A Global Network Alignment Method Using Discrete Particle Swarm Optimization.
Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia
2016-10-19
Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.
Taylor, Dane; Skardal, Per Sebastian; Sun, Jie
2016-01-01
Synchronization is central to many complex systems in engineering physics (e.g., the power-grid, Josephson junction circuits, and electro-chemical oscillators) and biology (e.g., neuronal, circadian, and cardiac rhythms). Despite these widespread applications—for which proper functionality depends sensitively on the extent of synchronization—there remains a lack of understanding for how systems can best evolve and adapt to enhance or inhibit synchronization. We study how network modifications affect the synchronization properties of network-coupled dynamical systems that have heterogeneous node dynamics (e.g., phase oscillators with non-identical frequencies), which is often the case for real-world systems. Our approach relies on a synchrony alignment function (SAF) that quantifies the interplay between heterogeneity of the network and of the oscillators and provides an objective measure for a system’s ability to synchronize. We conduct a spectral perturbation analysis of the SAF for structural network modifications including the addition and removal of edges, which subsequently ranks the edges according to their importance to synchronization. Based on this analysis, we develop gradient-descent algorithms to efficiently solve optimization problems that aim to maximize phase synchronization via network modifications. We support these and other results with numerical experiments. PMID:27872501
Probabilistic biological network alignment.
Todor, Andrei; Dobra, Alin; Kahveci, Tamer
2013-01-01
Interactions between molecules are probabilistic events. An interaction may or may not happen with some probability, depending on a variety of factors such as the size, abundance, or proximity of the interacting molecules. In this paper, we consider the problem of aligning two biological networks. Unlike existing methods, we allow one of the two networks to contain probabilistic interactions. Allowing interaction probabilities makes the alignment more biologically relevant at the expense of explosive growth in the number of alternative topologies that may arise from different subsets of interactions that take place. We develop a novel method that efficiently and precisely characterizes this massive search space. We represent the topological similarity between pairs of aligned molecules (i.e., proteins) with the help of random variables and compute their expected values. We validate our method showing that, without sacrificing the running time performance, it can produce novel alignments. Our results also demonstrate that our method identifies biologically meaningful mappings under a comprehensive set of criteria used in the literature as well as the statistical coherence measure that we developed to analyze the statistical significance of the similarity of the functions of the aligned protein pairs.
Pareto optimal pairwise sequence alignment.
DeRonne, Kevin W; Karypis, George
2013-01-01
Sequence alignment using evolutionary profiles is a commonly employed tool when investigating a protein. Many profile-profile scoring functions have been developed for use in such alignments, but there has not yet been a comprehensive study of Pareto optimal pairwise alignments for combining multiple such functions. We show that the problem of generating Pareto optimal pairwise alignments has an optimal substructure property, and develop an efficient algorithm for generating Pareto optimal frontiers of pairwise alignments. All possible sets of two, three, and four profile scoring functions are used from a pool of 11 functions and applied to 588 pairs of proteins in the ce_ref data set. The performance of the best objective combinations on ce_ref is also evaluated on an independent set of 913 protein pairs extracted from the BAliBASE RV11 data set. Our dynamic-programming-based heuristic approach produces approximated Pareto optimal frontiers of pairwise alignments that contain comparable alignments to those on the exact frontier, but on average in less than 1/58th the time in the case of four objectives. Our results show that the Pareto frontiers contain alignments whose quality is better than the alignments obtained by single objectives. However, the task of identifying a single high-quality alignment among those in the Pareto frontier remains challenging.
BinAligner: a heuristic method to align biological networks.
Yang, Jialiang; Li, Jun; Grünewald, Stefan; Wan, Xiu-Feng
2013-01-01
The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and
GraphAlignment: Bayesian pairwise alignment of biological networks
Directory of Open Access Journals (Sweden)
Kolář Michal
2012-11-01
Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.
Optimal Nonlinear Filter for INS Alignment
Institute of Scientific and Technical Information of China (English)
赵瑞; 顾启泰
2002-01-01
All the methods to handle the inertial navigation system (INS) alignment were sub-optimal in the past. In this paper, particle filtering (PF) as an optimal method is used for solving the problem of INS alignment. A sub-optimal two-step filtering algorithm is presented to improve the real-time performance of PF. The approach combines particle filtering with Kalman filtering (KF). Simulation results illustrate the superior performance of these approaches when compared with extended Kalman filtering (EKF).
Aligning Biomolecular Networks Using Modular Graph Kernels
Towfic, Fadi; Greenlee, M. Heather West; Honavar, Vasant
Comparative analysis of biomolecular networks constructed using measurements from different conditions, tissues, and organisms offer a powerful approach to understanding the structure, function, dynamics, and evolution of complex biological systems. We explore a class of algorithms for aligning large biomolecular networks by breaking down such networks into subgraphs and computing the alignment of the networks based on the alignment of their subgraphs. The resulting subnetworks are compared using graph kernels as scoring functions. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit. Our experiments using Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository of protein-protein interaction data demonstrate that the performance of the proposed algorithms (as measured by % GO term enrichment of subnetworks identified by the alignment) is competitive with some of the state-of-the-art algorithms for pair-wise alignment of large protein-protein interaction networks. Our results also show that the inter-species similarity scores computed based on graph kernels can be used to cluster the species into a species tree that is consistent with the known phylogenetic relationships among the species.
L-GRAAL: Lagrangian graphlet-based network aligner.
Malod-Dognin, Noël; Pržulj, Nataša
2015-07-01
Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at
GraphAlignment: Bayesian pairwise alignment of biological networks
Czech Academy of Sciences Publication Activity Database
Kolář, Michal; Meier, J.; Mustonen, V.; Lässig, M.; Berg, J.
2012-01-01
Roč. 6, November 21 (2012) ISSN 1752-0509 Grant - others:Deutsche Forschungsgemeinschaft(DE) SFB 680; Deutsche Forschungsgemeinschaft(DE) SFB-TR12; Deutsche Forschungsgemeinschaft(DE) BE 2478/2-1 Institutional research plan: CEZ:AV0Z50520514 Keywords : Graph alignment * Biological networks * Parameter estimation * Bioconductor Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.982, year: 2012
Multiple network alignment on quantum computers
Daskin, Anmer; Grama, Ananth; Kais, Sabre
2014-12-01
Comparative analyses of graph-structured datasets underly diverse problems. Examples of these problems include identification of conserved functional components (biochemical interactions) across species, structural similarity of large biomolecules, and recurring patterns of interactions in social networks. A large class of such analyses methods quantify the topological similarity of nodes across networks. The resulting correspondence of nodes across networks, also called node alignment, can be used to identify invariant subgraphs across the input graphs. Given graphs as input, alignment algorithms use topological information to assign a similarity score to each -tuple of nodes, with elements (nodes) drawn from each of the input graphs. Nodes are considered similar if their neighbors are also similar. An alternate, equivalent view of these network alignment algorithms is to consider the Kronecker product of the input graphs and to identify high-ranked nodes in the Kronecker product graph. Conventional methods such as PageRank and HITS (Hypertext-Induced Topic Selection) can be used for this purpose. These methods typically require computation of the principal eigenvector of a suitably modified Kronecker product matrix of the input graphs. We adopt this alternate view of the problem to address the problem of multiple network alignment. Using the phase estimation algorithm, we show that the multiple network alignment problem can be efficiently solved on quantum computers. We characterize the accuracy and performance of our method and show that it can deliver exponential speedups over conventional (non-quantum) methods.
Unified Alignment of Protein-Protein Interaction Networks.
Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša
2017-04-19
Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.
Ancestral sequence alignment under optimal conditions
Directory of Open Access Journals (Sweden)
Brown Daniel G
2005-11-01
Full Text Available Abstract Background Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences. Results We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang. Conclusion We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the
Toward Optimal Transport Networks
Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.
2008-01-01
Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.
Towards Optimal Transport Networks
Directory of Open Access Journals (Sweden)
Erik P. Vargo
2010-08-01
Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment
Energy Technology Data Exchange (ETDEWEB)
Mohammadi, Shahin [Purdue Univ., West Lafayette, IN (United States); Gleich, David F. [Purdue Univ., West Lafayette, IN (United States); Kolda, Tamara G. [Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Grama, Ananth [Purdue Univ., West Lafayette, IN (United States)
2015-11-01
Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.
DEFF Research Database (Denmark)
Hansen, C. T.; Madsen, Kaj; Nielsen, Hans Bruun
1991-01-01
algorithm using successive linear programming is presented. The performance of the algorithm is illustrated by optimizing a network with 201 pipes and 172 nodes. It is concluded that the new algorithm seems to be very efficient and stable, and that it always finds a solution with a cost near the best...
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Improving scanner wafer alignment performance by target optimization
Leray, Philippe; Jehoul, Christiane; Socha, Robert; Menchtchikov, Boris; Raghunathan, Sudhar; Kent, Eric; Schoonewelle, Hielke; Tinnemans, Patrick; Tuffy, Paul; Belen, Jun; Wise, Rich
2016-03-01
In the process nodes of 10nm and below, the patterning complexity along with the processing and materials required has resulted in a need to optimize alignment targets in order to achieve the required precision, accuracy and throughput performance. Recent industry publications on the metrology target optimization process have shown a move from the expensive and time consuming empirical methodologies, towards a faster computational approach. ASML's Design for Control (D4C) application, which is currently used to optimize YieldStar diffraction based overlay (DBO) metrology targets, has been extended to support the optimization of scanner wafer alignment targets. This allows the necessary process information and design methodology, used for DBO target designs, to be leveraged for the optimization of alignment targets. In this paper, we show how we applied this computational approach to wafer alignment target design. We verify the correlation between predictions and measurements for the key alignment performance metrics and finally show the potential alignment and overlay performance improvements that an optimized alignment target could achieve.
Node fingerprinting: an efficient heuristic for aligning biological networks.
Radu, Alex; Charleston, Michael
2014-10-01
With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.
Value Systems Alignment Analysis in Collaborative Networked Organizations Management
Patricia Macedo; Luis Camarinha-Matos
2017-01-01
The assessment of value systems alignment can play an important role in the formation and evolution of collaborative networks, contributing to reduce potential risks of collaboration. For this purpose, an assessment tool is proposed as part of a collaborative networks information system, supporting both the formation and evolution of long-term strategic alliances and goal-oriented networks. An implementation approach for value system alignment analysis is described, which is intended to assis...
Optimal Network-Topology Design
Li, Victor O. K.; Yuen, Joseph H.; Hou, Ting-Chao; Lam, Yuen Fung
1987-01-01
Candidate network designs tested for acceptability and cost. Optimal Network Topology Design computer program developed as part of study on topology design and analysis of performance of Space Station Information System (SSIS) network. Uses efficient algorithm to generate candidate network designs consisting of subsets of set of all network components, in increasing order of total costs and checks each design to see whether it forms acceptable network. Technique gives true cost-optimal network and particularly useful when network has many constraints and not too many components. Program written in PASCAL.
Finding optimal interaction interface alignments between biological complexes
Cui, Xuefeng
2015-06-13
Motivation: Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which are keys to understanding their evolutionary histories and functions. Although various structure alignment methods have been developed to successfully access the similarities of protein structures or certain types of interaction interfaces, existing alignment tools cannot directly align arbitrary types of interfaces formed by protein, DNA or RNA molecules. Specifically, they require a \\'blackbox preprocessing\\' to standardize interface types and chain identifiers. Yet their performance is limited and sometimes unsatisfactory. Results: Here we introduce a novel method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures. Our method uses sequentially remote fragments to search for the optimal superimposition. The optimal residue matching problem is then formulated as a maximum weighted bipartite matching problem to detect the optimal sequence order-independent alignment. Benchmark evaluation on all non-redundant protein-DNA complexes in PDB shows significant performance improvement of our method over TM-align and iAlign (with the \\'blackbox preprocessing\\'). Two case studies where our method discovers, for the first time, structural similarities between two pairs of functionally related protein-DNA complexes are presented. We further demonstrate the power of our method on detecting structural similarities between a protein-protein complex and a protein-RNA complex, which is biologically known as a protein-RNA mimicry case. © The Author 2015. Published by Oxford University Press.
Post-pit optimization strategic alignment
Breed, M.F.; van Heerden, D.
2016-01-01
Successful development of projects or life-of-mine strategies requires an understanding of the relative sensitivity of value drivers such as grade, tonnage, energy costs, direct operational costs, and recoveries. For example, the results could vary significantly depending on the grade strategy, given a specific orebody amenable to open pitting. Pit optimization is a very powerful tool widely used in the industry to determine the pit shell with the most attractive value potential. Based on the...
Airborne Network Optimization with Dynamic Network Update
2015-03-26
source si and a target ti . For each commodity (si, ki) the commodity specifies a non- negative demand di [5]. The objective of the multi-commodity...queue predictions, and network con- gestion [15]. The implementation of the DRQC uses the Kalman filter to predict the state of the network and optimize
Waferscale assembly of Field-Aligned nanotube Networks (FANs)
DEFF Research Database (Denmark)
Dimaki, Maria; Bøggild, Peter
2006-01-01
We demonstrate the integration of nanotube networks on 512 individual devices on a full 4-inch wafer in less than 60 seconds with a roughly 80% yield using dielectrophoresis. We present here investigations of the morphology and electrical resistance of such field aligned networks for different fr...
A Network Model of Interpersonal Alignment in Dialog
Directory of Open Access Journals (Sweden)
Alexander Mehler
2010-06-01
Full Text Available In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations.
AlignNemo: a local network alignment method to integrate homology and topology.
Directory of Open Access Journals (Sweden)
Giovanni Ciriello
Full Text Available Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.
Optimal Fragile Financial Networks
Castiglionesi, F.; Navarro, N.
2007-01-01
We study a financial network characterized by the presence of depositors, banks and their shareholders. Belonging to a financial network is beneficial for both the depositors and banks' shareholders since the return to investment increases with the number of banks connected. However, the network is
Numerical optimization of alignment reproducibility for customizable surgical guides.
Kroes, Thomas; Valstar, Edward; Eisemann, Elmar
2015-10-01
Computer-assisted orthopedic surgery aims at minimizing invasiveness, postoperative pain, and morbidity with computer-assisted preoperative planning and intra-operative guidance techniques, of which camera-based navigation and patient-specific templates (PST) are the most common. PSTs are one-time templates that guide the surgeon initially in cutting slits or drilling holes. This method can be extended to reusable and customizable surgical guides (CSG), which can be adapted to the patients' bone. Determining the right set of CSG input parameters by hand is a challenging task, given the vast amount of input parameter combinations and the complex physical interaction between the PST/CSG and the bone. This paper introduces a novel algorithm to solve the problem of choosing the right set of input parameters. Our approach predicts how well a CSG instance is able to reproduce the planned alignment based on a physical simulation and uses a genetic optimization algorithm to determine optimal configurations. We validate our technique with a prototype of a pin-based CSG and nine rapid prototyped distal femora. The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience. Using the optimization technique, the alignment errors remained within practical boundaries of 1.2 mm translation and [Formula: see text] rotation error. In all cases, the proposed method outperformed manual optimization. Manually optimizing CSG parameters turns out to be a counterintuitive task. Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations. Our optimization algorithm ensures that the CSG is configured correctly, and we could demonstrate that the intended alignment of the CSG is accurately reproduced on all tested bone geometries.
An Alignment Model for Collaborative Value Networks
Bremer, Carlos; Azevedo, Rodrigo Cambiaghi; Klen, Alexandra Pereira
This paper presents parts of the work carried out in several global organizations through the development of strategic projects with high tactical and operational complexity. By investing in long-term relationships, strongly operating in the transformation of the competitive model and focusing on the value chain management, the main aim of these projects was the alignment of multiple value chains. The projects were led by the Axia Transformation Methodology as well as by its Management Model and following the principles of Project Management. As a concrete result of the efforts made in the last years in the Brazilian market this work also introduces the Alignment Model which supports the transformation process that the companies undergo.
Distributed interference alignment iterative algorithms in symmetric wireless network
Directory of Open Access Journals (Sweden)
YANG Jingwen
2015-02-01
Full Text Available Interference alignment is a novel interference alignment way,which is widely noted all of the world.Interference alignment overlaps interference in the same signal space at receiving terminal by precoding so as to thoroughly eliminate the influence of interference impacted on expected signals,thus making the desire user achieve the maximum degree of freedom.In this paper we research three typical algorithms for realizing interference alignment,including minimizing the leakage interference,maximizing Signal to Interference plus Noise Ratio (SINR and minimizing mean square error(MSE.All of these algorithms utilize the reciprocity of wireless network,and iterate the precoders between original network and the reverse network so as to achieve interference alignment.We use the uplink transmit rate to analyze the performance of these three algorithms.Numerical simulation results show the advantages of these algorithms.which is the foundation for the further study in the future.The feasibility and future of interference alignment are also discussed at last.
Functional alignment of regulatory networks: a study of temperate phages.
Directory of Open Access Journals (Sweden)
Ala Trusina
2005-12-01
Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.
Value Systems Alignment Analysis in Collaborative Networked Organizations Management
Directory of Open Access Journals (Sweden)
Patricia Macedo
2017-11-01
Full Text Available The assessment of value systems alignment can play an important role in the formation and evolution of collaborative networks, contributing to reduce potential risks of collaboration. For this purpose, an assessment tool is proposed as part of a collaborative networks information system, supporting both the formation and evolution of long-term strategic alliances and goal-oriented networks. An implementation approach for value system alignment analysis is described, which is intended to assist managers in virtual and networked organizations management. The implementation of the assessment and analysis methods is supported by a set of software services integrated in the information system that supports the management of the networked organizations. A case study in the solar energy sector was conducted, and the data collected through this study allow us to confirm the practical applicability of the proposed methods and the software services.
Optimal alignment of mirror based pentaprisms for scanning deflectometric devices
Energy Technology Data Exchange (ETDEWEB)
Barber, Samuel K.; Geckeler, Ralf D.; Yashchuk, Valeriy V.; Gubarev, Mikhail V.; Buchheim, Jana; Siewert, Frank; Zeschke, Thomas
2011-03-04
In the recent work [Proc. of SPIE 7801, 7801-2/1-12 (2010), Opt. Eng. 50(5) (2011), in press], we have reported on improvement of the Developmental Long Trace Profiler (DLTP), a slope measuring profiler available at the Advanced Light Source Optical Metrology Laboratory, achieved by replacing the bulk pentaprism with a mirror based pentaprism (MBPP). An original experimental procedure for optimal mutual alignment of the MBPP mirrors has been suggested and verified with numerical ray tracing simulations. It has been experimentally shown that the optimally aligned MBPP allows the elimination of systematic errors introduced by inhomogeneity of the optical material and fabrication imperfections of the bulk pentaprism. In the present article, we provide the analytical derivation and verification of easily executed optimal alignment algorithms for two different designs of mirror based pentaprisms. We also provide an analytical description for the mechanism for reduction of the systematic errors introduced by a typical high quality bulk pentaprism. It is also shown that residual misalignments of an MBPP introduce entirely negligible systematic errors in surface slope measurements with scanning deflectometric devices.
Combinatorial optimization networks and matroids
Lawler, Eugene
2011-01-01
Perceptively written text examines optimization problems that can be formulated in terms of networks and algebraic structures called matroids. Chapters cover shortest paths, network flows, bipartite matching, nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. A suitable text or reference for courses in combinatorial computing and concrete computational complexity in departments of computer science and mathematics.
Use artificial neural network to align biological ontologies.
Huang, Jingshan; Dang, Jiangbo; Huhns, Michael N; Zheng, W Jim
2008-09-16
Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.
Location based Network Optimizations for Mobile Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen
selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...
Self-optimizing approach for automated laser resonator alignment
Brecher, C.; Schmitt, R.; Loosen, P.; Guerrero, V.; Pyschny, N.; Pavim, A.; Gatej, A.
2012-02-01
Nowadays, the assembly of laser systems is dominated by manual operations, involving elaborate alignment by means of adjustable mountings. From a competition perspective, the most challenging problem in laser source manufacturing is price pressure, a result of cost competition exerted mainly from Asia. From an economical point of view, an automated assembly of laser systems defines a better approach to produce more reliable units at lower cost. However, the step from today's manual solutions towards an automated assembly requires parallel developments regarding product design, automation equipment and assembly processes. This paper introduces briefly the idea of self-optimizing technical systems as a new approach towards highly flexible automation. Technically, the work focuses on the precision assembly of laser resonators, which is one of the final and most crucial assembly steps in terms of beam quality and laser power. The paper presents a new design approach for miniaturized laser systems and new automation concepts for a robot-based precision assembly, as well as passive and active alignment methods, which are based on a self-optimizing approach. Very promising results have already been achieved, considerably reducing the duration and complexity of the laser resonator assembly. These results as well as future development perspectives are discussed.
Optimization in a Networked Economy
Directory of Open Access Journals (Sweden)
Ahmet Sekreter
2017-10-01
Full Text Available An age of network has been living for the last decades. The information technologies have been used by hundreds of millions of users. These technologies are enabling to connect businesses and economic activities. One of the characteristics of the networked economy is the amount of data that produced due to the interlinking of firms, individuals, processes by businesses, and economic activities. Another issue with the networked economy is the complexity of the data. Extraction of the knowledge from the networked economy has challenges by the traditional approach since data is large scale, second decentralized, and third they connect many heterogeneous agents. The challenges can be overcome by the new optimization methods including human element or the social interactions with technological infrastructure.
DEFF Research Database (Denmark)
Nielsen, Morten; Lund, Ole
2009-01-01
this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...
Optimization of Actuating Origami Networks
Buskohl, Philip; Fuchi, Kazuko; Bazzan, Giorgio; Joo, James; Gregory, Reich; Vaia, Richard
2015-03-01
Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form, function and mobility of the structure. By leveraging design concepts from action origami, a subset of origami art focused on kinematic mechanisms, reversible folding patterns for applications such as solar array packaging, tunable antennae, and deployable sensing platforms may be designed. However, the enormity of the design space and the need to identify the requisite actuation forces within the structure places a severe limitation on design strategies based on intuition and geometry alone. The present work proposes a topology optimization method, using truss and frame element analysis, to distribute foldline mechanical properties within a reference crease pattern. Known actuating patterns are placed within a reference grid and the optimizer adjusts the fold stiffness of the network to optimally connect them. Design objectives may include a target motion, stress level, or mechanical energy distribution. Results include the validation of known action origami structures and their optimal connectivity within a larger network. This design suite offers an important step toward systematic incorporation of origami design concepts into new, novel and reconfigurable engineering devices. This research is supported under the Air Force Office of Scientific Research (AFOSR) funding, LRIR 13RQ02COR.
Optimizations in Heterogeneous Mobile Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana
nodes. The independent control of the user’s transmit power at each node may cause degradation of the overall performance. In this line, a dedicated study of power distribution among the carriers is performed. An optimization of the power allocation is proposed and evaluated. The results show...... significant performance improvement to the achieved user throughput in low as well as in high loads in the cell. The flow control of the data between the nodes is another challenge for effective aggregation of the resources in case of dual connectivity. As such, this thesis discusses the challenges...... with the densification of the base stations, bring into a very complex network management and operation control for the mobile operators. Furthermore, the need to provide always best connection and service with high quality demands for a joint overall network resource management. This thesis addresses this challenge...
Quantized hopfield networks for reliability optimization
International Nuclear Information System (INIS)
Nourelfath, Mustapha; Nahas, Nabil
2003-01-01
The use of neural networks in the reliability optimization field is rare. This paper presents an application of a recent kind of neural networks in a reliability optimization problem for a series system with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. The problem is formulated as a nonlinear binary integer programming problem and characterized as an NP-hard problem. Our design of neural network to solve efficiently this problem is based on a quantized Hopfield network. This network allows us to obtain optimal design solutions very frequently and much more quickly than others Hopfield networks
Junqueira Leão, Rodrigo; Raffaelo Baldo, Crhistian; Collucci da Costa Reis, Maria Luisa; Alves Trabanco, Jorge Luiz
2018-03-01
The building blocks of particle accelerators are magnets responsible for keeping beams of charged particles at a desired trajectory. Magnets are commonly grouped in support structures named girders, which are mounted on vertical and horizontal stages. The performance of this type of machine is highly dependent on the relative alignment between its main components. The length of particle accelerators ranges from small machines to large-scale national or international facilities, with typical lengths of hundreds of meters to a few kilometers. This relatively large volume together with micrometric positioning tolerances make the alignment activity a classical large-scale dimensional metrology problem. The alignment concept relies on networks of fixed monuments installed on the building structure to which all accelerator components are referred. In this work, the Sirius accelerator is taken as a case study, and an alignment network is optimized via computational methods in terms of geometry, densification, and surveying procedure. Laser trackers are employed to guide the installation and measure the girders’ positions, using the optimized network as a reference and applying the metric developed in part I of this paper. Simulations demonstrate the feasibility of aligning the 220 girders of the Sirius synchrotron to better than 0.080 mm, at a coverage probability of 95%.
What is the optimal alignment of the tibial and femoral components in knee arthroplasty?
DEFF Research Database (Denmark)
Gromov, Kirill; Korchi, Mounim; Thomsen, Morten G
2014-01-01
of positioning on survival and functional outcome was considered. Results - Many definitions exist when evaluating placement of femoral and tibial components. Implant alignment plays a role in both survival and functional outcome following primary TKA, as component malalignment can lead to increased failure......Background - Surgeon-dependent factors such as optimal implant alignment are thought to play a significant role in outcome following primary total knee arthroplasty (TKA). Exact definitions and references for optimal alignment are, however, still being debated. This overview of the literature...... describes different definitions of component alignment following primary TKA for (1) tibiofemoral alignment in the AP plane, (2) tibial and femoral component placement in the AP plane, (3) tibial and femoral component placement in the sagittal plane, and (4) rotational alignment of tibial and femoral...
RNA-Pareto: interactive analysis of Pareto-optimal RNA sequence-structure alignments.
Schnattinger, Thomas; Schöning, Uwe; Marchfelder, Anita; Kestler, Hans A
2013-12-01
Incorporating secondary structure information into the alignment process improves the quality of RNA sequence alignments. Instead of using fixed weighting parameters, sequence and structure components can be treated as different objectives and optimized simultaneously. The result is not a single, but a Pareto-set of equally optimal solutions, which all represent different possible weighting parameters. We now provide the interactive graphical software tool RNA-Pareto, which allows a direct inspection of all feasible results to the pairwise RNA sequence-structure alignment problem and greatly facilitates the exploration of the optimal solution set.
Finding optimal interaction interface alignments between biological complexes
Cui, Xuefeng; Naveed, Hammad; Gao, Xin
2015-01-01
Motivation: Biological molecules perform their functions through interactions with other molecules. Structure alignment of interaction interfaces between biological complexes is an indispensable step in detecting their structural similarities, which
SOCIAL NETWORK OPTIMIZATION A NEW METHAHEURISTIC FOR GENERAL OPTIMIZATION PROBLEMS
Directory of Open Access Journals (Sweden)
Hassan Sherafat
2017-12-01
Full Text Available In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Optimal topologies for maximizing network transmission capacity
Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.
2018-04-01
It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.
Business-IT alignment in PSS value networks : a capability-based framework
Bagheri, S.; Kusters, R.J.; Trienekens, J.J.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.
2014-01-01
Advanced information technology (IT) is regarded as a foundation for the operation of product-service system (PSS) value networks. This requires alignment between IT and PSS business strategy. Business‐IT alignment (BIA) in a value network can raise the ability of partners to collaborate effectively
Optimizing the next generation optical access networks
DEFF Research Database (Denmark)
Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso
2009-01-01
Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect...
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Optimization of temporal networks under uncertainty
Wiesemann, Wolfram
2012-01-01
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl
Optimal urban networks via mass transportation
Buttazzo, Giuseppe; Stepanov, Eugene; Solimini, Sergio
2009-01-01
Recently much attention has been devoted to the optimization of transportation networks in a given geographic area. One assumes the distributions of population and of services/workplaces (i.e. the network's sources and sinks) are known, as well as the costs of movement with/without the network, and the cost of constructing/maintaining it. Both the long-term optimization and the short-term, "who goes where" optimization are considered. These models can also be adapted for the optimization of other types of networks, such as telecommunications, pipeline or drainage networks. In the monograph we study the most general problem settings, namely, when neither the shape nor even the topology of the network to be constructed is known a priori.
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions
Odat, Enas M.
2011-05-01
The purpose of this dissertation is to present a methodology to model global sequence alignment problem as directed acyclic graph which helps to extract all possible optimal alignments. Moreover, a mechanism to sequentially optimize sequence alignment problem relative to different cost functions is suggested. Sequence alignment is mostly important in computational biology. It is used to find evolutionary relationships between biological sequences. There are many algo- rithms that have been developed to solve this problem. The most famous algorithms are Needleman-Wunsch and Smith-Waterman that are based on dynamic program- ming. In dynamic programming, problem is divided into a set of overlapping sub- problems and then the solution of each subproblem is found. Finally, the solutions to these subproblems are combined into a final solution. In this thesis it has been proved that for two sequences of length m and n over a fixed alphabet, the suggested optimization procedure requires O(mn) arithmetic operations per cost function on a single processor machine. The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.
Energy optimization in mobile sensor networks
Yu, Shengwei
Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while
Optimization of sequence alignment for simple sequence repeat regions
Directory of Open Access Journals (Sweden)
Ogbonnaya Francis C
2011-07-01
Full Text Available Abstract Background Microsatellites, or simple sequence repeats (SSRs, are tandemly repeated DNA sequences, including tandem copies of specific sequences no longer than six bases, that are distributed in the genome. SSR has been used as a molecular marker because it is easy to detect and is used in a range of applications, including genetic diversity, genome mapping, and marker assisted selection. It is also very mutable because of slipping in the DNA polymerase during DNA replication. This unique mutation increases the insertion/deletion (INDELs mutation frequency to a high ratio - more than other types of molecular markers such as single nucleotide polymorphism (SNPs. SNPs are more frequent than INDELs. Therefore, all designed algorithms for sequence alignment fit the vast majority of the genomic sequence without considering microsatellite regions, as unique sequences that require special consideration. The old algorithm is limited in its application because there are many overlaps between different repeat units which result in false evolutionary relationships. Findings To overcome the limitation of the aligning algorithm when dealing with SSR loci, a new algorithm was developed using PERL script with a Tk graphical interface. This program is based on aligning sequences after determining the repeated units first, and the last SSR nucleotides positions. This results in a shifting process according to the inserted repeated unit type. When studying the phylogenic relations before and after applying the new algorithm, many differences in the trees were obtained by increasing the SSR length and complexity. However, less distance between different linage had been observed after applying the new algorithm. Conclusions The new algorithm produces better estimates for aligning SSR loci because it reflects more reliable evolutionary relations between different linages. It reduces overlapping during SSR alignment, which results in a more realistic
WiMAX network performance monitoring & optimization
DEFF Research Database (Denmark)
Zhang, Qi; Dam, H
2008-01-01
frequency reuse, capacity planning, proper network dimensioning, multi-class data services and so on. Furthermore, as a small operator we also want to reduce the demand for sophisticated technicians and man labour hours. To meet these critical demands, we design a generic integrated network performance......In this paper we present our WiMAX (worldwide interoperability for microwave access) network performance monitoring and optimization solution. As a new and small WiMAX network operator, there are many demanding issues that we have to deal with, such as limited available frequency resource, tight...... this integrated network performance monitoring and optimization system in our WiMAX networks. This integrated monitoring and optimization system has such good flexibility and scalability that individual function component can be used by other operators with special needs and more advanced function components can...
International Nuclear Information System (INIS)
Touze, T.
2011-01-01
The compact linear collider (CLIC) is the particle accelerator project proposed by the european organization for nuclear research (CERN) for high energy physics after the large hadron collider (LHC). Because of the nano-metric scale of the CLIC leptons beams, the emittance growth budget is very tight. It induces alignment tolerances on the positions of the CLIC components that have never been achieved before. The last step of the CLIC alignment will be done according to the beam itself. It falls within the competence of the physicists. However, in order to implement the beam-based feedback, a challenging pre-alignment is required: 10 μm at 3σ along a 200 m sliding window. For such a precision, the proposed solution must be compatible with a feedback between the measurement and repositioning systems. The CLIC pre-alignment will have to be active. This thesis does not demonstrate the feasibility of the CLIC active pre-alignment but shows the way to the last developments that have to be done for that purpose. A method is proposed. Based on the management of the Helmert transformations between Euclidean coordinate systems, from the geodetic networks to the metrological measurements, this method is likely to solve the CLIC pre-alignment problem. Large scale facilities have been built and Monte-Carlo simulations have been made in order to validate the mathematical modeling of the measurement systems and of the alignment references. When this is done, it will be possible to extrapolate the modeling to the entire CLIC length. It will be the last step towards the demonstration of the CLIC pre-alignment feasibility. (author)
Directory of Open Access Journals (Sweden)
Julio Michael Stern
2014-03-01
Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.
Statistical distributions of optimal global alignment scores of random protein sequences
Directory of Open Access Journals (Sweden)
Tang Jiaowei
2005-10-01
Full Text Available Abstract Background The inference of homology from statistically significant sequence similarity is a central issue in sequence alignments. So far the statistical distribution function underlying the optimal global alignments has not been completely determined. Results In this study, random and real but unrelated sequences prepared in six different ways were selected as reference datasets to obtain their respective statistical distributions of global alignment scores. All alignments were carried out with the Needleman-Wunsch algorithm and optimal scores were fitted to the Gumbel, normal and gamma distributions respectively. The three-parameter gamma distribution performs the best as the theoretical distribution function of global alignment scores, as it agrees perfectly well with the distribution of alignment scores. The normal distribution also agrees well with the score distribution frequencies when the shape parameter of the gamma distribution is sufficiently large, for this is the scenario when the normal distribution can be viewed as an approximation of the gamma distribution. Conclusion We have shown that the optimal global alignment scores of random protein sequences fit the three-parameter gamma distribution function. This would be useful for the inference of homology between sequences whose relationship is unknown, through the evaluation of gamma distribution significance between sequences.
GraphCrunch 2: Software tool for network modeling, alignment and clustering.
Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša
2011-01-19
Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an
GraphCrunch 2: Software tool for network modeling, alignment and clustering
Directory of Open Access Journals (Sweden)
Hayes Wayne
2011-01-01
Full Text Available Abstract Background Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. Results We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL" for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other
Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions
Odat, Enas M.
2011-01-01
The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Developmental long trace profiler using optimally aligned mirror based pentaprism
International Nuclear Information System (INIS)
Barber, Samuel K.; Morrison, Gregory Y.; Yashchuk, Valeriy V.; Gubarev, Mikhail V.; Geckeler, Ralf D.; Buchheim, Jana; Siewert, Frank; Zeschke, Thomas
2010-01-01
A low-budget surface slope measuring instrument, the Developmental Long Trace Profiler (DLTP), was recently brought into operation at the Advanced Light Source Optical Metrology Laboratory (Nucl. Instr. and Meth. A 616, 212-223 (2010)). The instrument is based on a precisely calibrated autocollimator and a movable pentaprism. The capability of the DLTP to achieve sub-microradian surface slope metrology has been verified via cross-comparison measurements with other high-performance slope measuring instruments when measuring the same high-quality test optics. In the present work, a further improvement of the DLTP is achieved by replacing the existing bulk pentaprism with a specially designed mirror based pentaprism. A mirror based pentaprism offers the possibility to eliminate systematic errors introduced by inhomogeneity of the optical material and fabrication imperfections of a bulk pentaprism. We provide the details of the mirror based pentaprism design and describe an original experimental procedure for precision mutual alignment of the mirrors. The algorithm of the alignment procedure and its efficiency are verified with rigorous ray tracing simulations. Results of measurements of a spherically curved test mirror and a flat test mirror using the original bulk pentaprism are compared with measurements using the new mirror based pentaprism, demonstrating the improved performance.
Mutual information optimization for mass spectra data alignment.
Zoppis, Italo; Gianazza, Erica; Borsani, Massimiliano; Chinello, Clizia; Mainini, Veronica; Galbusera, Carmen; Ferrarese, Carlo; Galimberti, Gloria; Sorbi, Sandro; Borroni, Barbara; Magni, Fulvio; Antoniotti, Marco; Mauri, Giancarlo
2012-01-01
"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.
Value-Based Business-IT Alignment in Networked Constellations of Enterprises
Gordijn, Jaap; van Eck, Pascal; Cox, K.; Dubois, E.; Pigneur, Y.; Bleistein, S.J.; Verner, J.; Davis, A.M.; Wieringa, Roelf J.
Business-ICT alignment is the problem of matching ICTservices with the requirements of the business. In businesses of any significant size, business-ICT alignment is a hard problem, which is currently not solved completely. With the advent of networked constellations of enterprises, the problem gets
Regulatory Holidays and Optimal Network Expansion
Willems, Bert; Zwart, Gijsbert
2016-01-01
We model the optimal regulation of continuous, irreversible, capacity expansion, in a model in which the regulated network firm has private information about its capacity costs, investments need to be financed out of the firm’s cash flows from selling network access and demand is stochastic. If
Power consumption optimization strategy for wireless networks
DEFF Research Database (Denmark)
Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola
2011-01-01
in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
YAHA: fast and flexible long-read alignment with optimal breakpoint detection.
Faust, Gregory G; Hall, Ira M
2012-10-01
With improved short-read assembly algorithms and the recent development of long-read sequencers, split mapping will soon be the preferred method for structural variant (SV) detection. Yet, current alignment tools are not well suited for this. We present YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints. YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from http://faculty.virginia.edu/irahall/YAHA. imh4y@virginia.edu.
Pattern optimizing verification of self-align quadruple patterning
Yamato, Masatoshi; Yamada, Kazuki; Oyama, Kenichi; Hara, Arisa; Natori, Sakurako; Yamauchi, Shouhei; Koike, Kyohei; Yaegashi, Hidetami
2017-03-01
Lithographic scaling continues to advance by extending the life of 193nm immersion technology, and spacer-type multi-patterning is undeniably the driving force behind this trend. Multi-patterning techniques such as self-aligned double patterning (SADP) and self-aligned quadruple patterning (SAQP) have come to be used in memory devices, and they have also been adopted in logic devices to create constituent patterns in the formation of 1D layout designs. Multi-patterning has consequently become an indispensible technology in the fabrication of all advanced devices. In general, items that must be managed when using multi-patterning include critical dimension uniformity (CDU), line edge roughness (LER), and line width roughness (LWR). Recently, moreover, there has been increasing focus on judging and managing pattern resolution performance from a more detailed perspective and on making a right/wrong judgment from the perspective of edge placement error (EPE). To begin with, pattern resolution performance in spacer-type multi-patterning is affected by the process accuracy of the core (mandrel) pattern. Improving the controllability of CD and LER of the mandrel is most important, and to reduce LER, an appropriate smoothing technique should be carefully selected. In addition, the atomic layer deposition (ALD) technique is generally used to meet the need for high accuracy in forming the spacer film. Advances in scaling are accompanied by stricter requirements in the controllability of fine processing. In this paper, we first describe our efforts in improving controllability by selecting the most appropriate materials for the mandrel pattern and spacer film. Then, based on the materials selected, we present experimental results on a technique for improving etching selectivity.
Optimal hub location in pipeline networks
Energy Technology Data Exchange (ETDEWEB)
Dott, D.R.; Wirasinghe, S.C.; Chakma, A. [Univ. of Calgary, Alberta (Canada)
1996-12-31
This paper discusses optimization strategies and techniques for the location of natural gas marketing hubs in the North American gas pipeline network. A hub is a facility at which inbound and outbound network links meet and freight is redirected towards their destinations. Common examples of hubs used in the gas pipeline industry include gas plants, interconnects and market centers. Characteristics of the gas pipeline industry which are relevant to the optimization of transportation costs using hubs are presented. Allocation techniques for solving location-allocation problems are discussed. An outline of the research in process by the authors in the field of optimal gas hub location concludes the paper.
Small cell networks deployment, management, and optimization
Claussen, Holger; Ho, Lester; Razavi, Rouzbeh; Kucera, Stepan
2018-01-01
Small Cell Networks: Deployment, Management, and Optimization addresses key problems of the cellular network evolution towards HetNets. It focuses on the latest developments in heterogeneous and small cell networks, as well as their deployment, operation, and maintenance. It also covers the full spectrum of the topic, from academic, research, and business to the practice of HetNets in a coherent manner. Additionally, it provides complete and practical guidelines to vendors and operators interested in deploying small cells. The first comprehensive book written by well-known researchers and engineers from Nokia Bell Labs, Small Cell Networks begins with an introduction to the subject--offering chapters on capacity scaling and key requirements of future networks. It then moves on to sections on coverage and capacity optimization, and interference management. From there, the book covers mobility management, energy efficiency, and small cell deployment, ending with a section devoted to future trends and applicat...
Alignment of global supply networks based on strategic groups of supply chains
Directory of Open Access Journals (Sweden)
Nikos G. Moraitakis
2017-09-01
Full Text Available Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic supply chain perspective in order to align regional competitive priorities and supply chain structures. The objective of this research is hence to develop an approach for the supply-chain-based alignment of complex global supply networks. Method: We review existing literature from the fields of supply chain and network management, strategic sourcing, and strategic management. Based on the literature review and theoretical and practical considerations we deduce a conceptual approach to consider upstream supply chain structures in supply network alignment initiatives. Results: On the basis of these considerations and current empirical literature we transfer strategic group theory to the supply network management context. The proposed approach introduces strategic groups of supply chains as a segmentation criterion for complex global supply networks which enables the network-wide alignment of competitive priorities. Conclusion: Supply-chain-based segmentation of global supply network structures can effectively reduce the complexity, firms face when aiming to strategically align their supply chains on a holistic level. The results of this research are applicable for certain types of global supply networks and can be used for network alignment and strategy development. The approach can furthermore generate insights useable for negotiation support with suppliers.
Optimization of bicelle lipid composition and temperature for EPR spectroscopy of aligned membranes.
McCaffrey, Jesse E; James, Zachary M; Thomas, David D
2015-01-01
We have optimized the magnetic alignment of phospholipid bilayered micelles (bicelles) for EPR spectroscopy, by varying lipid composition and temperature. Bicelles have been extensively used in NMR spectroscopy for several decades, in order to obtain aligned samples in a near-native membrane environment and take advantage of the intrinsic sensitivity of magnetic resonance to molecular orientation. Recently, bicelles have also seen increasing use in EPR, which offers superior sensitivity and orientational resolution. However, the low magnetic field strength (less than 1 T) of most conventional EPR spectrometers results in homogeneously oriented bicelles only at a temperature well above physiological. To optimize bicelle composition for magnetic alignment at reduced temperature, we prepared bicelles containing varying ratios of saturated (DMPC) and unsaturated (POPC) phospholipids, using EPR spectra of a spin-labeled fatty acid to assess alignment as a function of lipid composition and temperature. Spectral analysis showed that bicelles containing an equimolar mixture of DMPC and POPC homogeneously align at 298 K, 20 K lower than conventional DMPC-only bicelles. It is now possible to perform EPR studies of membrane protein structure and dynamics in well-aligned bicelles at physiological temperatures and below. Copyright © 2014 Elsevier Inc. All rights reserved.
A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Lihui Jiang
2015-07-01
Full Text Available Interference alignment (IA is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs when the signal-to-noise ratio (SNR is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.
A Rapid Convergent Low Complexity Interference Alignment Algorithm for Wireless Sensor Networks.
Jiang, Lihui; Wu, Zhilu; Ren, Guanghui; Wang, Gangyi; Zhao, Nan
2015-07-29
Interference alignment (IA) is a novel technique that can effectively eliminate the interference and approach the sum capacity of wireless sensor networks (WSNs) when the signal-to-noise ratio (SNR) is high, by casting the desired signal and interference into different signal subspaces. The traditional alternating minimization interference leakage (AMIL) algorithm for IA shows good performance in high SNR regimes, however, the complexity of the AMIL algorithm increases dramatically as the number of users and antennas increases, posing limits to its applications in the practical systems. In this paper, a novel IA algorithm, called directional quartic optimal (DQO) algorithm, is proposed to minimize the interference leakage with rapid convergence and low complexity. The properties of the AMIL algorithm are investigated, and it is discovered that the difference between the two consecutive iteration results of the AMIL algorithm will approximately point to the convergence solution when the precoding and decoding matrices obtained from the intermediate iterations are sufficiently close to their convergence values. Based on this important property, the proposed DQO algorithm employs the line search procedure so that it can converge to the destination directly. In addition, the optimal step size can be determined analytically by optimizing a quartic function. Numerical results show that the proposed DQO algorithm can suppress the interference leakage more rapidly than the traditional AMIL algorithm, and can achieve the same level of sum rate as that of AMIL algorithm with far less iterations and execution time.
Modeling and optimization of potable water network
Energy Technology Data Exchange (ETDEWEB)
Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)
2000-07-01
Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.
Ibarra, Ignacio L; Melo, Francisco
2010-07-01
Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. All inquiries or comments about this software should be directed to Francisco Melo at fmelo@bio.puc.cl.
Optimal Information Processing in Biochemical Networks
Wiggins, Chris
2012-02-01
A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.
Optimization and Control of Communication Networks
Chiang, Mung; Low, Steven
2005-01-01
Recently, there has been a surge in research activities that utilize the power of recent developments in nonlinear optimization to tackle a wide scope of work in the analysis and design of communication systems, touching every layer of the layered network architecture, and resulting in both intellectual and practical impacts significantly beyond the earlier frameworks. These research activities are driven by both new demands in the areas of communications and networking, and n...
Directory of Open Access Journals (Sweden)
Anming Dong
2017-09-01
Full Text Available This paper considers power splitting (PS-based simultaneous wireless information and power transfer (SWIPT for multiple-input multiple-output (MIMO interference channel networks where multiple transceiver pairs share the same frequency spectrum. As the PS model is adopted, an individual receiver splits the received signal into two parts for information decoding (ID and energy harvesting (EH, respectively. Aiming to minimize the total transmit power, transmit precoders, receive filters and PS ratios are jointly designed under a predefined signal-to-interference-plus-noise ratio (SINR and EH constraints. The formulated joint transceiver design and power splitting problem is non-convex and thus difficult to solve directly. In order to effectively obtain its solution, the feasibility conditions of the formulated non-convex problem are first analyzed. Based on the analysis, an iterative algorithm is proposed by alternatively optimizing the transmitters together with the power splitting factors and the receivers based on semidefinite programming (SDP relaxation. Moreover, considering the prohibitive computational cost of the SDP for practical applications, a low-complexity suboptimal scheme is proposed by separately designing interference-suppressing transceivers based on interference alignment (IA and optimizing the transmit power allocation together with splitting factors. The transmit power allocation and receive power splitting problem is then recast as a convex optimization problem and solved efficiently. To further reduce the computational complexity, a low-complexity scheme is proposed by calculating the transmit power allocation and receive PS ratios in closed-form. Simulation results show the effectiveness of the proposed schemes in achieving SWIPT for MIMO interference channel (IC networks.
WiMax network planning and optimization
Zhang, Yan
2009-01-01
This book offers a comprehensive explanation on how to dimension, plan, and optimize WiMAX networks. The first part of the text introduces WiMAX networks architecture, physical layer, standard, protocols, security mechanisms, and highly related radio access technologies. It covers system framework, topology, capacity, mobility management, handoff management, congestion control, medium access control (MAC), scheduling, Quality of Service (QoS), and WiMAX mesh networks and security. Enabling easy understanding of key concepts and technologies, the second part presents practical examples and illu
Optimal transport on supply-demand networks.
Chen, Yu-Han; Wang, Bing-Hong; Zhao, Li-Chao; Zhou, Changsong; Zhou, Tao
2010-06-01
In the literature, transport networks are usually treated as homogeneous networks, that is, every node has the same function, simultaneously providing and requiring resources. However, some real networks, such as power grids and supply chain networks, show a far different scenario in which nodes are classified into two categories: supply nodes provide some kinds of services, while demand nodes require them. In this paper, we propose a general transport model for these supply-demand networks, associated with a criterion to quantify their transport capacities. In a supply-demand network with heterogeneous degree distribution, its transport capacity strongly depends on the locations of supply nodes. We therefore design a simulated annealing algorithm to find the near optimal configuration of supply nodes, which remarkably enhances the transport capacity compared with a random configuration and outperforms the degree target algorithm, the betweenness target algorithm, and the greedy method. This work provides a start point for systematically analyzing and optimizing transport dynamics on supply-demand networks.
Method of optimization onboard communication network
Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.
2018-02-01
In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.
Network inference via adaptive optimal design
Directory of Open Access Journals (Sweden)
Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
Fair Optimization and Networks: A Survey
Directory of Open Access Journals (Sweden)
Wlodzimierz Ogryczak
2014-01-01
Full Text Available Optimization models related to designing and operating complex systems are mainly focused on some efficiency metrics such as response time, queue length, throughput, and cost. However, in systems which serve many entities there is also a need for respecting fairness: each system entity ought to be provided with an adequate share of the system’s services. Still, due to system operations-dependant constraints, fair treatment of the entities does not directly imply that each of them is assigned equal amount of the services. That leads to concepts of fair optimization expressed by the equitable models that represent inequality averse optimization rather than strict inequality minimization; a particular widely applied example of that concept is the so-called lexicographic maximin optimization (max-min fairness. The fair optimization methodology delivers a variety of techniques to generate fair and efficient solutions. This paper reviews fair optimization models and methods applied to systems that are based on some kind of network of connections and dependencies, especially, fair optimization methods for the location problems and for the resource allocation problems in communication networks.
An optimal beam alignment method for large-scale distributed space surveillance radar system
Huang, Jian; Wang, Dongya; Xia, Shuangzhi
2018-06-01
Large-scale distributed space surveillance radar is a very important ground-based equipment to maintain a complete catalogue for Low Earth Orbit (LEO) space debris. However, due to the thousands of kilometers distance between each sites of the distributed radar system, how to optimally implement the Transmitting/Receiving (T/R) beams alignment in a great space using the narrow beam, which proposed a special and considerable technical challenge in the space surveillance area. According to the common coordinate transformation model and the radar beam space model, we presented a two dimensional projection algorithm for T/R beam using the direction angles, which could visually describe and assess the beam alignment performance. Subsequently, the optimal mathematical models for the orientation angle of the antenna array, the site location and the T/R beam coverage are constructed, and also the beam alignment parameters are precisely solved. At last, we conducted the optimal beam alignment experiments base on the site parameters of Air Force Space Surveillance System (AFSSS). The simulation results demonstrate the correctness and effectiveness of our novel method, which can significantly stimulate the construction for the LEO space debris surveillance equipment.
Directory of Open Access Journals (Sweden)
Lund Ole
2009-09-01
Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.
Impact of carbon nanotube length on electron transport in aligned carbon nanotube networks
Energy Technology Data Exchange (ETDEWEB)
Lee, Jeonyoon; Stein, Itai Y. [Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Devoe, Mackenzie E. [Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Lewis, Diana J.; Lachman, Noa; Buschhorn, Samuel T.; Wardle, Brian L., E-mail: wardle@mit.edu [Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139 (United States); Kessler, Seth S. [Metis Design Corporation, 205 Portland St., Boston, Massachusetts 02114 (United States)
2015-02-02
Here, we quantify the electron transport properties of aligned carbon nanotube (CNT) networks as a function of the CNT length, where the electrical conductivities may be tuned by up to 10× with anisotropies exceeding 40%. Testing at elevated temperatures demonstrates that the aligned CNT networks have a negative temperature coefficient of resistance, and application of the fluctuation induced tunneling model leads to an activation energy of ≈14 meV for electron tunneling at the CNT-CNT junctions. Since the tunneling activation energy is shown to be independent of both CNT length and orientation, the variation in electron transport is attributed to the number of CNT-CNT junctions an electron must tunnel through during its percolated path, which is proportional to the morphology of the aligned CNT network.
Impact of carbon nanotube length on electron transport in aligned carbon nanotube networks
International Nuclear Information System (INIS)
Lee, Jeonyoon; Stein, Itai Y.; Devoe, Mackenzie E.; Lewis, Diana J.; Lachman, Noa; Buschhorn, Samuel T.; Wardle, Brian L.; Kessler, Seth S.
2015-01-01
Here, we quantify the electron transport properties of aligned carbon nanotube (CNT) networks as a function of the CNT length, where the electrical conductivities may be tuned by up to 10× with anisotropies exceeding 40%. Testing at elevated temperatures demonstrates that the aligned CNT networks have a negative temperature coefficient of resistance, and application of the fluctuation induced tunneling model leads to an activation energy of ≈14 meV for electron tunneling at the CNT-CNT junctions. Since the tunneling activation energy is shown to be independent of both CNT length and orientation, the variation in electron transport is attributed to the number of CNT-CNT junctions an electron must tunnel through during its percolated path, which is proportional to the morphology of the aligned CNT network
Phase transitions in Pareto optimal complex networks.
Seoane, Luís F; Solé, Ricard
2015-09-01
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
Optimal image alignment with random projections of manifolds: algorithm and geometric analysis.
Kokiopoulou, Effrosyni; Kressner, Daniel; Frossard, Pascal
2011-06-01
This paper addresses the problem of image alignment based on random measurements. Image alignment consists of estimating the relative transformation between a query image and a reference image. We consider the specific problem where the query image is provided in compressed form in terms of linear measurements captured by a vision sensor. We cast the alignment problem as a manifold distance minimization problem in the linear subspace defined by the measurements. The transformation manifold that represents synthesis of shift, rotation, and isotropic scaling of the reference image can be given in closed form when the reference pattern is sparsely represented over a parametric dictionary. We show that the objective function can then be decomposed as the difference of two convex functions (DC) in the particular case where the dictionary is built on Gaussian functions. Thus, the optimization problem becomes a DC program, which in turn can be solved globally by a cutting plane method. The quality of the solution is typically affected by the number of random measurements and the condition number of the manifold that describes the transformations of the reference image. We show that the curvature, which is closely related to the condition number, remains bounded in our image alignment problem, which means that the relative transformation between two images can be determined optimally in a reduced subspace.
Optimal design of an alignment-free two-DOF rehabilitation robot for the shoulder complex.
Galinski, Daniel; Sapin, Julien; Dehez, Bruno
2013-06-01
This paper presents the optimal design of an alignment-free exoskeleton for the rehabilitation of the shoulder complex. This robot structure is constituted of two actuated joints and is linked to the arm through passive degrees of freedom (DOFs) to drive the flexion-extension and abduction-adduction movements of the upper arm. The optimal design of this structure is performed through two steps. The first step is a multi-objective optimization process aiming to find the best parameters characterizing the robot and its position relative to the patient. The second step is a comparison process aiming to select the best solution from the optimization results on the basis of several criteria related to practical considerations. The optimal design process leads to a solution outperforming an existing solution on aspects as kinematics or ergonomics while being more simple.
Directory of Open Access Journals (Sweden)
Yashon O. Ouma
2014-01-01
Full Text Available Selection of urban bypass highway alternatives involves the consideration of competing and conflicting criteria and factors, which require multicriteria decision analysis. Analytic hierarchy process (AHP is one of the most commonly used multicriteria decision making (MCDM methods that can integrate personal preferences in performing spatial analyses on the physical and nonphysical parameters. In this paper, the traditional AHP is modified to fuzzy AHP for the determination of the optimal bypass route for Eldoret town in Kenya. The fuzzy AHP is proposed in order to take care of the vagueness type uncertainty encountered in alternative bypass location determination. In the implementation, both engineering and environmental factors comprising of physical and socioeconomic objectives were considered at different levels of decision hierarchy. The results showed that the physical objectives (elevation, slope, soils, geology, and drainage networks and socioeconomic objectives (land-use and road networks contributed the same weight of 0.5 towards the bypass location prioritization process. At the subcriteria evaluation level, land-use and existing road networks contributed the highest significance of 47.3% amongst the seven decision factors. Integrated with GIS-based least cost path (LCP analysis, the fuzzy AHP results produced the most desirable and optimal route alignment, as compared to the AHP only prioritization approach.
An extensive assessment of network alignment algorithms for comparison of brain connectomes.
Milano, Marianna; Guzzi, Pietro Hiram; Tymofieva, Olga; Xu, Duan; Hess, Christofer; Veltri, Pierangelo; Cannataro, Mario
2017-06-06
Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem. In this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset. The results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.
Directory of Open Access Journals (Sweden)
Dawoon Jung
2016-01-01
Full Text Available As an influential candidate for highly sensitive biomolecule sensor, which can capture disease related biomolecules, carbon nanotube is useful material due to its unique properties. To adopt as a sensing platform, it is strongly needed to find optimal refined synthetic condition. In order to find the optimal synthetic conditions of horizontally aligned CNT, we performed quantity control of the mixed gases of H2 and CH4 injected. We successfully find that the formation of amorphous-like carbon was critically affected by some gas condition such as the flow rate of injected gases and ratios of gas mixture. Moreover, it should be noted that our horizontally aligned carbon nanotube array platform developed would offer another potential in developing nanoscale light source, where light emission results from electron-hole carrier recombination.
Multi-subject Manifold Alignment of Functional Network Structures via Joint Diagonalization.
Nenning, Karl-Heinz; Kollndorfer, Kathrin; Schöpf, Veronika; Prayer, Daniela; Langs, Georg
2015-01-01
Functional magnetic resonance imaging group studies rely on the ability to establish correspondence across individuals. This enables location specific comparison of functional brain characteristics. Registration is often based on morphology and does not take variability of functional localization into account. This can lead to a loss of specificity, or confounds when studying diseases. In this paper we propose multi-subject functional registration by manifold alignment via coupled joint diagonalization. The functional network structure of each subject is encoded in a diffusion map, where functional relationships are decoupled from spatial position. Two-step manifold alignment estimates initial correspondences between functionally equivalent regions. Then, coupled joint diagonalization establishes common eigenbases across all individuals, and refines the functional correspondences. We evaluate our approach on fMRI data acquired during a language paradigm. Experiments demonstrate the benefits in matching accuracy achieved by coupled joint diagonalization compared to previously proposed functional alignment approaches, or alignment based on structural correspondences.
Optimal and fast rotational alignment of volumes with missing data in Fourier space.
Shatsky, Maxim; Arbelaez, Pablo; Glaeser, Robert M; Brenner, Steven E
2013-11-01
Electron tomography of intact cells has the potential to reveal the entire cellular content at a resolution corresponding to individual macromolecular complexes. Characterization of macromolecular complexes in tomograms is nevertheless an extremely challenging task due to the high level of noise, and due to the limited tilt angle that results in missing data in Fourier space. By identifying particles of the same type and averaging their 3D volumes, it is possible to obtain a structure at a more useful resolution for biological interpretation. Currently, classification and averaging of sub-tomograms is limited by the speed of computational methods that optimize alignment between two sub-tomographic volumes. The alignment optimization is hampered by the fact that the missing data in Fourier space has to be taken into account during the rotational search. A similar problem appears in single particle electron microscopy where the random conical tilt procedure may require averaging of volumes with a missing cone in Fourier space. We present a fast implementation of a method guaranteed to find an optimal rotational alignment that maximizes the constrained cross-correlation function (cCCF) computed over the actual overlap of data in Fourier space. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Guzzi, Pietro Hiram; Milenković, Tijana
2017-01-05
Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Modeling and optimization of an electric power distribution network ...
African Journals Online (AJOL)
Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...
Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.
1993-01-01
This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.
Optimization of investments in gas networks
International Nuclear Information System (INIS)
Andre, J.
2010-09-01
The natural gas networks require very important investments to cope with a still growing demand and to satisfy the new regulatory constraints. The gas market deregulation imposed to the gas network operators, first, transparency rules of a natural monopoly to justify their costs and ultimately their tariffs, and, second, market fluidity objectives in order to facilitate access for competition to the end-users. These major investments are the main reasons for the use of optimization techniques aiming at reducing the costs. Due to the discrete choices (investment location, limited choice of additional capacities, timing) crossed with physical non linear constraints (flow/pressures relations in the pipe or operating ranges of compressors), the programs to solve are Large Mixed Non Linear Programs (MINLP). As these types of programs are known to be hard to solve exactly in polynomial times (NP-hard), advanced optimization methods have to be implemented to obtain realistic results. The objectives of this thesis are threefold. First, one states several investment problems modeling of natural gas networks from industrial world motivations. Second, one identifies the most suitable methods and algorithms to the formulated problems. Third, one exposes the main advantages and drawbacks of these methods with the help of numerical applications on real cases. (author)
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Loop optimization for tensor network renormalization
Yang, Shuo; Gu, Zheng-Cheng; Wen, Xiao-Gang
We introduce a tensor renormalization group scheme for coarse-graining a two-dimensional tensor network, which can be successfully applied to both classical and quantum systems on and off criticality. The key idea of our scheme is to deform a 2D tensor network into small loops and then optimize tensors on each loop. In this way we remove short-range entanglement at each iteration step, and significantly improve the accuracy and stability of the renormalization flow. We demonstrate our algorithm in the classical Ising model and a frustrated 2D quantum model. NSF Grant No. DMR-1005541 and NSFC 11274192, BMO Financial Group, John Templeton Foundation, Government of Canada through Industry Canada, Province of Ontario through the Ministry of Economic Development & Innovation.
Distributed Robust Optimization in Networked System.
Wang, Shengnan; Li, Chunguang
2016-10-11
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.
Gapped sequence alignment using artificial neural networks: application to the MHC class I system
DEFF Research Database (Denmark)
Andreatta, Massimo; Nielsen, Morten
2016-01-01
. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...... trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn...... the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped...
A simulation model for aligning smart home networks and deploying smart objects
DEFF Research Database (Denmark)
Lynggaard, Per
Smart homes use sensor based networks to capture activities and offer learned services to the user. These smart home networks are challenging because they mainly use wireless communication at frequencies that are shared with other services and equipments. One of the major challenges...... is the interferences produced by WiFi access points in smart home networks which are expensive to overcome in terms of battery energy. Currently, different method exists to handle this. However, they use complex mechanisms such as sharing frequencies, sharing time slots, and spatial reuse of frequencies. This paper...... introduces a unique concept which saves battery energy and lowers the interference level by simulating the network alignment and assign the necessary amount of transmit power to each individual network node and finally, deploy the smart objects. The needed transmit powers are calculated by the presented...
Optimal Brain Surgeon on Artificial Neural Networks in
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine
2012-01-01
It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...
Optimizing the spatial pattern of networks for monitoring radioactive releases
Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, van A.; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.
2011-01-01
This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines
Alignment of paired molecules of C60 within a hexagonal platform networked through hydrogen-bonds.
Hisaki, Ichiro; Nakagawa, Shoichi; Sato, Hiroyasu; Tohnai, Norimitsu
2016-07-28
We demonstrate, for the first time, that a hydrogen-bonded low-density organic framework can be applied as a platform to achieve periodic alignment of paired molecules of C60, which is the smallest example of a finite-numbered cluster of C60. The framework is a layered assembly of a hydrogen-bonded 2D hexagonal network (LA-H-HexNet) composed of dodecadehydrotribenzo[18]annulene derivatives.
de Groot, Marius; Vernooij, Meike W; Klein, Stefan; Ikram, M Arfan; Vos, Frans M; Smith, Stephen M; Niessen, Wiro J; Andersson, Jesper L R
2013-08-01
Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment. Copyright © 2013 Elsevier Inc. All rights reserved.
Global Optimization for Transport Network Expansion and Signal Setting
Liu, Haoxiang; Wang, David Z. W.; Yue, Hao
2015-01-01
This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two pr...
Synchronization-optimized networks for coupled nearly identical ...
Indian Academy of Sciences (India)
From the stability criteria of the MSF, we construct optimal networks ... of intense research in physical, biological, chemical, technological and social sci- ..... In figure 3a, a sample of initial network of 32 coupled nearly identical Rössler oscilla-.
Alignment and integration of complex networks by hypergraph-based spectral clustering
Michoel, Tom; Nachtergaele, Bruno
2012-11-01
Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.
Parallel Evolutionary Optimization for Neuromorphic Network Training
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)
2016-01-01
One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.
BWR fuel cycle optimization using neural networks
International Nuclear Information System (INIS)
Ortiz-Servin, Juan Jose; Castillo, Jose Alejandro; Pelta, David Alejandro
2011-01-01
Highlights: → OCONN a new system to optimize all nuclear fuel management steps in a coupled way. → OCON is based on an artificial recurrent neural network to find the best combination of partial solutions to each fuel management step. → OCONN works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. → Results show OCONN is able to find good combinations according the global objective function. - Abstract: In nuclear fuel management activities for BWRs, four combinatorial optimization problems are solved: fuel lattice design, axial fuel bundle design, fuel reload design and control rod patterns design. Traditionally, these problems have been solved in separated ways due to their complexity and the required computational resources. In the specialized literature there are some attempts to solve fuel reloads and control rod patterns design or fuel lattice and axial fuel bundle design in a coupled way. In this paper, the system OCONN to solve all of these problems in a coupled way is shown. This system is based on an artificial recurrent neural network to find the best combination of partial solutions to each problem, in order to maximize a global objective function. The new system works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. The system was tested to design an equilibrium cycle with a cycle length of 18 months. Results show that the new system is able to find good combinations. Cycle length is reached and safety parameters are fulfilled.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...
Two-Stream Transformer Networks for Video-based Face Alignment.
Liu, Hao; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie
2017-08-01
In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the fact that consistent movements of facial landmarks usually occur across consecutive frames, our TSTN aims to capture the complementary information of both the spatial appearance on still frames and the temporal consistency information across frames. To achieve this, we develop a two-stream architecture, which decomposes the video-based face alignment into spatial and temporal streams accordingly. Specifically, the spatial stream aims to transform the facial image to the landmark positions by preserving the holistic facial shape structure. Accordingly, the temporal stream encodes the video input as active appearance codes, where the temporal consistency information across frames is captured to help shape refinements. Experimental results on the benchmarking video-based face alignment datasets show very competitive performance of our method in comparisons to the state-of-the-arts.
Practical synchronization on complex dynamical networks via optimal pinning control
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Optimization-based topology identification of complex networks
International Nuclear Information System (INIS)
Tang Sheng-Xue; Chen Li; He Yi-Gang
2011-01-01
In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)
Software defined network inference with evolutionary optimal observation matrices
Malboubi, M; Gong, Y; Yang, Z; Wang, X; Chuah, CN; Sharma, P
2017-01-01
© 2017 Elsevier B.V. A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. This paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead t...
Nick, C.; Yadav, S.; Joshi, R.; Schneider, J. J.; Thielemann, C.
2015-07-01
Electrodes based on carbon nanotubes are a promising approach to manufacture highly sensitive sensors with a low limit of signal detection and a high signal-to-noise ratio. This is achieved by dramatically increasing the electrochemical active surface area without increasing the overall geometrical dimensions. Typically, carbon nanotube electrodes are nearly planar and composed of randomly distributed carbon nanotube networks having a limited surface gain for a specific geometrical surface area. To overcome this limitation, we have introduced vertically aligned carbon nanotube (VACNT) networks as electrodes, which are arranged in a microelectrode pattern of 60 single electrodes. Each microelectrode features a very high aspect ratio of more than 300 and thus a dramatically increased surface area. These microelectrodes composed of VACNT networks display dramatically decreased impedance over the entire frequency range compared to planar microelectrodes caused by the enormous capacity increase. This is experimentally verified by electrochemical impedance spectroscopy and cyclic voltammetry.
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Ovseiko, Pavel V; Heitmueller, Axel; Allen, Pauline; Davies, Stephen M; Wells, Glenn; Ford, Gary A; Darzi, Ara; Buchan, Alastair M
2014-01-20
As in many countries around the world, there are high expectations on academic health science centres and networks in England to provide high-quality care, innovative research, and world-class education, while also supporting wealth creation and economic growth. Meeting these expectations increasingly depends on partnership working between university medical schools and teaching hospitals, as well as other healthcare providers. However, academic-clinical relationships in England are still characterised by the "unlinked partners" model, whereby universities and their partner teaching hospitals are neither fiscally nor structurally linked, creating bifurcating accountabilities to various government and public agencies. This article focuses on accountability relationships in universities and teaching hospitals, as well as other healthcare providers that form core constituent parts of academic health science centres and networks. The authors analyse accountability for the tripartite mission of patient care, research, and education, using a four-fold typology of accountability relationships, which distinguishes between hierarchical (bureaucratic) accountability, legal accountability, professional accountability, and political accountability. Examples from North West London suggest that a number of mechanisms can be used to improve accountability for the tripartite mission through alignment, but that the simple creation of academic health science centres and networks is probably not sufficient. At the heart of the challenge for academic health science centres and networks is the separation of accountabilities for patient care, research, and education in different government departments. Given that a fundamental top-down system redesign is now extremely unlikely, local academic and clinical leaders face the challenge of aligning their institutions as a matter of priority in order to improve accountability for the tripartite mission from the bottom up. It remains to be
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer...... and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...... and it is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature...
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Self-Optimization of LTE Networks Utilizing Celnet Xplorer
Buvaneswari, A; Polakos, Paul; Buvaneswari, Arumugam
2010-01-01
In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performan...
UMTS network planning, optimization, and inter-operation with GSM
Rahnema, Moe
2008-01-01
UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle
Context-Aware Local Optimization of Sensor Network Deployment
Directory of Open Access Journals (Sweden)
Meysam Argany
2015-07-01
Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.
Optimizing online social networks for information propagation.
Directory of Open Access Journals (Sweden)
Duan-Bing Chen
Full Text Available Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Optimizing online social networks for information propagation.
Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng
2014-01-01
Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
High-accuracy local positioning network for the alignment of the Mu2e experiment.
Energy Technology Data Exchange (ETDEWEB)
Hejdukova, Jana B. [Czech Technical Univ., Prague (Czech Republic)
2017-06-01
This Diploma thesis describes the establishment of a high-precision local positioning network and accelerator alignment for the Mu2e physics experiment. The process of establishing new network consists of few steps: design of the network, pre-analysis, installation works, measurements of the network and making adjustments. Adjustments were performed using two approaches. First is a geodetic approach of taking into account the Earth’s curvature and the metrological approach of a pure 3D Cartesian system on the other side. The comparison of those two approaches is performed and evaluated in the results and compared with expected differences. The effect of the Earth’s curvature was found to be significant for this kind of network and should not be neglected. The measurements were obtained with Absolute Tracker AT401, leveling instrument Leica DNA03 and gyrotheodolite DMT Gyromat 2000. The coordinates of the points of the reference network were determined by the Least Square Meth od and the overall view is attached as Annexes.
LinkMind: link optimization in swarming mobile sensor networks.
Ngo, Trung Dung
2011-01-01
A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
Park, Kihong
2013-02-01
In this paper, we study a two-hop relaying network consisting of one source, one destination, and three amplify-and-forward (AF) relays with multiple antennas. To compensate for the capacity prelog factor loss of 1/2$ due to the half-duplex relaying, alternate transmission is performed among three relays, and the inter-relay interference due to the alternate relaying is aligned to make additional degrees of freedom. In addition, suboptimal linear filter designs at the nodes are proposed to maximize the achievable sum rate for different fading scenarios when the destination utilizes a minimum mean-square error filter. © 1967-2012 IEEE.
Optimal placement of distributed generation in distribution networks ...
African Journals Online (AJOL)
This paper proposes the application of Particle Swarm Optimization (PSO) technique to find the optimal size and optimum location for the placement of DG in the radial distribution networks for active power compensation by reduction in real power losses and enhancement in voltage profile. In the first segment, the optimal ...
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.
Network synchronization: optimal and pessimal scale-free topologies
Energy Technology Data Exchange (ETDEWEB)
Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es
2008-06-06
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.
Network synchronization: optimal and pessimal scale-free topologies
International Nuclear Information System (INIS)
Donetti, Luca; Hurtado, Pablo I; Munoz, Miguel A
2008-01-01
By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability
Padial, José M; Grant, Taran; Frost, Darrel R
2014-06-26
Brachycephaloidea is a monophyletic group of frogs with more than 1000 species distributed throughout the New World tropics, subtropics, and Andean regions. Recently, the group has been the target of multiple molecular phylogenetic analyses, resulting in extensive changes in its taxonomy. Here, we test previous hypotheses of phylogenetic relationships for the group by combining available molecular evidence (sequences of 22 genes representing 431 ingroup and 25 outgroup terminals) and performing a tree-alignment analysis under the parsimony optimality criterion using the program POY. To elucidate the effects of alignment and optimality criterion on phylogenetic inferences, we also used the program MAFFT to obtain a similarity-alignment for analysis under both parsimony and maximum likelihood using the programs TNT and GARLI, respectively. Although all three analytical approaches agreed on numerous points, there was also extensive disagreement. Tree-alignment under parsimony supported the monophyly of the ingroup and the sister group relationship of the monophyletic marsupial frogs (Hemiphractidae), while maximum likelihood and parsimony analyses of the MAFFT similarity-alignment did not. All three methods differed with respect to the position of Ceuthomantis smaragdinus (Ceuthomantidae), with tree-alignment using parsimony recovering this species as the sister of Pristimantis + Yunganastes. All analyses rejected the monophyly of Strabomantidae and Strabomantinae as originally defined, and the tree-alignment analysis under parsimony further rejected the recently redefined Craugastoridae and Pristimantinae. Despite the greater emphasis in the systematics literature placed on the choice of optimality criterion for evaluating trees than on the choice of method for aligning DNA sequences, we found that the topological differences attributable to the alignment method were as great as those caused by the optimality criterion. Further, the optimal tree-alignment indicates
Optimal defense resource allocation in scale-free networks
Zhang, Xuejun; Xu, Guoqiang; Xia, Yongxiang
2018-02-01
The robustness research of networked systems has drawn widespread attention in the past decade, and one of the central topics is to protect the network from external attacks through allocating appropriate defense resource to different nodes. In this paper, we apply a specific particle swarm optimization (PSO) algorithm to optimize the defense resource allocation in scale-free networks. Results reveal that PSO based resource allocation shows a higher robustness than other resource allocation strategies such as uniform, degree-proportional, and betweenness-proportional allocation strategies. Furthermore, we find that assigning less resource to middle-degree nodes under small-scale attack while more resource to low-degree nodes under large-scale attack is conductive to improving the network robustness. Our work provides an insight into the optimal defense resource allocation pattern in scale-free networks and is helpful for designing a more robust network.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
. The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...
Wireless Sensor Network Optimization: Multi-Objective Paradigm.
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-07-20
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-01-01
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271
Maleki, Ehsan; Babashah, Hossein; Koohi, Somayyeh; Kavehvash, Zahra
2017-07-01
This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool.
Simultaneous optimization of water and heat exchange networks
Energy Technology Data Exchange (ETDEWEB)
Chen, Zhiyou; Hou, Yanlong; Li, Xiaoduan; Wang, Jingtao [Tianjin University, Tianjin (China)
2014-04-15
This paper focuses on the simultaneous optimization of the heat-integrated water allocation networks. A mathematic model is established to illustrate the modified state-space representation of this problem. An easy logical method is employed to help identify the streams of hot or cold ones. In this model, the water exchange networks (WEN), heat exchange networks (HEN), and the interactions between the WEN and HEN combine together as one unity. Thus, the whole network can be solved at one time, which enhances the possibility to get a global optimal result. Examples from the literature and a PVC plant are analyzed to illustrate the accuracy and applicability of this method.
AS Migration and Optimization of the Power Integrated Data Network
Zhou, Junjie; Ke, Yue
2018-03-01
In the transformation process of data integration network, the impact on the business has always been the most important reference factor to measure the quality of network transformation. With the importance of the data network carrying business, we must put forward specific design proposals during the transformation, and conduct a large number of demonstration and practice to ensure that the transformation program meets the requirements of the enterprise data network. This paper mainly demonstrates the scheme of over-migrating point-to-point access equipment in the reconstruction project of power data comprehensive network to migrate the BGP autonomous domain to the specified domain defined in the industrial standard, and to smooth the intranet OSPF protocol Migration into ISIS agreement. Through the optimization design, eventually making electric power data network performance was improved on traffic forwarding, traffic forwarding path optimized, extensibility, get larger, lower risk of potential loop, the network stability was improved, and operational cost savings, etc.
ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2014-04-01
Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.
Optimal Design of Gravitational Sewer Networks with General Cellular Automata
Directory of Open Access Journals (Sweden)
Mohammad Hadi Afshar
2014-05-01
Full Text Available In this paper, a Cellular Automata method is applied for the optimal design of sewer networks. The solution of sewer network optimization problems requires the determination of pipe diameters and average pipe cover depths, minimizing the total cost of the sewer network subject to operational constraints. In this paper, the network nodes and upstream and downstream pipe cover depths are considered as CA cells and cell states, respectively, and the links around each cell are taken into account as neighborhood. The proposed method is a general and flexible method for the optimization of sewer networks as it can be used to optimally design both gravity and pumped network due to the use of pipe nodal cover depths as the decision variables. The proposed method is tested against two gravitational sewer networks and the comparison of results with other methods such as Genetic algorithm, Cellular Automata, Ant Colony Optimization Algorithm and Particle Swarm Optimization show the efficiency and effectiveness of the proposed method.
Stochastic network optimization with application to communication and queueing systems
Neely, Michael
2010-01-01
This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are prov
Designing optimal greenhouse gas monitoring networks for Australia
Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.
2016-01-01
Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
Wang, Hui; Liu, Chunyue; Rong, Luge; Wang, Xiaoxu; Sun, Lina; Luo, Qing; Wu, Hao
2018-01-09
River monitoring networks play an important role in water environmental management and assessment, and it is critical to develop an appropriate method to optimize the monitoring network. In this study, an effective method was proposed based on the attainment rate of National Grade III water quality, optimal partition analysis and Euclidean distance, and Hun River was taken as a method validation case. There were 7 sampling sites in the monitoring network of the Hun River, and 17 monitoring items were analyzed once a month during January 2009 to December 2010. The results showed that the main monitoring items in the surface water of Hun River were ammonia nitrogen (NH 4 + -N), chemical oxygen demand, and biochemical oxygen demand. After optimization, the required number of monitoring sites was reduced from seven to three, and 57% of the cost was saved. In addition, there were no significant differences between non-optimized and optimized monitoring networks, and the optimized monitoring networks could correctly represent the original monitoring network. The duplicate setting degree of monitoring sites decreased after optimization, and the rationality of the monitoring network was improved. Therefore, the optimal method was identified as feasible, efficient, and economic.
Medical Optimization Network for Space Telemedicine Resources
Shah, R. V.; Mulcahy, R.; Rubin, D.; Antonsen, E. L.; Kerstman, E. L.; Reyes, D.
2017-01-01
INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. NASA recognizes the need to improve capabilities for autonomous care on such missions. As the medical system is developed, it is important to have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources was developed for this reason, and is now a system to gauge the relative importance of medical resources in addressing medical conditions. METHODS: A list of medical conditions of potential concern for an exploration mission was referenced from the Integrated Medical Model, a probabilistic model designed to quantify in-flight medical risk. The diagnostic and treatment modalities required to address best and worst-case scenarios of each medical condition, at the terrestrial standard of care, were entered into a database. This list included tangible assets (e.g. medications) and intangible assets (e.g. clinical skills to perform a procedure). A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program ranked each of the items listed according to its criticality. Data was then obtained from the IMM for the probability of occurrence of the medical conditions, including a breakdown of best case and worst case, during a Mars reference mission. The probability of occurrence information and criticality for each resource were taken into account during analytics performed using Tableau software. RESULTS: A database and weighting system to evaluate all the diagnostic and treatment modalities was created by combining the probability of condition occurrence data with the criticalities assigned by the physician team. DISCUSSION: Exploration Medical Capabilities research at NASA is focused on providing a medical system to
Topologically determined optimal stochastic resonance responses of spatially embedded networks
International Nuclear Information System (INIS)
Gosak, Marko; Marhl, Marko; Korosak, Dean
2011-01-01
We have analyzed the stochastic resonance phenomenon on spatial networks of bistable and excitable oscillators, which are connected according to their location and the amplitude of external forcing. By smoothly altering the network topology from a scale-free (SF) network with dominating long-range connections to a network where principally only adjacent oscillators are connected, we reveal that besides an optimal noise intensity, there is also a most favorable interaction topology at which the best correlation between the response of the network and the imposed weak external forcing is achieved. For various distributions of the amplitudes of external forcing, the optimal topology is always found in the intermediate regime between the highly heterogeneous SF network and the strong geometric regime. Our findings thus indicate that a suitable number of hubs and with that an optimal ratio between short- and long-range connections is necessary in order to obtain the best global response of a spatial network. Furthermore, we link the existence of the optimal interaction topology to a critical point indicating the transition from a long-range interactions-dominated network to a more lattice-like network structure.
Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.
2015-05-01
This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
. Since tool life is critically affected by the tool wear, accurate prediction of this wear ... In their work, they established an improvement in the quality ... objective optimization of hard turning using neural network modelling and swarm intelligence ...
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
Optimized Neural Network for Fault Diagnosis and Classification
International Nuclear Information System (INIS)
Elaraby, S.M.
2005-01-01
This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)
Optimal Control of Interdependent Epidemics in Complex Networks
Chen, Juntao; Zhang, Rui; Zhu, Quanyan
2017-01-01
Optimal control of interdependent epidemics spreading over complex networks is a critical issue. We first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed control strategy globally optimizes the trade-off between the control cost and the severity of epidemics in the network. A gradient descent algorithm based on a fixed point itera...
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernán A.
2015-08-01
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Alignment of the UMLS semantic network with BioTop: methodology and assessment.
Schulz, Stefan; Beisswanger, Elena; van den Hoek, László; Bodenreider, Olivier; van Mulligen, Erik M
2009-06-15
For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop. The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations. BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning; Canepa, Edward S.; Claudel, Christian G.
2014-01-01
of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can
Optimization-based Method for Automated Road Network Extraction
International Nuclear Information System (INIS)
Xiong, D
2001-01-01
Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction
Optimal search strategies on complex networks
Di Patti, Francesca; Fanelli, Duccio; Piazza, Francesco
2014-01-01
Complex networks are ubiquitous in nature and play a role of paramount importance in many contexts. Internet and the cyberworld, which permeate our everyday life, are self-organized hierarchical graphs. Urban traffic flows on intricate road networks, which impact both transportation design and epidemic control. In the brain, neurons are cabled through heterogeneous connections, which support the propagation of electric signals. In all these cases, the true challenge is to unveil the mechanism...
Optimizing transport in a homogeneous network
WEAIRE, DENIS LAWRENCE
2004-01-01
PUBLISHED Many situations in physics, biology, and engineering consist of the transport of some physical quantity through a network of narrow channels. The ability of a network to transport such a quantity in every direction can be described by the average conductivity associated with it. When the flow through each channel is conserved and derives from a potential function, we show that there exists an upper bound of the average conductivity and explicitly give the expression f...
Optimal scope of supply chain network & operations design
Ma, N.
2014-01-01
The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are
Optimization with PDE constraints ESF networking program 'OPTPDE'
2014-01-01
This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).
Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks
DEFF Research Database (Denmark)
Bode, Felix; Binning, Philip John; Nowak, Wolfgang
Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability...... and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high...... be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics...
Control and Optimization of Network in Networked Control System
Directory of Open Access Journals (Sweden)
Wang Zhiwen
2014-01-01
Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
2016-01-01
In this paper, we propose a generic computer-aided methodology for synthesis of different processing networks using superstructure optimization. The methodology can handle different network optimization problems of various application fields. It integrates databases with a common data architecture......, a generic model to represent the processing steps, and appropriate optimization tools. A special software interface has been created to automate the steps in the methodology workflow, allow the transfer of data between tools and obtain the mathematical representation of the problem as required...
PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks
DEFF Research Database (Denmark)
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2013-01-01
This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii) r...
Genetic optimization of neural network architecture
International Nuclear Information System (INIS)
Harp, S.A.; Samad, T.
1994-03-01
Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)
Munier, Pierre; Gordeyeva, Korneliya; Bergström, Lennart; Fall, Andreas B
2016-05-09
We show that unidirectional freezing of nanocellulose dispersions produces cellular foams with high alignment of the rod-like nanoparticles in the freezing direction. Quantification of the alignment in the long direction of the tubular pores with X-ray diffraction shows high orientation of cellulose nanofibrils (CNF) and cellulose nanocrystals (CNC) at particle concentrations above 0.2 wt % (CNC) and 0.08 wt % (CNF). Aggregation of CNF by pH decrease or addition of salt significantly reduces the particle orientation; in contrast, exceeding the concentration where particles gel by mobility constraints had a relatively small effect on the orientation. The dense nanocellulose network formed by directional freezing was sufficiently strong to resist melting. The formed hydrogels were birefringent and displayed anisotropic laser diffraction patterns, suggesting preserved nanocellulose alignment and cellular structure. Nondirectional freezing of the hydrogels followed by sublimation generates foams with a pore structure and nanocellulose alignment resembling the structure of the initial directional freezing.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-11-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
A probabilistic computational framework for bridge network optimal maintenance scheduling
International Nuclear Information System (INIS)
Bocchini, Paolo; Frangopol, Dan M.
2011-01-01
This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.
Optimal satisfaction degree in energy harvesting cognitive radio networks
International Nuclear Information System (INIS)
Li Zan; Liu Bo-Yang; Si Jiang-Bo; Zhou Fu-Hui
2015-01-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. (paper)
Optimal satisfaction degree in energy harvesting cognitive radio networks
Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui
2015-12-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).
DEFF Research Database (Denmark)
Alkan, Ferhat; Erten, Cesim
2014-01-01
MOTIVATION: Global many-to-many alignment of biological networks has been a central problem in comparative biological network studies. Given a set of biological interaction networks, the informal goal is to group together related nodes. For the case of protein-protein interaction networks...... of execution speed and memory requirements is more reasonable than the competing algorithms. AVAILABILITY AND IMPLEMENTATION: Supplementary material including code implementations in LEDA C++, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/BEAMS.tar.gz....
Optimal neural networks for protein-structure prediction
International Nuclear Information System (INIS)
Head-Gordon, T.; Stillinger, F.H.
1993-01-01
The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident
Localization of multilayer networks by optimized single-layer rewiring.
Jalan, Sarika; Pradhan, Priodyuti
2018-04-01
We study localization properties of principal eigenvectors (PEVs) of multilayer networks (MNs). Starting with a multilayer network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multilayer network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that rewiring only one layer is enough to attain a MN having a highly localized PEV. Our investigation reveals that a single edge rewiring of the optimized MN can lead to the complete delocalization of a highly localized PEV. This sensitivity in the localization behavior of PEVs is accompanied with the second largest eigenvalue lying very close to the largest one. This observation opens an avenue to gain a deeper insight into the origin of PEV localization of networks. Furthermore, analysis of multilayer networks constructed using real-world social and biological data shows that the localization properties of these real-world multilayer networks are in good agreement with the simulation results for the model multilayer network. This paper is relevant to applications that require understanding propagation of perturbation in multilayer networks.
Topological Effects and Performance Optimization in Transportation Continuous Network Design
Directory of Open Access Journals (Sweden)
Jianjun Wu
2014-01-01
Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.
Optimal control of epidemic information dissemination over networks.
Chen, Pin-Yu; Cheng, Shin-Ming; Chen, Kwang-Cheng
2014-12-01
Information dissemination control is of crucial importance to facilitate reliable and efficient data delivery, especially in networks consisting of time-varying links or heterogeneous links. Since the abstraction of information dissemination much resembles the spread of epidemics, epidemic models are utilized to characterize the collective dynamics of information dissemination over networks. From a systematic point of view, we aim to explore the optimal control policy for information dissemination given that the control capability is a function of its distribution time, which is a more realistic model in many applications. The main contributions of this paper are to provide an analytically tractable model for information dissemination over networks, to solve the optimal control signal distribution time for minimizing the accumulated network cost via dynamic programming, and to establish a parametric plug-in model for information dissemination control. In particular, we evaluate its performance in mobile and generalized social networks as typical examples.
Optimization of deformation monitoring networks using finite element strain analysis
Alizadeh-Khameneh, M. Amin; Eshagh, Mehdi; Jensen, Anna B. O.
2018-04-01
An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.
Singularities in minimax optimization of networks
DEFF Research Database (Denmark)
Madsen, Kaj; Schjær-Jacobsen, Hans
1976-01-01
A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used in the li......A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used...
Optimization of robustness of interdependent network controllability by redundant design.
Directory of Open Access Journals (Sweden)
Zenghu Zhang
Full Text Available Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy or DBS (degree based strategy for node backup and HDF(high degree first for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability.
Speed Optimization in Liner Shipping Network Design
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Karsten, Christian Vad; Pisinger, David
In the Liner Shipping Network Design Problem (LSNDP) services sail at a given speed throughout a round trip. In reality most services operate with a speed differentiated head- and back-haul, or even individual speeds on every sailing between two ports. The speed of a service is decisive...
Optimizing Key Updates in Sensor Networks
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
2011-01-01
Sensor networks offer the advantages of simple and low–resource communication. Nevertheless, security is of particular importance in many cases such as when sensitive data is communicated or tamper-resistance is required. Updating the security keys is one of the key points in security, which...
Spatial prisoner's dilemma optimally played in small-world networks
International Nuclear Information System (INIS)
Masuda, Naoki; Aihara, Kazuyuki
2003-01-01
Cooperation is commonly found in ecological and social systems even when it apparently seems that individuals can benefit from selfish behavior. We investigate how cooperation emerges with the spatial prisoner's dilemma played in a class of networks ranging from regular lattices to random networks. We find that, among these networks, small-world topology is the optimal structure when we take into account the speed at which cooperative behavior propagates. Our results may explain why the small-world properties are self-organized in real networks
Optimization of multicast optical networks with genetic algorithm
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
An Optimal Routing Algorithm in Service Customized 5G Networks
Directory of Open Access Journals (Sweden)
Haipeng Yao
2016-01-01
Full Text Available With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN, Content-Centric Networking (CCN, and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.
LTE-Advanced Radio and Network Optimization
DEFF Research Database (Denmark)
Velez, Fernando J.; Sousa, Sofia; Flores, Jessica Acevedo
2015-01-01
In cellular optimization, the UL and DL the values from carrier-to-noise-plus-interference ratio (CNIR) from/at the mobile station are very important parameters. From a detailed analysis of its variation with the coverage and reuse distances for different values of the Channel Quality Indicator (...
Optimal Sales Schemes for Network Goods
DEFF Research Database (Denmark)
Parakhonyak, Alexei; Vikander, Nick
consumers simultaneously, serve them all sequentially, or employ any intermediate scheme. We show that the optimal sales scheme is purely sequential, where each consumer observes all previous sales before choosing whether to buy himself. A sequential scheme maximizes the amount of information available...
Optimal Simulations by Butterfly Networks: Extended Abstract,
1987-11-01
Typescript , Univ. of Massachusetts; submitted for nublication. 1_2.2 Ll, - W 12. ifliU 1.8 UI1.25 . l i I 61 MICROCOPY RESOLUTION TEST CHART NATIONAL...1987): An optimal mapping of the FFT algorithm onto the tlypercube architecture. Typescript , Univ. of Massachusetts; submitted for publication. (HR I
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shibo He
2010-01-01
Full Text Available In wireless sensor networks (WSNs, there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.
Neural network for nonsmooth pseudoconvex optimization with general convex constraints.
Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping
2018-05-01
In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.
Cascade-robustness optimization of coupling preference in interconnected networks
International Nuclear Information System (INIS)
Zhang, Xue-Jun; Xu, Guo-Qiang; Zhu, Yan-Bo; Xia, Yong-Xiang
2016-01-01
Highlights: • A specific memetic algorithm was proposed to optimize coupling links. • A small toy model was investigated to examine the underlying mechanism. • The MA optimized strategy exhibits a moderate assortative pattern. • A novel coupling coefficient index was proposed to quantify coupling preference. - Abstract: Recently, the robustness of interconnected networks has attracted extensive attentions, one of which is to investigate the influence of coupling preference. In this paper, the memetic algorithm (MA) is employed to optimize the coupling links of interconnected networks. Afterwards, a comparison is made between MA optimized coupling strategy and traditional assortative, disassortative and random coupling preferences. It is found that the MA optimized coupling strategy with a moderate assortative value shows an outstanding performance against cascading failures on both synthetic scale-free interconnected networks and real-world networks. We then provide an explanation for this phenomenon from a micro-scope point of view and propose a coupling coefficient index to quantify the coupling preference. Our work is helpful for the design of robust interconnected networks.
Deterministic network interdiction optimization via an evolutionary approach
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Ramirez-Marquez, Jose Emmanuel
2009-01-01
This paper introduces an evolutionary optimization approach that can be readily applied to solve deterministic network interdiction problems. The network interdiction problem solved considers the minimization of the maximum flow that can be transmitted between a source node and a sink node for a fixed network design when there is a limited amount of resources available to interdict network links. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link. For this problem, the solution approach developed is based on three steps that use: (1) Monte Carlo simulation, to generate potential network interdiction strategies, (2) Ford-Fulkerson algorithm for maximum s-t flow, to analyze strategies' maximum source-sink flow and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate the approach. In terms of computational effort, the results illustrate that solutions are obtained from a significantly restricted solution search space. Finally, the authors discuss the need for a reliability perspective to network interdiction, so that solutions developed address more realistic scenarios of such problem
de Groot, Marius; Vernooij, Meike W.; Klein, Stefan; Ikram, M. Arfan; Vos, Frans M.; Smith, Stephen M.; Niessen, Wiro J.; Andersson, Jesper L. R.
2013-01-01
Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS
De Groot, M.; Vernooij, M.W.; Klein, S.; Arfan Ikram, M.; Vos, F.M.; Smith, S.M.; Niessen, W.J.; Andersson, J.L.R.
2013-01-01
Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS
Applying neural networks to optimize instrumentation performance
Energy Technology Data Exchange (ETDEWEB)
Start, S.E.; Peters, G.G.
1995-06-01
Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate.
Applying neural networks to optimize instrumentation performance
International Nuclear Information System (INIS)
Start, S.E.; Peters, G.G.
1995-01-01
Well calibrated instrumentation is essential in providing meaningful information about the status of a plant. Signals from plant instrumentation frequently have inherent non-linearities, may be affected by environmental conditions and can therefore cause calibration difficulties for the people who maintain them. Two neural network approaches are described in this paper for improving the accuracy of a non-linear, temperature sensitive level probe ised in Expermental Breeder Reactor II (EBR-II) that was difficult to calibrate
Robust Optimization of Fourth Party Logistics Network Design under Disruptions
Directory of Open Access Journals (Sweden)
Jia Li
2015-01-01
Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
Optimal Quantum Spatial Search on Random Temporal Networks.
Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser
2017-12-01
To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G(n,p), where p is the probability that any two given nodes are connected: After every time interval τ, a new graph G(n,p) replaces the previous one. We prove analytically that, for any given p, there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O(sqrt[n]), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.
Optimal Quantum Spatial Search on Random Temporal Networks
Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser
2017-12-01
To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.
Optimization of stochastic discrete systems and control on complex networks computational networks
Lozovanu, Dmitrii
2014-01-01
This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...
Optimizing Human Input in Social Network Analysis
2018-01-23
7] T. L. Lai and H. Robbins, “Asymptotically efficient adaptive allocation rules,” Advances in applied mathematics , vol. 6, no. 1, pp. 4–22, 1985. [8...W. Whitt, “Heavy traffic limit theorems for queues: a survey,” in Mathematical Methods in Queueing Theory. Springer, 1974, pp. 307–350. [9] H...Regret lower bounds and optimal algorithms,” in Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer
Optimal interdependence between networks for the evolution of cooperation.
Wang, Zhen; Szolnoki, Attila; Perc, Matjaž
2013-01-01
Recent research has identified interactions between networks as crucial for the outcome of evolutionary games taking place on them. While the consensus is that interdependence does promote cooperation by means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we here address the question just how much interdependence there should be. Intuitively, one might assume the more the better. However, we show that in fact only an intermediate density of sufficiently strong interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links between the networks, and the independent formation of cooperative patterns on each individual network. Presented results are robust to variations of the strategy updating rule, the topology of interdependent networks, and the governing social dilemma, thus suggesting a high degree of universality.
A value-based maturity model for IT alignment in networked businesses
Santana Tapia, R.G.
Business-IT alignment can be achieved at various levels of maturity. Supposing that an organization has tried to achieve business-IT alignment, a question to face is: how is that organization going to know the level of maturity of its alignment situation in order to plan future ways of action? That
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
In this paper, the configuration of a district heating (DH) network which connects from the heating plant to the end users was optimized with emphasizing the network thermal performance. Each end user in the network represents a building block. The locations of the building blocks are fixed while...... the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... by multi factors as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding pressure and temperature limitation, as well as the corresponding network heat loss....
Optimal networks of future gravitational-wave telescopes
Raffai, Péter; Gondán, László; Heng, Ik Siong; Kelecsényi, Nándor; Logue, Josh; Márka, Zsuzsa; Márka, Szabolcs
2013-08-01
We aim to find the optimal site locations for a hypothetical network of 1-3 triangular gravitational-wave telescopes. We define the following N-telescope figures of merit (FoMs) and construct three corresponding metrics: (a) capability of reconstructing the signal polarization; (b) accuracy in source localization; and (c) accuracy in reconstructing the parameters of a standard binary source. We also define a combined metric that takes into account the three FoMs with practically equal weight. After constructing a geomap of possible telescope sites, we give the optimal 2-telescope networks for the four FoMs separately in example cases where the location of the first telescope has been predetermined. We found that based on the combined metric, placing the first telescope to Australia provides the most options for optimal site selection when extending the network with a second instrument. We suggest geographical regions where a potential second and third telescope could be placed to get optimal network performance in terms of our FoMs. Additionally, we use a similar approach to find the optimal location and orientation for the proposed LIGO-India detector within a five-detector network with Advanced LIGO (Hanford), Advanced LIGO (Livingston), Advanced Virgo, and KAGRA. We found that the FoMs do not change greatly in sites within India, though the network can suffer a significant loss in reconstructing signal polarizations if the orientation angle of an L-shaped LIGO-India is not set to the optimal value of ˜58.2°( + k × 90°) (measured counterclockwise from East to the bisector of the arms).
Optimal networks of future gravitational-wave telescopes
International Nuclear Information System (INIS)
Raffai, Péter; Márka, Zsuzsa; Márka, Szabolcs; Gondán, László; Kelecsényi, Nándor; Heng, Ik Siong; Logue, Josh
2013-01-01
We aim to find the optimal site locations for a hypothetical network of 1–3 triangular gravitational-wave telescopes. We define the following N-telescope figures of merit (FoMs) and construct three corresponding metrics: (a) capability of reconstructing the signal polarization; (b) accuracy in source localization; and (c) accuracy in reconstructing the parameters of a standard binary source. We also define a combined metric that takes into account the three FoMs with practically equal weight. After constructing a geomap of possible telescope sites, we give the optimal 2-telescope networks for the four FoMs separately in example cases where the location of the first telescope has been predetermined. We found that based on the combined metric, placing the first telescope to Australia provides the most options for optimal site selection when extending the network with a second instrument. We suggest geographical regions where a potential second and third telescope could be placed to get optimal network performance in terms of our FoMs. Additionally, we use a similar approach to find the optimal location and orientation for the proposed LIGO-India detector within a five-detector network with Advanced LIGO (Hanford), Advanced LIGO (Livingston), Advanced Virgo, and KAGRA. We found that the FoMs do not change greatly in sites within India, though the network can suffer a significant loss in reconstructing signal polarizations if the orientation angle of an L-shaped LIGO-India is not set to the optimal value of ∼58.2°( + k × 90°) (measured counterclockwise from East to the bisector of the arms). (paper)
MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks.
Directory of Open Access Journals (Sweden)
Konstantin Popov
2016-04-01
Full Text Available Active matter systems, and in particular the cell cytoskeleton, exhibit complex mechanochemical dynamics that are still not well understood. While prior computational models of cytoskeletal dynamics have lead to many conceptual insights, an important niche still needs to be filled with a high-resolution structural modeling framework, which includes a minimally-complete set of cytoskeletal chemistries, stochastically treats reaction and diffusion processes in three spatial dimensions, accurately and efficiently describes mechanical deformations of the filamentous network under stresses generated by molecular motors, and deeply couples mechanics and chemistry at high spatial resolution. To address this need, we propose a novel reactive coarse-grained force field, as well as a publicly available software package, named the Mechanochemical Dynamics of Active Networks (MEDYAN, for simulating active network evolution and dynamics (available at www.medyan.org. This model can be used to study the non-linear, far from equilibrium processes in active matter systems, in particular, comprised of interacting semi-flexible polymers embedded in a solution with complex reaction-diffusion processes. In this work, we applied MEDYAN to investigate a contractile actomyosin network consisting of actin filaments, alpha-actinin cross-linking proteins, and non-muscle myosin IIA mini-filaments. We found that these systems undergo a switch-like transition in simulations from a random network to ordered, bundled structures when cross-linker concentration is increased above a threshold value, inducing contraction driven by myosin II mini-filaments. Our simulations also show how myosin II mini-filaments, in tandem with cross-linkers, can produce a range of actin filament polarity distributions and alignment, which is crucially dependent on the rate of actin filament turnover and the actin filament's resulting super-diffusive behavior in the actomyosin-cross-linker system
Design and optimizing factors of PACS network architecture
International Nuclear Information System (INIS)
Tao Yonghao; Miao Jingtao
2001-01-01
Objective: Exploring the design and optimizing factors of picture archiving and communication system (PACS) network architecture. Methods: Based on the PACS of shanghai first hospital to performed the measurements and tests on the requirements of network bandwidth and transmitting rate for different PACS functions and procedures respectively in static and dynamic network traffic situation, utilizing the network monitoring tools which built-in workstations and provided by Windows NT. Results: No obvious difference between switch equipment and HUB when measurements and tests implemented in static situation except route which slow down the rate markedly. In dynamic environment Switch is able to provide higher bandwidth utilizing than HUB and local system scope communication achieved faster transmitting rate than global system. Conclusion: The primary optimizing factors of PACS network architecture design include concise network topology and disassemble tremendous global traffic to multiple distributed local scope network communication to reduce the traffic of network backbone. The most important issue is guarantee essential bandwidth for diagnosis procedure of medical imaging
Multi-objective optimization in computer networks using metaheuristics
Donoso, Yezid
2007-01-01
Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...
Cross-layer optimization of wireless multi-hop networks
Soldati, Pablo
2007-01-01
The interest in wireless communications has grown constantly for the past decades, leading to an enormous number of applications and services embraced by billions of users. In order to meet the increasing demand for mobile Internet access, several high data-rate radio networking technologies have been proposed to offer wide area high-speed wireless communications, eventually replacing fixed (wired) networks for many applications. This thesis considers cross-layer optimization of multi-hop rad...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)
A framework for reactive optimization in mobile ad hoc networks
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2008-01-01
We present a framework to optimize the performance of a mobile ad hoc network over a wide range of operating conditions. It includes screening experiments to quantify the parameters and interactions among parameters influential to throughput. Profile-driven regression is applied to obtain a model....... The predictive accuracy of the model is monitored and used to update the model dynamically. The results indicate the framework may be useful for the optimization of dynamic systems of high dimension....
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
Directory of Open Access Journals (Sweden)
J. Trdlicka
2010-12-01
Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.
Service network design of bike sharing systems analysis and optimization
Vogel, Patrick
2016-01-01
This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.
An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2016-01-01
As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.
5G heterogeneous networks self-organizing and optimization
Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing
2016-01-01
This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.
Directory of Open Access Journals (Sweden)
Nur Faziera Napis
2018-05-01
Full Text Available The presence of optimized distributed generation (DG with suitable distribution network reconfiguration (DNR in the electrical distribution network has an advantage for voltage support, power losses reduction, deferment of new transmission line and distribution structure and system stability improvement. However, installation of a DG unit at non-optimal size with non-optimal DNR may lead to higher power losses, power quality problem, voltage instability and incremental of operational cost. Thus, an appropriate DG and DNR planning are essential and are considered as an objective of this research. An effective heuristic optimization technique named as improved evolutionary particle swarm optimization (IEPSO is proposed in this research. The objective function is formulated to minimize the total power losses (TPL and to improve the voltage stability index (VSI. The voltage stability index is determined for three load demand levels namely light load, nominal load, and heavy load with proper optimal DNR and DG sizing. The performance of the proposed technique is compared with other optimization techniques, namely particle swarm optimization (PSO and iteration particle swarm optimization (IPSO. Four case studies on IEEE 33-bus and IEEE 69-bus distribution systems have been conducted to validate the effectiveness of the proposed IEPSO. The optimization results show that, the best achievement is done by IEPSO technique with power losses reduction up to 79.26%, and 58.41% improvement in the voltage stability index. Moreover, IEPSO has the fastest computational time for all load conditions as compared to other algorithms.
A multiobjective optimization framework for multicontaminant industrial water network design.
Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge
2011-07-01
The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.
Distributed Optimization of Multi Beam Directional Communication Networks
2017-06-30
Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications...Multilayer communications may occur within a coalition; for example, a team consisting of ground vehicles and an airborne set of assets may desire to
Optimization of composite panels using neural networks and genetic algorithms
Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries
2003-01-01
The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a
Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning
Optimal resource allocation in downlink CDMA wireless networks
Endrayanto, A.I.
2013-01-01
This thesis presents a full analytical characterization of the optimal joint downlink rate and power assignment for maximal total system throughput in a multi cell CDMA network. In Chapter 2, we analyze the feasibility of downlink power assignment in a linear model of two CDMA cell, under the
Optimization of Gas Flow Network using the Traveling Salesman ...
African Journals Online (AJOL)
The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...
On Optimal Geographical Caching in Heterogeneous Cellular Networks
Serbetci, Berksan; Goseling, Jasper
2017-01-01
In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit
Multi-Objective Design Of Optimal Greenhouse Gas Observation Networks
Lucas, D. D.; Bergmann, D. J.; Cameron-Smith, P. J.; Gard, E.; Guilderson, T. P.; Rotman, D.; Stolaroff, J. K.
2010-12-01
One of the primary scientific functions of a Greenhouse Gas Information System (GHGIS) is to infer GHG source emission rates and their uncertainties by combining measurements from an observational network with atmospheric transport modeling. Certain features of the observational networks that serve as inputs to a GHGIS --for example, sampling location and frequency-- can greatly impact the accuracy of the retrieved GHG emissions. Observation System Simulation Experiments (OSSEs) provide a framework to characterize emission uncertainties associated with a given network configuration. By minimizing these uncertainties, OSSEs can be used to determine optimal sampling strategies. Designing a real-world GHGIS observing network, however, will involve multiple, conflicting objectives; there will be trade-offs between sampling density, coverage and measurement costs. To address these issues, we have added multi-objective optimization capabilities to OSSEs. We demonstrate these capabilities by quantifying the trade-offs between retrieval error and measurement costs for a prototype GHGIS, and deriving GHG observing networks that are Pareto optimal. [LLNL-ABS-452333: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Optimal scheduling for distribution network with redox flow battery storage
International Nuclear Information System (INIS)
Hosseina, Majid; Bathaee, Seyed Mohammad Taghi
2016-01-01
Highlights: • A novel method for optimal scheduling of storages in radial network is presented. • Peak shaving and load leveling are the main objectives. • Vanadium redox flow battery is considered as the energy storage unit. • Real data is used for simulation. - Abstract: There are many advantages to utilize storages in electric power system. Peak shaving, load leveling, load frequency control, integration of renewable, energy trading and spinning reserve are the most important of them. Batteries, especially redox flow batteries, are one of the appropriate storages for utilization in distribution network. This paper presents a novel, heuristic and practical method for optimal scheduling in distribution network with flow battery storage. This heuristic method is more suitable for scheduling and operation of distribution networks which require installation of storages. Peak shaving and load leveling is considered as the main objective in this paper. Several indices are presented in this paper for determine the place of storages and also scheduling for optimal use of energy in them. Simulations of this paper are based on real information of distribution network substation that located in Semnan, Iran.
Optimal Intermittent Operation of Water Distribution Networks under Water Shortage
Directory of Open Access Journals (Sweden)
mohamad Solgi
2017-07-01
Full Text Available Under water shortage conditions, it is necessary to exercise water consumption management practices in water distribution networks (WDN. Intermittent supply of water is one such practice that makes it possible to supply consumption nodal demands with the required pressure via water cutoff to some consumers during certain hours of the day. One of the most important issues that must be observed in this management practice is the equitable and uniform water distribution among the consumers. In the present study, uniformity in water distribution and minimum supply of water to all consumers are defined as justice and equity, respectively. Also, an optimization model has been developed to find an optimal intermittent supply schedule that ensures maximum number of demand nodes are supplied with water while the constraints on the operation of water distribution networks are also observed. To show the efficiency of the proposed model, it has been used in the Two-Loop distribution network under several different scenarios of water shortage. The optimization model has been solved using the honey bee mating optimization algorithm (HBMO linked to the hydraulic simulator EPANET. The results obtained confirm the efficiency of the proposed model in achieving an optimal intermittent supply schedule. Moreover, the model is found capable of distributing the available water in an equitable and just manner among all the consumers even under severe water shoratges.
Improving the performance of the actinic inspection tool with an optimized alignment procedure
International Nuclear Information System (INIS)
Mochi, I.; Goldberg, K.A.; Naulleau, P.; Huh, Sungmin
2009-01-01
Extreme ultraviolet (EUV) microscopy is an important tool for the investigation of the performance of EUV masks, for detecting the presence and the characteristics of defects, and for evaluating the effectiveness of defect repair techniques. Aerial image measurement bypasses the difficulties inherent to photoresist imaging and enables high data collection speed and flexibility. It provides reliable and quick feedback for the development of masks and lithography system modeling methods. We operate the SEMATECH Berkeley Actinic Inspection Tool (AIT), a EUV microscope installed at the Advanced Light Source at Lawrence Berkeley National Laboratory. The AIT is equipped with several high-magnification Fresnel zoneplate lenses, with various numerical aperture values, that enable it image the reflective mask surface with various resolution and magnification settings. Although the AIT has undergone significant recent improvements in terms of imaging resolution and illumination uniformity, there is still room for improvement. In the AIT, an off-axis zoneplate lens collects the light coming from the sample and an image of the sample is projected onto an EUV-sensitive CCD camera. The simplicity of the optical system is particularly helpful considering that the AIT alignment has to be performed every time that a sample or a zoneplate is replaced. The alignment is sensitive to several parameters such as the lens position and orientation, the illumination direction and the sample characteristics. Since the AIT works in high vacuum, there is no direct access to the optics or to the sample during the alignment and the measurements. For all these reasons the alignment procedures and feedback can be complex, and in some cases can reduce the overall data throughput of the system. In this paper we review the main strategies and procedures that have been developed for quick and reliable alignments, and we describe the performance improvements we have achieved, in terms of aberration
Improving the performance of the actinic inspection tool with an optimized alignment procedure
Energy Technology Data Exchange (ETDEWEB)
Mochi, I.; Goldberg, K.A.; Naulleau, P.; Huh, Sungmin
2009-03-04
Extreme ultraviolet (EUV) microscopy is an important tool for the investigation of the performance of EUV masks, for detecting the presence and the characteristics of defects, and for evaluating the effectiveness of defect repair techniques. Aerial image measurement bypasses the difficulties inherent to photoresist imaging and enables high data collection speed and flexibility. It provides reliable and quick feedback for the development of masks and lithography system modeling methods. We operate the SEMATECH Berkeley Actinic Inspection Tool (AIT), a EUV microscope installed at the Advanced Light Source at Lawrence Berkeley National Laboratory. The AIT is equipped with several high-magnification Fresnel zoneplate lenses, with various numerical aperture values, that enable it image the reflective mask surface with various resolution and magnification settings. Although the AIT has undergone significant recent improvements in terms of imaging resolution and illumination uniformity, there is still room for improvement. In the AIT, an off-axis zoneplate lens collects the light coming from the sample and an image of the sample is projected onto an EUV-sensitive CCD camera. The simplicity of the optical system is particularly helpful considering that the AIT alignment has to be performed every time that a sample or a zoneplate is replaced. The alignment is sensitive to several parameters such as the lens position and orientation, the illumination direction and the sample characteristics. Since the AIT works in high vacuum, there is no direct access to the optics or to the sample during the alignment and the measurements. For all these reasons the alignment procedures and feedback can be complex, and in some cases can reduce the overall data throughput of the system. In this paper we review the main strategies and procedures that have been developed for quick and reliable alignments, and we describe the performance improvements we have achieved, in terms of aberration
Synchrony-optimized networks of non-identical Kuramoto oscillators
International Nuclear Information System (INIS)
Brede, Markus
2008-01-01
In this Letter we discuss a method for generating synchrony-optimized coupling architectures of Kuramoto oscillators with a heterogeneous distribution of native frequencies. The method allows us to relate the properties of the coupling network to its synchronizability. These relations were previously only established from a linear stability analysis of the identical oscillator case. We further demonstrate that the heterogeneity in the oscillator population produces heterogeneity in the optimal coupling network as well. Two rules for enhancing the synchronizability of a given network by a suitable placement of oscillators are given: (i) native frequencies of adjacent oscillators must be anti-correlated and (ii) frequency magnitudes should positively correlate with the degree of the node they are placed at
On Optimal Policies for Network-Coded Cooperation
DEFF Research Database (Denmark)
Khamfroush, Hana; Roetter, Daniel Enrique Lucani; Pahlevani, Peyman
2015-01-01
Network-coded cooperative communication (NC-CC) has been proposed and evaluated as a powerful technology that can provide a better quality of service in the next-generation wireless systems, e.g., D2D communications. Previous contributions have focused on performance evaluation of NC-CC scenarios...... rather than searching for optimal policies that can minimize the total cost of reliable packet transmission. We break from this trend by initially analyzing the optimal design of NC-CC for a wireless network with one source, two receivers, and half-duplex erasure channels. The problem is modeled...... as a special case of Markov decision process (MDP), which is called stochastic shortest path (SSP), and is solved for any field size, arbitrary number of packets, and arbitrary erasure probabilities of the channels. The proposed MDP solution results in an optimal transmission policy per time slot, and we use...
Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.
Sohn, Insoo; Liu, Huaping; Ansari, Nirwan
2015-01-01
An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Management and optimization of the CPCU network working
Energy Technology Data Exchange (ETDEWEB)
Silvain, D. (Compagnie Parisienne de Chauffage Urbain, 75 - Paris (FR))
1991-10-01
The CPCU steam distribution network is supplemented by a return network for the condensation water. The data system installed in 1988 provides, for the real time, management of the function of the two networks and a reduction in production costs. For the steam, data required in the network, the boiler houses and from external sources are processed by local network of five microprocessors and permit: - with time delay: technical and economic production optimizing calculations, or forecasts, for the following day, of the total required output and the procedure necessary for supplying this at the lowest cost; - in real time: on the basis of the forecasts for the previous day, creating the production instructions for the boiler houses and the instructions for the network remote control elements; - in case of an unexpected occurrence: immediate creation of new operating forecasts for the boiler houses for the establishing management data in real time. For the water, the system forecasts the volume to be returned to the boiler depending on the quantity of steam to be produced. Subsequently, an analysis is carried out in real time of pressures and outputs measured in the network for deriving valve movements and the pump stop/start procedure for guaranteeing the return of the water. The architecture, basic principles and software developed for this application can be used in other steam or water networks and, in a general manner, are adaptable for the management of any complex multi-supplier or multicustomer systems.
DEFF Research Database (Denmark)
Petersen, Gitte; Seberg, Ole; Aagesen, Lone
2004-01-01
The ability of the program POY, implementing optimization alignment, to deal with major indels is explored and discussed in connection with a phylogenetic analysis of the genus Secale based on partial Adhl sequences. The Adhl sequences used span exon 2-4. Nearly all variation is found in intron 2...... recovers both genera as monophyletic when knowledge of the duplication is incorporated in the analysis. The phylogenetic relationships within Secale are not clearly resolved. Subspecific taxa of Secale strictum have identical sequences and they are confined to a monophyletic group. However, the two...
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Kolosionis, Konstantinos; Papadopoulou, Maria P.
2017-04-01
Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.
Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen
2017-02-01
Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-01-01
Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
Energy Technology Data Exchange (ETDEWEB)
L. DOWELL
1999-08-01
The optimization of the configuration of communications and control networks is important for assuring the reliability and performance of the networks. This paper presents techniques for determining the optimal configuration for such a network in the presence of communication and connectivity constraints. reconfiguration to restore connectivity to a data-fusion network following the failure of a network component.
Competitive game theoretic optimal routing in optical networks
Yassine, Abdulsalam; Kabranov, Ognian; Makrakis, Dimitrios
2002-09-01
Optical transport service providers need control and optimization strategies for wavelength management, network provisioning, restoration and protection, allowing them to define and deploy new services and maintain competitiveness. In this paper, we investigate a game theory based model for wavelength and flow assignment in multi wavelength optical networks, consisting of several backbone long-haul optical network transport service providers (TSPs) who are offering their services -in terms of bandwidth- to Internet service providers (ISPs). The ISPs act as brokers or agents between the TSP and end user. The agent (ISP) buys services (bandwidth) from the TSP. The TSPs compete among themselves to sell their services and maintain profitability. We present a case study, demonstrating the impact of different bandwidth broker demands on the supplier's profit and the price paid by the network broker.
Optimizing bulk data transfers using network measurements: A practical case
International Nuclear Information System (INIS)
Ciuffoletti, A; Merola, L; Palmieri, F; Russo, G; Pardi, S
2010-01-01
In modern Data Grid infrastructures, we increasingly face the problem of providing the running applications with fast and reliable access to large data volumes, often geographically distributed across the network. As a direct consequence, the concept of replication has been adopted by the grid community to increase data availability and maximize job throughput. To be really effective, such process has to be driven by specific optimization strategies that define when and where replicas should be created or deleted on a per-site basis, and which replicas a job should use. These strategies have to take into account the available network bandwidth as a primary resource, prior to any consideration about storage or processing power. We present a novel replica management service, integrated within the Gluedomains active network monitoring architecture, designed and implemented within the centralized collective middleware framework of the SCoPE project to provide network-aware transfer services for data intensive Grid applications.
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Automated Axis Alignment for a Nanomanipulator inside SEM and Its Error Optimization
Directory of Open Access Journals (Sweden)
Chao Zhou
2017-01-01
Full Text Available In the motion of probing nanostructures, repeating position and movement is frequently happing and tolerance for position error is stringent. The consistency between the axis of manipulators and image is very significant since the visual servo is the most important tool in the automated manipulation. This paper proposed an automated axis alignment method for a nanomanipulator inside the SEM by recognizing the position of a closed-loop controlling the end-effector, which can characterize the relationship of these two axes, and then the rotation matrix can be calculated accordingly. The error of this method and its transfer function are also calculated to compare the iteration method and average method. The method in this paper can accelerate the process of axis alignment to avoid the electron beam induced deposition effect on the end tips. Experiment demonstration shows that it can achieve a 0.1-degree precision in 90 seconds.
Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks
Directory of Open Access Journals (Sweden)
M. Hadi Amini
2018-01-01
Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.
Energy network dispatch optimization under emergency of local energy shortage
International Nuclear Information System (INIS)
Cai, Tianxing; Zhao, Chuanyu; Xu, Qiang
2012-01-01
The consequence of short-time energy shortage under extreme conditions, such as earthquake, tsunami, and hurricane, may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. In this paper, a novel methodology is developed for energy network dispatch optimization under emergency of local energy shortage, which includes four stages of work. First, emergency-area-centered energy network needs to be characterized, where the capacity, quantity, and availability of various energy sources are determined. Second, the energy initial situation under emergency conditions needs to be identified. Then, the energy dispatch optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage. Finally, the sensitivity of the minimum dispatch time with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces. The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions. -- Highlights: ► Address the energy network dispatch problem under emergency of local energy shortage. ► Minimize the energy restoration time for the entire energy network under emergency events. ► Develop a new MILP model and a sensitivity analysis method with respect to uncertainties.
Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST
Directory of Open Access Journals (Sweden)
Oliver Melvin J
2005-04-01
Full Text Available Abstract Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST, which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN. W.ND-BLAST provides intuitive Graphic User Interfaces (GUI for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is
Optimizing Markovian modeling of chaotic systems with recurrent neural networks
International Nuclear Information System (INIS)
Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de
2008-01-01
In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included
Implementing size-optimal discrete neural networks require analog circuitry
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-01
This paper starts by overviewing results dealing with the approximation capabilities of neural networks, as well as bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions the authors show that implementing Boolean functions can be done using neurons having an identity transfer function. Because in this case the size of the network is minimized, it follows that size-optimal solutions for implementing Boolean functions can be obtained using analog circuitry. Conclusions and several comments on the required precision are ending the paper.
Energy Technology Data Exchange (ETDEWEB)
Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)
2010-02-05
Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.
International Nuclear Information System (INIS)
Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min
2010-01-01
Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.
Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun
2010-02-01
Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.
Optimization of TTEthernet Networks to Support Best-Effort Traffic
DEFF Research Database (Denmark)
Tamas-Selicean, Domitian; Pop, Paul
2014-01-01
This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet...
Optimal synthesis of a heat-exchanger network
Energy Technology Data Exchange (ETDEWEB)
Hamed, O A; Aly, S [University of United Arab Emirates, Al-Ain (United Arab Emirates). Faculty of Engineering
1991-01-01
Thermodynamic, heat transfer and economic concepts influencing the synthesis of a heat-exchanger network (HEN) coupled to a crude fractionation unit are examined. The impact of the variation of the minimum temperature approach on energy and capital targets is studied using recent developments in pinch technology. The optimal pinch approach temperature has been determined using the 'supertargeting' concept where proper trade-off between energy and capital targets is observed prior to design. A heuristic evolutionary approach has then been used for the generation of the optimal HEN. (author).
Designing optimal bioethanol networks with purification for integrated biorefineries
International Nuclear Information System (INIS)
Shenoy, Akshay U.; Shenoy, Uday V.
2014-01-01
Highlights: • An analytical method is devised for bioethanol network integration with purification. • Minimum fresh bioethanol flow and pinch are found by the Unified Targeting Algorithm. • Optimal bioethanol networks are then synthesized by the Nearest Neighbors Algorithm. • Continuous targets and networks are developed over the purifier inlet flowrate range. • Case study of a biorefinery producing bioethanol from wheat shows large savings. - Abstract: Bioethanol networks with purification for processing pathways in integrated biorefineries are targeted and designed in this work by an analytical approach not requiring graphical constructions. The approach is based on six fundamental equations involving eight variables: two balance equations for the stream flowrate and the bioethanol load over the total network system; one equation for the above-pinch bioethanol load being picked up by the minimum fresh resource and the purified stream; and three equations for the purification unit. A solution strategy is devised by specifying the two variables associated with the purifier inlet stream. Importantly, continuous targeting is then possible over the entire purifier inlet flowrate range on deriving elegant formulae for the remaining six variables. The Unified Targeting Algorithm (UTA) is utilized to establish the minimum fresh bioethanol resource flowrate and identify the pinch purity. The fresh bioethanol resource flowrate target is shown to decrease linearly with purifier inlet flowrate provided the pinch is held by the same point. The Nearest Neighbors Algorithm (NNA) is used to methodically synthesize optimal networks matching bioethanol demands and sources. A case study of a biorefinery producing bioethanol from wheat with arabinoxylan (AX) coproduction is presented. It illustrates the versatility of the approach in generating superior practical designs with up to nearly 94% savings for integrated bioethanol networks, both with and without process
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
2013-01-01
Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions...... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms...
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
Finding a location for a new facility such that the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions...... that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. The query returns the road segment(s) with the highest score. To achieve low latency, it is essential to prune the very large search space. We propose two algorithms...
2013-11-04
... (NOA) for Strategic Network Optimization (SNO) Environmental Assessment Finding of No Significant... Network Optimization (SNO) Environmental Assessment (EA) Finding of No Significant Impact (FONSI). SUMMARY... interpreted comprehensively to include the natural and physical environment and the relationship of people...
2014-12-01
The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...
PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks
Directory of Open Access Journals (Sweden)
Mansour Sheikhan
2012-06-01
Full Text Available Mobile ad-hoc network (MANET is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN which its parameters are optimized by particle swarm optimization (PSO algorithm is proposed as multipath routing algorithm. Link expiration time (LET between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA in terms of the path set reliability and number of paths in the set.
Accelerator optimization using a network control and acquisition system
International Nuclear Information System (INIS)
Geddes, Cameron G.R.; Catravas, P.E.; Faure, Jerome; Toth, Csaba; Tilborg, J. van; Leemans, Wim P.
2002-01-01
Accelerator optimization requires detailed study of many parameters, indicating the need for remote control and automated data acquisition systems. A control and data acquisition system based on a network of commodity PCs and applications with standards based inter-application communication is being built for the l'OASIS accelerator facility. This system allows synchronous acquisition of data at high (> 1 Hz) rates and remote control of the accelerator at low cost, allowing detailed study of the acceleration process
A measure theoretic approach to traffic flow optimization on networks
Cacace, Simone; Camilli, Fabio; De Maio, Raul; Tosin, Andrea
2018-01-01
We consider a class of optimal control problems for measure-valued nonlinear transport equations describing traffic flow problems on networks. The objective isto minimise/maximise macroscopic quantities, such as traffic volume or average speed,controlling few agents, for example smart traffic lights and automated cars. The measuretheoretic approach allows to study in a same setting local and nonlocal drivers interactionsand to consider the control variables as additional measures interacting ...
Optimal Joint Expected Delay Forwarding in Delay Tolerant Networks
Jia Xu; Xin Feng; Wen Jun Yang; Ru Chuan Wang; Bing Qing Han
2013-01-01
Multicopy forwarding schemes have been employed in delay tolerant network (DTN) to improve the delivery delay and delivery rate. Much effort has been focused on reducing the routing cost while retaining high performance. This paper aims to provide an optimal joint expected delay forwarding (OJEDF) protocol which minimizes the expected delay while satisfying a certain constant on the number of forwardings per message. We propose a comprehensive forwarding metric called joint expected delay (JE...
An optimization planning technique for Suez Canal Network in Egypt
Energy Technology Data Exchange (ETDEWEB)
Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, Gasir M. [Electrical Engineering Dept., Faculty of Eng., Shebin El-Kom (Egypt)
2010-02-15
This paper introduces a proposed optimization technique POT for predicting the peak load demand and planning of transmission line systems. Many of traditional methods have been presented for long-term load forecasting of electrical power systems. But, the results of these methods are approximated. Therefore, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and discussed as a modern technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. The POT is applied also to obtain the optimal planning of transmission lines for the 220 kV of Suez Canal Network (SCN) using the ANN technique. The minimization of the transmission network costs are considered as an objective function, while the transmission lines (TL) planning constraints are satisfied. Zafarana site on the Red Sea coast is considered as an optimal site for installing big wind farm (WF) units in Egypt. So, the POT is applied to plan both the peak load and the electrical transmission of SCN with and without considering WF to develop the impact of WF units on the electrical transmission system of Egypt, considering the reliability constraints which were taken as a separate model in the previous techniques. The application on SCN shows the capability and the efficiently of the proposed techniques to obtain the predicting peak load demand and the optimal planning of transmission lines of SCN up to year 2017. (author)
PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN
Directory of Open Access Journals (Sweden)
Arie Rachmad Syulistyo
2016-02-01
Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.
Extending Resolution of Fault Slip With Geodetic Networks Through Optimal Network Design
Sathiakumar, Sharadha; Barbot, Sylvain Denis; Agram, Piyush
2017-12-01
Geodetic networks consisting of high precision and high rate Global Navigation Satellite Systems (GNSS) stations continuously monitor seismically active regions of the world. These networks measure surface displacements and the amount of geodetic strain accumulated in the region and give insight into the seismic potential. SuGar (Sumatra GPS Array) in Sumatra, GEONET (GNSS Earth Observation Network System) in Japan, and PBO (Plate Boundary Observatory) in California are some examples of established networks around the world that are constantly expanding with the addition of new stations to improve the quality of measurements. However, installing new stations to existing networks is tedious and expensive. Therefore, it is important to choose suitable locations for new stations to increase the precision obtained in measuring the geophysical parameters of interest. Here we describe a methodology to design optimal geodetic networks that augment the existing system and use it to investigate seismo-tectonics at convergent and transform boundaries considering land-based and seafloor geodesy. The proposed network design optimization would be pivotal to better understand seismic and tsunami hazards around the world. Land-based and seafloor networks can monitor fault slip around subduction zones with significant resolution, but transform faults are more challenging to monitor due to their near-vertical geometry.
International Nuclear Information System (INIS)
Ramirez-Marquez, Jose Emmanuel; Rocco S, Claudio M.
2009-01-01
This paper introduces an evolutionary optimization approach that can be readily applied to solve stochastic network interdiction problems (SNIP). The network interdiction problem solved considers the minimization of the cost associated with an interdiction strategy such that the maximum flow that can be transmitted between a source node and a sink node for a fixed network design is greater than or equal to a given reliability requirement. Furthermore, the model assumes that the nominal capacity of each network link and the cost associated with their interdiction can change from link to link and that such interdiction has a probability of being successful. This version of the SNIP is for the first time modeled as a capacitated network reliability problem allowing for the implementation of computation and solution techniques previously unavailable. The solution process is based on an evolutionary algorithm that implements: (1) Monte-Carlo simulation, to generate potential network interdiction strategies, (2) capacitated network reliability techniques to analyze strategies' source-sink flow reliability and, (3) an evolutionary optimization technique to define, in probabilistic terms, how likely a link is to appear in the final interdiction strategy. Examples for different sizes of networks are used throughout the paper to illustrate the approach
National Research Council Canada - National Science Library
Valentine, Jennifer R
2006-01-01
.... This thesis proposes that while the two areas? success fundamentally resides in the implementation and exploitation of technology, it is only through sound IT Governance policies and strategic alignment practices that success can be measured...
Optimization of neural network algorithm of the land market description
Directory of Open Access Journals (Sweden)
M. A. Karpovich
2016-01-01
Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.
International Nuclear Information System (INIS)
Hopps, N. W.
1998-01-01
The work to improve the energy stability of the regenerative amplifier ('regen') for the National Ignition Facility is described. This includes a fast feed-forward system, designed to regulate the output energy of the regen by monitoring how quickly a pulse builds up over many round trips. Shot-to-shot energy fluctuations of all elements prior to (and including) the regen may be compensated for in this way, at the expense of a loss of approximately 50%. Also included is a detailed study into the alignment sensitivity of the regen cavity, with the goal of quantifying the effect of misalignment on the output energy. This is done by calculating the displacement of the eigenmode by augmenting the cavity ABCD matrix with the misalignment matrix elements, E, F. In this way, cavity misalignment issues due to thermal loading of the gain medium are investigated. Alternative cavity designs, which reduce the alignment sensitivity and therefore the energy drift over periods of continuous operation, are considered. Alterations to the amplifier head design are also considered
Optimal residential smart appliances scheduling considering distribution network constraints
Directory of Open Access Journals (Sweden)
Yu-Ree Kim
2016-01-01
Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.
Optimal Operations and Resilient Investments in Steam Networks
Energy Technology Data Exchange (ETDEWEB)
Bungener, Stéphane L., E-mail: stephane.bungener@a3.epfl.ch [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland); Van Eetvelde, Greet [Environmental and Spatial Management, Faculty of Engineering and Architecture, Ghent University, Ghent (Belgium); Maréchal, François [Industrial Process and Energy Systems Engineering, École Polytechnique Fédérale de Lausanne, Lausanne (Switzerland)
2016-01-20
Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.
Optimal Operations and Resilient Investments in Steam Networks
International Nuclear Information System (INIS)
Bungener, Stéphane L.; Van Eetvelde, Greet; Maréchal, François
2016-01-01
Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network’s resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.
Optimization of operation cycles in BWRs using neural networks
International Nuclear Information System (INIS)
Ortiz S, J. J.; Castillo, A.; Alejandro P, D.
2011-11-01
The first results of a system for the optimization of operation cycles in boiling water reactors by means of a multi state recurrent neural network are present in this work. The neural network finds the best combination of fuel cells; fuel reloads and control bars patterns previously designed, according to an energy function that qualifies the performance of the three partial solutions for the solution of the whole problem. The partial solutions are designed by means of optimization systems non couple among them and that can use any optimization technique. The phase of the fuel axial design is not made and the size of the axial areas is fixed during the optimization process. The methodology was applied to design a balance cycle of 18 months for the reactors of the nuclear power station of Laguna Verde. The results show that is possible to find combinations of partial solutions that in set represent good solutions to the complete design problem of an operation cycle of a nuclear reactor. The results are compared with others obtained previously by other techniques. This system was developed in platform Li nux and programmed in Fortran 95 taking advantage of the 8 nuclei of a work station Dell Precision T7400. (Author)
Network ownership and optimal tariffs for natural gas transport
International Nuclear Information System (INIS)
Hagen, Kaare P.; Kind, Hans Jarle; Sannarnes, Jan Gaute
2004-11-01
This paper addresses the issue of national optimal tariffs for transportation of natural gas in a setting where national gas production in its entirety is exported to end-user markets abroad. In a situation where the transportation network is owned altogether by a vertically integrated national gas producer, it is shown that the optimal tariff depends on the ownership structure in the integrated transportation company as well as in the non-facility based gas company. There are two reasons why it is possibly optimal with a mark-up on marginal transportation costs. First, there is a premium on public revenue if domestic taxation is distorting. Second, with incomplete national taxation of rents from the gas sector, the transportation tariffs can serve as a second best way of appropriating rents accruing to foreigners. In a situation where the network is run as a separate entity subject to a rate of return regulation, it will be optimal to discriminate the tariffs between shippers for the usual Ramseyean reasons. (Author)
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Using neural networks to speed up optimization algorithms
Bazan, M
2000-01-01
The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).
Nonlinear Non-convex Optimization of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef
2013-01-01
Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in p....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced.......Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption...
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar; Alouini, Mohamed-Slim; Chen, Yunfei; Radaydeh, Redha M.
2012-01-01
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Pricing Resources in LTE Networks through Multiobjective Optimization
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution. PMID:24526889
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar
2012-09-16
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Pricing resources in LTE networks through multiobjective optimization.
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Pricing Resources in LTE Networks through Multiobjective Optimization
Directory of Open Access Journals (Sweden)
Yung-Liang Lai
2014-01-01
Full Text Available The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1 maximizing operator profit and (2 maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Optimizing Seismic Monitoring Networks for EGS and Conventional Geothermal Projects
Kraft, Toni; Herrmann, Marcus; Bethmann, Falko; Stefan, Wiemer
2013-04-01
In the past several years, geological energy technologies receive growing attention and have been initiated in or close to urban areas. Some of these technologies involve injecting fluids into the subsurface (e.g., oil and gas development, waste disposal, and geothermal energy development) and have been found or suspected to cause small to moderate sized earthquakes. These earthquakes, which may have gone unnoticed in the past when they occurred in remote sparsely populated areas, are now posing a considerable risk for the public acceptance of these technologies in urban areas. The permanent termination of the EGS project in Basel, Switzerland after a number of induced ML~3 (minor) earthquakes in 2006 is one prominent example. It is therefore essential for the future development and success of these geological energy technologies to develop strategies for managing induced seismicity and keeping the size of induced earthquakes at a level that is acceptable to all stakeholders. Most guidelines and recommendations on induced seismicity published since the 1970ies conclude that an indispensable component of such a strategy is the establishment of seismic monitoring in an early stage of a project. This is because an appropriate seismic monitoring is the only way to detect and locate induced microearthquakes with sufficient certainty to develop an understanding of the seismic and geomechanical response of the reservoir to the geotechnical operation. In addition, seismic monitoring lays the foundation for the establishment of advanced traffic light systems and is therefore an important confidence building measure towards the local population and authorities. We have developed an optimization algorithm for seismic monitoring networks in urban areas that allows to design and evaluate seismic network geometries for arbitrary geotechnical operation layouts. The algorithm is based on the D-optimal experimental design that aims to minimize the error ellipsoid of the linearized
Directory of Open Access Journals (Sweden)
Lijun Song
2018-01-01
Full Text Available The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.
Strus, Mark C; Chiaramonti, Ann N; Kim, Young Lae; Jung, Yung Joon; Keller, Robert R
2011-07-01
We investigate the electrical reliability of nanoscale lines of highly aligned, networked, metallic/semiconducting single-walled carbon nanotubes (SWCNTs) fabricated through a template-based fluidic assembly process. We find that these SWCNT networks can withstand DC current densities larger than 10 MA cm(-2) for several hours and, in some cases, several days. We develop test methods that show that the degradation rate, failure predictability and total device lifetime can be linked to the initial resistance. Scanning electron and transmission electron microscopy suggest that fabrication variability plays a critical role in the rate of degradation, and we offer an empirical method of quickly determining the long-term performance of a network. We find that well-fabricated lines subject to constant electrical stress show a linear accumulation of damage reminiscent of electromigration in metallic interconnects, and we explore the underlying physical mechanisms that could cause such behavior.
Directory of Open Access Journals (Sweden)
Nuri Yazdani
2014-03-01
Full Text Available Many energy conversion and storage devices exploit structured ceramics with large interfacial surface areas. Vertically aligned carbon nanotube (VACNT arrays have emerged as possible scaffolds to support large surface area ceramic layers. However, obtaining conformal and uniform coatings of ceramics on structures with high aspect ratio morphologies is non-trivial, even with atomic layer deposition (ALD. Here we implement a diffusion model to investigate the effect of the ALD parameters on coating kinetics and use it to develop a guideline for achieving conformal and uniform thickness coatings throughout the depth of ultra-high aspect ratio structures. We validate the model predictions with experimental data from ALD coatings of VACNT arrays. However, the approach can be applied to predict film conformality as a function of depth for any porous topology, including nanopores and nanowire arrays.
Yazdani, Nuri; Chawla, Vipin; Edwards, Eve; Wood, Vanessa; Park, Hyung Gyu; Utke, Ivo
2014-01-01
Many energy conversion and storage devices exploit structured ceramics with large interfacial surface areas. Vertically aligned carbon nanotube (VACNT) arrays have emerged as possible scaffolds to support large surface area ceramic layers. However, obtaining conformal and uniform coatings of ceramics on structures with high aspect ratio morphologies is non-trivial, even with atomic layer deposition (ALD). Here we implement a diffusion model to investigate the effect of the ALD parameters on coating kinetics and use it to develop a guideline for achieving conformal and uniform thickness coatings throughout the depth of ultra-high aspect ratio structures. We validate the model predictions with experimental data from ALD coatings of VACNT arrays. However, the approach can be applied to predict film conformality as a function of depth for any porous topology, including nanopores and nanowire arrays.
Liu, Qingshan; Guo, Zhishan; Wang, Jun
2012-02-01
In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Haibo Zhang
2016-08-01
Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Design and optimization of all-optical networks
Xiao, Gaoxi
1999-10-01
In this thesis, we present our research results on the design and optimization of all-optical networks. We divide our results into the following four parts: 1.In the first part, we consider broadcast-and-select networks. In our research, we propose an alternative and cheaper network configuration to hide the tuning time. In addition, we derive lower bounds on the optimal schedule lengths and prove that they are tighter than the best existing bounds. 2.In the second part, we consider all-optical wide area networks. We propose a set of algorithms for allocating a given number of WCs to the nodes. We adopt a simulation-based optimization approach, in which we collect utilization statistics of WCs from computer simulation and then perform optimization to allocate the WCs. Therefore, our algorithms are widely applicable and they are not restricted to any particular model and assumption. We have conducted extensive computer simulation on regular and irregular networks under both uniform and non-uniform traffic. We see that our method can get nearly the same performance as that of full wavelength conversion by using a much smaller number of WCs. Compared with the best existing method, the results show that our algorithms can significantly reduce (1)the overall blocking probability (i.e., better mean quality of service) and (2)the maximum of the blocking probabilities experienced at all the source nodes (i.e., better fairness). Equivalently, for a given performance requirement on blocking probability, our algorithms can significantly reduce the number of WCs required. 3.In the third part, we design and optimize the physical topology of all-optical wide area networks. We show that the design problem is NP-complete and we propose a heuristic algorithm called two-stage cut saturation algorithm for this problem. Simulation results show that (1)the proposed algorithm can efficiently design networks with low cost and high utilization, and (2)if wavelength converters are
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
Information spread in networks: Games, optimal control, and stabilization
Khanafer, Ali
This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
International Nuclear Information System (INIS)
Hwangbo, Soonho; Lee, In-Beum; Han, Jeehoon
2014-01-01
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network
Directory of Open Access Journals (Sweden)
Nadjla Hariri
2013-03-01
Full Text Available This study aimed to determine the status of Persian professional web social networks' features and provide a suitable solution for optimization of these networks in Iran. The research methods were library research and evaluative method, and study population consisted of 10 Persian professional web social networks. In this study, for data collection, a check list of social networks important tools and features was used. According to the results, “Cloob”, “IR Experts” and “Doreh” were the most compatible networks with the criteria of social networks. Finally, some solutions were presented for optimization of capabilities of Persian professional web social networks.
Moser, K.; Bergmann, B.; Diemert, J.; Elsner, P.
2014-05-01
In this paper two promising ways to improve the material characteristics of PLA and PHB-V are presented by showing their positive effects on mechanical, optical, and thermal properties. The optimization is achieved by increasing the crystallization from the melt of the polymer chains and the other by means of a reinforcement of the matrices by bio-based materials. In the case of crystallization specific nucleating agents and optimized process parameters promote optimized crystallization conditions and lead particularly in toughness to significant improvements. In addition to crystallization the introduction of cellulose-based reinforcing materials is also a good alternative to improve the ductility of a biopolymer matrix considerably. Due to their polar surface structure cellulose fibres are favouring a very good interaction to the also polar biopolymers. In addition, the polar surfaces of both materials results in very homogeneous dispersion within the compound.
Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio
2013-09-01
Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.
Computation of optimal transport and related hedging problems via penalization and neural networks
Eckstein, Stephan; Kupper, Michael
2018-01-01
This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks. The core idea is to penalize the optimization problem in its dual formulation and reduce it to a finite dimensional one which corresponds to optimizing a neural network with smooth objective function. We present numerical examples from optimal transport, martingale optimal transport, portfolio optimization under uncertainty and generative adversa...
Optimal interval for major maintenance actions in electricity distribution networks
Energy Technology Data Exchange (ETDEWEB)
Louit, Darko; Pascual, Rodrigo [Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna MacKenna, 4860 Santiago (Chile); Banjevic, Dragan [Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, 5 King' s College Rd., Toronto, Ontario (Canada)
2009-09-15
Many systems require the periodic undertaking of major (preventive) maintenance actions (MMAs) such as overhauls in mechanical equipment, reconditioning of train lines, resurfacing of roads, etc. In the long term, these actions contribute to achieving a lower rate of occurrence of failures, though in many cases they increase the intensity of the failure process shortly after performed, resulting in a non-monotonic trend for failure intensity. Also, in the special case of distributed assets such as communications and energy networks, pipelines, etc., it is likely that the maintenance action takes place sequentially over an extended period of time, implying that different sections of the network underwent the MMAs at different periods. This forces the development of a model based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme. Additionally, extended maintenance times and sequential execution of the MMAs make it difficult to identify failures occurring before and after the preventive maintenance action. This results in the loss of important information for the characterization of the failure process. A simple model is introduced to determine the optimal MMA interval considering such restrictions. Furthermore, a case study illustrates the optimal tree trimming interval around an electricity distribution network. (author)
Optimal allocation of resources for suppressing epidemic spreading on networks
Chen, Hanshuang; Li, Guofeng; Zhang, Haifeng; Hou, Zhonghuai
2017-07-01
Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve the optimization problem of how best to allocate the limited resources so as to minimize prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that an epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate λ has a maximal threshold λcopt=1 / , where is the average degree of the underlying network. For a weak infection region (λ ≳λcopt ), we combine perturbation theory with the Lagrange multiplier method (LMM) to derive the analytical expression of optimal allocation of the curing rates and the corresponding minimized prevalence. For a general infection region (λ >λcopt ), the high-dimensional optimization problem is converted into numerically solving low-dimensional nonlinear equations by the HMF theory and LMM. Counterintuitively, in the strong infection region the low-degree nodes should be allocated more medical resources than the high-degree nodes to minimize prevalence. Finally, we use simulated annealing to validate the theoretical results.
Directory of Open Access Journals (Sweden)
Toshio Takayama, MD, PhD
2017-06-01
Full Text Available Angulated anatomy in the aorta, such as tortuous infrarenal aortic necks or steep aortic arches, is a significant challenge for endovascular aortic repair because it often causes inadequate sealing and fixation, which may lead to treatment failure. We have developed a technique using off-the-shelf equipment to precisely control the deployment of stent grafts in challenging landing zones. The key of this technique is to create a through-and-through wire between two access sites and to use a guiding device over the wire. This technique is best used with stent grafts without nose cones. We present an endovascular aneurysm repair case and a thoracic endovascular aortic repair case with challenging proximal landing zones treated by this technique. In both cases, technical success was attained, and follow-up imaging demonstrated well-aligned stent grafts. Our directional tip control technique is easy and effective. It can be a good technical solution for endovascular aortic treatment in angulated anatomy.
Alignment of cryo-EM movies of individual particles by optimization of image translations.
Rubinstein, John L; Brubaker, Marcus A
2015-11-01
Direct detector device (DDD) cameras have revolutionized single particle electron cryomicroscopy (cryo-EM). In addition to an improved camera detective quantum efficiency, acquisition of DDD movies allows for correction of movement of the specimen, due to both instabilities in the microscope specimen stage and electron beam-induced movement. Unlike specimen stage drift, beam-induced movement is not always homogeneous within an image. Local correlation in the trajectories of nearby particles suggests that beam-induced motion is due to deformation of the ice layer. Algorithms have already been described that can correct movement for large regions of frames and for >1 MDa protein particles. Another algorithm allows individual images to be aligned without frame averaging or linear trajectories. The algorithm maximizes the overall correlation of the shifted frames with the sum of the shifted frames. The optimum in this single objective function is found efficiently by making use of analytically calculated derivatives of the function. To smooth estimates of particle trajectories, rapid changes in particle positions between frames are penalized in the objective function and weighted averaging of nearby trajectories ensures local correlation in trajectories. This individual particle motion correction, in combination with weighting of Fourier components to account for increasing radiation damage in later frames, can be used to improve 3-D maps from single particle cryo-EM. Copyright © 2015 Elsevier Inc. All rights reserved.
Optimal information transfer in enzymatic networks: A field theoretic formulation
Samanta, Himadri S.; Hinczewski, Michael; Thirumalai, D.
2017-07-01
Signaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus [Hinczewski and Thirumalai, Phys. Rev. X 4, 041017 (2014), 10.1103/PhysRevX.4.041017]. We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudointermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudointermediate. Surprisingly, in these examples the minimum error computed using simulations that take nonlinearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second-order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in
Optimizing the African VLBI Network for Astronomy and Geodesy
de Witt, A.; Mayer, D.; MacLeod, G.; Combrinck, L.; Petrov, L.; Nickola, M.
2016-12-01
The African VLBI Network will be a pan-African network of radio telescopes comprised of converted redundant satellite Earth-station antennas and new purpose-built radio telescopes. The first of these antennas, in Ghana, is currently being converted to a radio telescope and current funding is estimated to permit the conversion of two more antennas in Africa. These antennas will initially be equipped with a 5-GHz and 6.7-GHz receiver and the next receiver likely to be fitted is a 1.4-1.7-GHz receiver. While it would be advantageous for the AVN antennas to be able to participate also in geodetic and astrometric VLBI observations, there is no funding currently for this. In this paper we re-visit the scientific justifications for the AVN in an attempt to optimize the AVN for each science case, both astronomical and geodetic.
Optimizing the Heat Exchanger Network of a Steam Reforming System
DEFF Research Database (Denmark)
Nielsen, Mads Pagh; Korsgaard, Anders Risum; Kær, Søren Knudsen
2004-01-01
Proton Exchange Membrane (PEM) based combined heat and power production systems are highly integrated energy systems. They may include a hydrogen production system and fuel cell stacks along with post combustion units optionally coupled with gas turbines. The considered system is based on a natural...... stationary numerical system model was used and process integration techniques for optimizing the heat exchanger network for the reforming unit are proposed. Objective is to minimize the system cost. Keywords: Fuel cells; Steam Reforming; Heat Exchanger Network (HEN) Synthesis; MINLP....... gas steam reformer along with gas purification reactors to generate clean hydrogen suited for a PEM stack. The temperatures in the various reactors in the fuel processing system vary from around 1000°C to the stack temperature at 80°C. Furthermore, external heating must be supplied to the endothermic...
Loss optimization in distribution networks with distributed generation
DEFF Research Database (Denmark)
Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte
2017-01-01
This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators...... in highly active distribution grids. This issue is tackled by formulating a hybrid loss optimization problem and solved using the Interior Point Method. Sensitivity analysis is used to identify the optimum location of storage units. Different scenarios of reconfiguration, storage and distributed generation...... penetration are created to test the proposed algorithm. It is tested in a benchmark medium voltage network to show the effectiveness and performance of the algorithm. Results obtained are found to be encouraging for radial distribution system. It shows that we can reduce the power loss by more than 30% using...
Optimal Node Placement in Underwater Wireless Sensor Networks
Felamban, M.
2013-03-25
Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.
Big Data Reduction and Optimization in Sensor Monitoring Network
Directory of Open Access Journals (Sweden)
Bin He
2014-01-01
Full Text Available Wireless sensor networks (WSNs are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.
Distributed optimization of a multisubchannel Ad Hoc cognitive radio network
Leith, Alex
2012-05-01
In this paper, we study the distributed-duality-based optimization of a multisubchannel ad hoc cognitive radio network (CRN) that coexists with a multicell primary radio network (PRN). For radio resource allocation in multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, the orthogonal-access-based exclusive subchannel assignment (ESA) technique has been a popular method, but it is suboptimal in ad hoc networks, because nonorthogonal access between multiple secondary-user links by using shared subchannel assignment (SSA) can bring a higher weighted sum rate. We utilize the Lagrangian dual composition tool and design low-complexity near-optimal SSA resource allocation methods, assuming practical discrete-rate modulation and that the CRN-to-PRN interference constraint has to strictly be satisfied. However, available SSA methods for CRNs are either suboptimal or involve high complexity and suffer from slow convergence. To address this problem, we design fast-convergence SSA duality schemes and introduce several novel methods to increase the speed of convergence and to satisfy various system constraints with low complexity. For practical implementation in ad hoc CRNs, we design distributed-duality schemes that involve only a small number of CRN local information exchanges for dual update. The effects of many system parameters are presented through simulation results, which show that the near-optimal SSA duality scheme can perform significantly better than the suboptimal ESA duality and SSA-iterative waterfilling schemes and that the performance loss of the distributed schemes is small, compared with their centralized counterparts. © 2012 IEEE.
Iterative free-energy optimization for recurrent neural networks (INFERNO)
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
Optimal pinnate leaf-like network/matrix structure for enhanced conductive cooling
International Nuclear Information System (INIS)
Hu, Liguo; Zhou, Han; Zhu, Hanxing; Fan, Tongxiang; Zhang, Di
2015-01-01
Highlights: • We present a pinnate leaf-like network/matrix structure for conductive cooling. • We study the effect of matrix thickness on network conductive cooling performance. • Matrix thickness determines optimal distance between collection channels in network. • We determine the optimal network architecture from a global perspective. • Optimal network greatly reduces the maximum temperature difference in the network. - Abstract: Heat generated in electronic devices has to be effectively removed because excessive temperature strongly impairs their performance and reliability. Embedding a high thermal conductivity network into an electronic device is an effective method to conduct the generated heat to the outside. In this study, inspired by the pinnate leaf, we present a pinnate leaf-like network embedded in the matrix (i.e., electronic device) to cool the matrix by conduction and develop a method to construct the optimal network. In this method, we first investigate the effect of the matrix thickness on the conductive cooling performance of the network, and then optimize the network architecture from a global perspective so that to minimize the maximum temperature difference between the heat sink and the matrix. The results indicate that the matrix thickness determines the optimal distance of the neighboring collection channels in the network, which minimizes the maximum temperature difference between the matrix and the network, and that the optimal network greatly reduces the maximum temperature difference in the network. The results can serve as a design guide for efficient conductive cooling of electronic devices
Cellular Neural Networks for NP-Hard Optimization
Directory of Open Access Journals (Sweden)
Mária Ercsey-Ravasz
2009-02-01
Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.
Optimizing a Drone Network to Deliver Automated External Defibrillators.
Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y
2017-06-20
Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event. © 2017 American Heart Association, Inc.
Optimal resource allocation solutions for heterogeneous cognitive radio networks
Directory of Open Access Journals (Sweden)
Babatunde Awoyemi
2017-05-01
Full Text Available Cognitive radio networks (CRN are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others among their numerous users. ‘Resource allocation (RA in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored, i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.
Optimal Switch Configuration in Software-Defined Networks
Directory of Open Access Journals (Sweden)
Béla GENGE
2016-06-01
Full Text Available The emerging Software-Defined Networks (SDN paradigm facilitates innovative applications and enables the seamless provisioning of resilient communications. Nevertheless, the installation of communication flows in SDN requires careful planning in order to avoid configuration errors and to fulfill communication requirements. In this paper we propose an approach that installs automatically and optimally static flows in SDN switches. The approach aims to select high capacity links and shortest path routing, and enforces communication link and switch capacity limitations. Experimental results demonstrate the effectiveness and scalability of the developed methodology.
A method of network topology optimization design considering application process characteristic
Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo
2018-03-01
Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Directory of Open Access Journals (Sweden)
Lianbo Ma
2014-01-01
Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Optimization of Multiple Related Negotiation through Multi-Negotiation Network
Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi
In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.
Hierarchical artificial bee colony algorithm for RFID network planning optimization.
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Optimal Alignment of Search and Rescue Response Posture with Historical Incident Occurrence
2014-04-01
and Development Canada Scientific Report DRDC-RDDC-2014-R12 April 2014 © Her Majesty the Queen in Right of Canada, as represented by the Minister of...makers and was used as input to the 2014 CJOC SAR Directive. This report also documents the optimization of RP30 schedules in the Victoria and Halifax...de SAR 2014 du COIC. Ce rapport décrit également l’optimisation des horaires liés à la posture d’intervention de 30 minutes dans les RRS de Victoria et
International Nuclear Information System (INIS)
Wissmann, Markus; Barié, Nicole; Guttmann, Markus; Schneider, Marc; Kolew, Alexander; Besser, Heino; Pfleging, Wilhelm; Hofmann, Andreas; Van Erps, Jürgen; Beri, Stefano; Watté, Jan
2015-01-01
For mass production of multiscale-optical components, microstructured moulding tools are needed. Metal tools are used for hot embossing or injection moulding of microcomponents made of a thermoplastic polymer. Microstructures with extremely tight specifications, e.g. low side wall roughness and high aspect ratios are generally made by lithographic procedures such as x-ray lithography or deep proton writing. However, these processes are unsuitable for low-cost mass production. An alternative manufacturing method of moulding tools has been developed at the Karlsruhe Institute of Technology (KIT). This article describes a mould insert fabrication and a new replication process for self-centring fibre alignment structures for low loss field installable single-mode fibre connectors, developed and fabricated by the Vrije Universiteit Brussel (VUB) in collaboration with TE Connectivity. These components are to be used in fibre-to-the-home networks and support the deployment and maintenance of fibre optic links. The special feature of this particular fibre connector is a self-centring fibre alignment, achieved by means of a through hole with deflectable cantilevers acting as micro-springs. The particular challenge is the electroforming of through holes with a centre hole diameter smaller than 125 µm. The fibre connector structure is prototyped by deep proton writing in polymethylmethacrylate and used as a sacrificial part. Using joining, physical vapour deposition and electroforming technology, a negative copy of the prototyped connector is transferred into nickel to be used as a moulding tool. The benefits of this replication technique are a rapid and economical fabrication of moulding tools with high-precision microstructures and a long tool life. With these moulding tools low-cost mass production is possible. We present the manufacturing chain we have established. Each individual manufacturing step of the mould insert fabrication will be shown in this report. The
Optimal assignment of multiple utilities in heat exchange networks
International Nuclear Information System (INIS)
Salama, A.I.A.
2009-01-01
Existing numerical geometry-based techniques, developed by [A.I.A. Salama, Numerical techniques for determining heat energy targets in pinch analysis, Computers and Chemical Engineering 29 (2005) 1861-1866; A.I.A. Salama, Determination of the optimal heat energy targets in heat pinch analysis using a geometry-based approach, Computers and Chemical Engineering 30 (2006) 758-764], have been extended to optimally assign multiple utilities in heat exchange network (HEN). These techniques utilize the horizontal shift between the cold composite curve (CC) and the stationary hot CC to determine the HEN optimal energy targets, grand composite curve (GCC), and the complement grand composite curve (CGCC). The proposed numerical technique developed in this paper is direct and simultaneously determines the optimal heat-energy targets and optimally assigns multiple utilities as compared with an existing technique based on sequential assignment of multiple utilities. The technique starts by arranging in an ascending order the HEN stream and target temperatures, and the resulting set is labelled T. Furthermore, the temperature sets where multiple utilities are introduced are arranged in an ascending order and are labelled T ic and T ih for the cold and hot sides, respectively. The graphical presentation of the results is facilitated by the insertion at each multiple-utility temperature a perturbed temperature equals the insertion temperature minus a small perturbation. Furthermore, using the heat exchanger network (HEN) minimum temperature-differential approach (ΔT min ) and stream heat-capacity flow rates, the presentation is facilitated by using the conventional temperature shift of the HEN CCs. The set of temperature-shifted stream and target temperatures and perturbed temperatures in the overlap range between the CCs is labelled T ol . Using T ol , a simple formula employing enthalpy-flow differences between the hot composite curve CC h and the cold composite curve CC c is
Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
Directory of Open Access Journals (Sweden)
Seung Wan Kim
2016-01-01
Full Text Available Active Network Management (ANM enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG and Battery Energy Storage System (BESS units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.
Proposal for optimal placement platform of bikes using queueing networks.
Mizuno, Shinya; Iwamoto, Shogo; Seki, Mutsumi; Yamaki, Naokazu
2016-01-01
In recent social experiments, rental motorbikes and rental bicycles have been arranged at nodes, and environments where users can ride these bikes have been improved. When people borrow bikes, they return them to nearby nodes. Some experiments have been conducted using the models of Hamachari of Yokohama, the Niigata Rental Cycle, and Bicing. However, from these experiments, the effectiveness of distributing bikes was unclear, and many models were discontinued midway. Thus, we need to consider whether these models are effectively designed to represent the distribution system. Therefore, we construct a model to arrange the nodes for distributing bikes using a queueing network. To adopt realistic values for our model, we use the Google Maps application program interface. Thus, we can easily obtain values of distance and transit time between nodes in various places in the world. Moreover, we apply the distribution of a population to a gravity model and we compute the effective transition probability for this queueing network. If the arrangement of the nodes and number of bikes at each node is known, we can precisely design the system. We illustrate our system using convenience stores as nodes and optimize the node configuration. As a result, we can optimize simultaneously the number of nodes, node places, and number of bikes for each node, and we can construct a base for a rental cycle business to use our system.
Optimal power flow for distribution networks with distributed generation
Directory of Open Access Journals (Sweden)
Radosavljević Jordan
2015-01-01
Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046
A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.
Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen
2018-03-01
In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.
2010-03-01
EVOLUTIONARY ARTIFICIAL NEURAL NETWORK WEIGHT TUNING TO OPTIMIZE DECISION MAKING FOR AN ABSTRACT...AFIT/GCS/ENG/10-06 EVOLUTIONARY ARTIFICIAL NEURAL NETWORK WEIGHT TUNING TO OPTIMIZE DECISION MAKING FOR AN ABSTRACT GAME THESIS Presented...35 14: Diagram of pLoGANN’s Artificial Neural Network and
Energy Technology Data Exchange (ETDEWEB)
L. DOWELL
1999-07-01
The optimization of the configuration of communications and control networks is important for assuring the reliability and performance of the networks. This paper presents techniques for determining the optimal configuration for such a network in the presence of communication and connectivity constraints.
Optimization of RFID network planning using Zigbee and WSN
Hasnan, Khalid; Ahmed, Aftab; Badrul-aisham, Bakhsh, Qadir
2015-05-01
Everyone wants to be ease in their life. Radio frequency identification (RFID) wireless technology is used to make our life easier. RFID technology increases productivity, accuracy and convenience in delivery of service in supply chain. It is used for various applications such as preventing theft of automobiles, tolls collection without stopping, no checkout lines at grocery stores, managing traffic, hospital management, corporate campuses and airports, mobile asset tracking, warehousing, tracking library books, and to track a wealth of assets in supply chain management. Efficiency of RFID can be enhanced by integrating with wireless sensor network (WSN), zigbee mesh network and internet of things (IOT). The proposed system is used for identifying, sensing and real-time locating system (RTLS) of items in an indoor heterogeneous region. The system gives real-time richer information of object's characteristics, location and their environmental parameters like temperature, noise and humidity etc. RTLS reduce human error, optimize inventory management, increase productivity and information accuracy at indoor heterogeneous network. The power consumption and the data transmission rate of the system can be minimized by using low power hardware design.
Optimal Retrofit Scheme for Highway Network under Seismic Hazards
Directory of Open Access Journals (Sweden)
Yongxi Huang
2014-06-01
Full Text Available Many older highway bridges in the United States (US are inadequate for seismic loads and could be severely damaged or collapsed in a relatively small earthquake. According to the most recent American Society of Civil Engineers’ infrastructure report card, one-third of the bridges in the US are rated as structurally deficient and many of these structurally deficient bridges are located in seismic zones. To improve this situation, at-risk bridges must be identified and evaluated and effective retrofitting programs should be in place to reduce their seismic vulnerabilities. In this study, a new retrofit strategy decision scheme for highway bridges under seismic hazards is developed and seamlessly integrate the scenario-based seismic analysis of bridges and the traffic network into the proposed optimization modeling framework. A full spectrum of bridge retrofit strategies is considered based on explicit structural assessment for each seismic damage state. As an empirical case study, the proposed retrofit strategy decision scheme is utilized to evaluate the bridge network in one of the active seismic zones in the US, Charleston, South Carolina. The developed modeling framework, on average, will help increase network throughput traffic capacity by 45% with a cost increase of only $15million for the Mw 5.5 event and increase the capacity fourfold with a cost of only $32m for the Mw 7.0 event.
Algorithms for finding optimal paths in network games with p players
Directory of Open Access Journals (Sweden)
R. Boliac
1997-08-01
Full Text Available We study the problem of finding optimal paths in network games with p players. Some polynomial-time algorithms for finding optimal paths and optimal by Nash strategies of the players in network games with p players are proposed.
Optimal File-Distribution in Heterogeneous and Asymmetric Storage Networks
Langner, Tobias; Schindelhauer, Christian; Souza, Alexander
We consider an optimisation problem which is motivated from storage virtualisation in the Internet. While storage networks make use of dedicated hardware to provide homogeneous bandwidth between servers and clients, in the Internet, connections between storage servers and clients are heterogeneous and often asymmetric with respect to upload and download. Thus, for a large file, the question arises how it should be fragmented and distributed among the servers to grant "optimal" access to the contents. We concentrate on the transfer time of a file, which is the time needed for one upload and a sequence of n downloads, using a set of m servers with heterogeneous bandwidths. We assume that fragments of the file can be transferred in parallel to and from multiple servers. This model yields a distribution problem that examines the question of how these fragments should be distributed onto those servers in order to minimise the transfer time. We present an algorithm, called FlowScaling, that finds an optimal solution within running time {O}(m log m). We formulate the distribution problem as a maximum flow problem, which involves a function that states whether a solution with a given transfer time bound exists. This function is then used with a scaling argument to determine an optimal solution within the claimed time complexity.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints
Kmet', Tibor; Kmet'ová, Mária
2009-09-01
A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.
International Nuclear Information System (INIS)
Fonda, James W; Zawodniok, Maciej; Jagannathan, S; Watkins, Steve E
2008-01-01
The development and the implementation issues of a reactive optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) are introduced and its performance is contrasted with the popular ad hoc on-demand distance vector (AODV) routing protocol. Analytical results illustrate the performance of the proposed OEDSR protocol, while experimental results utilizing a hardware testbed under various scenarios demonstrate improvements in energy efficiency of the OEDSR protocol. A hardware platform constructed at the University of Missouri-Rolla (UMR), now the Missouri University of Science and Technology (MST), based on the Generation 4 Smart Sensor Node (G4-SSN) prototyping platform is also described. Performance improvements are shown in terms of end-to-end (E2E) delay, throughput, route-set-up time and drop rates and energy usage is given for three topologies, including a mobile topology. Additionally, results from the hardware testbed provide valuable lessons for network deployments. Under testing OEDSR provides a factor of ten improvement in the energy used in the routing session and extends network lifetime compared to AODV. Depletion experiments show that the time until the first node failure is extended by a factor of three with the network depleting and network lifetime is extended by 6.7%
Algorithms for optimization of branching gravity-driven water networks
Dardani, Ian; Jones, Gerard F.
2018-05-01
The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.
Algorithms for optimization of branching gravity-driven water networks
Directory of Open Access Journals (Sweden)
I. Dardani
2018-05-01
Full Text Available The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs, this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011 to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, J; Zhang, Qi; Fitzek, F H P
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...
Kulkarni, Shruti R; Rajendran, Bipin
2018-07-01
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.
Outage Analysis and Optimization of SWIPT in Network-Coded Two-Way Relay Networks
Directory of Open Access Journals (Sweden)
Ruihong Jiang
2017-01-01
Full Text Available This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT in network-coded two-way relay systems, where a relay first harvests energy from the signals transmitted by two sources and then uses the harvested energy to forward the received information to the two sources. We consider two transmission protocols, power splitting two-way relay (PS-TWR and time switching two-way relay (TS-TWR protocols. We present two explicit expressions for the system outage probability of the two protocols and further derive approximate expressions for them in high and low SNR cases. To explore the system performance limits, two optimization problems are formulated to minimize the system outage probability. Since the problems are nonconvex and have no known solution methods, a genetic algorithm- (GA- based algorithm is designed. Numerical and simulation results validate our theoretical analysis. It is shown that, by jointly optimizing the time assignment and SWIPT receiver parameters, a great performance gain can be achieved for both PS-TWR and TS-TWR. Moreover, the optimized PS-TWR always outperforms the optimized TS-TWR in terms of outage performance. Additionally, the effects of parameters including relay location and transmit powers are also discussed, which provide some insights for the SWIPT-enabled two-way relay networks.
WANG, Qingrong; ZHU, Changfeng; LI, Ying; ZHANG, Zhengkun
2017-06-01
Considering the time dependence of emergency logistic network and complexity of the environment that the network exists in, in this paper the time dependent network optimization theory and robust discrete optimization theory are combined, and the emergency logistics dynamic network optimization model with characteristics of robustness is built to maximize the timeliness of emergency logistics. On this basis, considering the complexity of dynamic network and the time dependence of edge weight, an improved ant colony algorithm is proposed to realize the coupling of the optimization algorithm and the network time dependence and robustness. Finally, a case study has been carried out in order to testify validity of this robustness optimization model and its algorithm, and the value of different regulation factors was analyzed considering the importance of the value of the control factor in solving the optimal path. Analysis results show that this model and its algorithm above-mentioned have good timeliness and strong robustness.
Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm
Directory of Open Access Journals (Sweden)
D.B. Prakash
2017-12-01
Full Text Available In present days, continuous effort is being made in bringing down the line losses of the electrical distribution networks. Therefore proper allocation of capacitors is of utmost importance because, it will help in reducing the line losses and maintaining the bus voltage. This in turn results in improving the stability and reliability of the system. In this paper Whale Optimization Algorithm (WOA is used to find optimal sizing and placement of capacitors for a typical radial distribution system. Multi objectives such as operating cost reduction and power loss minimization with inequality constraints on voltage limits are considered and the proposed algorithm is validated by applying it on standard radial systems: IEEE-34 bus and IEEE-85 bus radial distribution test systems. The results obtained are compared with those of existing algorithms. The results show that the proposed algorithm is more effective in bringing down the operating costs and in maintaining better voltage profile. Keywords: Whale Optimization Algorithm (WOA, Optimal allocation and sizing of capacitors, Power loss reduction and voltage stability improvement, Radial distribution system, Operating cost minimization
Modeling and optimization of cloud-ready and content-oriented networks
Walkowiak, Krzysztof
2016-01-01
This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...
Optimization of patterns of control bars using neural networks
International Nuclear Information System (INIS)
Mejia S, D.M.; Ortiz S, J.J.
2005-01-01
In this work the RENOPBC system that is based on a recurrent multi state neural network, for the optimization of patterns of control bars in a cycle of balance of a boiling water reactor (BWR for their initials in English) is presented. The design of patterns of bars is based on the execution of operation thermal limits, to maintain criticizes the reactor and that the axial profile of power is adjusted to one predetermined along several steps of burnt. The patterns of control bars proposed by the system are comparable to those proposed by human experts with many hour-man of experience. These results are compared with those proposed by other techniques as genetic algorithms, colonies of ants and tabu search for the same operation cycle. As consequence it is appreciated that the proposed patterns of control bars, have bigger operation easiness that those proposed by the other techniques. (Author)
Routing Optimization of AVB Streams in TSN Networks
DEFF Research Database (Denmark)
Laursen, Sune Mølgaard; Pop, Paul; Steiner, Wilfried
2016-01-01
In this paper we are interested in safety-critical real-time applications implemented on distributed architectures using the Time-Sensitive Networking (TSN) standard. The ongoing standardization of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet...... standard supporting safety-critical systems and guaranteed Quality-of-Service. TSN will support Time-Triggered (TT) communication based on schedule tables, Audio-Video-Bridging (AVB) streams with bounded end-to-end latency as well as Best-Effort messages. We consider that we know the topology...... Procedure (GRASP)-based heuristic for this routing optimization problem. The proposed approaches has been evaluated using several test cases....
Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks
Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet
2017-01-01
This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model. PMID:28587171
Optimal Dimensioning of Broadband Integrated Services Digital Network
Directory of Open Access Journals (Sweden)
Zdenka Chmelikova
2005-01-01
Full Text Available Tasks of this paper are research of input flow statistic parametres influence and parameters demands relating to VP (Virtual Path or VC (Virtual Channel dimensioning. However it is necessary to consider different time of flow arrival and differend time of holding time during connection level. Process of input flow arrival is considered as Poisson process. Holding time is considered as exponential function. Permanent allocation of VP is made by separate VP, where each of VPs enable transmitting offered laod with explicit bandwidth and explicit loss. The mathematic model was created to verify the above mentioned dependences for different types of telecomunications signals and different input flows. "Comnet III" software was selected for experimental verification of process optimization. The simulation model was based on thiss software, which simulate ATM network traffic in behalf of different input flow.
Optimization of power distribution networks; Optimierung von Energieverteilungsnetzen
Energy Technology Data Exchange (ETDEWEB)
Casteren, J. van [Digsilent GmbH (Netherlands); Chalmers Univ. of Technology, Goeteborg (Sweden)
2000-03-01
In a competition-oriented power market the optimization of distribution networks is more and more becoming a search for minimum investment and operating cost where all relevant cost factors should be taken into account. A so far neglected factor is the expectation of reliability-related cost. A new analytical calculation method permits flexible, realistic estimation of interruption costs to be expected. (orig.) [German] In einem wettbewerbsorientierten Strom-Markt wird die Optimierung der Verteilungsnetze mehr und mehr zu einer Suche nach den minimalen Investitions- und Betriebskosten, wobei moeglichst alle relevanten Kostenfaktoren beruecksichtigt werden muessen. Ein bisher vernachlaessigter Faktor ist dabei die Erwartung der zuverlaessigkeitsbedingten Kosten. Ein neues analytisches Berechnungsverfahren erlaubt nun flexibel eine realistische Abschaetzung der zu erwarteten Unterbrechungskosten. (orig.)
Optimal and secure measurement protocols for quantum sensor networks
Eldredge, Zachary; Foss-Feig, Michael; Gross, Jonathan A.; Rolston, S. L.; Gorshkov, Alexey V.
2018-04-01
Studies of quantum metrology have shown that the use of many-body entangled states can lead to an enhancement in sensitivity when compared with unentangled states. In this paper, we quantify the metrological advantage of entanglement in a setting where the measured quantity is a linear function of parameters individually coupled to each qubit. We first generalize the Heisenberg limit to the measurement of nonlocal observables in a quantum network, deriving a bound based on the multiparameter quantum Fisher information. We then propose measurement protocols that can make use of Greenberger-Horne-Zeilinger (GHZ) states or spin-squeezed states and show that in the case of GHZ states the protocol is optimal, i.e., it saturates our bound. We also identify nanoscale magnetic resonance imaging as a promising setting for this technology.
Alignment of global supply networks based on strategic groups of supply chains
Nikos G. Moraitakis; Jiazhen Huo; Hans-Christian Pfohl
2017-01-01
Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic su...
Song, Chen; Zhong-Cheng, Wu; Hong, Lv
2018-03-01
Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.
Optimal brain network synchrony visualization: application in an alcoholism paradigm.
Sakkalis, Vangelis; Tsiaras, Vassilis; Zervakis, Michalis; Tollis, Ioannis
2007-01-01
Although Electroencephalographic (EEG) signal synchronization studies have been a topic of increasing interest lately, there is no similar effort in the visualization of such measures. In this direction a graph-theoretic approach devised to study and stress the coupling dynamics of task-performing dynamical networks is proposed. Both linear and nonlinear interdependence measures are investigated in an alcoholism paradigm during mental rehearsal of pictures, which is known to reflect synchronization impairment. More specifically, the widely used magnitude squared coherence; phase synchronization and a robust nonlinear state-space generalized synchronization assessment method are investigated. This paper mostly focuses on a signal-based technique of selecting the optimal visualization threshold using surrogate datasets to correctly identify the most significant correlation patterns. Furthermore, a graph statistical parameter attempts to capture and quantify collective motifs present in the functional brain network. The results are in accordance with previous psychophysiology studies suggesting that an alcoholic subject has impaired synchronization of brain activity and loss of lateralization during the rehearsal process, most prominently in alpha (8-12 Hz) band, as compared to a control subject. Lower beta (13-30 Hz) synchronization was also evident in the alcoholic subject.
Optimized Reputable Sensing Participants Extraction for Participatory Sensor Networks
Directory of Open Access Journals (Sweden)
Weiwei Yuan
2014-01-01
Full Text Available By collecting data via sensors embedded personal smart devices, sensing participants play a key role in participatory sensor networks. Using information provided by reputable sensing participants ensures the reliability of participatory sensing data. Setting a threshold for the reputation, and those whose reputations are bigger than this value are regarded as reputable. The bigger the threshold value is, the more reliable the extracted reputable sensing participant is. However, if the threshold value is too big, only very limited participatory sensing data can be involved. This may cause unexpected bias in information collection. Existing works did not consider the relationship between the reliability of extracted reputable sensing participants and the ratio of usable participatory sensing data. In this work, we propose a criterion for optimized reputable sensing participant extraction in participatory sensor networks. This is achieved based on the mathematical analysis on the ratio of available participatory sensing data and the reliability of extracted reputable sensing participants. Our suggested threshold value for reputable sensing participant extraction is only related to the power of sensing participant’s reputation distribution. It is easy to be applied in real applications. Simulation results tested on real application data further verified the effectiveness of our proposed method.
Optimal Node Placement in Underwater Acoustic Sensor Network
Felemban, Muhamad
2011-10-01
Almost 70% of planet Earth is covered by water. A large percentage of underwater environment is unexplored. In the past two decades, there has been an increase in the interest of exploring and monitoring underwater life among scientists and in industry. Underwater operations are extremely difficult due to the lack of cheap and efficient means. Recently, Wireless Sensor Networks have been introduced in underwater environment applications. However, underwater communication via acoustic waves is subject to several performance limitations, which makes the relevant research issues very different from those on land. In this thesis, we investigate node placement for building an initial Underwater Wireless Sensor Network infrastructure. Firstly, we formulated the problem into a nonlinear mathematic program with objectives of minimizing the total transmission loss under a given number of sensor nodes and targeted volume. We conducted experiments to verify the proposed formulation, which is solved using Matlab optimization tool. We represented each node with a truncated octahedron to fill out the 3D space. The truncated octahedrons are tiled in the 3D space with each node in the center where locations of the nodes are given using 3D coordinates. Results are supported using ns-3 simulator. Results from simulation are consistent with the obtained results from mathematical model with less than 10% error.
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Finding the optimal Bayesian network given a constraint graph
Directory of Open Access Journals (Sweden)
Jacob M. Schreiber
2017-07-01
Full Text Available Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
Coherent Network Optimizing of Rail-Based Urban Mass Transit
Directory of Open Access Journals (Sweden)
Ying Zhang
2012-01-01
Full Text Available An efficient public transport is more than ever a crucial factor when it comes to the quality of life and competitiveness of many cities and regions in Asia. In recent years, the rail-based urban mass transit has been regarded as one of the key means to overcoming the great challenges in Chinese megacities. The purpose of this study is going to develop a coherent network optimizing for rail-based urban mass transit to find the best alternatives for the user and to demonstrate how to meet sustainable development needs and to match the enormous capacity requirements simultaneously. This paper presents an introduction to the current situation of the important lines, and transfer points in the metro system Shanghai. The insufficient aspects are analyzed and evaluated; while the optimizing ideas and measurements are developed and concreted. A group of examples are used to illustrate the approach. The whole study could be used for the latest reference for other megacities which have to be confronted with the similar situations and processes with enormous dynamic travel and transport demands.
Optimizing Soil Moisture Sampling Locations for Validation Networks for SMAP
Roshani, E.; Berg, A. A.; Lindsay, J.
2013-12-01
Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories
Fairweather-Tait, Susan J
2011-08-01
Dietary reference values for micronutrients vary considerably among countries, and harmonization is needed to facilitate nutrition policy and public health strategies at the European and global levels. The EURopean micronutrient RECommendations Aligned (EURRECA) Network of Excellence is developing generic instruments for systematically deriving and updating micronutrient reference values and dietary recommendations. These include best practice guidelines, interlinked web pages, online databases, and decision trees. Journal supplements have been published on micronutrient intakes and status, and an ongoing activity of EURRECA is the completion of systematic reviews on associations between intakes, status, and various health outcomes for priority micronutrients (ie, iron, zinc, folate, vitamin B-12, and iodine), which were selected by using a triage technique. Future activities include meta-analyses to identify dose-response relations and the variability, factorial estimates of requirements, bioavailability from whole diets, effects of genotype, and modeling techniques for addressing dietary recommendations for combinations of nutrients with common health endpoints.
Park, Seungman
2017-09-01
Interstitial flow (IF) is a creeping flow through the interstitial space of the extracellular matrix (ECM). IF plays a key role in diverse biological functions, such as tissue homeostasis, cell function and behavior. Currently, most studies that have characterized IF have focused on the permeability of ECM or shear stress distribution on the cells, but less is known about the prediction of shear stress on the individual fibers or fiber networks despite its significance in the alignment of matrix fibers and cells observed in fibrotic or wound tissues. In this study, I developed a computational model to predict shear stress for different structured fibrous networks. To generate isotropic models, a random growth algorithm and a second-order orientation tensor were employed. Then, a three-dimensional (3D) solid model was created using computer-aided design (CAD) software for the aligned models (i.e., parallel, perpendicular and cubic models). Subsequently, a tetrahedral unstructured mesh was generated and flow solutions were calculated by solving equations for mass and momentum conservation for all models. Through the flow solutions, I estimated permeability using Darcy's law. Average shear stress (ASS) on the fibers was calculated by averaging the wall shear stress of the fibers. By using nonlinear surface fitting of permeability, viscosity, velocity, porosity and ASS, I devised new computational models. Overall, the developed models showed that higher porosity induced higher permeability, as previous empirical and theoretical models have shown. For comparison of the permeability, the present computational models were matched well with previous models, which justify our computational approach. ASS tended to increase linearly with respect to inlet velocity and dynamic viscosity, whereas permeability was almost the same. Finally, the developed model nicely predicted the ASS values that had been directly estimated from computational fluid dynamics (CFD). The present
Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang
2011-05-01
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.
Directory of Open Access Journals (Sweden)
Fahui Wu
2016-01-01
Full Text Available This paper considers a simultaneous wireless information and power transfer (SWIPT mechanism in an interference alignment (IA relay system, in which source nodes send wireless information and energy simultaneously to relay nodes, and relay nodes forward the received signal to destination nodes powered by harvested energy. To manage interference and utilize interference as energy source, two-SWIPT receiver is designed, namely, power splitting (PS, and antennas switching (AS has been considered for relay system. The performance of AS- and PS-based IA relay systems is considered, as is a new energy cooperation (ECop scheme that is proposed to improve system performance. Numerical results are provided to evaluate the performance of all schemes and it is shown from the simulations that the performance of proposed ECop outperformed both AS and PS.
Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen
2013-02-01
This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.
Phadke, Sujay
2011-09-30
Factors affecting charge transport through ZnO nanowire mat films were studied by aligning ZnO nanowires on substrates and coupling experimental measurements with 2D nanowire network simulations. Gallium doped ZnO nanowires were aligned on thermally oxidized silicon wafer by shearing a nanowire dispersion in ethanol. Sheet resistances of nanowire thin films that had current flowing parallel to nanowire alignment direction were compared to thin films that had current flowing perpendicular to nanowire alignment direction. Perpendicular devices showed ∼5 fold greater sheet resistance than parallel devices supporting the hypothesis that aligning nanowires would increase conductivity of ZnO nanowire electrodes. 2-D nanowire network simulations of thin films showed that the device sheet resistance was dominated by inter-wire contact resistance. For a given resistivity of ZnO nanowires, the thin film electrodes would have the lowest possible sheet resistance if the inter-wire contact resistance was one order of magnitude lower than the single nanowire resistance. Simulations suggest that the conductivity of such thin film devices could be further enhanced by using longer nanowires. Solution processed Gallium doped ZnO nanowires are aligned on substrates using an innovative shear coating technique. Nanowire alignment has shown improvement in ZnO nanowire transparent electrode conductivity. 2D network simulations in conjunction with electrical measurements have revealed different regimes of operation of nanowire thin films and provided a guideline for improving electrical performance of nanowire electrodes. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Park, Kihong; Alouini, Mohamed-Slim
2013-01-01
In this paper, we study a two-hop relaying network consisting of one source, one destination, and three amplify-and-forward (AF) relays with multiple antennas. To compensate for the capacity prelog factor loss of 1/2$ due to the half-duplex relaying
Mol, Ben Willem; Ruifrok, Anneloes Elisabeth
2013-01-01
Large clinical studies provide information and insight that are used to develop clinical guidelines. In view of the large sample sizes needed, many researchers have initiated multicenter studies. In some situations, the activities of these groups have led to networks, through which multiple trials
Alignment of the UMLS semantic network with BioTop: Methodology and assessment
S. Schulz; E. Beisswanger (Elena); L. van den Hoek (László); O. Bodenreider (Olivier); E.M. van Mulligen (Erik)
2009-01-01
textabstractMotivation: For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for
Nano-soldering of magnetically aligned three-dimensional nanowire networks
International Nuclear Information System (INIS)
Gao Fan; Gu Zhiyong
2010-01-01
It is extremely challenging to fabricate 3D integrated nanostructures and hybrid nanoelectronic devices. In this paper, we report a simple and efficient method to simultaneously assemble and solder nanowires into ordered 3D and electrically conductive nanowire networks. Nano-solders such as tin were fabricated onto both ends of multi-segmented nanowires by a template-assisted electrodeposition method. These nanowires were then self-assembled and soldered into large-scale 3D network structures by magnetic field assisted assembly in a liquid medium with a high boiling point. The formation of junctions/interconnects between the nanowires and the scale of the assembly were dependent on the solder reflow temperature and the strength of the magnetic field. The size of the assembled nanowire networks ranged from tens of microns to millimeters. The electrical characteristics of the 3D nanowire networks were measured by regular current-voltage (I-V) measurements using a probe station with micropositioners. Nano-solders, when combined with assembling techniques, can be used to efficiently connect and join nanowires with low contact resistance, which are very well suited for sensor integration as well as nanoelectronic device fabrication.
Optimizing Low Speed VoIP Network for Rural Next Generation Network (R-NGN
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Yoanes Bandung
2007-11-01
Full Text Available In this research, we propose an optimization method based-on E-Model for designing an efficient low speed VoIP network for Rural Next Generation Network (R-NGN. We are choosing 128 kbps and 256 kbps bandwidth as the typical community link to be used in the designing of R-NGN infrastructure. The method is based on selection of some VoIP network parameters such as voice coder, communication protocol, packet loss level, network utilization and resource allocation. We draw analytic approach for achieving rating value (R of E-model that represent level of quality of service. In this approach, we focus on delay and packet loss calculation to find the rating value. We state the rating value = 70 as minimum level of quality of service for each call, equivalent to 3.6 of Mean Opinion Score (MOS. In our experiments, either G.723.1 5.3 kbps or G.729 is chosen for maximizing the number of VoIP calls, it depends on link utilization and level of packet loss.
Peng, Xi; Tang, Zhiqiang; Yang, Fei; Feris, Rogerio; Metaxas, Dimitris
2018-01-01
Random data augmentation is a critical technique to avoid overfitting in training deep neural network models. However, data augmentation and network training are usually treated as two isolated processes, limiting the effectiveness of network training. Why not jointly optimize the two? We propose adversarial data augmentation to address this limitation. The main idea is to design an augmentation network (generator) that competes against a target network (discriminator) by generating `hard' au...
Xia, Li
2014-11-20
This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.
Xia, Li; Shihada, Basem
2014-01-01
This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.
Optimization of heat exchanger networks using genetic algorithms
International Nuclear Information System (INIS)
Teyssedou, A.; Dipama, J.; Sorin, M.
2004-01-01
Most thermal processes encountered in the power industry (chemical, metallurgical, nuclear and thermal power stations) necessitate the transfer of large amounts of heat between fluids having different thermal potentials. A common practice applied to achieve such a requirement consists of using heat exchangers. In general, each current of fluid is conveniently cooled or heated independently from each other in the power plant. When the number of heat exchangers is large enough, however, a convenient arrangement of different flow currents may allow a considerable reduction in energy consumption to be obtained (Linnhoff and Hidmarsh, 1983). In such a case the heat exchangers form a 'Heat Exchanger Network' (HEN) that can be optimized to reduce the overall energy consumption. This type of optimization problem, involves two separates calculation procedures. First, it is necessary to optimize the topology of the HEN that will permit a reduction in energy consumption to be obtained. In a second step the power distribution across the HEN should be optimized without violating the second law of thermodynamics. The numerical treatment of this kind of problem requires the use of both discrete variables (for taking into account each heat exchanger unit) and continuous variables for handling the thermal load of each unit. It is obvious that for a large number of heat exchangers, the use of conventional calculation methods, i.e., Simplexe, becomes almost impossible. Therefore, in this paper we present a 'Genetic Algorithm' (GA), that has been implemented and successfully used to treat complex HENs, containing a large number of heat exchangers. As opposed to conventional optimization techniques that require the knowledge of the derivatives of a function, GAs start the calculation process from a large population of possible solutions of a given problem (Goldberg, 1999). Each possible solution is in turns evaluated according to a 'fitness' criterion obtained from an objective
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
2015-03-01
biometric data collection. Capture role- player mock biometric data including finger prints, iris scans, and facial recognition photos. (MOC training...MARITIME INFORMATION DOMINANCE: OPTIMIZING TACTICAL NETWORK FOR BIOMETRIC DATA SHARING IN MARITIME INTERDICTION OPERATIONS by Adam R. Sinsel...MARITIME INFORMATION DOMINANCE: OPTIMIZING TACTICAL NETWORK FOR BIOMETRIC DATA SHARING IN MARITIME INTERDICTION OPERATIONS 6. AUTHOR(S) Adam R
Directory of Open Access Journals (Sweden)
Zhilu Wu
2015-01-01
Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.
Kim, Youngsoo; Hong, Jinsuk; Choi, Byungin; Lee, Jong-Ung; Kim, Yeonsoo; Kim, Hyunsook
2017-08-21
A fore optics for the hyperspectral spectrometer is designed, manufactured, assembled, and aligned. The optics has a telecentric off-axis three-mirror configuration with a field of view wider than 14 degrees and an f-number as small as 2.3. The primary mirror (M1) and the secondary mirror (M2) are axially symmetric aspheric surfaces to minimize the sensitivity. The tertiary mirror (M3) is a decentered aspheric surface to minimize the coma and astigmatism aberration. The M2 also has a hole for the slit to maintain the optical performance while maximizing the telecentricity. To ensure the spatial resolution performance of the optical system, an alignment procedure is established to assemble and align the entrance slit of the spectrometer to the rear end of the fore optics. It has a great advantage to confirm and maintain the alignment integrity of the fore optics module throughout the alignment procedure. To perform the alignment procedure successfully, the precision movement control requirements are calculated and applied. As a result, the alignment goal of the RMS wave front error (WFE) to be smaller than 90 nm at all fields is achieved.
Directory of Open Access Journals (Sweden)
Li Ran
2017-01-01
Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.
Risk-based optimization of pipe inspections in large underground networks with imprecise information
International Nuclear Information System (INIS)
Mancuso, A.; Compare, M.; Salo, A.; Zio, E.; Laakso, T.
2016-01-01
In this paper, we present a novel risk-based methodology for optimizing the inspections of large underground infrastructure networks in the presence of incomplete information about the network features and parameters. The methodology employs Multi Attribute Value Theory to assess the risk of each pipe in the network, whereafter the optimal inspection campaign is built with Portfolio Decision Analysis (PDA). Specifically, Robust Portfolio Modeling (RPM) is employed to identify Pareto-optimal portfolios of pipe inspections. The proposed methodology is illustrated by reporting a real case study on the large-scale maintenance optimization of the sewerage network in Espoo, Finland. - Highlights: • Risk-based approach to optimize pipe inspections on large underground networks. • Reasonable computational effort to select efficient inspection portfolios. • Possibility to accommodate imprecise expert information. • Feasibility of the approach shown by Espoo water system case study.
Network synthesis and parameter optimization for vehicle suspension with inerter
Directory of Open Access Journals (Sweden)
Long Chen
2016-12-01
Full Text Available In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.
NLP model and stochastic multi-start optimization approach for heat exchanger networks
International Nuclear Information System (INIS)
Núñez-Serna, Rosa I.; Zamora, Juan M.
2016-01-01
Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.
Optimization of hot water transport and distribution networks by analytical method: OPTAL program
International Nuclear Information System (INIS)
Barreau, Alain; Caizergues, Robert; Moret-Bailly, Jean
1977-06-01
This report presents optimization studies of hot water transport and distribution network by minimizing operating cost. Analytical optimization is used: Lagrange's method of undetermined multipliers. Optimum diameter of each pipe is calculated for minimum network operating cost. The characteristics of the computer program used for calculations, OPTAL, are given in this report. An example of network is calculated and described: 52 branches and 27 customers. Results are discussed [fr
Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms
Luo, Zihan; Armour, Simon; McGeehan, Joe
2015-01-01
This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...
Amith, Muhammad; Cunningham, Rachel; Savas, Lara S; Boom, Julie; Schvaneveldt, Roger; Tao, Cui; Cohen, Trevor
2017-10-01
This study demonstrates the use of distributed vector representations and Pathfinder Network Scaling (PFNETS) to represent online vaccine content created by health experts and by laypeople. By analyzing a target audience's conceptualization of a topic, domain experts can develop targeted interventions to improve the basic health knowledge of consumers. The underlying assumption is that the content created by different groups reflects the mental organization of their knowledge. Applying automated text analysis to this content may elucidate differences between the knowledge structures of laypeople (heath consumers) and professionals (health experts). This paper utilizes vaccine information generated by laypeople and health experts to investigate the utility of this approach. We used an established technique from cognitive psychology, Pathfinder Network Scaling to infer the structure of the associational networks between concepts learned from online content using methods of distributional semantics. In doing so, we extend the original application of PFNETS to infer knowledge structures from individual participants, to infer the prevailing knowledge structures within communities of content authors. The resulting graphs reveal opportunities for public health and vaccination education experts to improve communication and intervention efforts directed towards health consumers. Our efforts demonstrate the feasibility of using an automated procedure to examine the manifestation of conceptual models within large bodies of free text, revealing evidence of conflicting understanding of vaccine concepts among health consumers as compared with health experts. Additionally, this study provides insight into the differences between consumer and expert abstraction of domain knowledge, revealing vaccine-related knowledge gaps that suggest opportunities to improve provider-patient communication. Copyright © 2017 Elsevier Inc. All rights reserved.
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives
International Nuclear Information System (INIS)
Warmflash, Aryeh; Siggia, Eric D; Francois, Paul
2012-01-01
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.
Warmflash, Aryeh; Francois, Paul; Siggia, Eric D
2012-10-01
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network
Jing Wang; Yourui Huang
2013-01-01
In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...
Optimal stabilization of Boolean networks through collective influence
Wang, Jiannan; Pei, Sen; Wei, Wei; Feng, Xiangnan; Zheng, Zhiming
2018-03-01
Boolean networks have attracted much attention due to their wide applications in describing dynamics of biological systems. During past decades, much effort has been invested in unveiling how network structure and update rules affect the stability of Boolean networks. In this paper, we aim to identify and control a minimal set of influential nodes that is capable of stabilizing an unstable Boolean network. For locally treelike Boolean networks with biased truth tables, we propose a greedy algorithm to identify influential nodes in Boolean networks by minimizing the largest eigenvalue of a modified nonbacktracking matrix. We test the performance of the proposed collective influence algorithm on four different networks. Results show that the collective influence algorithm can stabilize each network with a smaller set of nodes compared with other heuristic algorithms. Our work provides a new insight into the mechanism that determines the stability of Boolean networks, which may find applications in identifying virulence genes that lead to serious diseases.
Directory of Open Access Journals (Sweden)
Young-Bo Sim
2017-11-01
Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.
Directory of Open Access Journals (Sweden)
محسن نفیسی
2014-10-01
Full Text Available Lack of an efficient sewer network in urban areas threatens public health and may give rise to contagious diseases. Various optimization methods have been developed for use in designing sewers networks in response to a number of requirements such as the high costs of constructing sewer networks, financial limitations, the presence of both discrete and continuous decision variables, and the nonlinear time complexity of such design problems. In this study, the particle swarm optimization algorithm (PSO with the capability of “fly-back” mechanism equipped with the harmony search (HPSO is used for the optimization of sewers network designs. The objective function consists of minimizing the excavation and embedding costs of commercial pipes. The fly-back mechanism and the harmony memory method are used to prevent leaving out variables from the feasible space of the problem in an attempt to enhance model efficiency. Model constraints are satisfied at two levels, which leads to the desirable convergence of the PSO algorithm as compared to the conventional penalty methods in alternative evolutionary algorithms. In order to determine the admissible decision variables, the Manning equation is used as a hydraulic model. The performance of the proposed algorithm is shown by presenting two examples of sewer networks. Compared to the PSO algorithm used in sewer network optimization models, the proposed model exhibits a tangible improvement in cost reduction and a higher computational stability.
Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks
Directory of Open Access Journals (Sweden)
Qihua Wang
2017-11-01
Full Text Available Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.
Compensatory Analysis and Optimization for MADM for Heterogeneous Wireless Network Selection
Directory of Open Access Journals (Sweden)
Jian Zhou
2016-01-01
Full Text Available In the next-generation heterogeneous wireless networks, a mobile terminal with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a network selection scheme, which is for users that select an optimal network with best comprehensive performance between different types of networks. However, the optimal network may be not the most reasonable one due to compensation of MADM (Multiple Attribute Decision Making, and the network is called pseudo-optimal network. This paper conducts a performance evaluation of a number of widely used MADM-based methods for network selection that aim to keep the mobile users always best connected anywhere and anytime, where subjective weight and objective weight are all considered. The performance analysis shows that the selection scheme based on MEW (weighted multiplicative method and combination weight can better avoid accessing pseudo-optimal network for balancing network load and reducing ping-pong effect in comparison with three other MADM solutions.
Towards Optimal Buffer Size in Wi-Fi Networks
Showail, Ahmad
2016-01-01
extensively for wired networks. However, there is little work addressing the unique challenges of wireless environment. In this dissertation, we discuss buffer sizing challenges in wireless networks, classify the state-of-the-art solutions, and propose two
Bolodurina, I. P.; Parfenov, D. I.
2018-01-01
We have elaborated a neural network model of virtual network flow identification based on the statistical properties of flows circulating in the network of the data center and characteristics that describe the content of packets transmitted through network objects. This enabled us to establish the optimal set of attributes to identify virtual network functions. We have established an algorithm for optimizing the placement of virtual data functions using the data obtained in our research. Our approach uses a hybrid method of visualization using virtual machines and containers, which enables to reduce the infrastructure load and the response time in the network of the virtual data center. The algorithmic solution is based on neural networks, which enables to scale it at any number of the network function copies.
Optimality principles in the regulation of metabolic networks
Berkhout, J.; Bruggeman, F.J.; Teusink, B.
2012-01-01
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks
Optimal Software Strategies in the Presence of Network Externalities
Liu, Yipeng
2009-01-01
Network externalities or alternatively termed network effects are pervasive in computer software markets. While software vendors consider pricing strategies, they must also take into account the impact of network externalities on their sales. My main interest in this research is to describe a firm's strategies and behaviors in the presence of…
Optimal cost for strengthening or destroying a given network
Patron, Amikam; Cohen, Reuven; Li, Daqing; Havlin, Shlomo
2017-05-01
Strengthening or destroying a network is a very important issue in designing resilient networks or in planning attacks against networks, including planning strategies to immunize a network against diseases, viruses, etc. Here we develop a method for strengthening or destroying a random network with a minimum cost. We assume a correlation between the cost required to strengthen or destroy a node and the degree of the node. Accordingly, we define a cost function c (k ) , which is the cost of strengthening or destroying a node with degree k . Using the degrees k in a network and the cost function c (k ) , we develop a method for defining a list of priorities of degrees and for choosing the right group of degrees to be strengthened or destroyed that minimizes the total price of strengthening or destroying the entire network. We find that the list of priorities of degrees is universal and independent of the network's degree distribution, for all kinds of random networks. The list of priorities is the same for both strengthening a network and for destroying a network with minimum cost. However, in spite of this similarity, there is a difference between their pc, the critical fraction of nodes that has to be functional to guarantee the existence of a giant component in the network.
Epidemics in Networks : Modeling, Optimization and Security Games
Omic, J.
2010-01-01
Epidemic theory has wide range of applications in computer networks, from spreading of malware to the information dissemination algorithms. Our society depends more strongly than ever on such computer networks. Many of these networks rely to a large extent on decentralization and self-organization.
A one-layer recurrent neural network for constrained nonsmooth optimization.
Liu, Qingshan; Wang, Jun
2011-10-01
This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.
International Nuclear Information System (INIS)
Guo, Chunxiang; Liu, Xiaoli; Jin, Maozhu; Lv, Zhihan
2016-01-01
Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.
Optimization of municipal pressure pumping station layout and sewage pipe network design
Tian, Jiandong; Cheng, Jilin; Gong, Yi
2018-03-01
Accelerated urbanization places extraordinary demands on sewer networks; thus optimization research to improve the design of these systems has practical significance. In this article, a subsystem nonlinear programming model is developed to optimize pumping station layout and sewage pipe network design. The subsystem model is expanded into a large-scale complex nonlinear programming system model to find the minimum total annual cost of the pumping station and network of all pipe segments. A comparative analysis is conducted using the sewage network in Taizhou City, China, as an example. The proposed method demonstrated that significant cost savings could have been realized if the studied system had been optimized using the techniques described in this article. Therefore, the method has practical value for optimizing urban sewage projects and provides a reference for theoretical research on optimization of urban drainage pumping station layouts.
Xie, Rui; Wan, Xianrong; Hong, Sheng; Yi, Jianxin
2017-06-14
The performance of a passive radar network can be greatly improved by an optimal radar network structure. Generally, radar network structure optimization consists of two aspects, namely the placement of receivers in suitable places and selection of appropriate illuminators. The present study investigates issues concerning the joint optimization of receiver placement and illuminator selection for a passive radar network. Firstly, the required radar cross section (RCS) for target detection is chosen as the performance metric, and the joint optimization model boils down to the partition p -center problem (PPCP). The PPCP is then solved by a proposed bisection algorithm. The key of the bisection algorithm lies in solving the partition set covering problem (PSCP), which can be solved by a hybrid algorithm developed by coupling the convex optimization with the greedy dropping algorithm. In the end, the performance of the proposed algorithm is validated via numerical simulations.
Complex fluid network optimization and control integrative design based on nonlinear dynamic model
International Nuclear Information System (INIS)
Sui, Jinxue; Yang, Li; Hu, Yunan
2016-01-01
In view of distribution according to complex fluid network’s needs, this paper proposed one optimization computation method of the nonlinear programming mathematical model based on genetic algorithm. The simulation result shows that the overall energy consumption of the optimized fluid network has a decrease obviously. The control model of the fluid network is established based on nonlinear dynamics. We design the control law based on feedback linearization, take the optimal value by genetic algorithm as the simulation data, can also solve the branch resistance under the optimal value. These resistances can provide technical support and reference for fluid network design and construction, so can realize complex fluid network optimization and control integration design.
Optimal network structure in an open market environment
International Nuclear Information System (INIS)
2002-01-01
The focus of this report is on network planning in the new environment of a liberalized electricity market. The development of the network is viewed from different stakeholders objectives. The stakeholders in the transmission network are groups or individuals who have a stake in, or an expectation of the development and performance of the network. An open network exists when all market players meet equal admission rights and obligations. This required that the grid be administered through a transparent set of rules such as a grid code. (author)
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
Directory of Open Access Journals (Sweden)
О.С. Якушенко
2010-01-01
Full Text Available The article is devoted to the problem of gas turbine engine (GTE technical state class automatic recognition with operation parameters by neuron networks. The one of main problems for creation the neuron networks is determination of their optimal structures size (amount of layers in network and count of neurons in each layer.The method of neuron network size optimization intended for classification of GTE technical state is considered in the article. Optimization is cared out with taking into account of overlearning effect possibility when a learning network loses property of generalization and begins strictly describing educational data set. To determinate a moment when overlearning effect is appeared in learning neuron network the method of three data sets is used. The method is based on the comparison of recognition quality parameters changes which were calculated during recognition of educational and control data sets. As the moment when network overlearning effect is appeared the moment when control data set recognition quality begins deteriorating but educational data set recognition quality continues still improving is used. To determinate this moment learning process periodically is terminated and simulation of network with education and control data sets is fulfilled. The optimization of two-, three- and four-layer networks is conducted and some results of optimization are shown. Also the extended educational set is created and shown. The set describes 16 GTE technical state classes and each class is represented with 200 points (200 possible technical state class realizations instead of 20 points using in the former articles. It was done to increase representativeness of data set.In the article the algorithm of optimization is considered and some results which were obtained with it are shown. The results of experiments were analyzed to determinate most optimal neuron network structure. This structure provides most high-quality GTE
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most...... nature of solar PV energy may affect the selection of the critical PV inverters and also the final optimal objective value. In order to address this issue, a two-stage robust optimization model is proposed in this paper to achieve a robust optimal solution to the PV inverter dispatch, which can hedge...... against any possible realization within the uncertain PV outputs. In addition, the conic relaxation-based branch flow formulation and second-order cone programming based column-and-constraint generation algorithm are employed to deal with the proposed robust optimization model. Case studies on a 33-bus...
International Nuclear Information System (INIS)
Liu, Xiaolan; Zhou, Mi
2016-01-01
In this paper, a one-layer recurrent network is proposed for solving a non-smooth convex optimization subject to linear inequality constraints. Compared with the existing neural networks for optimization, the proposed neural network is capable of solving more general convex optimization with linear inequality constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds.
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher; Meymand, Hamed Zeinoddini; Nayeripour, Majid [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran)
2010-08-15
Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders. (author)
Directory of Open Access Journals (Sweden)
Chao-Chih Lin
2017-10-01
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
International Nuclear Information System (INIS)
Moosavi, K.; Setayeshi, S.; Maragheh, M.Gh.; Ahmadi, S.J.; Kardan, M.R.; Banaem, L.M.
2009-01-01
In this study, an experimental design using artificial neural networks for an optimization on the strontium separation model for fission products (inactive trace elements) is investigated. The goal is to optimize the separation parameters to achieve maximum amount of strontium that is separated from the fission products. The result of the optimization method causes a proper purity of Strontium-89 that was separated from the fission products. It is also shown that ANN may be establish a method to optimize the separation model.
Optimal experiment design in a filtering context with application to sampled network data
Singhal, Harsh; Michailidis, George
2010-01-01
We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, w...
Application of particle swarm optimization to identify gamma spectrum with neural network
International Nuclear Information System (INIS)
Shi Dongsheng; Di Yuming; Zhou Chunlin
2007-01-01
In applying neural network to identification of gamma spectra back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of globe optimal searching. In this paper, we propose a new algorithm for neural network training, i.e. combined BP and PSO optimization, or PSO-BP algorithm. Practical example shows that the new algorithm can overcome shortcomings of BP algorithm and the neural network trained by it has a high ability of generalization with identification result of 100% correctness. It can be used effectively and reliably to identify gamma spectra. (authors)
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Huang, Can
2018-01-01
–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....
Liu, Yang; Yang, Linghui; Guo, Yin; Lin, Jiarui; Cui, Pengfei; Zhu, Jigui
2018-02-01
An interferometer technique based on temporal coherence function of femtosecond pulses is demonstrated for practical distance measurement. Here, the pulse-to-pulse alignment is analyzed for large delay distance measurement. Firstly, a temporal coherence function model between two femtosecond pulses is developed in the time domain for the dispersive unbalanced Michelson interferometer. Then, according to this model, the fringes analysis and the envelope extraction process are discussed. Meanwhile, optimization methods of pulse-to-pulse alignment for practical long distance measurement are presented. The order of the curve fitting and the selection of points for envelope extraction are analyzed. Furthermore, an averaging method based on the symmetry of the coherence function is demonstrated. Finally, the performance of the proposed methods is evaluated in the absolute distance measurement of 20 μ m with path length difference of 9 m. The improvement of standard deviation in experimental results shows that these approaches have the potential for practical distance measurement.
Optimality principles in the regulation of metabolic networks.
Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas
2012-08-29
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Finding influential nodes for integration in brain networks using optimal percolation theory.
Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon; Morone, Flaviano; Pérez-Ramírez, Úrsula; Pérez-Cervera, Laura; Parra, Lucas C; Holodny, Andrei; Canals, Santiago; Makse, Hernán A
2018-06-11
Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.
Optimal Base Station Density of Dense Network: From the Viewpoint of Interference and Load.
Feng, Jianyuan; Feng, Zhiyong
2017-09-11
Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.
Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui
2017-10-01
Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.
Optimization of the Critical Diameter and Average Path Length of Social Networks
Directory of Open Access Journals (Sweden)
Haifeng Du
2017-01-01
Full Text Available Optimizing average path length (APL by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL.
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....
2012-01-01
Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685
Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing
2017-07-19
Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.
Bi and tri-objective optimization in the deterministic network interdiction problem
International Nuclear Information System (INIS)
Rocco S, Claudio M.; Emmanuel Ramirez-Marquez, Jose; Salazar A, Daniel E.
2010-01-01
Solution approaches to the deterministic network interdiction problem have previously been developed for optimizing a single figure-of-merit of the network configuration (i.e. flow that can be transmitted between a source node and a sink node for a fixed network design) under constraints related to limited amount of resources available to interdict network links. These approaches work under the assumption that: (1) nominal capacity of each link is completely reduced when interdicted and (2) there is a single criterion to optimize. This paper presents a newly developed evolutionary algorithm that for the first time allows solving multi-objective optimization models for the design of network interdiction strategies that take into account a variety of figures-of-merit. The algorithm provides an approximation to the optimal Pareto frontier using: (a) techniques in Monte Carlo simulation to generate potential network interdiction strategies, (b) graph theory to analyze strategies' maximum source-sink flow and (c) an evolutionary search that is driven by the probability that a link will belong to the optimal Pareto set. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate and validate the approach.
Towards Optimal Buffer Size in Wi-Fi Networks
Showail, Ahmad J.
2016-01-19
Buffer sizing is an important network configuration parameter that impacts the quality of data traffic. Falling memory cost and the fallacy that ‘more is better’ lead to over provisioning network devices with large buffers. Over-buffering or the so called ‘bufferbloat’ phenomenon creates excessive end-to-end delay in today’s networks. On the other hand, under-buffering results in frequent packet loss and subsequent under-utilization of network resources. The buffer sizing problem has been studied extensively for wired networks. However, there is little work addressing the unique challenges of wireless environment. In this dissertation, we discuss buffer sizing challenges in wireless networks, classify the state-of-the-art solutions, and propose two novel buffer sizing schemes. The first scheme targets buffer sizing in wireless multi-hop networks where the radio spectral resource is shared among a set of con- tending nodes. Hence, it sizes the buffer collectively and distributes it over a set of interfering devices. The second buffer sizing scheme is designed to cope up with recent Wi-Fi enhancements. It adapts the buffer size based on measured link characteristics and network load. Also, it enforces limits on the buffer size to maximize frame aggregation benefits. Both mechanisms are evaluated using simulation as well as testbed implementation over half-duplex and full-duplex wireless networks. Experimental evaluation shows that our proposal reduces latency by an order of magnitude.
Optimality Principles in the Regulation of Metabolic Networks
Directory of Open Access Journals (Sweden)
Jan Berkhout
2012-08-01
Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Zhang, Guangzhi; Cai, Shaobin; Xiong, Naixue
2018-02-03
One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C /2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C /2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.
Robinson, Y Harold; Rajaram, M
2015-01-01
Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.
Optimal Design of Multitype Groundwater Monitoring Networks Using Easily Accessible Tools.
Wöhling, Thomas; Geiges, Andreas; Nowak, Wolfgang
2016-11-01
Monitoring networks are expensive to establish and to maintain. In this paper, we extend an existing data-worth estimation method from the suite of PEST utilities with a global optimization method for optimal sensor placement (called optimal design) in groundwater monitoring networks. Design optimization can include multiple simultaneous sensor locations and multiple sensor types. Both location and sensor type are treated simultaneously as decision variables. Our method combines linear uncertainty quantification and a modified genetic algorithm for discrete multilocation, multitype search. The efficiency of the global optimization is enhanced by an archive of past samples and parallel computing. We demonstrate our methodology for a groundwater monitoring network at the Steinlach experimental site, south-western Germany, which has been established to monitor river-groundwater exchange processes. The target of optimization is the best possible exploration for minimum variance in predicting the mean travel time of the hyporheic exchange. Our results demonstrate that the information gain of monitoring network designs can be explored efficiently and with easily accessible tools prior to taking new field measurements or installing additional measurement points. The proposed methods proved to be efficient and can be applied for model-based optimal design of any type of monitoring network in approximately linear systems. Our key contributions are (1) the use of easy-to-implement tools for an otherwise complex task and (2) yet to consider data-worth interdependencies in simultaneous optimization of multiple sensor locations and sensor types. © 2016, National Ground Water Association.
Ayala Solares, Jose Roberto; Rezki, Zouheir; Alouini, Mohamed-Slim
2012-01-01
The optimal transmit power of a wireless sensor network with one transmitter and multiple receivers in a cognitive radio environment while satisfying independent peak, independent average, sum of peak and sum of average transmission rate constraints
Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting
Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.
2016-01-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048
DRO: domain-based route optimization scheme for nested mobile networks
Directory of Open Access Journals (Sweden)
Chuang Ming-Chin
2011-01-01
Full Text Available Abstract The network mobility (NEMO basic support protocol is designed to support NEMO management, and to ensure communication continuity between nodes in mobile networks. However, in nested mobile networks, NEMO suffers from the pinball routing problem, which results in long packet transmission delays. To solve the problem, we propose a domain-based route optimization (DRO scheme that incorporates a domain-based network architecture and ad hoc routing protocols for route optimization. DRO also improves the intra-domain handoff performance, reduces the convergence time during route optimization, and avoids the out-of-sequence packet problem. A detailed performance analysis and simulations were conducted to evaluate the scheme. The results demonstrate that DRO outperforms existing mechanisms in terms of packet transmission delay (i.e., better route-optimization, intra-domain handoff latency, convergence time, and packet tunneling overhead.
2012-06-01
The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...
2012-11-01
The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...
A non-penalty recurrent neural network for solving a class of constrained optimization problems.
Hosseini, Alireza
2016-01-01
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network. Copyright © 2015 Elsevier Ltd. All rights reserved.
A one-layer recurrent neural network for constrained nonconvex optimization.
Li, Guocheng; Yan, Zheng; Wang, Jun
2015-01-01
In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
National Research Council Canada - National Science Library
Rodin, Ervin Y
2005-01-01
The purpose of this present research was to develop a generic model and methodology for analyzing and optimizing large-scale air transportation networks including both their routing and their scheduling...
2015-06-01
This Technical Report on Prototype Intelligent Network Flow Optimization (INFLO) Dynamic Speed Harmonization and : Queue Warning is the final report for the project. It describes the prototyping, acceptance testing and small-scale : demonstration of ...
Directory of Open Access Journals (Sweden)
M. A. Karakuts
2015-01-01
Full Text Available The basic problems of route network and aircraft fleet optimization and its role in airline strategic planning are considered. Measures to improve the methods of its implementation are proposed.
Optimal design of water supply networks for enhancing seismic reliability
International Nuclear Information System (INIS)
Yoo, Do Guen; Kang, Doosun; Kim, Joong Hoon
2016-01-01
The goal of the present study is to construct a reliability evaluation model of a water supply system taking seismic hazards and present techniques to enhance hydraulic reliability of the design into consideration. To maximize seismic reliability with limited budgets, an optimal design model is developed using an optimization technique called harmony search (HS). The model is applied to actual water supply systems to determine pipe diameters that can maximize seismic reliability. The reliabilities between the optimal design and existing designs were compared and analyzed. The optimal design would both enhance reliability by approximately 8.9% and have a construction cost of approximately 1.3% less than current pipe construction cost. In addition, the reinforcement of the durability of individual pipes without considering the system produced ineffective results in terms of both cost and reliability. Therefore, to increase the supply ability of the entire system, optimized pipe diameter combinations should be derived. Systems in which normal status hydraulic stability and abnormal status available demand could be maximally secured if configured through the optimal design. - Highlights: • We construct a seismic reliability evaluation model of water supply system. • We present technique to enhance hydraulic reliability in the aspect of design. • Harmony search algorithm is applied in optimal designs process. • The effects of the proposed optimal design are improved reliability about by 9%. • Optimized pipe diameter combinations should be derived indispensably.
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
The mathematical formulation is a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price of pipes, the hydraulic gradient and the loss of pressure. The objective function minimizes the cost of the network which is computed as the sum ...
Optimal topology to minimizing congestion in connected communication complex network
Benyoussef, M.; Ez-Zahraouy, H.; Benyoussef, A.
In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that pc the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.
THE OPERATION MODES OPTIMIZATION OF THE NEUTRAL DISTRIBUTION NETWORKS
Directory of Open Access Journals (Sweden)
F. P. Shkarbets
2009-03-01
Full Text Available The variants of grounding the neutral wire of electric networks are considered and the recommendations are presented on increasing the level of operational reliability and electric safety of distribution networks with 6 kV voltage on the basis of limitation and suppression of transitional processes at asymmetrical damages.
modeling and optimization of an electric power distribution network
African Journals Online (AJOL)
user
EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...
M2M Optimizations in Public Mobile Networks
Norp, A.H.J.; Landais, B.
2012-01-01
Many M2M applications use public telecommunications networks to transfer data from M2M devices to an M2M server. These telecommunications networks will have to be adapted to cope with the traffic generated by the projected growth of M2M applications. In the near future, many more devices will be
Optimal task scheduling policy in energy harvesting wireless sensor networks
Rao, Vijay S.; Prasad, R. Venkatesha; Niemegeers, Ignas G M M
2015-01-01
Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for long-lasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of
International Nuclear Information System (INIS)
Pombo, A. Vieira; Murta-Pina, João; Pires, V. Fernão
2015-01-01
A multi-objective planning approach for the reliability of electric distribution networks using a memetic optimization is presented. In this reliability optimization, the type of the equipment (switches or reclosers) and their location are optimized. The multiple objectives considered to find the optimal values for these planning variables are the minimization of the total equipment cost and at the same time the minimization of two distribution network reliability indexes. The reliability indexes are the system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). To solve this problem a memetic evolutionary algorithm is proposed, which combines the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with a local search algorithm. The obtained Pareto-optimal front contains solutions of different trade-offs with respect to the three objectives. A real distribution network is used to test the proposed algorithm. The obtained results show that this approach allows the utility to obtain the optimal type and location of the equipments to achieve the best reliability with the lower cost. - Highlights: • Reliability indexes SAIFI and SAIDI and Equipment Cost are optimized. • Optimization of equipment type, number and location on a MV network. • Memetic evolutionary algorithm with a local search algorithm is proposed. • Pareto optimal front solutions with respect to the three objective functions
Optimal design of an IP/MPLS over DWDM network
Directory of Open Access Journals (Sweden)
Eduardo Canale
2014-04-01
Full Text Available Different approaches for deploying resilient optical networks of low cost constitute a traditional group of NP-Hard problems that have been widely studied. Most of them are based on the construction of low cost networks that fulfill connectivity constraints. However, recent trends to virtualize optical networks over the legacy fiber infrastructure, modified the nature of network design problems and turned inappropriate many of these models and algorithms. In this paper we study a design problem arising from the deployment of an IP/MPLS network over an existing DWDM infrastructure. Besides cost and resiliency, this problem integrates traffic and capacity constraints. We present: an integer programming formulation for the problem, theoretical results, and describe how several metaheuristics were applied in order to find good quality solutions, for a real application case of a telecommunications company.
Huang, Daizheng; Wu, Zhihui
2017-01-01
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
Nikelshpur, Dmitry O.
2014-01-01
Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…
CORAL off-line: an object-oriented tool for optimal control of sewer networks
Figueras, J.; Cembrano, Gabriela; Puig, Vicenç; Quevedo, Joseba; Salamero Sansalvado, María; Marti Marques, Joaquim
2002-01-01
This paper describes a tool to aid in the analysis and design of combined sewer networks. Complex drainage systems include actuators, like flow-diversion gates and detention tanks, which should be optimally controlled in order to minimize flooding and combined sewer overflow (CSO). Through these optimisations volume to waste water treatment plants (WWTP) is maximised. CORAL is a tool able to model a combined sewer network, simulate rain events, calculate actuators optimal policies, reproduce ...
Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks
Directory of Open Access Journals (Sweden)
Migliavacca S.C.P.
2002-01-01
Full Text Available Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.
Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey
Adnan, Md. Akhtaruzzaman; Razzaque, Mohammd Abdur; Ahmed, Ishtiaque; Isnin, Ismail Fauzi
2014-01-01
For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted. PMID:24368702
FE, JORGE DEOLINDO; Aliaga Varea, Ramón José; Gadea Gironés, Rafael
2015-01-01
In the optimization of artificial neural networks (ANNs) via evolutionary algorithms and the implementation of the necessary training for the objective function, there is often a trade-off between efficiency and flexibility. Pure software solutions on general-purpose processors tend to be slow because they do not take advantage of the inherent parallelism, whereas hardware realizations usually rely on optimizations that reduce the range of applicable network topologies, or they...
Bio-mimic optimization strategies in wireless sensor networks: a survey.
Adnan, Md Akhtaruzzaman; Abdur Razzaque, Mohammd; Ahmed, Ishtiaque; Isnin, Ismail Fauzi
2013-12-24
For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.
Optimization of rainfall networks using information entropy and temporal variability analysis
Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin
2018-04-01
Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.
A one-layer recurrent neural network for constrained nonsmooth invex optimization.
Li, Guocheng; Yan, Zheng; Wang, Jun
2014-02-01
Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
Optimization of controllability and robustness of complex networks by edge directionality
Liang, Man; Jin, Suoqin; Wang, Dingjie; Zou, Xiufen
2016-09-01
Recently, controllability of complex networks has attracted enormous attention in various fields of science and engineering. How to optimize structural controllability has also become a significant issue. Previous studies have shown that an appropriate directional assignment can improve structural controllability; however, the evolution of the structural controllability of complex networks under attacks and cascading has always been ignored. To address this problem, this study proposes a new edge orientation method (NEOM) based on residual degree that changes the link direction while conserving topology and directionality. By comparing the results with those of previous methods in two random graph models and several realistic networks, our proposed approach is demonstrated to be an effective and competitive method for improving the structural controllability of complex networks. Moreover, numerical simulations show that our method is near-optimal in optimizing structural controllability. Strikingly, compared to the original network, our method maintains the structural controllability of the network under attacks and cascading, indicating that the NEOM can also enhance the robustness of controllability of networks. These results alter the view of the nature of controllability in complex networks, change the understanding of structural controllability and affect the design of network models to control such networks.
Directory of Open Access Journals (Sweden)
KHANBABAZADEH Javad
2016-10-01
Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.
Directory of Open Access Journals (Sweden)
Xiao-feng Xu
2017-01-01
Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.
Panda, Satyasen
2018-05-01
This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.
Directory of Open Access Journals (Sweden)
Yang Sun
2018-01-01
Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies
Asorey-Cacheda, Rafael; García-Sánchez, Antonio Javier; García-Sánchez, Felipe; García-Haro, Joan; Gonzalez-Castaño, Francisco Javier
2013-01-01
The extension of the network lifetime of Wireless Sensor Networks (WSN) is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes) carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes) are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment) to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively. PMID:23939582
On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies
Directory of Open Access Journals (Sweden)
Francisco Javier González-Castano
2013-08-01
Full Text Available The extension of the network lifetime of Wireless Sensor Networks (WSN is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.
Optimal design of cluster-based ad-hoc networks using probabilistic solution discovery
International Nuclear Information System (INIS)
Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel
2009-01-01
The reliability of ad-hoc networks is gaining popularity in two areas: as a topic of academic interest and as a key performance parameter for defense systems employing this type of network. The ad-hoc network is dynamic and scalable and these descriptions are what attract its users. However, these descriptions are also synonymous for undefined and unpredictable when considering the impacts to the reliability of the system. The configuration of an ad-hoc network changes continuously and this fact implies that no single mathematical expression or graphical depiction can describe the system reliability-wise. Previous research has used mobility and stochastic models to address this challenge successfully. In this paper, the authors leverage the stochastic approach and build upon it a probabilistic solution discovery (PSD) algorithm to optimize the topology for a cluster-based mobile ad-hoc wireless network (MAWN). Specifically, the membership of nodes within the back-bone network or networks will be assigned in such as way as to maximize reliability subject to a constraint on cost. The constraint may also be considered as a non-monetary cost, such as weight, volume, power, or the like. When a cost is assigned to each component, a maximum cost threshold is assigned to the network, and the method is run; the result is an optimized allocation of the radios enabling back-bone network(s) to provide the most reliable network possible without exceeding the allowable cost. The method is intended for use directly as part of the architectural design process of a cluster-based MAWN to efficiently determine an optimal or near-optimal design solution. It is capable of optimizing the topology based upon all-terminal reliability (ATR), all-operating terminal reliability (AoTR), or two-terminal reliability (2TR)
Optimization of blanking process using neural network simulation
International Nuclear Information System (INIS)
Hambli, R.
2005-01-01
The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)
Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing
Directory of Open Access Journals (Sweden)
Tapan Kumar Jain
2015-01-01
Full Text Available The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.
Energy Technology Data Exchange (ETDEWEB)
Gonzalez-Barrios, P.; Castro, M.; Pérez, O.; Vilaró, D.; Gutiérrez, L.
2017-07-01
Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.
Robust optimal control of material flows in demand-driven supply networks
Laumanns, M.; Lefeber, A.A.J.
2006-01-01
We develop a model based on stochastic discrete-time controlleddynamical systems in order to derive optimal policies for controllingthe material flow in supply networks. Each node in the network isdescribed as a transducer such that the dynamics of the material andinformation flows within the entire
Optimal Power Flow for resistive DC Network : A Port-Hamiltonian approach
Benedito, Ernest; del Puerto-Flores, D.; Doria-Cerezo, A.; Scherpen, Jacquelien M.A.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri
This paper studies the optimal power flow problem for resistive DC networks. The gradient method algorithm is written in a port-Hamiltonian form and the stability of the resulting dynamics is studied. Stability conditions are provided for general cyclic networks and a solution, when these conditions
Optimization of neural networks for time-domain simulation of mooring lines
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Winther, Ole
2016-01-01
, they also can be used to rank the importance of the various network inputs. The dynamic response of slender marine structures often depends on several external load components, and by applying the optimization procedures to a trained artificial neural network, it is possible to classify the external force...
Energy-Aware Routing Optimization in Dynamic GMPLS Controlled Optical Networks
DEFF Research Database (Denmark)
Wang, Jiayuan; Ricciardi, Sergio; Fagertun, Anna Manolova
2012-01-01
In this paper, routing optimizations based on energy sources are proposed in dynamic GMPLS controlled optical networks. The influences of re-routing and load balancing factors on the algorithm are evaluated, with a focus on different re-routing thresholds. Results from dynamic network simulations...
Hoomod, Haider K.; Kareem Jebur, Tuka
2018-05-01
Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.
Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.
Wang, Yunpeng; Cheng, Long; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min
2016-02-01
The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.