos, D S; Grove, M; Cameron, K; de Supinski, B R
2011-11-01
Multicore multiprocessors use a Non Uniform Memory Architecture (NUMA) to improve their scalability. However, NUMA introduces performance penalties due to remote memory accesses. Without efficiently managing data layout and thread mapping to cores, scientific applications, even if they are optimized for NUMA, may suffer performance loss. In this paper, we present algorithms and a runtime system that optimize the execution of OpenMP applications on NUMA architectures. By collecting information from hardware counters, the runtime system directs thread placement and reduces performance penalties by minimizing the critical path of OpenMP parallel regions. The runtime system uses a scalable algorithm that derives placement decisions with negligible overhead. We evaluate our algorithms and runtime system with four NPB applications implemented in OpenMP. On average the algorithms achieve between 8.13% and 25.68% performance improvement compared to the default Linux thread placement scheme. The algorithms miss the optimal thread placement in only 8.9% of the cases.
Two arm robot path planning in a static environment using polytopes and string stretching. Thesis
NASA Technical Reports Server (NTRS)
Schima, Francis J., III
1990-01-01
The two arm robot path planning problem has been analyzed and reduced into components to be simplified. This thesis examines one component in which two Puma-560 robot arms are simultaneously holding a single object. The problem is to find a path between two points around obstacles which is relatively fast and minimizes the distance. The thesis involves creating a structure on which to form an advanced path planning algorithm which could ideally find the optimum path. An actual path planning method is implemented which is simple though effective in most common situations. Given the limits of computer technology, a 'good' path is currently found. Objects in the workspace are modeled with polytopes. These are used because they can be used for rapid collision detection and still provide a representation which is adequate for path planning.
Handbook of Feynman Path Integrals
NASA Astrophysics Data System (ADS)
Grosche, Christian, Steiner, Frank
The Handbook of Feynman Path Integrals appears just fifty years after Richard Feynman published his pioneering paper in 1948 entitled "Space-Time Approach to Non-Relativistic Quantum Mechanics", in which he introduced his new formulation of quantum mechanics in terms of path integrals. The book presents for the first time a comprehensive table of Feynman path integrals together with an extensive list of references; it will serve the reader as a thorough introduction to the theory of path integrals. As a reference book, it is unique in its scope and will be essential for many physicists, chemists and mathematicians working in different areas of research.
Computing Path Tables for Quickest Multipaths In Computer Networks
Grimmell, W.C.
2004-12-21
We consider the transmission of a message from a source node to a terminal node in a network with n nodes and m links where the message is divided into parts and each part is transmitted over a different path in a set of paths from the source node to the terminal node. Here each link is characterized by a bandwidth and delay. The set of paths together with their transmission rates used for the message is referred to as a multipath. We present two algorithms that produce a minimum-end-to-end message delay multipath path table that, for every message length, specifies a multipath that will achieve the minimum end-to-end delay. The algorithms also generate a function that maps the minimum end-to-end message delay to the message length. The time complexities of the algorithms are O(n{sup 2}((n{sup 2}/logn) + m)min(D{sub max}, C{sub max})) and O(nm(C{sub max} + nmin(D{sub max}, C{sub max}))) when the link delays and bandwidths are non-negative integers. Here D{sub max} and C{sub max} are respectively the maximum link delay and maximum link bandwidth and C{sub max} and D{sub max} are greater than zero.
Quantum Adiabatic Algorithms and Large Spin Tunnelling
NASA Technical Reports Server (NTRS)
Boulatov, A.; Smelyanskiy, V. N.
2003-01-01
We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in this paper. The algorithm is applied to a random binary optimization problem (a version of the 3-Satisfiability problem) where the n-bit cost function is symmetric with respect to the permutation of individual bits. The evolution paths are produced, using the generic control Hamiltonians H (r) that preserve the bit symmetry of the underlying optimization problem. In the case where the ground state of H(0) coincides with the totally-symmetric state of an n-qubit system the algorithm dynamics is completely described in terms of the motion of a spin-n/2. We show that different control Hamiltonians can be parameterized by a set of independent parameters that are expansion coefficients of H (r) in a certain universal set of operators. Only one of these operators can be responsible for avoiding the tunnelling in the spin-n/2 system during the quantum adiabatic algorithm. We show that it is possible to select a coefficient for this operator that guarantees a polynomial complexity of the algorithm for all problem instances. We show that a successful evolution path of the algorithm always corresponds to the trajectory of a classical spin-n/2 and provide a complete characterization of such paths.
THE SHORTEST PERIOD sdB PLUS WHITE DWARF BINARY CD-30 11223 (GALEX J1411-3053)
Vennes, S.; Kawka, A.; Nemeth, P.; O'Toole, S. J.; Burton, D.
2012-11-01
We report on the discovery of the shortest period binary comprising a hot subdwarf star (CD-30 11223, GALEX J1411-3053) and a massive unseen companion. Photometric data from the All Sky Automated Survey show ellipsoidal variations of the hot subdwarf primary and spectroscopic series revealed an orbital period of 70.5 minutes. The large velocity amplitude suggests the presence of a massive white dwarf in the system (M{sub 2}/M{sub Sun} {approx}> 0.77) assuming a canonical mass for the hot subdwarf (0.48 M{sub Sun }), although a white dwarf mass as low as 0.75 M{sub Sun} is allowable by postulating a subdwarf mass as low as 0.44 M{sub Sun }. The amplitude of ellipsoidal variations and a high rotation velocity imposed a high-inclination to the system (i {approx}> 68 Degree-Sign ) and, possibly, observable secondary transits (i {approx}> 74 Degree-Sign ). At the lowest permissible inclination and assuming a subdwarf mass of {approx}0.48 M{sub Sun }, the total mass of the system reaches the Chandrasekhar mass limit at 1.35 M{sub Sun} and would exceed it for a subdwarf mass above 0.48 M{sub Sun }. The system should be considered, like its sibling KPD 1930+2752, a candidate progenitor for a Type Ia supernova. The system should become semi-detached and initiate mass transfer within Almost-Equal-To 30 Myr.
Collabortive Authoring of Walden's Paths
Li, Yuanling; Bogen II, Paul Logasa; Pogue, Daniel; Furuta, Richard Keith; Shipman, Frank Major
2012-01-01
This paper presents a prototype of an authoring tool to allow users to collaboratively build, annotate, manage, share and reuse collections of distributed resources from the World Wide Web. This extends on the Walden’s Path project’s work to help educators bring resources found on the World Wide Web into a linear contextualized structure. The introduction of collaborative authoring feature fosters collaborative learning activities through social interaction among participants, where participants can coauthor paths in groups. Besides, the prototype supports path sharing, branching and reusing; specifically, individual participant can contribute to the group with private collections of knowledge resources; paths completed by group can be shared among group members, such that participants can tailor, extend, reorder and/or replace nodes to have sub versions of shared paths for different information needs.
Hard paths, soft paths or no paths? Cross-cultural perceptions of water solutions
NASA Astrophysics Data System (ADS)
Wutich, A.; White, A. C.; White, D. D.; Larson, K. L.; Brewis, A.; Roberts, C.
2014-01-01
In this study, we examine how development status and water scarcity shape people's perceptions of "hard path" and "soft path" water solutions. Based on ethnographic research conducted in four semi-rural/peri-urban sites (in Bolivia, Fiji, New Zealand, and the US), we use content analysis to conduct statistical and thematic comparisons of interview data. Our results indicate clear differences associated with development status and, to a lesser extent, water scarcity. People in the two less developed sites were more likely to suggest hard path solutions, less likely to suggest soft path solutions, and more likely to see no path to solutions than people in the more developed sites. Thematically, people in the two less developed sites envisioned solutions that involve small-scale water infrastructure and decentralized, community-based solutions, while people in the more developed sites envisioned solutions that involve large-scale infrastructure and centralized, regulatory water solutions. People in the two water-scarce sites were less likely to suggest soft path solutions and more likely to see no path to solutions (but no more likely to suggest hard path solutions) than people in the water-rich sites. Thematically, people in the two water-rich sites seemed to perceive a wider array of unrealized potential soft path solutions than those in the water-scarce sites. On balance, our findings are encouraging in that they indicate that people are receptive to soft path solutions in a range of sites, even those with limited financial or water resources. Our research points to the need for more studies that investigate the social feasibility of soft path water solutions, particularly in sites with significant financial and natural resource constraints.
Hard paths, soft paths or no paths? Cross-cultural perceptions of water solutions
NASA Astrophysics Data System (ADS)
Wutich, A.; White, A. C.; Roberts, C. M.; White, D. D.; Larson, K. L.; Brewis, A.
2013-06-01
In this study, we examine how development status and water scarcity shape people's perceptions of "hard path" and "soft path" water solutions. Based on ethnographic research conducted in four semi-rural/peri-urban sites (in Bolivia, Fiji, New Zealand, and the US), we use content analysis to conduct statistical and thematic comparisons of interview data. Our results indicate clear differences based on development status and, to a lesser extent, water scarcity. People in less developed sites were more likely to suggest hard path solutions, less likely to suggest soft path solutions, and more likely to see no path to solutions than people in more developed sites. Thematically, people in less developed sites envisioned solutions that involve small-scale water infrastructure and decentralized, community based solutions, while people in more developed sites envisioned solutions that involve large-scale infrastructure and centralized, regulatory water solutions. People in water-scarce sites were less likely to suggest soft path solutions and more likely to see no path to solutions (but no more likely to suggest hard path solutions) than people in water-rich sites. Thematically, people in water-rich sites seemed to perceive a wider array of unrealized potential soft path solutions than those in water-scarce sites. On balance, our findings are encouraging in that they indicate that people are receptive to soft path solutions in a range of sites, even those with limited financial or water resources. Our research points to the need for more studies that investigate the social feasibility of soft path water solutions, particularly in sites with significant financial and natural resource constraints.
A Differential Tree Approach to Price Path-Dependent American Options using Malliavin Calculus
NASA Astrophysics Data System (ADS)
Schellhorn, Henry; Morris, Hedley
2009-05-01
We propose a recursive schemes to calculate backward the values of conditional expectations of functions of path values of Brownian motion. This scheme is based on the Clark-Ocone formula in discrete time. We suggest an algorithm based on our scheme to effectively calculate the price of American options on securities with path-dependent payoffs. For problems where the path-dependence comes only from the path-dependence of the state variables our method is less subject to the curse of dimensionality observed in all other methods.
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
A Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts
NASA Technical Reports Server (NTRS)
Abbott, Terence S.
2007-01-01
This document describes an algorithm for the generation of a four dimensional aircraft trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival Route (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. Wind data at each waypoint are also inputs into this algorithm. The algorithm calculates the altitude, speed, along path distance, and along path time for each waypoint.
NASA Astrophysics Data System (ADS)
Wagner, Martin G.; Strother, Charles M.; Mistretta, Charles A.
2016-03-01
Recent efforts to perform a 3D reconstruction of interventional devices such as guidewires from monoplane and biplane fluoroscopic images require the segmentation of the exact device path in the respective 2D projection images. The segmentation of the device in low dose fluoroscopy images can be challenging since noise and motion artifacts degrade the image quality. Additionally, extracting the device path from the segmented region may result in ambiguous results due to overlapping device parts or discontinuities in the device segmentation. The purpose of this work is to present a novel guidewire tracking and segmentation algorithm, which segments the device region based on three different features based on a ridge detection filter, noise reduction for curvilinear structures as well as an a priori probability map. The features are calculated from background subtracted as well as unsubtracted fluoroscopic images. The device path extraction is based on a topology preserving thinning algorithm followed by a path search, which minimizes a cost function based on distance and directional difference between adjacent segments as well as the similarity to the device path extracted from the previous frame. The quantitative evaluation was performed using 7 data sets acquired from a canine study. Device shapes with different complexities are compared to semi-automatic segmentations. An average segmentation accuracy of 0.50 0.41 mm was achieved where each point along the device was compared to the point on the reference device centerline with the same distance to the device tip. In all cases the device path could be resolved correctly, which would allow a more accurate and reliable reconstruction of the 3D path of the device.
A synthesized heuristic task scheduling algorithm.
Dai, Yanyan; Zhang, Xiangli
2014-01-01
Aiming at the static task scheduling problems in heterogeneous environment, a heuristic task scheduling algorithm named HCPPEFT is proposed. In task prioritizing phase, there are three levels of priority in the algorithm to choose task. First, the critical tasks have the highest priority, secondly the tasks with longer path to exit task will be selected, and then algorithm will choose tasks with less predecessors to schedule. In resource selection phase, the algorithm is selected task duplication to reduce the interresource communication cost, besides forecasting the impact of an assignment for all children of the current task permits better decisions to be made in selecting resources. The algorithm proposed is compared with STDH, PEFT, and HEFT algorithms through randomly generated graphs and sets of task graphs. The experimental results show that the new algorithm can achieve better scheduling performance.
The Edge-Disjoint Path Problem on Random Graphs by Message-Passing
2015-01-01
We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length. PMID:26710102
The Edge-Disjoint Path Problem on Random Graphs by Message-Passing.
Altarelli, Fabrizio; Braunstein, Alfredo; Dall'Asta, Luca; De Bacco, Caterina; Franz, Silvio
2015-01-01
We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length. PMID:26710102
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Takeoff path. 23.57 Section 23.57... path. For each commuter category airplane, the takeoff path is as follows: (a) The takeoff path extends... completed; and (1) The takeoff path must be based on the procedures prescribed in § 23.45; (2) The...
Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Takeoff path. 23.57 Section 23.57... path. For each commuter category airplane, the takeoff path is as follows: (a) The takeoff path extends... completed; and (1) The takeoff path must be based on the procedures prescribed in § 23.45; (2) The...
Pon, Allison; Jewison, Timothy; Su, Yilu; Liang, Yongjie; Knox, Craig; Maciejewski, Adam; Wilson, Michael; Wishart, David S.
2015-01-01
PathWhiz (http://smpdb.ca/pathwhiz) is a web server designed to create colourful, visually pleasing and biologically accurate pathway diagrams that are both machine-readable and interactive. As a web server, PathWhiz is accessible from almost any place and compatible with essentially any operating system. It also houses a public library of pathways and pathway components that can be easily viewed and expanded upon by its users. PathWhiz allows users to readily generate biologically complex pathways by using a specially designed drawing palette to quickly render metabolites (including automated structure generation), proteins (including quaternary structures, covalent modifications and cofactors), nucleic acids, membranes, subcellular structures, cells, tissues and organs. Both small-molecule and protein/gene pathways can be constructed by combining multiple pathway processes such as reactions, interactions, binding events and transport activities. PathWhiz's pathway replication and propagation functions allow for existing pathways to be used to create new pathways or for existing pathways to be automatically propagated across species. PathWhiz pathways can be saved in BioPAX, SBGN-ML and SBML data exchange formats, as well as PNG, PWML, HTML image map or SVG images that can be viewed offline or explored using PathWhiz's interactive viewer. PathWhiz has been used to generate over 700 pathway diagrams for a number of popular databases including HMDB, DrugBank and SMPDB. PMID:25934797
NASA Technical Reports Server (NTRS)
Chandler, J. A.
1983-01-01
Long helical vent path cools and releases hot pyrotechnical gas that exits along its spiraling threads. Current design uses 1/4-28 threads with outer diameter of stud reduced by 0.025 in. (0.62 mm). To open or close gassampler bottle, pyrotechnic charges on either one side or other of valve cylinder are actuated. Gases vented slowly over long path are cool enough to present no ignition hazard. Vent used to meter flow in refrigeration, pneumaticcontrol, and fluid-control systems by appropriately adjusting size and length of vent path.
NASA Astrophysics Data System (ADS)
Janssen, Hans-Karl; Stenull, Olaf
2012-01-01
Long linear polymers in strongly disordered media are well described by self-avoiding walks (SAWs) on percolation clusters and a lot can be learned about the statistics of these polymers by studying the length distribution of SAWs on percolation clusters. This distribution encompasses 2 distinct averages, viz., the average over the conformations of the underlying cluster and the SAW conformations. For the latter average, there are two basic options, one being static and one being kinetic. It is well known for static averaging that if the disorder of the underlying medium is weak, this disorder is redundant in the sense the renormalization group; i.e., differences to the ordered case appear merely in nonuniversal quantities. Using dynamical field theory, we show that the same holds true for kinetic averaging. Our main focus, however, lies on strong disorder, i.e., the medium being close to the percolation point, where disorder is relevant. Employing a field theory for the nonlinear random resistor network in conjunction with a real-world interpretation of the corresponding Feynman diagrams, we calculate the scaling exponents for the shortest, the longest, and the mean or average SAW to 2-loop order. In addition, we calculate to 2-loop order the entire family of multifractal exponents that governs the moments of the the statistical weights of the elementary constituents (bonds or sites of the underlying fractal cluster) contributing to the SAWs. Our RG analysis reveals that kinetic averaging leads to renormalizability whereas static averaging does not, and hence, we argue that the latter does not lead to a well-defined scaling limit. We discuss the possible implications of this finding for experiments and numerical simulations which have produced widespread results for the exponent of the average SAW. To corroborate our results, we also study the well-known Meir-Harris model for SAWs on percolation clusters. We demonstrate that the Meir-Harris model leads back up to
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making. PMID:17186801
Career Path Suggestion using String Matching and Decision Trees
NASA Astrophysics Data System (ADS)
Nagpal, Akshay; P. Panda, Supriya
2015-05-01
High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.
The path integral picture of quantum systems
NASA Astrophysics Data System (ADS)
Ceperley, David
2011-03-01
The imaginary time path integral ``formalism'' was introduced in 1953 by Feynman to understand the superfluid transition in liquid helium. The equilibrium properties of quantum many body systems is isomorphic to the classical statistical mechanics of cross-linking polymer-like objects. With the Markov Chain Monte Carlo method, invented by Metropolis et al., also in 1953, a potential way of calculating properties of correlated quantum systems was in place. But calculations for many-body quantum systems did not become routine until computers and algorithms had become sufficiently powerful three decades later. Once such simulations could happen, it was realized that simulations provided a deeper insight into boson superfluids, in particular the relation of bose condensation to the polymer end-to-end distance, and the superfluid density to the polymer ``winding number.'' Some recent developments and applications to supersolids, and helium droplets will be given. Finally, limitations of the methodology e.g. to fermion systems are discussed.
Heuristically optimal path scanning for high-speed multiphoton circuit imaging.
Sadovsky, Alexander J; Kruskal, Peter B; Kimmel, Joseph M; Ostmeyer, Jared; Neubauer, Florian B; MacLean, Jason N
2011-09-01
Population dynamics of patterned neuronal firing are fundamental to information processing in the brain. Multiphoton microscopy in combination with calcium indicator dyes allows circuit dynamics to be imaged with single-neuron resolution. However, the temporal resolution of fluorescent measures is constrained by the imaging frequency imposed by standard raster scanning techniques. As a result, traditional raster scans limit the ability to detect the relative timing of action potentials in the imaged neuronal population. To maximize the speed of fluorescence measures from large populations of neurons using a standard multiphoton laser scanning microscope (MPLSM) setup, we have developed heuristically optimal path scanning (HOPS). HOPS optimizes the laser travel path length, and thus the temporal resolution of neuronal fluorescent measures, using standard galvanometer scan mirrors. Minimizing the scan path alone is insufficient for prolonged high-speed imaging of neuronal populations. Path stability and the signal-to-noise ratio become increasingly important factors as scan rates increase. HOPS addresses this by characterizing the scan mirror galvanometers to achieve prolonged path stability. In addition, the neuronal dwell time is optimized to sharpen the detection of action potentials while maximizing scan rate. The combination of shortest path calculation and minimization of mirror positioning time allows us to optically monitor a population of neurons in a field of view at high rates with single-spike resolution, ∼ 125 Hz for 50 neurons and ∼ 8.5 Hz for 1,000 neurons. Our approach introduces an accessible method for rapid imaging of large neuronal populations using traditional MPLSMs, facilitating new insights into neuronal circuit dynamics.
Schiefer, H. Peters, S.; Plasswilm, L.; Ingulfsen, N.; Kluckert, J.
2015-03-15
Purpose: For stereotactic radiosurgery, the AAPM Report No. 54 [AAPM Task Group 42 (AAPM, 1995)] requires the overall stability of the isocenter (couch, gantry, and collimator) to be within a 1 mm radius. In reality, a rotating system has no rigid axis and thus no isocenter point which is fixed in space. As a consequence, the isocenter concept is reviewed here. It is the aim to develop a measurement method following the revised definitions. Methods: The mechanical isocenter is defined here by the point which rotates on the shortest path in the room coordinate system. The path is labeled as “isocenter path.” Its center of gravity is assumed to be the mechanical isocenter. Following this definition, an image-based and radiation-free measurement method was developed. Multiple marker pairs in a plane perpendicular to the assumed gantry rotation axis of a linear accelerator are imaged with a smartphone application from several rotation angles. Each marker pair represents an independent measuring system. The room coordinates of the isocenter path and the mechanical isocenter are calculated based on the marker coordinates. The presented measurement method is by this means strictly focused on the mechanical isocenter. Results: The measurement result is available virtually immediately following completion of measurement. When 12 independent measurement systems are evaluated, the standard deviations of the isocenter path points and mechanical isocenter coordinates are 0.02 and 0.002 mm, respectively. Conclusions: The measurement is highly accurate, time efficient, and simple to adapt. It is therefore suitable for regular checks of the mechanical isocenter characteristics of the gantry and collimator rotation axis. When the isocenter path is reproducible and its extent is in the range of the needed geometrical accuracy, it should be taken into account in the planning process. This is especially true for stereotactic treatments and radiosurgery.
Heuristically optimal path scanning for high-speed multiphoton circuit imaging.
Sadovsky, Alexander J; Kruskal, Peter B; Kimmel, Joseph M; Ostmeyer, Jared; Neubauer, Florian B; MacLean, Jason N
2011-09-01
Population dynamics of patterned neuronal firing are fundamental to information processing in the brain. Multiphoton microscopy in combination with calcium indicator dyes allows circuit dynamics to be imaged with single-neuron resolution. However, the temporal resolution of fluorescent measures is constrained by the imaging frequency imposed by standard raster scanning techniques. As a result, traditional raster scans limit the ability to detect the relative timing of action potentials in the imaged neuronal population. To maximize the speed of fluorescence measures from large populations of neurons using a standard multiphoton laser scanning microscope (MPLSM) setup, we have developed heuristically optimal path scanning (HOPS). HOPS optimizes the laser travel path length, and thus the temporal resolution of neuronal fluorescent measures, using standard galvanometer scan mirrors. Minimizing the scan path alone is insufficient for prolonged high-speed imaging of neuronal populations. Path stability and the signal-to-noise ratio become increasingly important factors as scan rates increase. HOPS addresses this by characterizing the scan mirror galvanometers to achieve prolonged path stability. In addition, the neuronal dwell time is optimized to sharpen the detection of action potentials while maximizing scan rate. The combination of shortest path calculation and minimization of mirror positioning time allows us to optically monitor a population of neurons in a field of view at high rates with single-spike resolution, ∼ 125 Hz for 50 neurons and ∼ 8.5 Hz for 1,000 neurons. Our approach introduces an accessible method for rapid imaging of large neuronal populations using traditional MPLSMs, facilitating new insights into neuronal circuit dynamics. PMID:21715667
NASA Astrophysics Data System (ADS)
Shakeri, Nadim; Jalili, Saeed; Ahmadi, Vahid; Rasoulzadeh Zali, Aref; Goliaei, Sama
2015-01-01
The problem of finding the Hamiltonian path in a graph, or deciding whether a graph has a Hamiltonian path or not, is an NP-complete problem. No exact solution has been found yet, to solve this problem using polynomial amount of time and space. In this paper, we propose a two dimensional (2-D) optical architecture based on optical electronic devices such as micro ring resonators, optical circulators and MEMS based mirror (MEMS-M) to solve the Hamiltonian Path Problem, for undirected graphs in linear time. It uses a heuristic algorithm and employs n+1 different wavelengths of a light ray, to check whether a Hamiltonian path exists or not on a graph with n vertices. Then if a Hamiltonian path exists, it reports the path. The device complexity of the proposed architecture is O(n2).
Scattering theory with path integrals
Rosenfelder, R.
2014-03-15
Starting from well-known expressions for the T-matrix and its derivative in standard nonrelativistic potential scattering, I rederive recent path-integral formulations due to Efimov and Barbashov et al. Some new relations follow immediately.
Image-decrypting common path interferometer
NASA Astrophysics Data System (ADS)
Glueckstad, Jesper
1999-03-01
A new scheme for parallel optical decryption and the display of encrypted image information is presented. The scheme is based on a common path interferometer configuration providing a simple and robust optical setup. Images are encrypted directly during recording by use of a combined phase encoding and phase scrambling method. The encoding and encryption does not require sophisticated, iterative and time consuming optimization algorithms. Pixels are independently encoded and encrypted by use of a simple look-up table technique that can be performed in milliseconds on a standard personal computer. Optical decryption can subsequently be implemented in the common path interferometer by use of single phase or if desired a combined phase/amplitude key. An advantage of the presented method is that the encrypted image may selectively require recording and decryption of phase values or amplitude values or a combination thereof. Another advantage is that decryption is performed in a plane adjacent to the encrypted mask or an equivalent plane whereby generation of speckles in the decrypted image is strongly suppressed. Finally, there is no requirement of positioning a decrypting mask or spatial light modulator in the optical Fourier plane whereby an accurate three dimensional positioning requirement can be avoided.
The Path Decomposition Expansion and Multidimensional Tunneling
NASA Astrophysics Data System (ADS)
Auerbach, Assa
The dissertation consists of two main topics. (a) The Path Decomposition Expansion (PDX): A new path integral technique which allows us to break configuration space into disjoint regions, and express the dynamics of the full system in terms of its parts. (b) The application of the PDX and semiclassical methods for solving quantum -mechanical problems in multidimensions. The result is a conceptually simple, computationally straightforward method for calculating tunneling effects in complicated multidimensional potentials, even in cases where the nature of the states in the classically allowed regions in nontrivial. Algorithms for computing tunneling effects in general classes of problems are obtained. The detailed solutions to several model problems are presented. These enable us to define various well -controlled approximation schemes, which help to reduce the dimensions of complicated tunneling calculations in real physical systems. The dramatic effects of transverse fluctuations on the asymptotic behavior of the groundstate tunnel-splitting are studied also in potentials with non -quadratic minima where standard instanton techniques fail. The power of the PDX is demonstrated by a calculation of the optical absorption coefficient of trans-polyacetylene where large amplitude (non-perturbative) quantum fluctuations of the lattice play an important role in determining the sub-gap absorption tail. Good agreement with experimental data is found, and suggestions for further measurements in this regime are made.
Robust Path Planning and Feedback Design Under Stochastic Uncertainty
NASA Technical Reports Server (NTRS)
Blackmore, Lars
2008-01-01
Autonomous vehicles require optimal path planning algorithms to achieve mission goals while avoiding obstacles and being robust to uncertainties. The uncertainties arise from exogenous disturbances, modeling errors, and sensor noise, which can be characterized via stochastic models. Previous work defined a notion of robustness in a stochastic setting by using the concept of chance constraints. This requires that mission constraint violation can occur with a probability less than a prescribed value.In this paper we describe a novel method for optimal chance constrained path planning with feedback design. The approach optimizes both the reference trajectory to be followed and the feedback controller used to reject uncertainty. Our method extends recent results in constrained control synthesis based on convex optimization to solve control problems with nonconvex constraints. This extension is essential for path planning problems, which inherently have nonconvex obstacle avoidance constraints. Unlike previous approaches to chance constrained path planning, the new approach optimizes the feedback gain as wellas the reference trajectory.The key idea is to couple a fast, nonconvex solver that does not take into account uncertainty, with existing robust approaches that apply only to convex feasible regions. By alternating between robust and nonrobust solutions, the new algorithm guarantees convergence to a global optimum. We apply the new method to an unmanned aircraft and show simulation results that demonstrate the efficacy of the approach.
MotifMiner: A Table Driven Greedy Algorithm for DNA Motif Mining
NASA Astrophysics Data System (ADS)
Seeja, K. R.; Alam, M. A.; Jain, S. K.
DNA motif discovery is a much explored problem in functional genomics. This paper describes a table driven greedy algorithm for discovering regulatory motifs in the promoter sequences of co-expressed genes. The proposed algorithm searches both DNA strands for the common patterns or motifs. The inputs to the algorithm are set of promoter sequences, the motif length and minimum Information Content. The algorithm generates subsequences of given length from the shortest input promoter sequence. It stores these subsequences and their reverse complements in a table. Then it searches the remaining sequences for good matches of these subsequences. The Information Content score is used to measure the goodness of the motifs. The algorithm has been tested with synthetic data and real data. The results are found promising. The algorithm could discover meaningful motifs from the muscle specific regulatory sequences.
Path planning for robotic truss assembly
NASA Technical Reports Server (NTRS)
Sanderson, Arthur C.
1993-01-01
A new Potential Fields approach to the robotic path planning problem is proposed and implemented. Our approach, which is based on one originally proposed by Munger, computes an incremental joint vector based upon attraction to a goal and repulsion from obstacles. By repetitively adding and computing these 'steps', it is hoped (but not guaranteed) that the robot will reach its goal. An attractive force exerted by the goal is found by solving for the the minimum norm solution to the linear Jacobian equation. A repulsive force between obstacles and the robot's links is used to avoid collisions. Its magnitude is inversely proportional to the distance. Together, these forces make the goal the global minimum potential point, but local minima can stop the robot from ever reaching that point. Our approach improves on a basic, potential field paradigm developed by Munger by using an active, adaptive field - what we will call a 'flexible' potential field. Active fields are stronger when objects move towards one another and weaker when they move apart. An adaptive field's strength is individually tailored to be just strong enough to avoid any collision. In addition to the local planner, a global planning algorithm helps the planner to avoid local field minima by providing subgoals. These subgoals are based on the obstacles which caused the local planner to fail. A best-first search algorithm A* is used for graph search.
Predictive Algorithm For Aiming An Antenna
NASA Technical Reports Server (NTRS)
Gawronski, Wodek K.
1993-01-01
Method of computing control signals to aim antenna based on predictive control-and-estimation algorithm that takes advantage of control inputs. Conceived for controlling antenna in tracking spacecraft and celestial objects, near-future trajectories of which are known. Also useful in enhancing aiming performances of other antennas and instruments that track objects that move along fairly well known paths.
Autonomous Path-Following by Approximate Inverse Dynamics and Vector Field Prediction
NASA Astrophysics Data System (ADS)
Gerlach, Adam R.
In this dissertation, we develop two general frameworks for the navigation and control of autonomous vehicles that must follow predefined paths. These frameworks are designed such that they inherently provide accurate navigation and control of a wide class of systems directly from a model of the vehicle's dynamics. The first framework introduced is the inverse dynamics by radial basis function (IDRBF) algorithm, which exploits the best approximation property of radial basis functions to accurately approximate the inverse dynamics of non-linear systems. This approximation is then used with the known, desired state of the system at a future time point to generate the system input that must be applied to reach the desired state in the specified time interval. The IDRBF algorithm is then tested on two non-linear dynamic systems, and accurate path-following is demonstrated. The second framework introduced is the predictive vector field (PVF) algorithm. The PVF algorithm uses the equations of motion and constraints of the system to predict a set of reachable states by sampling the system's configuration space. By finding and minimizing a continuous mapping between the system's configuration space and a cost space relating the reachable states of the system with a vector field (VF), one can determine the system inputs required to follow the VF. The PVF algorithm is then tested on the Dubin's vehicle and aircraft models, and accurate path-following is demonstrated. As the PVF algorithm's performance is dependent on the quality of the underlying system model and VF, algorithms are introduced for automatically generating VFs for constant altitude paths defined by a series of waypoints and for handling modeling uncertainties. Additionally, we provide a mathematical proof showing that this method can automatically produce VFs of the desired form. To handle modeling uncertainties, we enhance the PVF algorithm with the Gaussian process machine learning framework, enabling the
Code of Federal Regulations, 2010 CFR
2010-01-01
... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Takeoff path. 25.111 Section 25.111... STANDARDS: TRANSPORT CATEGORY AIRPLANES Flight Performance § 25.111 Takeoff path. (a) The takeoff path... and VFTO is reached, whichever point is higher. In addition— (1) The takeoff path must be based on...
Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Takeoff path. 23.57 Section 23.57... path. Link to an amendment published at 76 FR 75753, December 2, 2011. For each commuter category airplane, the takeoff path is as follows: (a) The takeoff path extends from a standing start to a point...
Code of Federal Regulations, 2012 CFR
2012-01-01
... 14 Aeronautics and Space 1 2012-01-01 2012-01-01 false Takeoff path. 25.111 Section 25.111... STANDARDS: TRANSPORT CATEGORY AIRPLANES Flight Performance § 25.111 Takeoff path. (a) The takeoff path... and VFTO is reached, whichever point is higher. In addition— (1) The takeoff path must be based on...
Code of Federal Regulations, 2011 CFR
2011-01-01
... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false Takeoff path. 25.111 Section 25.111... STANDARDS: TRANSPORT CATEGORY AIRPLANES Flight Performance § 25.111 Takeoff path. (a) The takeoff path... and VFTO is reached, whichever point is higher. In addition— (1) The takeoff path must be based on...
Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Takeoff path. 23.57 Section 23.57... path. For normal, utility, and acrobatic category multiengine jets of more than 6,000 pounds maximum weight and commuter category airplanes, the takeoff path is as follows: (a) The takeoff path extends...
Code of Federal Regulations, 2013 CFR
2013-01-01
... 14 Aeronautics and Space 1 2013-01-01 2013-01-01 false Takeoff path. 25.111 Section 25.111... STANDARDS: TRANSPORT CATEGORY AIRPLANES Flight Performance § 25.111 Takeoff path. (a) The takeoff path... and VFTO is reached, whichever point is higher. In addition— (1) The takeoff path must be based on...
Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Takeoff path. 23.57 Section 23.57... path. For normal, utility, and acrobatic category multiengine jets of more than 6,000 pounds maximum weight and commuter category airplanes, the takeoff path is as follows: (a) The takeoff path extends...
Code of Federal Regulations, 2014 CFR
2014-01-01
... 14 Aeronautics and Space 1 2014-01-01 2014-01-01 false Takeoff path. 25.111 Section 25.111... STANDARDS: TRANSPORT CATEGORY AIRPLANES Flight Performance § 25.111 Takeoff path. (a) The takeoff path... and VFTO is reached, whichever point is higher. In addition— (1) The takeoff path must be based on...
Balanced Paths in Colored Graphs
NASA Astrophysics Data System (ADS)
Bianco, Alessandro; Faella, Marco; Mogavero, Fabio; Murano, Aniello
We consider finite graphs whose edges are labeled with elements, called colors, taken from a fixed finite alphabet. We study the problem of determining whether there is an infinite path where either (i) all colors occur with the same asymptotic frequency, or (ii) there is a constant which bounds the difference between the occurrences of any two colors for all prefixes of the path. These two notions can be viewed as refinements of the classical notion of fair path, whose simplest form checks whether all colors occur infinitely often. Our notions provide stronger criteria, particularly suitable for scheduling applications based on a coarse-grained model of the jobs involved. We show that both problems are solvable in polynomial time, by reducing them to the feasibility of a linear program.
Adaptive feedback cancellation in hearing aids with clipping in the feedback path.
Freed, Daniel J
2008-03-01
Adaptive linear filtering algorithms are commonly used to cancel feedback in hearing aids. The use of these algorithms is based on the assumption that the feedback path is linear, so nonlinearities in the feedback path may affect performance. This study investigated the effect on feedback canceller performance of clipping of the feedback signal arriving at the microphone, as well as the benefit of applying identical clipping to the cancellation signal so that the cancellation path modeled the nonlinearity of the feedback path. Feedback signal clipping limited the amount of added stable gain that the feedback canceller could provide, and caused misadjustment in response to high-level inputs, by biasing adaptive filter coefficients toward lower magnitudes. Cancellation signal clipping mitigated these negative effects, permitting higher amounts of added stable gain and less misadjustment in response to high-level inputs, but the benefit was reduced in the presence of the highest-level inputs. PMID:18345849
Quantum hyperparallel algorithm for matrix multiplication.
Zhang, Xin-Ding; Zhang, Xiao-Ming; Xue, Zheng-Yuan
2016-01-01
Hyperentangled states, entangled states with more than one degree of freedom, are considered as promising resource in quantum computation. Here we present a hyperparallel quantum algorithm for matrix multiplication with time complexity O(N(2)), which is better than the best known classical algorithm. In our scheme, an N dimensional vector is mapped to the state of a single source, which is separated to N paths. With the assistance of hyperentangled states, the inner product of two vectors can be calculated with a time complexity independent of dimension N. Our algorithm shows that hyperparallel quantum computation may provide a useful tool in quantum machine learning and "big data" analysis. PMID:27125586
Quantum hyperparallel algorithm for matrix multiplication
NASA Astrophysics Data System (ADS)
Zhang, Xin-Ding; Zhang, Xiao-Ming; Xue, Zheng-Yuan
2016-04-01
Hyperentangled states, entangled states with more than one degree of freedom, are considered as promising resource in quantum computation. Here we present a hyperparallel quantum algorithm for matrix multiplication with time complexity O(N2), which is better than the best known classical algorithm. In our scheme, an N dimensional vector is mapped to the state of a single source, which is separated to N paths. With the assistance of hyperentangled states, the inner product of two vectors can be calculated with a time complexity independent of dimension N. Our algorithm shows that hyperparallel quantum computation may provide a useful tool in quantum machine learning and “big data” analysis.
Data-parallel algorithms for image computing
NASA Astrophysics Data System (ADS)
Carlotto, Mark J.
1990-11-01
Data-parallel algorithms for image computing on the Connection Machine are described. After a brief review of some basic programming concepts in *Lip, a parallel extension of Common Lisp, data-parallel programming paradigms based on a local (diffusion-like) model of computation, the scan model of computation, a general interprocessor communications model, and a region-based model are introduced. Algorithms for connected component labeling, distance transformation, Voronoi diagrams, finding minimum cost paths, local means, shape-from-shading, hidden surface calculations, affine transformation, oblique parallel projection, and spatial operations over regions are presented. An new algorithm for interpolating irregularly spaced data via Voronoi diagrams is also described.
Speckle Imaging Over Horizontal Paths
Carrano, C J
2002-05-21
Atmospheric aberrations reduce the resolution and contrast in surveillance images recorded over horizontal or slant paths. This paper describes our recent horizontal and slant path imaging experiments of extended scenes as well as the results obtained using speckle imaging. The experiments were performed with an 8-inch diameter telescope placed on either a rooftop or hillside and cover ranges of interest from 0.5 km up to 10 km. The scenery includes resolution targets, people, vehicles, and other structures. The improvement in image quality using speckle imaging is dramatic in many cases, and depends significantly upon the atmospheric conditions. We quantify resolution improvement through modulation transfer function measurement comparisons.
Multiple paths in complex tasks
NASA Technical Reports Server (NTRS)
Galanter, Eugene; Wiegand, Thomas; Mark, Gloria
1987-01-01
The relationship between utility judgments of subtask paths and the utility of the task as a whole was examined. The convergent validation procedure is based on the assumption that measurements of the same quantity done with different methods should covary. The utility measures of the subtasks were obtained during the performance of an aircraft flight controller navigation task. Analyses helped decide among various models of subtask utility combination, whether the utility ratings of subtask paths predict the whole tasks utility rating, and indirectly, whether judgmental models need to include the equivalent of cognitive noise.
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Lomax, Harvard
1987-01-01
The past decade has seen considerable activity in algorithm development for the Navier-Stokes equations. This has resulted in a wide variety of useful new techniques. Some examples for the numerical solution of the Navier-Stokes equations are presented, divided into two parts. One is devoted to the incompressible Navier-Stokes equations, and the other to the compressible form.
Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance.
Willms, Allan R; Yang, Simon X
2008-06-01
An efficient grid-based distance-propagating dynamic system is proposed for real-time robot path planning in dynamic environments, which incorporates safety margins around obstacles using local penalty functions. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The algorithm is similar to D* but does not maintain a sorted queue of points to update. The resulting gain in computational speed is offset by the need to update all points in turn. Consequently, in situations where many obstacles and targets are moving at substantial distances from the current robot location, this algorithm is more efficient than D*. The properties of the algorithm are demonstrated through a number of simulations. A sufficient condition for capture of a target is provided.
WDM Multicast Tree Construction Algorithms and Their Comparative Evaluations
NASA Astrophysics Data System (ADS)
Makabe, Tsutomu; Mikoshi, Taiju; Takenaka, Toyofumi
We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.
Path statistics, memory, and coarse-graining of continuous-time random walks on networks
NASA Astrophysics Data System (ADS)
Manhart, Michael; Kion-Crosby, Willow; Morozov, Alexandre V.
2015-12-01
Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.
Research on global path planning based on ant colony optimization for AUV
NASA Astrophysics Data System (ADS)
Wang, Hong-Jian; Xiong, Wei
2009-03-01
Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
Path statistics, memory, and coarse-graining of continuous-time random walks on networks.
Manhart, Michael; Kion-Crosby, Willow; Morozov, Alexandre V
2015-12-01
Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.
Yen-BSR: A New Approach for the Choice of Routes in WDM Networks
NASA Astrophysics Data System (ADS)
Santos, A. F.; Almeida, R. C.; Assis, K. D. R.
2014-12-01
In this paper we propose to use an iterative algorithm for optimizing the fixed-alternate shortest path routing in the dynamic routing and wavelength assignment (RWA) problem in wavelength routing optical networks. The algorithm performance is compared, in terms of blocking probability, with the Dijkstra and Yen traditional algorithms, as well as with the recently proposed best among the shortest routes (BSR) algorithm. The results suggest that it is feasible to choose an appropriate set of routes for each pair of nodes (source, destination), among the shortest paths and efficiently balance the network load. For all studied scenarios, the proposed heuristic achieved superior performance.
Perceived Shrinkage of Motion Paths
ERIC Educational Resources Information Center
Sinico, Michele; Parovel, Giulia; Casco, Clara; Anstis, Stuart
2009-01-01
We show that human observers strongly underestimate a linear or circular trajectory that a luminous spot follows in the dark. At slow speeds, observers are relatively accurate, but, as the speed increases, the size of the path is progressively underestimated, by up to 35%. The underestimation imposes little memory load and does not require…
ERIC Educational Resources Information Center
Salmani-Nodoushan, Mohammad Ali
2007-01-01
The present paper underscores the importance of the cognitive orientation of English as a Foreign Language (EFL) students in their success in writing courses. A few suggestions are made as to how EFL teachers can put their students on the right cognitive path in their writings.
Career Paths in Environmental Sciences
Career paths, current and future, in the environmental sciences will be discussed, based on experiences and observations during the author's 40 + years in the field. An emphasis will be placed on the need for integrated, transdisciplinary systems thinking approaches toward achie...
Career Paths of Academic Deans.
ERIC Educational Resources Information Center
Wolverton, Mimi; Gonzales, Mary Jo
This paper examines various career paths leading to deanship and considers the implications of the findings for women and minorities who aspire to this position. The paper is part of a larger study of academic deanship conducted by the Center for Academic Leadership at Washington State University between October 1996 and January 1997. Data for the…
Employer Resource Manual. Project Path.
ERIC Educational Resources Information Center
Kane, Karen R.; Del George, Eve
Project Path at Illinois' College of DuPage was established to provide pre-employment training and career counseling for disabled students. To encourage the integration of qualified individuals with disabilities into the workplace, the project compiled this resource manual for area businesses, providing tips for interacting with disabled people…
Schuster, C.E.
1990-06-01
Automated path planning and obstacle avoidance has been the subject of intensive research in recent times. Most efforts in the field of semiautonomous mobile-robotic navigation involve using Artificial Intelligence search algorithms on a structured environment to achieve either good or optimal paths. Other approaches, such as incorporating Artificial Neural Networks, have also been explored. By implementing a hybrid system using the parallel-processing features of connectionist networks and simple localized search techniques, good paths can be generated using only low-level environmental sensory data. This system can negotiate structured two- and three-dimensional grid environments, from a start position to a goal, while avoiding all obstacles. Major advantages of this method are that solution paths are good in a global sense and path planning can be accomplished in real time if the system is implemented in customized parallel-processing hardware. This system has been proven effective in solving two- and three-dimensional maze-type environments.
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.
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. PMID:25435779
Kinetic Constrained Optimization of the Golf Swing Hub Path
Nesbit, Steven M.; McGinnis, Ryan S.
2014-01-01
This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key Points The hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer. It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer. It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories. Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact. The hand path trajectory has important influences over the club swing trajectory. PMID:25435779
Research on algorithms for adaptive antenna arrays
NASA Astrophysics Data System (ADS)
Widrow, B.; Newman, W.; Gooch, R.; Duvall, K.; Shur, D.
1981-08-01
The fundamental efficiency of adaptive algorithms is analyzed. It is found that noise in the adaptive weights increases with convergence speed. This causes loss in mean-square-error performance. Efficiency is considered from the point of view of misadjustment versus speed of convergence. A new version of the LMS algorithm based on Newton's method is analyzed and shown to make maximally efficient use of real-time input data. The performance of this algorithm is not affected by eigenvalue disparity. Practical algorithms can be devised that closely approximate Newton's method. In certain cases, the steepest descent version of LMS performs as well as Newton's method. The efficiency of adaptive algorithms with nonstationary input environments is analyzed where signals, jammers, and background noises can be of a transient and nonstationary nature. A new adaptive filtering method for broadband adaptive beamforming is described which uses both poles and zeros in the adaptive signal filtering paths from the antenna elements to the final array output.
Human-machine teaming for effective estimation and path planning
NASA Astrophysics Data System (ADS)
McCourt, Michael J.; Mehta, Siddhartha S.; Doucette, Emily A.; Curtis, J. Willard
2016-05-01
While traditional sensors provide accurate measurements of quantifiable information, humans provide better qualitative information and holistic assessments. Sensor fusion approaches that team humans and machines can take advantage of the benefits provided by each while mitigating the shortcomings. These two sensor sources can be fused together using Bayesian fusion, which assumes that there is a method of generating a probabilistic representation of the sensor measurement. This general framework of fusing estimates can also be applied to joint human-machine decision making. In the simple case, binary decisions can be fused by using a probability of taking an action versus inaction from each decision-making source. These are fused together to arrive at a final probability of taking an action, which would be taken if above a specified threshold. In the case of path planning, rather than binary decisions being fused, complex decisions can be fused by allowing the human and machine to interact with each other. For example, the human can draw a suggested path while the machine planning algorithm can refine it to avoid obstacles and remain dynamically feasible. Similarly, the human can revise a suggested path to achieve secondary goals not encoded in the algorithm such as avoiding dangerous areas in the environment.
Benchmarking Gas Path Diagnostic Methods: A Public Approach
NASA Technical Reports Server (NTRS)
Simon, Donald L.; Bird, Jeff; Davison, Craig; Volponi, Al; Iverson, R. Eugene
2008-01-01
Recent technology reviews have identified the need for objective assessments of engine health management (EHM) technology. The need is two-fold: technology developers require relevant data and problems to design and validate new algorithms and techniques while engine system integrators and operators need practical tools to direct development and then evaluate the effectiveness of proposed solutions. This paper presents a publicly available gas path diagnostic benchmark problem that has been developed by the Propulsion and Power Systems Panel of The Technical Cooperation Program (TTCP) to help address these needs. The problem is coded in MATLAB (The MathWorks, Inc.) and coupled with a non-linear turbofan engine simulation to produce "snap-shot" measurements, with relevant noise levels, as if collected from a fleet of engines over their lifetime of use. Each engine within the fleet will experience unique operating and deterioration profiles, and may encounter randomly occurring relevant gas path faults including sensor, actuator and component faults. The challenge to the EHM community is to develop gas path diagnostic algorithms to reliably perform fault detection and isolation. An example solution to the benchmark problem is provided along with associated evaluation metrics. A plan is presented to disseminate this benchmark problem to the engine health management technical community and invite technology solutions.
Ronacher, B; Westwig, E; Wehner, R
2006-09-01
When performing foraging trips desert ants of the genus Cataglyphis continuously process and update a ;home vector' that enables them to return to their nest on the shortest route. This capacity of path integration requires two types of information: (i) information about the travelling directions, and (ii) odometric information about the distances travelled in a particular direction. We have investigated how these two necessary pieces of information interact within the path integration processor. The specific question is: how do the ants process distance information if there is no simultaneous input from the sky compass available. Ants were trained to forage in a ;Z'-shaped channel system, the three segments of which joined at right angles. Individual animals were transferred from the feeder to a test field where their homing paths could be observed. In the crucial tests the middle segment of the maze was covered by orange Perspex that did not transmit the UV part of the spectrum, and thus precluded the perception of polarization patterns. Changes of the ant's processing of odometric information within this channel segment directly translate into a change in homing direction on the test field. The results indicate that the odometric information about travelling distance is largely ignored for path integration if there is no simultaneous input from the sky-view-based compass. They further show that idiothetic information cannot adequately substitute for the polarization compass to infer travelling directions.
A temperature control algorithm of immersion liquid for immersion lithography
NASA Astrophysics Data System (ADS)
He, Junwei; Li, Xiaoping; Lei, Min; Chen, Bing; Wang, Jinchun
2014-03-01
Immersion lithography is one of the main technologies used to manufacture integrated circuits with the shortest feature size. In immersion lithography, temperature of immersion liquid is strictly constrained and its allowable range is less than +/-0.01°C at 22°C. To meet this requirement, a temperature control algorithm adopted by the test rig which controls the temperature of the immersion liquid with process cooling water (PCW) via heat exchangers is proposed. By adjusting the flow rate of PCW through the heat exchangers, the control system varies the amount of heat exchanged, and the temperature of the immersion liquid can be properly controlled. The temperature control rig is a multi-disturbed, timevariant, non-linear and time-delayed system and its transfer function varies with the inlet temperature and flow rates of the streams through the heat exchangers. Considering the characteristics of the system, a cascade-connected fuzzy PID feedback algorithm is designed.
A Revised Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts
NASA Technical Reports Server (NTRS)
Abbott, Terence S.
2010-01-01
This document describes an algorithm for the generation of a four dimensional trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. This version of the algorithm accommodates descent Mach values that are different from the cruise Mach values. Wind data at each waypoint are also inputs into this algorithm. The algorithm calculates the altitude, speed, along path distance, and along path time for each waypoint.
Path entanglement of surface plasmons
NASA Astrophysics Data System (ADS)
Fakonas, James S.; Mitskovets, Anna; Atwater, Harry A.
2015-02-01
Metals can sustain traveling electromagnetic waves at their surfaces supported by the collective oscillations of their free electrons in unison. Remarkably, classical electromagnetism captures the essential physics of these ‘surface plasma’ waves using simple models with only macroscopic features, accounting for microscopic electron-electron and electron-phonon interactions with a single, semi-empirical damping parameter. Nevertheless, in quantum theory these microscopic interactions could be important, as any substantial environmental interactions could decohere quantum superpositions of surface plasmons, the quanta of these waves. Here we report a measurement of path entanglement between surface plasmons with 95% contrast, confirming that a path-entangled state can indeed survive without measurable decoherence. Our measurement suggests that elastic scattering mechanisms of the type that might cause pure dephasing in plasmonic systems must be weak enough not to significantly perturb the state of the metal under the experimental conditions we investigated.
Kinematics, controls, and path planning results for a redundant manipulator
NASA Technical Reports Server (NTRS)
Gretz, Bruce; Tilley, Scott W.
1989-01-01
The inverse kinematics solution, a modal position control algorithm, and path planning results for a 7 degree of freedom manipulator are presented. The redundant arm consists of two links with shoulder and elbow joints and a spherical wrist. The inverse kinematics problem for tip position is solved and the redundant joint is identified. It is also shown that a locus of tip positions exists in which there are kinematic limitations on self-motion. A computationally simple modal position control algorithm has been developed which guarantees a nearly constant closed-loop dynamic response throughout the workspace. If all closed-loop poles are assigned to the same location, the algorithm can be implemented with very little computation. To further reduce the required computation, the modal gains are updated only at discrete time intervals. Criteria are developed for the frequency of these updates. For commanding manipulator movements, a 5th-order spline which minimizes jerk provides a smooth tip-space path. Schemes for deriving a corresponding joint-space trajectory are discussed. Modifying the trajectory to avoid joint torque saturation when a tip payload is added is also considered. Simulation results are presented.
Squeezed states and path integrals
NASA Technical Reports Server (NTRS)
Daubechies, Ingrid; Klauder, John R.
1992-01-01
The continuous-time regularization scheme for defining phase-space path integrals is briefly reviewed as a method to define a quantization procedure that is completely covariant under all smooth canonical coordinate transformations. As an illustration of this method, a limited set of transformations is discussed that have an image in the set of the usual squeezed states. It is noteworthy that even this limited set of transformations offers new possibilities for stationary phase approximations to quantum mechanical propagators.
Path selection process utilizing rapid estimation scheme. [for Martian rover
NASA Technical Reports Server (NTRS)
Ring, H.; Shen, C. N.
1978-01-01
The paper describes the use of a rapid estimation scheme for path selection by a roving vehicle. Essentially, the evaluation procedure simulates movement of the rover over each of several corridors lying radially outward from the scanning position. Two levels of corridors are used, and the path selection scheme selects the optimal primary corridor according to a dynamic programming algorithm. In the present version, the length of the corridors is variable. The rapid estimation scheme provides information to define corridor dimensions. This corridor structure, which varies as a function of the terrain, eliminates the need for backtracking, except in certain extreme cases. Computer results are promising in that obstacles were avoided while corridor lengths were kept to a maximum where safety permitted.
Robot path generation for surface processing applications via neural networks
NASA Astrophysics Data System (ADS)
Koikkalainen, Pasi; Varsta, Markus
1996-10-01
This paper presents a hierarchical method, based on a deterministic variant of the self-organizing map, that provides an elegant solution for automated surface processing, e.g. for robot painting and sand-blasting. Given a set of data points in arbitrary order from the object surface, the proposed method is able to generate a path, where the robot hand position and its direction are optimized using separate criteria, and the tool path is smooth and covers the object uniformly. Input data may come from a laser measurement system, CAD model, digital camera, or from human assisted object digitizing system. The algorithm is reliable and easy to implement, and a good alternative for costly manual training of a robot.
Accelerating cleanup: Paths to closure
Edwards, C.
1998-06-30
This document was previously referred to as the Draft 2006 Plan. As part of the DOE`s national strategy, the Richland Operations Office`s Paths to Closure summarizes an integrated path forward for environmental cleanup at the Hanford Site. The Hanford Site underwent a concerted effort between 1994 and 1996 to accelerate the cleanup of the Site. These efforts are reflected in the current Site Baseline. This document describes the current Site Baseline and suggests strategies for further improvements in scope, schedule and cost. The Environmental Management program decided to change the name of the draft strategy and the document describing it in response to a series of stakeholder concerns, including the practicality of achieving widespread cleanup by 2006. Also, EM was concerned that calling the document a plan could be misconstrued to be a proposal by DOE or a decision-making document. The change in name, however, does not diminish the 2006 vision. To that end, Paths to Closure retains a focus on 2006, which serves as a point in time around which objectives and goals are established.
Copper foil provides uniform heat sink path
NASA Technical Reports Server (NTRS)
Phillips, I. E., Jr.; Schreihans, F. A.
1966-01-01
Thermal path prevents voids and discontinuities which make heat sinks in electronic equipment inefficient. The thermal path combines the high thermal conductivity of copper with the resiliency of silicone rubber.
Electron Inelastic-Mean-Free-Path Database
National Institute of Standards and Technology Data Gateway
SRD 71 NIST Electron Inelastic-Mean-Free-Path Database (PC database, no charge) This database provides values of electron inelastic mean free paths (IMFPs) for use in quantitative surface analyses by AES and XPS.
Time optimal paths for high speed maneuvering
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.
The diagnostic path, a useful visualisation tool in virtual microscopy
Schrader, Thomas; Niepage, Sonja; Leuthold, Thomas; Saeger, Kai; Schluns, Karsten; Hufnagl, Peter; Kayser, Klaus; Dietel, Manfred
2006-01-01
Background The Virtual Microscopy based on completely digitalised histological slide. Concerning this digitalisation many new features in mircoscopy can be processed by the computer. New applications are possible or old, well known techniques of image analyses can be adapted for routine use. Aims A so called diagnostic path observes in the way of a professional sees through a histological virtual slide combined with the text information of the dictation process. This feature can be used for image retrieval, quality assurance or for educational purpose. Materials and methods The diagnostic path implements a metadata structure of image information. It stores and processes the different images seen by a pathologist during his "slide viewing" and the obtained image sequence ("observation path"). Contemporary, the structural details of the pathology reports were analysed. The results were transferred into an XML structure. Based on this structure, a report editor and a search function were implemented. The report editor compiles the "diagnostic path", which is the connection from the image viewing sequence ("observation path") and the oral report sequence of the findings ("dictation path"). The time set ups of speech and image viewing serve for the link between the two sequences. The search tool uses the obtained diagnostic path. It allows the user to search for particular histological hallmarks in pathology reports and in the corresponding images. Results The new algorithm was tested on 50 pathology reports and 74 attached histological images. The creation of a new individual diagnostic path is automatically performed during the routine diagnostic process. The test prototype experienced an insignificant prolongation of the diagnosis procedure (oral case description and stated diagnosis by the pathologist) and a fast and reliable retrieval, especially useful for continuous education and quality control of case description and diagnostic work. Discussion The Digital
Efficient enumeration of monocyclic chemical graphs with given path frequencies
2014-01-01
Background The enumeration of chemical graphs (molecular graphs) satisfying given constraints is one of the fundamental problems in chemoinformatics and bioinformatics because it leads to a variety of useful applications including structure determination and development of novel chemical compounds. Results We consider the problem of enumerating chemical graphs with monocyclic structure (a graph structure that contains exactly one cycle) from a given set of feature vectors, where a feature vector represents the frequency of the prescribed paths in a chemical compound to be constructed and the set is specified by a pair of upper and lower feature vectors. To enumerate all tree-like (acyclic) chemical graphs from a given set of feature vectors, Shimizu et al. and Suzuki et al. proposed efficient branch-and-bound algorithms based on a fast tree enumeration algorithm. In this study, we devise a novel method for extending these algorithms to enumeration of chemical graphs with monocyclic structure by designing a fast algorithm for testing uniqueness. The results of computational experiments reveal that the computational efficiency of the new algorithm is as good as those for enumeration of tree-like chemical compounds. Conclusions We succeed in expanding the class of chemical graphs that are able to be enumerated efficiently. PMID:24955135
Multiple Paths to Encephalization and Technical Civilizations
NASA Astrophysics Data System (ADS)
Schwartzman, David; Middendorf, George
2011-12-01
We propose consideration of at least two possible evolutionary paths for the emergence of intelligent life with the potential for technical civilization. The first is the path via encephalization of homeothermic animals; the second is the path to swarm intelligence of so-called superorganisms, in particular the social insects. The path to each appears to be facilitated by environmental change: homeothermic animals by decreased climatic temperature and for swarm intelligence by increased oxygen levels.
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches. PMID:25532155
Mining Relational Paths in Integrated Biomedical Data
He, Bing; Tang, Jie; Ding, Ying; Wang, Huijun; Sun, Yuyin; Shin, Jae Hong; Chen, Bin; Moorthy, Ganesh; Qiu, Judy; Desai, Pankaj; Wild, David J.
2011-01-01
Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies. PMID:22162991
Pareto-path multitask multiple kernel learning.
Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C
2015-01-01
A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.
Evaluation of the Learning Path Specification
ERIC Educational Resources Information Center
Janssen, Jose; Berlanga, Adriana J.; Koper, Rob
2011-01-01
Flexible lifelong learning requires that learners can compare and select learning paths that best meet individual needs, not just in terms of learning goals, but also in terms of planning, costs etc. To this end a learning path specification was developed, which describes both the contents and the structure of any learning path, be it formal,…
Experimental and analytical study of secondary path variations in active engine mounts
NASA Astrophysics Data System (ADS)
Hausberg, Fabian; Scheiblegger, Christian; Pfeffer, Peter; Plöchl, Manfred; Hecker, Simon; Rupp, Markus
2015-03-01
Active engine mounts (AEMs) provide an effective solution to further improve the acoustic and vibrational comfort of passenger cars. Typically, adaptive feedforward control algorithms, e.g., the filtered-x-least-mean-squares (FxLMS) algorithm, are applied to cancel disturbing engine vibrations. These algorithms require an accurate estimate of the AEM active dynamic characteristics, also known as the secondary path, in order to guarantee control performance and stability. This paper focuses on the experimental and theoretical study of secondary path variations in AEMs. The impact of three major influences, namely nonlinearity, change of preload and component temperature, on the AEM active dynamic characteristics is experimentally analyzed. The obtained test results are theoretically investigated with a linear AEM model which incorporates an appropriate description for elastomeric components. A special experimental set-up extends the model validation of the active dynamic characteristics to higher frequencies up to 400 Hz. The theoretical and experimental results show that significant secondary path variations are merely observed in the frequency range of the AEM actuator's resonance frequency. These variations mainly result from the change of the component temperature. As the stability of the algorithm is primarily affected by the actuator's resonance frequency, the findings of this paper facilitate the design of AEMs with simpler adaptive feedforward algorithms. From a practical point of view it may further be concluded that algorithmic countermeasures against instability are only necessary in the frequency range of the AEM actuator's resonance frequency.
A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis
NASA Astrophysics Data System (ADS)
Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang
2016-09-01
A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.
Interactive multi-objective path planning through a palette-based user interface
NASA Astrophysics Data System (ADS)
Shaikh, Meher T.; Goodrich, Michael A.; Yi, Daqing; Hoehne, Joseph
2016-05-01
n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist's palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist's shade of color. We call the GUI an "Adverb Palette" where the word "Adverb" represents a specific type of objective for the path, such as the adverbs "quickly" and "safely" in the commands: "travel the path quickly", "make the journey safely". The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a
NASA Astrophysics Data System (ADS)
Pérez, Alejandro; Tuckerman, Mark E.
2011-08-01
Higher order factorization schemes are developed for path integral molecular dynamics in order to improve the convergence of estimators for physical observables as a function of the Trotter number. The methods are based on the Takahashi-Imada and Susuki decompositions of the Boltzmann operator. The methods introduced improve the averages of the estimators by using the classical forces needed to carry out the dynamics to construct a posteriori weighting factors for standard path integral molecular dynamics. The new approaches are straightforward to implement in existing path integral codes and carry no significant overhead. The Suzuki higher order factorization was also used to improve the end-to-end distance estimator in open path integral molecular dynamics. The new schemes are tested in various model systems, including an ab initio path integral molecular dynamics calculation on the hydrogen molecule and a quantum water model. The proposed algorithms have potential utility for reducing the cost of path integral molecular dynamics calculations of bulk systems.
Computing LS factor by runoff paths on TIN
NASA Astrophysics Data System (ADS)
Kavka, Petr; Krasa, Josef; Bek, Stanislav
2013-04-01
The article shows results of topographic factor (the LS factor in USLE) derivation enhancement focused on detailed Airborne Laser Scanning (ALS) based DEMs. It describes a flow paths generation technique using triangulated irregular network (TIN) for terrain morphology description, which is not yet established in soil loss computations. This technique was compared with other procedures of flow direction and flow paths generation based on commonly used raster model (DEM). These overland flow characteristics together with therefrom derived flow accumulation are significant inputs for many scientific models. Particularly they are used in all USLE-based soil erosion models, from which USLE2D, RUSLE3D, Watem/Sedem or USPED can be named as the most acknowledged. Flow routing characteristics are also essential parameters in physically based hydrological and soil erosion models like HEC-HMS, Wepp, Erosion3D, LISEM, SMODERP, etc. Mentioned models are based on regular raster grids, where the identification of runoff direction is problematic. The most common method is Steepest descent (one directional flow), which corresponds well with the concentration of surface runoff into concentrated flow. The Steepest descent algorithm for the flow routing doesn't provide satisfying results, it often creates parallel and narrow flow lines while not respecting real morphological conditions. To overcome this problem, other methods (such as Flux Decomposition, Multiple flow, Deterministic Infinity algorithm etc.) separate the outflow into several components. This approach leads to unrealistic diffusion propagation of the runoff and makes it impossible to be used for simulation of dominant morphological features, such as artificial rills, hedges, sediment traps etc. The modern methods of mapping ground elevations, especially ALS, provide very detailed models even for large river basins, including morphological details. New algorithms for derivation a runoff direction have been developed as
Obstacle Bypassing in Optimal Ship Routing Using Simulated Annealing
Kosmas, O. T.; Vlachos, D. S.; Simos, T. E.
2008-11-06
In this paper we are going to discuss a variation on the problem of finding the shortest path between two points in optimal ship routing problems consisting of obstacles that are not allowed to be crossed by the path. Our main goal are going to be the construction of an appropriate algorithm, based in an earlier work by computing the shortest path between two points in the plane that avoids a set of polygonal obstacles.
NASA Astrophysics Data System (ADS)
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Characterizing the Evolutionary Path(s) to Early Homo
Schroeder, Lauren; Roseman, Charles C.; Cheverud, James M.; Ackermann, Rebecca R.
2014-01-01
Numerous studies suggest that the transition from Australopithecus to Homo was characterized by evolutionary innovation, resulting in the emergence and coexistence of a diversity of forms. However, the evolutionary processes necessary to drive such a transition have not been examined. Here, we apply statistical tests developed from quantitative evolutionary theory to assess whether morphological differences among late australopith and early Homo species in Africa have been shaped by natural selection. Where selection is demonstrated, we identify aspects of morphology that were most likely under selective pressure, and determine the nature (type, rate) of that selection. Results demonstrate that selection must be invoked to explain an Au. africanus—Au. sediba—Homo transition, while transitions from late australopiths to various early Homo species that exclude Au. sediba can be achieved through drift alone. Rate tests indicate that selection is largely directional, acting to rapidly differentiate these taxa. Reconstructions of patterns of directional selection needed to drive the Au. africanus—Au. sediba—Homo transition suggest that selection would have affected all regions of the skull. These results may indicate that an evolutionary path to Homo without Au. sediba is the simpler path and/or provide evidence that this pathway involved more reliance on cultural adaptations to cope with environmental change. PMID:25470780
Architecture and design of optical path networks utilizing waveband virtual links
NASA Astrophysics Data System (ADS)
Ito, Yusaku; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi
2016-02-01
We propose a novel optical network architecture that uses waveband virtual links, each of which can carry several optical paths, to directly bridge distant node pairs. Future photonic networks should not only transparently cover extended areas but also expand fiber capacity. However, the traversal of many ROADM nodes impairs the optical signal due to spectrum narrowing. To suppress the degradation, the bandwidth of guard bands needs to be increased, which degrades fiber frequency utilization. Waveband granular switching allows us to apply broader pass-band filtering at ROADMs and to insert sufficient guard bands between wavebands with minimum frequency utilization offset. The scheme resolves the severe spectrum narrowing effect. Moreover, the guard band between optical channels in a waveband can be minimized, which increases the number of paths that can be accommodated per fiber. In the network, wavelength path granular routing is done without utilizing waveband virtual links, and it still suffers from spectrum narrowing. A novel network design algorithm that can bound the spectrum narrowing effect by limiting the number of hops (traversed nodes that need wavelength path level routing) is proposed in this paper. This algorithm dynamically changes the waveband virtual link configuration according to the traffic distribution variation, where optical paths that need many node hops are effectively carried by virtual links. Numerical experiments demonstrate that the number of necessary fibers is reduced by 23% compared with conventional optical path networks.
Relations between Coherence and Path Information.
Bagan, Emilio; Bergou, János A; Cottrell, Seth S; Hillery, Mark
2016-04-22
We find two relations between coherence and path information in a multipath interferometer. The first builds on earlier results for the two-path interferometer, which used minimum-error state discrimination between detector states to provide the path information. For visibility, which was used in the two-path case, we substitute a recently defined l_{1} measure of quantum coherence. The second is an entropic relation in which the path information is characterized by the mutual information between the detector states and the outcome of the measurement performed on them, and the coherence measure is one based on relative entropy.
Evaluation of guidewire path reproducibility.
Schafer, Sebastian; Hoffmann, Kenneth R; Noël, Peter B; Ionita, Ciprian N; Dmochowski, Jacek
2008-05-01
The number of minimally invasive vascular interventions is increasing. In these interventions, a variety of devices are directed to and placed at the site of intervention. The device used in almost all of these interventions is the guidewire, acting as a monorail for all devices which are delivered to the intervention site. However, even with the guidewire in place, clinicians still experience difficulties during the interventions. As a first step toward understanding these difficulties and facilitating guidewire and device guidance, we have investigated the reproducibility of the final paths of the guidewire in vessel phantom models on different factors: user, materials and geometry. Three vessel phantoms (vessel diameters approximately 4 mm) were constructed having tortuousity similar to the internal carotid artery from silicon tubing and encased in Sylgard elastomer. Several trained users repeatedly passed two guidewires of different flexibility through the phantoms under pulsatile flow conditions. After the guidewire had been placed, rotational c-arm image sequences were acquired (9 in. II mode, 0.185 mm pixel size), and the phantom and guidewire were reconstructed (512(3), 0.288 mm voxel size). The reconstructed volumes were aligned. The centerlines of the guidewire and the phantom vessel were then determined using region-growing techniques. Guidewire paths appear similar across users but not across materials. The average root mean square difference of the repeated placement was 0.17 +/- 0.02 mm (plastic-coated guidewire), 0.73 +/- 0.55 mm (steel guidewire) and 1.15 +/- 0.65 mm (steel versus plastic-coated). For a given guidewire, these results indicate that the guidewire path is relatively reproducible in shape and position.
NASA Astrophysics Data System (ADS)
Wang, Po-Jen; Keyawa, Nicholas R.; Euler, Craig
2012-01-01
In order to achieve highly accurate motion control and path planning for a mobile robot, an obstacle avoidance algorithm that provided a desired instantaneous turning radius and velocity was generated. This type of obstacle avoidance algorithm, which has been implemented in California State University Northridge's Intelligent Ground Vehicle (IGV), is known as Radial Polar Histogram (RPH). The RPH algorithm utilizes raw data in the form of a polar histogram that is read from a Laser Range Finder (LRF) and a camera. A desired open block is determined from the raw data utilizing a navigational heading and an elliptical approximation. The left and right most radii are determined from the calculated edges of the open block and provide the range of possible radial paths the IGV can travel through. In addition, the calculated obstacle edge positions allow the IGV to recognize complex obstacle arrangements and to slow down accordingly. A radial path optimization function calculates the best radial path between the left and right most radii and is sent to motion control for speed determination. Overall, the RPH algorithm allows the IGV to autonomously travel at average speeds of 3mph while avoiding all obstacles, with a processing time of approximately 10ms.
Communication path for extreme environments
NASA Technical Reports Server (NTRS)
Jorgensen, Charles C. (Inventor); Betts, Bradley J. (Inventor)
2010-01-01
Methods and systems for using one or more radio frequency identification devices (RFIDs), or other suitable signal transmitters and/or receivers, to provide a sensor information communication path, to provide location and/or spatial orientation information for an emergency service worker (ESW), to provide an ESW escape route, to indicate a direction from an ESW to an ES appliance, to provide updated information on a region or structure that presents an extreme environment (fire, hazardous fluid leak, underwater, nuclear, etc.) in which an ESW works, and to provide accumulated thermal load or thermal breakdown information on one or more locations in the region.
Multiple order common path spectrometer
NASA Technical Reports Server (NTRS)
Newbury, Amy B. (Inventor)
2010-01-01
The present invention relates to a dispersive spectrometer. The spectrometer allows detection of multiple orders of light on a single focal plane array by splitting the orders spatially using a dichroic assembly. A conventional dispersion mechanism such as a defraction grating disperses the light spectrally. As a result, multiple wavelength orders can be imaged on a single focal plane array of limited spectral extent, doubling (or more) the number of spectral channels as compared to a conventional spectrometer. In addition, this is achieved in a common path device.
Gibbs Ensembles of Nonintersecting Paths
NASA Astrophysics Data System (ADS)
Borodin, Alexei; Shlosman, Senya
2010-01-01
We consider a family of determinantal random point processes on the two-dimensional lattice and prove that members of our family can be interpreted as a kind of Gibbs ensembles of nonintersecting paths. Examples include probability measures on lozenge and domino tilings of the plane, some of which are non-translation-invariant. The correlation kernels of our processes can be viewed as extensions of the discrete sine kernel, and we show that the Gibbs property is a consequence of simple linear relations satisfied by these kernels. The processes depend on infinitely many parameters, which are closely related to parametrization of totally positive Toeplitz matrices.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881
Khoubrouy, Soudeh A; Panahi, Issa M S
2011-01-01
Various methods have been proposed to overcome the problem of compensating the acoustic feedback path that negatively impacts the performance of hearing aid devices. However, in most of them feedback path model is assumed to be fixed which is not quite realistic. In this paper, we consider fixed and variable feedback paths and analyze for each case the performance of one of the robust Adaptive Feedback Cancellation (AFC) schemes, i.e. the Prediction Error Method AFC which uses Partitioned Block Frequency-Domain Normalized Least Mean Square (PBFD-NLMS) algorithm. Based on the analysis results we propose varying the step size values for the same adaptive algorithm on the fly by monitoring the misalignment criteria. The experimental results using the proposed method show improvement made on the system performance. PMID:22256175
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm
Han, Gaining; Fu, Weiping; Wang, Wen
2016-01-01
In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881
Recursive Branching Simulated Annealing Algorithm
NASA Technical Reports Server (NTRS)
Bolcar, Matthew; Smith, J. Scott; Aronstein, David
2012-01-01
solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.
Path planning for assembly of strut-based structures. Thesis
NASA Technical Reports Server (NTRS)
Muenger, Rolf
1991-01-01
A path planning method with collision avoidance for a general single chain nonredundant or redundant robot is proposed. Joint range boundary overruns are also avoided. The result is a sequence of joint vectors which are passed to a trajectory planner. A potential field algorithm in joint space computes incremental joint vectors delta-q = delta-q(sub a) + delta-q(sub c) + delta-q(sub r). Adding delta-q to the robot's current joint vector leads to the next step in the path. Delta-q(sub a) is obtained by computing the minimum norm solution of the underdetermined linear system J delta-q(sub a) = x(sub a) where x(sub a) is a translational and rotational force vector that attracts the robot to its goal position and orientation. J is the manipulator Jacobian. Delta-q(sub c) is a collision avoidance term encompassing collisions between the robot (links and payload) and obstacles in the environment as well as collisions among links and payload of the robot themselves. It is obtained in joint space directly. Delta-q(sub r) is a function of the current joint vector and avoids joint range overruns. A higher level discrete search over candidate safe positions is used to provide alternatives in case the potential field algorithm encounters a local minimum and thus fails to reach the goal. The best first search algorithm A* is used for graph search. Symmetry properties of the payload and equivalent rotations are exploited to further enlarge the number of alternatives passed to the potential field algorithm.
Path integration: effect of curved path complexity and sensory system on blindfolded walking.
Koutakis, Panagiotis; Mukherjee, Mukul; Vallabhajosula, Srikant; Blanke, Daniel J; Stergiou, Nicholas
2013-02-01
Path integration refers to the ability to integrate continuous information of the direction and distance traveled by the system relative to the origin. Previous studies have investigated path integration through blindfolded walking along simple paths such as straight line and triangles. However, limited knowledge exists regarding the role of path complexity in path integration. Moreover, little is known about how information from different sensory input systems (like vision and proprioception) contributes to accurate path integration. The purpose of the current study was to investigate how sensory information and curved path complexity affect path integration. Forty blindfolded participants had to accurately reproduce a curved path and return to the origin. They were divided into four groups that differed in the curved path, circle (simple) or figure-eight (complex), and received either visual (previously seen) or proprioceptive (previously guided) information about the path before they reproduced it. The dependent variables used were average trajectory error, walking speed, and distance traveled. The results indicated that (a) both groups that walked on a circular path and both groups that received visual information produced greater accuracy in reproducing the path. Moreover, the performance of the group that received proprioceptive information and later walked on a figure-eight path was less accurate than their corresponding circular group. The groups that had the visual information also walked faster compared to the group that had proprioceptive information. Results of the current study highlight the roles of different sensory inputs while performing blindfolded walking for path integration.
Path Integration: Effect of Curved Path Complexity and Sensory System on Blindfolded Walking
Koutakis, Panagiotis; Mukherjee, Mukul; Vallabhajosula, Srikant; Blanke, Daniel J.; Stergiou, Nicholas
2012-01-01
Path integration refers to the ability to integrate continuous information of the direction and distance travelled by the system relative to the origin. Previous studies have investigated path integration through blindfolded walking along simple paths such as straight line and triangles. However, limited knowledge exists regarding the role of path complexity in path integration. Moreover, little is known about how information from different sensory input systems (like vision and proprioception) contributes to accurate path integration. The purpose of the current study was to investigate how sensory information and curved path complexity affect path integration. Forty blindfolded participants had to accurately reproduce a curved path and return to the origin. They were divided into four groups that differed in the curved path, circle (simple) or figure-eight (complex), and received either visual (previously seen) or proprioceptive (previously guided) information about the path before they reproduced it. The dependent variables used were average trajectory error, walking speed, and distance travelled. The results indicated that (a) both groups that walked on a circular path and both groups that received visual information produced greater accuracy in reproducing the path. Moreover, the performance of the group that received proprioceptive information and later walked on a figure-eight path was less accurate than their corresponding circular group. The groups that had the visual information also walked faster compared to the group that had proprioceptive information. Results of the current study highlight the roles of different sensory inputs while performing blindfolded walking for path integration. PMID:22840893
NASA Technical Reports Server (NTRS)
Goldhirsh, J.
1978-01-01
Yearly, monthly, and time of day fade statistics are presented and characterized. A 19.04 GHz yearly fade distribution, corresponding to a second COMSTAR beacon frequency, is predicted using the concept of effective path length, disdrometer, and rain rate results. The yearly attenuation and rain rate distributions follow with good approximation log normal variations for most fade and rain rate levels. Attenuations were exceeded for the longest and shortest periods of times for all fades in August and February, respectively. The eight hour time period showing the maximum and minimum number of minutes over the year for which fades exceeded 12 db were approximately between 1600 to 2400, and 0400 to 1200 hours, respectively. In employing the predictive method for obtaining the 19.04 GHz fade distribution, it is demonstrated theoretically that the ratio of attenuations at two frequencies is minimally dependent of raindrop size distribution providing these frequencies are not widely separated.
Stereoscopic depth perception for robot vision: algorithms and architectures
Safranek, R.J.; Kak, A.C.
1983-01-01
The implementation of depth perception algorithms for computer vision is considered. In automated manufacturing, depth information is vital for tasks such as path planning and 3-d scene analysis. The presentation begins with a survey of computer algorithms for stereoscopic depth perception. The emphasis is on the Marr-Poggio paradigm of human stereo vision and its computer implementation. In addition, a stereo matching algorithm based on the relaxation labelling technique is examined. A computer architecture designed to efficiently implement stereo matching algorithms, an MIMD array interfaced to a global memory, is presented. 9 references.
Wiest, Jennifer H.; Buckner, Gregory D.
2014-01-01
This paper introduces a real-time path optimization and control strategy for shape memory alloy (SMA) actuated cardiac ablation catheters, potentially enabling the creation of more precise lesions with reduced procedure times and improved patient outcomes. Catheter tip locations and orientations are optimized using parallel genetic algorithms to produce continuous ablation paths with near normal tissue contact through physician-specified points. A nonlinear multivariable control strategy is presented to compensate for SMA hysteresis, bandwidth limitations, and coupling between system inputs. Simulated and experimental results demonstrate efficient generation of ablation paths and optimal reference trajectories. Closed-loop control of the SMA-actuated catheter along optimized ablation paths is validated experimentally. PMID:25684857
Path planning method for UUV homing and docking in movement disorders environment.
Yan, Zheping; Deng, Chao; Chi, Dongnan; Chen, Tao; Hou, Shuping
2014-01-01
Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path. PMID:25054169
Bioinspired Coordinated Path Following for Vessels with Speed Saturation Based on Virtual Leader
Fu, Mingyu
2016-01-01
This paper investigates the coordinated path following of multiple marine vessels with speed saturation. Based on virtual leader strategy, the authors show how the neural dynamic model and passivity-based techniques are brought together to yield a distributed control strategy. The desired path following is achieved by means of a virtual dynamic leader, whose controller is designed based on the biological neural shunting model. Utilizing the characteristic of bounded and smooth output of neural dynamic model, the tracking error jump is avoided and speed saturation problem is solved in straight path. Meanwhile, the coordinated path following of multiple vessels with a desired spatial formation is achieved through defining the formation reference point. The consensus of formation reference point is realized by using the synchronization controller based on passivity. Finally, simulation results validate the effectiveness of the proposed coordinated algorithm. PMID:27034652
Path Planning Method for UUV Homing and Docking in Movement Disorders Environment
Yan, Zheping; Deng, Chao; Chi, Dongnan; Hou, Shuping
2014-01-01
Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path. PMID:25054169
Direct path planning in image plane and tracking for visual servoing
NASA Astrophysics Data System (ADS)
Wang, Junping; Liu, An; Cho, Hyungsuck
2007-10-01
The image-based visual servoing would lead to image singularities that might cause control instabilities, and there exit other constraints such as the object should remain in the camera field of view and avoid obstacles. This problem can be solved by coupling path planning and image-based control. The trajectory is planned directly in the image space in our strategy to avoid the 3D estimation of the object, which is required in the motion space based path planning method. In the presented method, the initial path is given using the artificial potential field method without considering the constraints and then genetic algorithm based method is used to check and modify the initial path. This method can achieve satisfactory task while decrease the computation. The proposed method is used to align the micro peg and hole, and the simulation results show that the object can reach its desired position accurately without violation these constrains.
Birkholz, Adam B; Schlegel, H Bernhard
2016-05-14
Reaction path optimization is being used more frequently as an alternative to the standard practice of locating a transition state and following the path downhill. The Variational Reaction Coordinate (VRC) method was proposed as an alternative to chain-of-states methods like nudged elastic band and string method. The VRC method represents the path using a linear expansion of continuous basis functions, allowing the path to be optimized variationally by updating the expansion coefficients to minimize the line integral of the potential energy gradient norm, referred to as the Variational Reaction Energy (VRE) of the path. When constraints are used to control the spacing of basis functions and to couple the minimization of the VRE with the optimization of one or more individual points along the path (representing transition states and intermediates), an approximate path as well as the converged geometries of transition states and intermediates along the path are determined in only a few iterations. This algorithmic efficiency comes at a high per-iteration cost due to numerical integration of the VRE derivatives. In the present work, methods for incorporating redundant internal coordinates and potential energy surface interpolation into the VRC method are described. With these methods, the per-iteration cost, in terms of the number of potential energy surface evaluations, of the VRC method is reduced while the high algorithmic efficiency is maintained.
NASA Astrophysics Data System (ADS)
Birkholz, Adam B.; Schlegel, H. Bernhard
2016-05-01
Reaction path optimization is being used more frequently as an alternative to the standard practice of locating a transition state and following the path downhill. The Variational Reaction Coordinate (VRC) method was proposed as an alternative to chain-of-states methods like nudged elastic band and string method. The VRC method represents the path using a linear expansion of continuous basis functions, allowing the path to be optimized variationally by updating the expansion coefficients to minimize the line integral of the potential energy gradient norm, referred to as the Variational Reaction Energy (VRE) of the path. When constraints are used to control the spacing of basis functions and to couple the minimization of the VRE with the optimization of one or more individual points along the path (representing transition states and intermediates), an approximate path as well as the converged geometries of transition states and intermediates along the path are determined in only a few iterations. This algorithmic efficiency comes at a high per-iteration cost due to numerical integration of the VRE derivatives. In the present work, methods for incorporating redundant internal coordinates and potential energy surface interpolation into the VRC method are described. With these methods, the per-iteration cost, in terms of the number of potential energy surface evaluations, of the VRC method is reduced while the high algorithmic efficiency is maintained.
Birkholz, Adam B; Schlegel, H Bernhard
2016-05-14
Reaction path optimization is being used more frequently as an alternative to the standard practice of locating a transition state and following the path downhill. The Variational Reaction Coordinate (VRC) method was proposed as an alternative to chain-of-states methods like nudged elastic band and string method. The VRC method represents the path using a linear expansion of continuous basis functions, allowing the path to be optimized variationally by updating the expansion coefficients to minimize the line integral of the potential energy gradient norm, referred to as the Variational Reaction Energy (VRE) of the path. When constraints are used to control the spacing of basis functions and to couple the minimization of the VRE with the optimization of one or more individual points along the path (representing transition states and intermediates), an approximate path as well as the converged geometries of transition states and intermediates along the path are determined in only a few iterations. This algorithmic efficiency comes at a high per-iteration cost due to numerical integration of the VRE derivatives. In the present work, methods for incorporating redundant internal coordinates and potential energy surface interpolation into the VRC method are described. With these methods, the per-iteration cost, in terms of the number of potential energy surface evaluations, of the VRC method is reduced while the high algorithmic efficiency is maintained. PMID:27179465
A Moment-Based Condensed History Algorithm
Tolar, D.R.; Larsen, E.W.
2000-06-15
''Condensed History'' algorithms are Monte Carlo models for electron transport problems, They describe the aggregate effect of multiple collisions that occur when an electron travels a path length s{sub 0}. This path length is the distance each Monte Carlo electron travels between Condensed History steps. Conventional Condensed History schemes employ a splitting routine over the range 0 {le} s {le} s{sub 0}. For example, the Random Hinge method splits each path length step into two substeps; one with length {xi}s{sub 0} and one with length (1-{xi})s{sub 0}, where {xi} is a random number from 0 < {xi} < 1. Here we develop a new Condensed History algorithm to improve the accuracy of electron transport simulations by preserving the mean position and the variance in the mean of electrons that have traveled a path length s and are traveling with the direction cosine {mu}. These means and variances are obtained from the zeroth-, first-, and second-order spatial moments of the Boltzmann transport equation. Hence, our method is a Monte Carlo application of the ''Method of Moments''.
Fast extraction of minimal paths in 3D images and applications to virtual endoscopy.
Deschamps, T; Cohen, L D
2001-12-01
The aim of this article is to build trajectories for virtual endoscopy inside 3D medical images, using the most automatic way. Usually the construction of this trajectory is left to the clinician who must define some points on the path manually using three orthogonal views. But for a complex structure such as the colon, those views give little information on the shape of the object of interest. The path construction in 3D images becomes a very tedious task and precise a priori knowledge of the structure is needed to determine a suitable trajectory. We propose a more automatic path tracking method to overcome those drawbacks: we are able to build a path, given only one or two end points and the 3D image as inputs. This work is based on previous work by Cohen and Kimmel [Int. J. Comp. Vis. 24 (1) (1997) 57] for extracting paths in 2D images using Fast Marching algorithm. Our original contribution is twofold. On the first hand, we present a general technical contribution which extends minimal paths to 3D images and gives new improvements of the approach that are relevant in 2D as well as in 3D to extract linear structures in images. It includes techniques to make the path extraction scheme faster and easier, by reducing the user interaction. We also develop a new method to extract a centered path in tubular structures. Synthetic and real medical images are used to illustrate each contribution. On the other hand, we show that our method can be efficiently applied to the problem of finding a centered path in tubular anatomical structures with minimum interactivity, and that this path can be used for virtual endoscopy. Results are shown in various anatomical regions (colon, brain vessels, arteries) with different 3D imaging protocols (CT, MR). PMID:11731307
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
Energy-Aware Path Planning for UAS Persistent Sampling and Surveillance
NASA Astrophysics Data System (ADS)
Shaw-Cortez, Wenceslao
The focus of this work is to develop an energy-aware path planning algorithm that maximizes UAS endurance, while performing sampling and surveillance missions in a known, stationary wind environment. The energy-aware aspect is specifically tailored to extract energy from the wind to reduce thrust use, thereby increasing aircraft endurance. Wind energy extraction is performed by static soaring and dynamic soaring. Static soaring involves using upward wind currents to increase altitude and potential energy. Dynamic soaring involves taking advantage of wind gradients to exchange potential and kinetic energy. The path planning algorithm developed in this work uses optimization to combine these soaring trajectories with the overarching sampling and surveillance mission. The path planning algorithm uses a simplified aircraft model to tractably optimize soaring trajectories. This aircraft model is presented and along with the derivation of the equations of motion. A nonlinear program is used to create the soaring trajectories based on a given optimization problem. This optimization problem is defined using a heuristic decision tree, which defines appropriate problems given a sampling and surveillance mission and a wind model. Simulations are performed to assess the path planning algorithm. The results are used to identify properties of soaring trajectories as well as to determine what wind conditions support minimal thrust soaring. Additional results show how the path planning algorithm can be tuned between maximizing aircraft endurance and performing the sampling and surveillance mission. A means of trajectory stitching is demonstrated to show how the periodic soaring segments can be combined together to provide a full solution to an infinite/long horizon problem.
Last-passage Monte Carlo algorithm for mutual capacitance.
Hwang, Chi-Ok; Given, James A
2006-08-01
We develop and test the last-passage diffusion algorithm, a charge-based Monte Carlo algorithm, for the mutual capacitance of a system of conductors. The first-passage algorithm is highly efficient because it is charge based and incorporates importance sampling; it averages over the properties of Brownian paths that initiate outside the conductor and terminate on its surface. However, this algorithm does not seem to generalize to mutual capacitance problems. The last-passage algorithm, in a sense, is the time reversal of the first-passage algorithm; it involves averages over particles that initiate on an absorbing surface, leave that surface, and diffuse away to infinity. To validate this algorithm, we calculate the mutual capacitance matrix of the circular-disk parallel-plate capacitor and compare with the known numerical results. Good agreement is obtained.
A simple way to improve path consistency processing in interval algebra networks
Bessiere, C.
1996-12-31
Reasoning about qualitative temporal information is essential in many artificial intelligence problems. In particular, many tasks can be solved using the interval-based temporal algebra introduced by Allen (A1183). In this framework, one of the main tasks is to compute the transitive closure of a network of relations between intervals (also called path consistency in a CSP-like terminology). Almost all previous path consistency algorithms proposed in the temporal reasoning literature were based on the constraint reasoning algorithms PC-1 and PC-2 (Mac77). In this paper, we first show that the most efficient of these algorithms is the one which stays the closest to PC-2. Afterwards, we propose a new algorithm, using the idea {open_quotes}one support is sufficient{close_quotes} (as AC-3 (Mac77) does for arc consistency in constraint networks). Actually, to apply this idea, we simply changed the way composition-intersection of relations was achieved during the path consistency process in previous algorithms.
A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.
Sentelle, Christopher G; Anagnostopoulos, Georgios C; Georgiopoulos, Michael
2016-04-01
The support vector machine (SVM) remains a popular classifier for its excellent generalization performance and applicability of kernel methods; however, it still requires tuning of a regularization parameter, C , to achieve optimal performance. Regularization path-following algorithms efficiently solve the solution at all possible values of the regularization parameter relying on the fact that the SVM solution is piece-wise linear in C . The SVMPath originally introduced by Hastie et al., while representing a significant theoretical contribution, does not work with semidefinite kernels. Ong et al. introduce a method improved SVMPath (ISVMP) algorithm, which addresses the semidefinite kernel; however, Singular Value Decomposition or QR factorizations are required, and a linear programming solver is required to find the next C value at each iteration. We introduce a simple implementation of the path-following algorithm that automatically handles semidefinite kernels without requiring a method to detect singular matrices nor requiring specialized factorizations or an external solver. We provide theoretical results showing how this method resolves issues associated with the semidefinite kernel as well as discuss, in detail, the potential sources of degeneracy and cycling and how cycling is resolved. Moreover, we introduce an initialization method for unequal class sizes based upon artificial variables that work within the context of the existing path-following algorithm and do not require an external solver. Experiments compare performance with the ISVMP algorithm introduced by Ong et al. and show that the proposed method is competitive in terms of training time while also maintaining high accuracy. PMID:26011894
Least expected time paths in stochastic, time-varying transportation networks
Miller-Hooks, E.D.; Mahmassani, H.S.
1999-06-01
The authors consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e. in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms` computational performance as well as the properties of the solution.
Data Resource Profile: Pathways to Health and Social Equity for Children (PATHS Equity for Children)
Nickel, Nathan C; Chateau, Dan G; Martens, Patricia J; Brownell, Marni D; Katz, Alan; Burland, Elaine MJ; Walld, Randy; Hu, Mingming; Taylor, Carole R; Sarkar, Joykrishna; Goh, Chun Yan
2014-01-01
The PATHS Data Resource is a unique database comprising data that follow individuals from the prenatal period to adulthood. The PATHS Resource was developed for conducting longitudinal epidemiological research into child health and health equity. It contains individual-level data on health, socioeconomic status, social services and education. Individuals’ data are linkable across these domains, allowing researchers to follow children through childhood and across a variety of sectors. PATHS includes nearly all individuals that were born between 1984 and 2012 and registered with Manitoba’s universal health insurance programme at some point during childhood. All PATHS data are anonymized. Key concepts, definitions and algorithms necessary to work with the PATHS Resource are freely accessible online and an interactive forum is available to new researchers working with these data. The PATHS Resource is one of the richest and most complete databases assembled for conducting longitudinal epidemiological research, incorporating many variables that address the social determinants of health and health equity. Interested researchers are encouraged to contact [mchp_access@cpe.umanitoba.ca] to obtain access to PATHS to use in their own programmes of research. PMID:25212478
MLEM algorithm adaptation for improved SPECT scintimammography
NASA Astrophysics Data System (ADS)
Krol, Andrzej; Feiglin, David H.; Lee, Wei; Kunniyur, Vikram R.; Gangal, Kedar R.; Coman, Ioana L.; Lipson, Edward D.; Karczewski, Deborah A.; Thomas, F. Deaver
2005-04-01
Standard MLEM and OSEM algorithms used in SPECT Tc-99m sestamibi scintimammography produce hot-spot artifacts (HSA) at the image support peripheries. We investigated a suitable adaptation of MLEM and OSEM algorithms needed to reduce HSA. Patients with suspicious breast lesions were administered 10 mCi of Tc-99m sestamibi and SPECT scans were acquired for patients in prone position with uncompressed breasts. In addition, to simulate breast lesions, some patients were imaged with a number of breast skin markers each containing 1 mCi of Tc-99m. In order to reduce HSA in reconstruction, we removed from the backprojection step the rays that traverse the periphery of the support region on the way to a detector bin, when their path length through this region was shorter than some critical length. Such very short paths result in a very low projection counts contributed to the detector bin, and consequently to overestimation of the activity in the peripheral voxels in the backprojection step-thus creating HSA. We analyzed the breast-lesion contrast and suppression of HSA in the images reconstructed using standard and modified MLEM and OSEM algorithms vs. critical path length (CPL). For CPL >= 0.01 pixel size, we observed improved breast-lesion contrast and lower noise in the reconstructed images, and a very significant reduction of HSA in the maximum intensity projection (MIP) images.
Path Query Processing in Large-Scale XML Databases
NASA Astrophysics Data System (ADS)
Haw, Su-Cheng; Radha Krishna Rao, G. S. V.
With the ever-increasing popularity of XML (e-Xtensible Markup Language) as data representation and exchange on the Internet, querying XML data has become an important issue to be address. In Native XML Database (NXD), XML documents are usually modeled as trees and XML queries are typically specified in path expression. In path expression, the primitive structural relationships are Parent-Child (P-C) and Ancestor-Descendant (A-D). Thus, finding all occurrences of these relationships is crucial for XML query processing. Current methods for query processing on NXD usually employ either sequential traversing of tree-structured model or a decomposition-matching-merging processes. We adopt the later approach and propose a novel hybrid query optimization technique, INLAB comprising both indexing and labeling technologies. Furthermore, we also propose several algorithms to create INLAB encoding and analyze the path query. We implemented our technique and present performance results over several benchmarking datasets, which prove the viability of our approach.
Path planning in uncertain flow fields using ensemble method
NASA Astrophysics Data System (ADS)
Wang, Tong; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.
2016-08-01
An ensemble-based approach is developed to conduct 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 an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the 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 of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time 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 the sampling strategy and to develop insight into extensions dealing with general circulation ocean 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.
Energy-driven path search for Termite Retinex.
Lecca, Michela; Rizzi, Alessandro; Gianini, Gabriele
2016-01-01
The human color sensation depends on the local and global spatial arrangements of the colors in the scene. Emulating this dependence requires the exploration of the image in search of a white reference. The algorithm Termite Retinex explores the image by a set of paths resembling traces of a swarm of termites. Starting from this approach, we develop a novel spatial exploration scheme where the termite paths are local minimums of an energy function, which depend on the image visual content. The energy is designed to favor the visitation of regions containing information relevant to the color sensation while minimizing the coverage of less essential regions. This exploration method contributes to the investigation of the spatial properties of the color sensation and, to the best of our knowledge, is the first model relying on mathematical global conditions for the Retinex paths. The experiments show that the estimation of the color sensation obtained by means of the proposed spatial sampling is a valid alternative to the one based on Termite Retinex. PMID:26831582
Path planning in uncertain flow fields using ensemble method
NASA Astrophysics Data System (ADS)
Wang, Tong; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.
2016-10-01
An ensemble-based approach is developed to conduct 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 an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the 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 of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time 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 the sampling strategy and to develop insight into extensions dealing with general circulation ocean 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.
Ice Water Path Retrieval Using Microwave and Submillimetre Wave Observations
NASA Astrophysics Data System (ADS)
Brath, Manfred; Grützun, Verena; Mendrok, Jana; Fox, Stuart; Eriksson, Patrick; Buehler, Stefan A.
2016-04-01
There is an ongoing need for data on ice clouds. The ice water path as an essential climate variable is a fundamental parameter to describe ice clouds. Combined passive microwave and submillimetre wave measurements are capable to sample the size distribution of the ice particles and are sensitive to relevant particle sizes. This makes combined microwave and submillimetre wave measurements useful for estimates of ice water path. Furthermore, instead of being sensitive for the upper ice column as for example for passive visible and passive infrared measurements, combined microwave and submillimetre wave measurements can sample the full ice column. We developed a retrieval algorithm for ice water path based on a neural network approach using combined microwave and submillimetre wave measurements, from about 20 channels in the range between 89 GHz and 664 GHz of the electromagnetic sprectra. We trained a neural network by using 1D radiative transfer simulations which were conducted using the Atmospheric Radiative Transfer Simulator (ARTS). The radiative transfer simulations were fed by atmospheric profiles from a numerical weather prediction model. We will present an analysis of the retrieval. Additionally, we will present results of retrieved IWP from combined ISMAR (International SubMillimetre Airborne Radiometer) and MARSS (Microwave Airborne Radiometer Scanning System) measurements on board of the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during March 2015 over the North Atlantic.
Integrated assignment and path planning
NASA Astrophysics Data System (ADS)
Murphey, Robert A.
2005-11-01
A surge of interest in unmanned systems has exposed many new and challenging research problems across many fields of engineering and mathematics. These systems have the potential of transforming our society by replacing dangerous and dirty jobs with networks of moving machines. This vision is fundamentally separate from the modern view of robotics in that sophisticated behavior is realizable not by increasing individual vehicle complexity, but instead through collaborative teaming that relies on collective perception, abstraction, decision making, and manipulation. Obvious examples where collective robotics will make an impact include planetary exploration, space structure assembly, remote and undersea mining, hazardous material handling and clean-up, and search and rescue. Nonetheless, the phenomenon driving this technology trend is the increasing reliance of the US military on unmanned vehicles, specifically, aircraft. Only a few years ago, following years of resistance to the use of unmanned systems, the military and civilian leadership in the United States reversed itself and have recently demonstrated surprisingly broad acceptance of increasingly pervasive use of unmanned platforms in defense surveillance, and even attack. However, as rapidly as unmanned systems have gained acceptance, the defense research community has discovered the technical pitfalls that lie ahead, especially for operating collective groups of unmanned platforms. A great deal of talent and energy has been devoted to solving these technical problems, which tend to fall into two categories: resource allocation of vehicles to objectives, and path planning of vehicle trajectories. An extensive amount of research has been conducted in each direction, yet, surprisingly, very little work has considered the integrated problem of assignment and path planning. This dissertation presents a framework for studying integrated assignment and path planning and then moves on to suggest an exact
Evolutionary path control strategy for solving many-objective optimization problem.
Roy, Proteek Chandan; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin
2015-04-01
The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs. The central component of our algorithm is the use of a reference vector that helps simultaneously minimizing all the objectives of an MaOP. In doing so, EPCS employs a new fitness assignment strategy for survival selection. This strategy consists of two procedures and our algorithm applies them sequentially. It encourages a population of solutions to follow a certain path reaching toward the Pareto optimal front. The essence of our strategy is that it reduces the number of nondominated solutions to increase selection pressure in evolution. Furthermore, unlike previous work, EPCS is able to apply the classical Pareto-dominance relation with the new fitness assignment strategy. Our algorithm has been tested extensively on several scalable test problems, namely five DTLZ problems with 5 to 40 objectives and six WFG problems with 2 to 13 objectives. Furthermore, the algorithm has been tested on six CEC09 problems having 2 or 3 objectives. The experimental results show that EPCS is capable of finding better solutions compared to other existing algorithms for problems with an increasing number of objectives.
A Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts: Third Revision
NASA Technical Reports Server (NTRS)
Abbott, Terence S.
2012-01-01
This document describes an algorithm for the generation of a four dimensional trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. This version of the algorithm accommodates constant radius turns and cruise altitude waypoints with calibrated airspeed, versus Mach, constraints. The algorithm calculates the altitude, speed, along path distance, and along path time for each waypoint. Wind data at each of these waypoints are also used for the calculation of ground speed and turn radius.
Finding multiple possible critical paths using fuzzy PERT.
Chen, S M; Chang, T H
2001-01-01
Program evaluation and review techniques (PERT) is an efficient tool for large project management. In actual project control decisions, PERT has successfully been applied to business management, industry production, project scheduling control, logistics support, etc. However, classical PERT requires a crisp duration time representation for each activity. This requirement is often difficult for the decision-makers due to the fact that they usually can not estimate these values precisely. In recent years, some fuzzy PERT methods have been proposed based on fuzzy set theory for project management. However, there is a drawback in the existing fuzzy PERT methods, i.e., sometimes they maybe cannot find a critical path in a fuzzy project network. In this paper, we propose a fuzzy PERT algorithm to find multiple possible critical paths in a fuzzy project network, where the duration time of each activity in a fuzzy project network is represented by a fuzzy number. The proposed algorithm can overcome the drawback of the existing fuzzy PERT methods.
Transition paths of Met-enkephalin from Markov state modeling of a molecular dynamics trajectory.
Banerjee, Rahul; Cukier, Robert I
2014-03-20
Conformational states and their interconversion pathways of the zwitterionic form of the pentapeptide Met-enkephalin (MetEnk) are identified. An explicit solvent molecular dynamics (MD) trajectory is used to construct a Markov state model (MSM) based on dihedral space clustering of the trajectory, and transition path theory (TPT) is applied to identify pathways between open and closed conformers. In the MD trajectory, only four of the eight backbone dihedrals exhibit bistable behavior. Defining a conformer as the string XXXX with X = "+" or "-" denoting, respectively, positive or negative values of a given dihedral angle and obtaining the populations of these conformers shows that only four conformers are highly populated, implying a strong correlation among these dihedrals. Clustering in dihedral space to construct the MSM finds the same four bistable dihedral angles. These state populations are very similar to those found directly from the MD trajectory. TPT is used to obtain pathways, parametrized by committor values, in dihedral state space that are followed in transitioning from closed to open states. Pathway costs are estimated by introducing a kinetics-based procedure that orders pathways from least (shortest) to greater cost paths. The least costly pathways in dihedral space are found to only involve the same XXXX set of dihedral angles, and the conformers accessed in the closed to open transition pathways are identified. For these major pathways, a correlation between reaction path progress (committors) and the end-to-end distance is identified. A dihedral space principal component analysis of the MD trajectory shows that the first three modes capture most of the overall fluctuation, and pick out the same four dihedrals having essentially all the weight in those modes. A MSM based on root-mean-square backbone clustering was also carried out, with good agreement found with dihedral clustering for the static information, but with results that differ
The path to adaptive microsystems
NASA Astrophysics Data System (ADS)
Zolper, John C.; Biercuk, Michael J.
2006-05-01
Scaling trends in microsystems are discussed frequently in the technical community, providing a short-term perspective on the future of integrated microsystems. This paper looks beyond the leading edge of technological development, focusing on new microsystem design paradigms that move far beyond today's systems based on static components. We introduce the concept of Adaptive Microsystems and outline a path to realizing these systems-on-a-chip. The role of DARPA in advancing future components and systems research is discussed, and specific DARPA efforts enabling and producing adaptive microsystems are presented. In particular, we discuss efforts underway in the DARPA Microsystems Technology Office (MTO) including programs in novel circuit architectures (3DIC), adaptive imaging and sensing (AFPA, VISA, MONTAGE, A-to-I) and reconfigurable RF/Microwave devices (SMART, TFAST, IRFFE).
Path integral simulations for nanoelectronics
NASA Astrophysics Data System (ADS)
Shumway, John
2007-10-01
As computer circuits shrink, devices are entering the nanoscale regime and quantum physics is becoming important. The biggest barrier to further decreases in size and increases in clock speed is excessive heat generation. Some physicists are proposing that many-body correlated quantum states of electrons may be exploited to make more energy efficient switches. In our research we are developing new simulation techniques to study highly correlated electron states in realistic device geometries and finite temperatures. The simulations are based on Feynman path integrals, which cast quantum statistical mechanics as a sum over worldlines, a mathematically equivalent alternative Schroedinger's differetial equation. Using Monte Carlo sampling on dozens to hundreds of electrons, we can simulate properties of an interacting electron fluid in a nanowire. Linear response theory relates fluctuations about equilibrium to conductivity. This gives us a new perspective on quantum phenomena, including quantized conductance steps and spin-charge separation.
The Logic Behind Feynman's Paths
NASA Astrophysics Data System (ADS)
García Álvarez, Edgardo T.
The classical notions of continuity and mechanical causality are left in order to reformulate the Quantum Theory starting from two principles: (I) the intrinsic randomness of quantum process at microphysical level, (II) the projective representations of symmetries of the system. The second principle determines the geometry and then a new logic for describing the history of events (Feynman's paths) that modifies the rules of classical probabilistic calculus. The notion of classical trajectory is replaced by a history of spontaneous, random and discontinuous events. So the theory is reduced to determining the probability distribution for such histories accordingly with the symmetries of the system. The representation of the logic in terms of amplitudes leads to Feynman rules and, alternatively, its representation in terms of projectors results in the Schwinger trace formula.
Counting paths with Schur transitions
NASA Astrophysics Data System (ADS)
Díaz, Pablo; Kemp, Garreth; Véliz-Osorio, Alvaro
2016-10-01
In this work we explore the structure of the branching graph of the unitary group using Schur transitions. We find that these transitions suggest a new combinatorial expression for counting paths in the branching graph. This formula, which is valid for any rank of the unitary group, reproduces known asymptotic results. We proceed to establish the general validity of this expression by a formal proof. The form of this equation strongly hints towards a quantum generalization. Thus, we introduce a notion of quantum relative dimension and subject it to the appropriate consistency tests. This new quantity finds its natural environment in the context of RCFTs and fractional statistics; where the already established notion of quantum dimension has proven to be of great physical importance.
Extracting Critical Path Graphs from MPI Applications
Schulz, M
2005-07-27
The critical path is one of the fundamental runtime characteristics of a parallel program. It identifies the longest execution sequence without wait delays. In other words, the critical path is the global execution path that inflicts wait operations on other nodes without itself being stalled. Hence, it dictates the overall runtime and knowing it is important to understand an application's runtime and message behavior and to target optimizations. We have developed a toolset that identifies the critical path of MPI applications, extracts it, and then produces a graphical representation of the corresponding program execution graph to visualize it. To implement this, we intercept all MPI library calls, use the information to build the relevant subset of the execution graph, and then extract the critical path from there. We have applied our technique to several scientific benchmarks and successfully produced critical path diagrams for applications running on up to 128 processors.
Flexible-Path Human Exploration
NASA Technical Reports Server (NTRS)
Sherwood, B.; Adler, M.; Alkalai, L.; Burdick, G.; Coulter, D.; Jordan, F.; Naderi, F.; Graham, L.; Landis, R.; Drake, B.; Hoffman, S.; Grunsfeld, J.; Seery, B. D.
2010-01-01
In the fourth quarter of 2009 an in-house, multi-center NASA study team briefly examined "Flexible Path" concepts to begin understanding characteristics, content, and roles of potential missions consistent with the strategy proposed by the Augustine Committee. We present an overview of the study findings. Three illustrative human/robotic mission concepts not requiring planet surface operations are described: assembly of very large in-space telescopes in cis-lunar space; exploration of near Earth objects (NEOs); exploration of Mars' moon Phobos. For each, a representative mission is described, technology and science objectives are outlined, and a basic mission operations concept is quantified. A fourth type of mission, using the lunar surface as preparation for Mars, is also described. Each mission's "capability legacy" is summarized. All four illustrative missions could achieve NASA's stated human space exploration objectives and advance human space flight toward Mars surface exploration. Telescope assembly missions would require the fewest new system developments. NEO missions would offer a wide range of deep-space trip times between several months and two years. Phobos exploration would retire several Marsclass risks, leaving another large remainder set (associated with entry, descent, surface operations, and ascent) for retirement by subsequent missions. And extended lunar surface operations would build confidence for Mars surface missions by addressing a complementary set of risks. Six enabling developments (robotic precursors, ISS exploration testbed, heavy-lift launch, deep-space-capable crew capsule, deep-space habitat, and reusable in-space propulsion stage) would apply across multiple program sequence options, and thus could be started even without committing to a specific mission sequence now. Flexible Path appears to be a viable strategy, with meaningful and worthy mission content.
Detecting curves with unknown endpoints and arbitrary topology using minimal paths.
Kaul, Vivek; Yezzi, Anthony; Tsai, Yichang James
2012-10-01
Existing state-of-the-art minimal path techniques work well to extract simple open curves in images when both endpoints of the curve are given as user input or when one input is given and the total length of the curve is known in advance. Curves which branch require even further prior input from the user, namely, each branch endpoint. In this work, we present a novel minimal path-based algorithm which works on much more general curve topologies with far fewer demands on the user for initial input compared to prior minimal path-based algorithms. The two key novelties and benefits of this new approach are that 1) it may be used to detect both open and closed curves, including more complex topologies containing both multiple branch points and multiple closed cycles without requiring a priori knowledge about which of these types is to be extracted, and 2) it requires only a single input point which, in contrast to existing methods, is no longer constrained to be an endpoint of the desired curve but may in fact be ANY point along the desired curve (even an internal point). We perform quantitative evaluation of the algorithm on 48 images (44 pavement crack images, 1 catheter tube image, and 3 retinal images) against human supplied ground truth. The results demonstrate that the algorithm is indeed able to extract curve-like objects accurately from images with far less prior knowledge and less user interaction compared to existing state-of-the-art minimal path-based image processing algorithms. In the future, the algorithm can be applied to other 2D curve-like objects and it can be extended to detect 3D curves.
Geodesics on path spaces and double category
NASA Astrophysics Data System (ADS)
Chatterjee, Saikat
2016-09-01
Let M be a Riemannian manifold and 𝒫M be the space of all smooth paths on M. We describe geodesics on path space 𝒫M. Normal neighborhoods on 𝒫M have been discussed. We identify paths on M under “back-track” equivalence. Under this identification, we show that if M is complete, then geodesics on the path space yield a double category. This double category has a natural interpretation in terms of the worldsheets generated by freely moving (without any external force) strings.
A note on the path interval distance.
Coons, Jane Ivy; Rusinko, Joseph
2016-06-01
The path interval distance accounts for global congruence between locally incongruent trees. We show that the path interval distance provides a lower bound for the nearest neighbor interchange distance. In contrast to the Robinson-Foulds distance, random pairs of trees are unlikely to be maximally distant from one another under the path interval distance. These features indicate that the path interval distance should play a role in phylogenomics where the comparison of trees on a fixed set of taxa is becoming increasingly important. PMID:27040521
A note on the path interval distance.
Coons, Jane Ivy; Rusinko, Joseph
2016-06-01
The path interval distance accounts for global congruence between locally incongruent trees. We show that the path interval distance provides a lower bound for the nearest neighbor interchange distance. In contrast to the Robinson-Foulds distance, random pairs of trees are unlikely to be maximally distant from one another under the path interval distance. These features indicate that the path interval distance should play a role in phylogenomics where the comparison of trees on a fixed set of taxa is becoming increasingly important.