A Novel Algorithm for Cooperative Distributed Sequential Spectrum Sensing in Cognitive Radio
S, Jithin K
2010-01-01
This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.
Cooperative Distributed Sequential Spectrum Sensing
S, Jithin K; Gopalarathnam, Raghav
2010-01-01
We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.
A NEW INEXACT SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM
Institute of Scientific and Technical Information of China (English)
倪勤
2002-01-01
This paper represents an inexact sequential quadratic programming (SQP ) algorithm which can solve nonlinear programming (NLP ) problems. An inexact solution of the quadratic programming subproblem is determined by a projection and contraction method such that only matrix-vector product is required. Some truncated criteria are chosen such that the algorithm is suitable to large scale NLP problem. The global convergence of the algorithm is proved.
Sequential algorithm for fast clique percolation.
Kumpula, Jussi M; Kivelä, Mikko; Kaski, Kimmo; Saramäki, Jari
2008-08-01
In complex network research clique percolation, introduced by Palla, Derényi, and Vicsek [Nature (London) 435, 814 (2005)], is a deterministic community detection method which allows for overlapping communities and is purely based on local topological properties of a network. Here we present a sequential clique percolation algorithm (SCP) to do fast community detection in weighted and unweighted networks, for cliques of a chosen size. This method is based on sequentially inserting the constituent links to the network and simultaneously keeping track of the emerging community structure. Unlike existing algorithms, the SCP method allows for detecting k -clique communities at multiple weight thresholds in a single run, and can simultaneously produce a dendrogram representation of hierarchical community structure. In sparse weighted networks, the SCP algorithm can also be used for implementing the weighted clique percolation method recently introduced by Farkas [New J. Phys. 9, 180 (2007)]. The computational time of the SCP algorithm scales linearly with the number of k -cliques in the network. As an example, the method is applied to a product association network, revealing its nested community structure.
A Sequential Algorithm for Training Text Classifiers
Lewis, D D; Lewis, David D.; Gale, William A.
1994-01-01
The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
Qi, Hong; Qiao, Yao-Bin; Ren, Ya-Tao; Shi, Jing-Wen; Zhang, Ze-Yu; Ruan, Li-Ming
2016-10-17
Sequential quadratic programming (SQP) is used as an optimization algorithm to reconstruct the optical parameters based on the time-domain radiative transfer equation (TD-RTE). Numerous time-resolved measurement signals are obtained using the TD-RTE as forward model. For a high computational efficiency, the gradient of objective function is calculated using an adjoint equation technique. SQP algorithm is employed to solve the inverse problem and the regularization term based on the generalized Gaussian Markov random field (GGMRF) model is used to overcome the ill-posed problem. Simulated results show that the proposed reconstruction scheme performs efficiently and accurately.
Large-scale sequential quadratic programming algorithms
Energy Technology Data Exchange (ETDEWEB)
Eldersveld, S.K.
1992-09-01
The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
A Trust-region-based Sequential Quadratic Programming Algorithm
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Poulsen, Niels Kjølstad
This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints....
Fast Algorithms for Discovering Sequential Patterns in Massive Datasets
Directory of Open Access Journals (Sweden)
S. Dharani
2011-01-01
Full Text Available Problem statement: Sequential pattern mining is one of the specific data mining tasks, particularly from retail data. The task is to discover all sequential patterns with a user-specified minimum support, where support of a pattern is the number of data-sequences that contain the pattern. Approach: To find a sequence patterns variety of algorithm like AprioriAll and Generalized Sequential Patterns (GSP were there. We present fast and efficient algorithms called AprioriAllSID and GSPSID for mining sequential patterns that were fundamentally different from known algorithms. Results: The proposed algorithm had been implemented and compared with AprioriAll and Generalized Sequential Patterns (GSP. Its performance was studied on an experimental basis. We combined the AprioriAllSID algorithm with AprioriAll algorithm into a Hybrid algorithm, called AprioriAll Hybrid. Conclusion: Implementation shows that the execution time of the algorithm to find sequential pattern depends on total no of candidates generated at each level and the time taken to scan the database. Our performance study shows that the proposed algorithms have an excellent performance over the best existing algorithms.
Sequential unconstrained minimization algorithms for constrained optimization
Byrne, Charles
2008-02-01
The problem of minimizing a function f(x):RJ → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G_k(x)=f(x)+g_k(x), to obtain xk. The auxiliary functions gk(x):D ⊆ RJ → R+ are nonnegative on the set D, each xk is assumed to lie within D, and the objective is to minimize the continuous function f:RJ → R over x in the set C=\\overline D , the closure of D. We assume that such minimizers exist, and denote one such by \\hat x . We assume that the functions gk(x) satisfy the inequalities 0\\leq g_k(x)\\leq G_{k-1}(x)-G_{k-1}(x^{k-1}), for k = 2, 3, .... Using this assumption, we show that the sequence {f(xk)} is decreasing and converges to f({\\hat x}) . If the restriction of f(x) to D has bounded level sets, which happens if \\hat x is unique and f(x) is closed, proper and convex, then the sequence {xk} is bounded, and f(x^*)=f({\\hat x}) , for any cluster point x*. Therefore, if \\hat x is unique, x^*={\\hat x} and \\{x^k\\}\\rightarrow {\\hat x} . When \\hat x is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton-Raphson method. The proof techniques used for SUMMA can be extended to obtain related results for the induced proximal
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
A Fast Algorithm for Mining Sequential Patterns from Large Databases
Institute of Scientific and Technical Information of China (English)
CHEN Ning; CHEN An; ZHOU Longxiang; LIU Lu
2001-01-01
Mining sequential patterns from large databases has been recognized by many researchers as an attractive task of data mining and knowledge discovery. Previous algorithms scan the databases for many times, which is often unendurable due to the very large amount of databases. In this paper, the authors introduce an effective algorithm for mining sequential patterns from large databases.In the algorithm, the original database is not used at all for counting the support of sequences after the first pass. Rather, a tidlist structure generated in the previous pass is employed for the purpose based on set intersection operations, avoiding the multiple scans of the databases.
Efficient sequential and parallel algorithms for record linkage.
Mamun, Abdullah-Al; Mi, Tian; Aseltine, Robert; Rajasekaran, Sanguthevar
2014-01-01
Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Our sequential and parallel algorithms have been tested on a real dataset of 1,083,878 records and synthetic datasets ranging in size from 50,000 to 9,000,000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.
DEFF Research Database (Denmark)
Pontefisso, Alessandro; Zappalorto, Michele; Quaresimin, Marino
2016-01-01
In this work, a study of the Random Sequential Absorption (RSA) algorithm in the generation of nanoplatelet Volume Elements (VEs) is carried out. The effect of the algorithm input parameters on the reinforcement distribution is studied through the implementation of statistical tools, showing...... that the platelet distribution is systematically affected by these parameters. The consequence is that a parametric analysis of the VE input parameters may be biased by hidden differences in the filler distribution. The same statistical tools used in the analysis are implemented in a modified RSA algorithm...
Recursive Algorithm and Alternate Operation Strategy in Sequential Tests
Institute of Scientific and Technical Information of China (English)
XU Hong-lin; CHEN Zhan-qi; GUO Lue
2009-01-01
Based on the sequential probability ratio test (SPRT) developed by Wald, an improved method for successful probability test of missile flight is proposed. A recursive algorithm and its program in Matlab are designed to calculate the real risk level of the sequential test decision and the average number of samples under various test conditions. A concept, that is "rejecting as soon as possible", is put forward and an alternate operation strategy is conducted. The simulation results show that it can reduce the test expenses.
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule....... We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
Graph-Theoretic Techniques for Parallel, Distributed, and Sequential Computation
1988-09-01
presenting a very simple wait-free leader - election algorithm. It should be noted that this algorithm is interesting by itself, because the standard methods of...wait-free leader election algorithm. In Sections 2.5 and 2.6 we describe how to construct an atomic simulation of any sequential object from Sticky...new data object, the Sticky Bit, and illustrate the use of this object by presenting a deterministic wait-free leader - election algorithm. Definition
New sequential quadratic programming algorithm with consistent subproblems
Institute of Scientific and Technical Information of China (English)
贺国平; 高自友; 赖炎连
1997-01-01
One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done The method proposed by De O. Panto-ja J F A and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and local superhnear convergence.
Study of sequential optimal control algorithm smart isolation structure based on Simulink-S function
Liu, Xiaohuan; Liu, Yanhui
2017-01-01
The study of this paper focuses on smart isolation structure, a method for realizing structural vibration control by using Simulink simulation is proposed according to the proposed sequential optimal control algorithm. In the Simulink simulation environment, A smart isolation structure is used to compare the control effect of three algorithms, i.e., classical optimal control algorithm, linear quadratic gaussian control algorithm and sequential optimal control algorithm under the condition of sensor contaminated with noise. Simulation results show that this method can be applied to the simulation of sequential optimal control algorithm and the proposed sequential optimal control algorithm has a good ability of resisting the noise and better control efficiency.
Institute of Scientific and Technical Information of China (English)
张勇; 孟庆浩; 吴玉秀; 曾明
2014-01-01
基于无线传感网络的气体泄漏源定位在环境监测、安全防护和污染控制等多个领域具有重要意义。提出一种基于分布式最小均方差( D-MMSE)序贯估计的气体泄漏源定位算法。其通过构建一个包含节点之间信息增益与网络能量消耗两方面参数的信息融合目标函数，并对目标函数寻优实现路由节点的调度与选择。所选节点在其测量值和前节点估计值并通过与邻居节点信息交互的基础上完成气体泄漏源位置参数估计量及其方差的更新与传递。为了降低网络能耗，邻居节点集的选择半径随估计量方差做动态调整。仿真分析表明所提算法对比单节点序贯估计定位算法在一定的能耗条件下可获得较高的定位精度和速度。%Distributed gas source localization with Wireless Sensor Networks has an important significance in the en-vironmental monitoring,security protection and pollution control and other fields. a gas leakage source localization ( GLSL) algorithm based on distributed minimum mean squared error( D-MMSE) sequential estimation is proposed. In the proposed GLSL algorithm, an information fusion objective function which combines the information utility measure and the communication cost between sensor nodes is constructed,and the sensor-node scheduling scheme is designed by optimizing the information fusion objective function;For each selected sensor node,the estimator and the corresponding mean square error are updated with its own observation and the noise corrupted decision from the previous node and transmitted to the next selected node by collaborating information within its neighborhood,and to decrease the energy consumption,the neighborhood radius is adjusted dynamically based on the mean square error. At last,the analysis and simulation results show that the proposed algorithm could be applied to the GLSL with a realtively high accuracy, less time and relatively energy
Method of sequential mesh on Koopman-Darmois distributions
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
For costly and/or destructive tests,the sequential method with a proper maximum sample size is needed.Based on Koopman-Darmois distributions,this paper proposes the method of sequential mesh,which has an acceptable maximum sample size.In comparison with the popular truncated sequential probability ratio test,our method has the advantage of a smaller maximum sample size and is especially applicable for costly and/or destructive tests.
Multisensor estimation: New distributed algorithms
Directory of Open Access Journals (Sweden)
K. N. Plataniotis
1996-01-01
Full Text Available The multisensor estimation problem is considered in this paper. New distributed algorithms, which are able to locally process the information and which deliver identical results to those generated by their centralized counterparts are presented. The algorithms can be used to provide robust and computationally efficient solutions to the multisensor estimation problem. The proposed distributed algorithms are theoretically interesting and computationally attractive.
Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm
Directory of Open Access Journals (Sweden)
Yi Li
2013-01-01
Full Text Available We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.
Distributed Minimum Hop Algorithms
1982-01-01
acknowledgement), node d starts iteration i+1, and otherwise the algorithm terminates. A detailed description of the algorithm is given in pidgin algol...precise behavior of the algorithm under these circumstances is described by the pidgin algol program in the appendix which is executed by each node. The...l) < N!(2) for each neighbor j, and thus by induction,J -1 N!(2-1) < n-i + (Z-1) + N!(Z-1), completing the proof. Algorithm Dl in Pidgin Algol It is
A Volume Rendering Algorithm for Sequential 2D Medical Images
Institute of Scientific and Technical Information of China (English)
吕忆松; 陈亚珠
2002-01-01
Volume rendering of 3D data sets composed of sequential 2D medical images has become an important branch in image processing and computer graphics.To help physicians fully understand deep-seated human organs and focuses(e.g.a tumour)as 3D structures.in this paper,we present a modified volume rendering algorithm to render volumetric data,Using this method.the projection images of structures of interest from different viewing directions can be obtained satisfactorily.By rotating the light source and the observer eyepoint,this method avoids rotates the whole volumetric data in main memory and thus reduces computational complexity and rendering time.Experiments on CT images suggest that the proposed method is useful and efficient for rendering 3D data sets.
SPATIAL DISTRIBUTION AND SEQUENTIAL SAMPLING OF Brevipalpus phoenicis IN CITRUS
Directory of Open Access Journals (Sweden)
WALTER MALDONADO JR
Full Text Available ABSTRACT Among the pests of citrus, one of the most important is the red and black flat mite Brevipalpus phoenicis (Geijskes, which transmits the Citrus leprosis virus C (CiLV-C.When a rational pest control plan is adopted, it is important to determine the correct timing for carrying out the control plan. Making this decision demands constant follow-up of the culture through periodic sampling where knowledge about the spatial distribution of the pest is a fundamental part to improve sampling and control decisions. The objective of this work was to study the spatial distribution pattern and build a sequential sampling plan for the pest. The data used were gathered from two blocks of Valencia sweet orange on a farm in São Paulo State, Brazil, by 40 inspectors trained for the data collection. The following aggregation indices were calculated: variance/ mean ratio, Morisita index, Green’s coefficient, and k parameter of the negative binomial distribution. The data were tested for fit with Poisson and negative binomial distributions using the chi-square goodness of fit test. The sequential sampling was developed using Wald’s Sequential Probability Ratio Test and validated through simulations. We concluded that the spatial distribution of B. phoenicis is aggregated, its behavior best fitted to the negative binomial distribution and we built and validated a sequential sampling plan for control decision-making.
A multi-sequential number-theoretic optimization algorithm using clustering methods
Institute of Scientific and Technical Information of China (English)
XU Qing-song; LIANG Yi-zeng; HOU Zhen-ting
2005-01-01
A multi-sequential number-theoretic optimization method based on clustering was developed and applied to the optimization of functions with many local extrema. Details of the procedure to generate the clusters and the sequential schedules were given. The algorithm was assessed by comparing its performance with generalized simulated annealing algorithm in a difficult instructive example and a D-optimum experimental design problem. It is shown the presented algorithm to be more effective and reliable based on the two examples.
GEOMETRIC METHOD OF SEQUENTIAL ESTIMATION RELATED TO MULTINOMIAL DISTRIBUTION MODELS
Institute of Scientific and Technical Information of China (English)
WEIBOCHENG; LISHOUYE
1995-01-01
In 1980's differential geometric methods are successfully used to study curved expomential families and normal nonlinear regression models.This paper presents a new geometric structure to study multinomial distribution models which contain a set of nonlinear parameters.Based on this geometric structure,the suthors study several asymptotic properties for sequential estimation.The bias,the variance and the information loss of the sequential estimates are given from geomentric viewpoint,and a limit theorem connected with the observed and expected Fisher information is obtained in terms of curvatvre measures.The results show that the sequential estimation procednce has some better properties which are generally impossible for nonsequential estimation procedures.
Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua
2015-01-15
Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.
Directory of Open Access Journals (Sweden)
Han Zou
2015-01-01
Full Text Available Nowadays, developing indoor positioning systems (IPSs has become an attractive research topic due to the increasing demands on location-based service (LBS in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.
Sarwono, A. A.; Ai, T. J.; Wigati, S. S.
2017-01-01
Vehicle Routing Problem (VRP) is a method for determining the optimal route of vehicles in order to serve customers starting from depot. Combination of the two most important problems in distribution logistics, which is called the two dimensional loading vehicle routing problem, is considered in this paper. This problem combines the loading of the freight into the vehicles and the successive routing of the vehicles along the route. Moreover, an additional feature of last-in-first-out loading sequencesis also considered. In the sequential two dimensional loading capacitated vehicle routing problem (sequential 2L-CVRP), the loading must be compatible with the trip sequence: when the vehicle arrives at a customer i, there must be no obstacle (items for other customers) between the item of i and the loading door (rear part) of the vehicle. In other words, it is not necessary to move non-i’s items whenever the unloading process of the items of i. According with aforementioned conditions, a program to solve sequential 2L-CVRP is required. A nearest neighbor algorithm for solving the routing problem is presented, in which the loading component of the problem is solved through a collection of 5 packing heuristics.
a Distributed Polygon Retrieval Algorithm Using Mapreduce
Guo, Q.; Palanisamy, B.; Karimi, H. A.
2015-07-01
The burst of large-scale spatial terrain data due to the proliferation of data acquisition devices like 3D laser scanners poses challenges to spatial data analysis and computation. Among many spatial analyses and computations, polygon retrieval is a fundamental operation which is often performed under real-time constraints. However, existing sequential algorithms fail to meet this demand for larger sizes of terrain data. Motivated by the MapReduce programming model, a well-adopted large-scale parallel data processing technique, we present a MapReduce-based polygon retrieval algorithm designed with the objective of reducing the IO and CPU loads of spatial data processing. By indexing the data based on a quad-tree approach, a significant amount of unneeded data is filtered in the filtering stage and it reduces the IO overhead. The indexed data also facilitates querying the relationship between the terrain data and query area in shorter time. The results of the experiments performed in our Hadoop cluster demonstrate that our algorithm performs significantly better than the existing distributed algorithms.
A DISTRIBUTED POLYGON RETRIEVAL ALGORITHM USING MAPREDUCE
Directory of Open Access Journals (Sweden)
Q. Guo
2015-07-01
Full Text Available The burst of large-scale spatial terrain data due to the proliferation of data acquisition devices like 3D laser scanners poses challenges to spatial data analysis and computation. Among many spatial analyses and computations, polygon retrieval is a fundamental operation which is often performed under real-time constraints. However, existing sequential algorithms fail to meet this demand for larger sizes of terrain data. Motivated by the MapReduce programming model, a well-adopted large-scale parallel data processing technique, we present a MapReduce-based polygon retrieval algorithm designed with the objective of reducing the IO and CPU loads of spatial data processing. By indexing the data based on a quad-tree approach, a significant amount of unneeded data is filtered in the filtering stage and it reduces the IO overhead. The indexed data also facilitates querying the relationship between the terrain data and query area in shorter time. The results of the experiments performed in our Hadoop cluster demonstrate that our algorithm performs significantly better than the existing distributed algorithms.
Distributed Algorithms for Optimal Power Flow Problem
Lam, Albert Y S; Tse, David
2011-01-01
Optimal power flow (OPF) is an important problem for power generation and it is in general non-convex. With the employment of renewable energy, it will be desirable if OPF can be solved very efficiently so its solution can be used in real time. With some special network structure, e.g. trees, the problem has been shown to have a zero duality gap and the convex dual problem yields the optimal solution. In this paper, we propose a primal and a dual algorithm to coordinate the smaller subproblems decomposed from the convexified OPF. We can arrange the subproblems to be solved sequentially and cumulatively in a central node or solved in parallel in distributed nodes. We test the algorithms on IEEE radial distribution test feeders, some random tree-structured networks, and the IEEE transmission system benchmarks. Simulation results show that the computation time can be improved dramatically with our algorithms over the centralized approach of solving the problem without decomposition, especially in tree-structured...
Poage, J. L.
1975-01-01
A sequential nonparametric pattern classification procedure is presented. The method presented is an estimated version of the Wald sequential probability ratio test (SPRT). This method utilizes density function estimates, and the density estimate used is discussed, including a proof of convergence in probability of the estimate to the true density function. The classification procedure proposed makes use of the theory of order statistics, and estimates of the probabilities of misclassification are given. The procedure was tested on discriminating between two classes of Gaussian samples and on discriminating between two kinds of electroencephalogram (EEG) responses.
A Distributed Spanning Tree Algorithm
DEFF Research Database (Denmark)
Johansen, Karl Erik; Jørgensen, Ulla Lundin; Nielsen, Sven Hauge
We present a distributed algorithm for constructing a spanning tree for connected undirected graphs. Nodes correspond to processors and edges correspond to two-way channels. Each processor has initially a distinct identity and all processors perform the same algorithm. Computation as well...... as communication is asynchronous. The total number of messages sent during a construction of a spanning tree is at most 2E+3NlogN. The maximal message size is loglogN+log(maxid)+3, where maxid is the maximal processor identity....
A distributed spanning tree algorithm
DEFF Research Database (Denmark)
Johansen, Karl Erik; Jørgensen, Ulla Lundin; Nielsen, Svend Hauge
1988-01-01
We present a distributed algorithm for constructing a spanning tree for connected undirected graphs. Nodes correspond to processors and edges correspond to two way channels. Each processor has initially a distinct identity and all processors perform the same algorithm. Computation as well...... as communication is asyncronous. The total number of messages sent during a construction of a spanning tree is at most 2E+3NlogN. The maximal message size is loglogN+log(maxid)+3, where maxid is the maximal processor identity....
A Distributed Spanning Tree Algorithm
DEFF Research Database (Denmark)
Johansen, Karl Erik; Jørgensen, Ulla Lundin; Nielsen, Sven Hauge
We present a distributed algorithm for constructing a spanning tree for connected undirected graphs. Nodes correspond to processors and edges correspond to two-way channels. Each processor has initially a distinct identity and all processors perform the same algorithm. Computation as well...... as communication is asynchronous. The total number of messages sent during a construction of a spanning tree is at most 2E+3NlogN. The maximal message size is loglogN+log(maxid)+3, where maxid is the maximal processor identity....
Institute of Scientific and Technical Information of China (English)
高自友; 贺国平; 吴方
1997-01-01
For current sequential quadratic programming (SQP) type algorithms, there exist two problems; (i) in order to obtain a search direction, one must solve one or more quadratic programming subproblems per iteration, and the computation amount of this algorithm is very large. So they are not suitable for the large-scale problems; (ii) the SQP algorithms require that the related quadratic programming subproblems be solvable per iteration, but it is difficult to be satisfied. By using e-active set procedure with a special penalty function as the merit function, a new algorithm of sequential systems of linear equations for general nonlinear optimization problems with arbitrary initial point is presented This new algorithm only needs to solve three systems of linear equations having the same coefficient matrix per iteration, and has global convergence and local superlinear convergence. To some extent, the new algorithm can overcome the shortcomings of the SQP algorithms mentioned above.
A sequential algorithm of inverse heat conduction problems using singular value decomposition
Energy Technology Data Exchange (ETDEWEB)
Gutierrez Cabeza, J.M. [Dep. of Applied Physics of Univ. of Cadiz, Escuela Politecnica Superior de Algeciras, Cadiz (Spain); Garcia, Juan Andres Martin [Department of Electrical Engineering of University of Cadiz, Escuela Politecnica Superior de Algeciras, Avda. Ramon Puyol, s/n, 11202 Algeciras (Cadiz) (Spain); Rodriguez, Alfonso Corz [Department of Industrial and Civil Engineering of University of Cadiz, Escuela Politecnica Superior de Algeciras, Avda. Ramon Puyol, s/n, 11202 Algeciras (Cadiz) (Spain)
2005-03-01
This paper examines numerically and theoretically the application of truncated Singular Value Decomposition (SVD) in a sequential form. The Sequential SVD algorithm presents two tunable hyper-parameters: the number of future temperature (r) and the rank of the truncated sensitivity matrix (p). The regularization effect of both hyper-parameters is consistent with the data filtering interpretation by truncated SVD (reported by Shenefelt [Internat. J. Heat Mass Transfer 45 (2002) 67]). This study reveals that the most suitable reduced rank is ''one''. Under this assumption (p=1), the sequential procedure proposed, presents several advantages with respect to the standard whole-domain procedure: The search of the optimum rank value is not required. The simplification of the model is the maximum that can be achieved. The unique tunable hyper-parameter is the number of future temperatures, and a very simple algorithm is obtained. This algorithm has been compared to: Function Specification Method (FSM) proposed by Beck and the standard whole-domain SVD. In this comparative study, the parameters considered have been: the shape of the input, the noise level of measurement and the size of time step. In all cases, the FSM and sequential SVD algorithm give very similar results. In one case, the results obtained by the sequential SVD algorithm are clearly superior to the ones obtained by the whole-domain algorithm. (authors)
A nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix
Institute of Scientific and Technical Information of China (English)
李文法
2016-01-01
Problems existin similarity measurement and index tree construction which affect the perform-ance of nearest neighbor search of high-dimensional data .The equidistance problem is solved using NPsim function to calculate similarity .And a sequential NPsim matrix is built to improve indexing performance .To sum up the above innovations , a nearest neighbor search algorithm of high-dimen-sional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set .Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others .In addition , the slow construction speed of sequential NPsim matrix can be increased by using parallel computing .
HASM-AD Algorithm Based on the Sequential Least Squares
Institute of Scientific and Technical Information of China (English)
WANG Shihai; YUE Tianxiang
2010-01-01
The HASM (high accuracy surface modeling) technique is based on the fundamental theory of surfaces, which has been proved to improve the interpolation accuracy in surface fitting. However, the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets. In the study, an innovative model (HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory. Sequential division is adopted in the technique, so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation. The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy. Also, the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province. Moreover, it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.
Directory of Open Access Journals (Sweden)
Wodziński Marek
2017-06-01
Full Text Available This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.
UCB Algorithm for Exponential Distributions
Jouini, Wassim
2012-01-01
We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading channels. This strategy, named Multiplicative Upper Confidence Bound (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that the MUCB policy has a low complexity and is order optimal.
Discrete Riccati equation solutions: Distributed algorithms
Directory of Open Access Journals (Sweden)
D. G. Lainiotis
1996-01-01
Full Text Available In this paper new distributed algorithms for the solution of the discrete Riccati equation are introduced. The algorithms are used to provide robust and computational efficient solutions to the discrete Riccati equation. The proposed distributed algorithms are theoretically interesting and computationally attractive.
FSRM: A Fast Algorithm for Sequential Rule Mining
Directory of Open Access Journals (Sweden)
Anjali Paliwal
2014-10-01
Full Text Available Recent developments in computing and automation technologies have resulted in computerizing business and scientific applications in various areas. Turing the massive amounts of accumulated information into knowledge is attracting researchers in numerous domains as well as databases, machine learning, statistics, and so on. From the views of information researchers, the stress is on discovering meaningful patterns hidden in the massive data sets. Hence, a central issue for knowledge discovery in databases, additionally the main focus of this paper, is to develop economical and scalable mining algorithms as integrated tools for management systems.
Institute of Scientific and Technical Information of China (English)
Zi-you Gao; Tian-de Guo; Guo-ping He; Fang Wu
2002-01-01
In this paper, a new superlinearly convergent algorithm of sequential systems of linear equations (SSLE) for nonlinear optimization problems with inequality constraints is proposed. Since the new algorithm only needs to solve several systems of linear equations having a same coefficient matrix per iteration, the computation amount of the algorithm is much less than that of the existing SQP algorithms per iteration. Moreover, for the SQPtype algorithms, there exist so-called inconsistent problems, i.e., quadratic programming subproblems of the SQP algorithms may not have a solution at some iterations, but this phenomenon will not occur with the SSLE algorithms because the related systems of linear equations always have solutions. Some numerical results are reported.
Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.
Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar
2017-03-01
We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.
Paul, Joshua S; Steinrücken, Matthias; Song, Yun S
2011-04-01
The sequentially Markov coalescent is a simplified genealogical process that aims to capture the essential features of the full coalescent model with recombination, while being scalable in the number of loci. In this article, the sequentially Markov framework is applied to the conditional sampling distribution (CSD), which is at the core of many statistical tools for population genetic analyses. Briefly, the CSD describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. A hidden Markov model (HMM) formulation of the sequentially Markov CSD is developed here, yielding an algorithm with time complexity linear in both the number of loci and the number of haplotypes. This work provides a highly accurate, practical approximation to a recently introduced CSD derived from the diffusion process associated with the coalescent with recombination. It is empirically demonstrated that the improvement in accuracy of the new CSD over previously proposed HMM-based CSDs increases substantially with the number of loci. The framework presented here can be adopted in a wide range of applications in population genetics, including imputing missing sequence data, estimating recombination rates, and inferring human colonization history.
An Effective Algorithm for Average Power Estimation of CMOS Sequential Chircuit
Institute of Scientific and Technical Information of China (English)
LIYueping; TANGPushan; ZHAOWenqing
2003-01-01
An incremental probabilistic algorithm is proposed for estimating average power of CMOS sequential circuit.We facilitate the flrst-order Taylor expansion to consider the spatial and temporal correlation among the internal nodes of the seauential circuits.Regarding finite state machines as non-decomposable and aperiodic Markov Chains,the steady-state probabilities exist.Consequently there have the steady probabilities of state lines.Thus the signal probability and switching activity of state line can be gotten through Picard-Peano iteration method.Sequential modules are separated from the whole circuit to shorten the runtime of our algorithm.We unroll the sequential module to accurately estimate the signal probability of state lines.Unilke the algorithms bassed on global BDD,the runtime of computing signal probability and switching activity of our algorithm does not depend on the circuit size.Experimental results show that our algorithm is much faster than the Monte-Carlo simulation method with the error below 10%.
IMPROVED ALGORITHM FOR ROAD REGION SEGMENTATION BASED ON SEQUENTIAL MONTE-CARLO ESTIMATION
Directory of Open Access Journals (Sweden)
Zdenek Prochazka
2014-12-01
Full Text Available In recent years, many researchers and car makers put a lot of intensive effort into development of autonomous driving systems. Since visual information is the main modality used by human driver, a camera mounted on moving platform is very important kind of sensor, and various computer vision algorithms to handle vehicle surrounding situation are under intensive research. Our final goal is to develop a vision based lane detection system with ability to handle various types of road shapes, working on both structured and unstructured roads, ideally under presence of shadows. This paper presents a modified road region segmentation algorithm based on sequential Monte-Carlo estimation. Detailed description of the algorithm is given, and evaluation results show that the proposed algorithm outperforms the segmentation algorithm developed as a part of our previous work, as well as an conventional algorithm based on colour histogram.
Directory of Open Access Journals (Sweden)
Sheng Wanxing
2016-01-01
Full Text Available In allusion to the randomness of output power of distributed generation (DG, a reliability evaluation model based on sequential Monte Carlo simulation (SMCS for distribution system with DG is proposed. Operating states of the distribution system can be sampled by SMCS in chronological order thus the corresponding output power of DG can be generated. The proposed method has been tested on feeder F4 of IEEE-RBTS Bus 6. The results show that reliability evaluation of distribution system considering the uncertainty of output power of DG can be effectively implemented by SMCS.
A Sequential Quadratic Programming Algorithm Using An Incomplete Solution of the Subproblem
1993-05-01
Research Stanford University tDept. de Estadistica y Econometria Universidad Carlos III de Madrid Abstract We analyze sequential quadratic programming...xEK- NP s.t. c(x) > 0, where F : R " --+ R and c : R1 --+ Rm. Since we shall not assume second derivatives are known, computing x*, a point satisfying...algorithm We first present an outline of the algorithm. Given Ho positive definite, z0 and A0, select P-1 0 O, 0 < a < r 1,/7 < > jc-(xo)lo,, _ Ž IIAoll
A sequential quadratic programming algorithm using an incomplete solution of the subproblem
Energy Technology Data Exchange (ETDEWEB)
Murray, W. [Stanford Univ., CA (United States). Systems Optimization Lab.; Prieto, F.J. [Universidad `Carlos III` de Madrid (Spain). Dept. de Estadistica y Econometria
1993-05-01
We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed; in particular, it is not assumed that the iterates lie on a compact set.
Adaptive link selection algorithms for distributed estimation
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Distributed Multiuser Sequential Channel Sensing Schemes in Multichannel Cognitive Radio Networks
Shokri-Ghadikolaei, Hossein; Nasiri-Kenari, Masoumeh
2012-01-01
Effective spectrum sensing strategies enable cognitive radios (CRs) to identify and opportunistically transmit on under-utilized spectral resources. In this paper, sequential channel sensing problems for single and multiple secondary users (SUs) cases are effectively modeled through finite state Markovian processes. More specifically, a model for single user case is introduced and validated through analytical analysis. In order to address multiple SUs, this model is extended to modified p-persistent access (MPPA) and its generalized version. While the introduced analytical framework facilitates the process of performance evaluation, these algorithms experience a high level of collision among the SUs. To mitigate this problem appropriately, p-persistent random access (PPRA) scheme is proposed, which offers higher average throughput for SUs by statistically distributing their loads among all channels. The structure and performance of the proposed schemes are discussed in detail, and a set of illustrative numeri...
Distributed algorithms for resource allocation and routing
Hu, Zengjian
2007-01-01
In this thesis, we study distributed algorithms in the context of two fundamental problems in distributed systems, resource allocation and routing. Resource allocation studies how to distribute workload evenly to resources. We consider two different resource allocation models, the diffusive load balancing and the weighted balls-into-bins games. Routing studies how to deliver messages from source to estination efficiently. We design routing algorithms for broadcasting and gossiping in ad hoc n...
Confidence limits for the mean of exponential distribution in any time-sequential samples
Institute of Scientific and Technical Information of China (English)
CHEN Jiading; FANG Xiangzhong
2005-01-01
We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replacement. Especially, we give the best lower confidence limits in the case of no failure data.
Distributed algorithms over communicating membrane systems.
Ciobanu, Gabriel
2003-07-01
This paper presents fundamental distributed algorithms over membrane systems with antiport carriers. We describe distributed algorithms for collecting and dispersing information, leader election in these systems, and the mutual exclusion problem. Finally, we consider membrane systems producing correct results despite some failures at some of the components or the communication links. We show that membrane systems with antiport carriers provide an appropriate model for distributed computing, particularly for message-passing algorithms interpreted here as membrane transport in both directions, namely when two chemicals behave as input and output messages and pass the membranes in both directions using antiport carriers.
Distribution Bottlenecks in Classification Algorithms
Zwartjes, G.J.; Havinga, P.J.M.; Smit, G.J.M.; Hurink, J.L.
2012-01-01
The abundance of data available on Wireless Sensor Networks makes online processing necessary. In industrial applications for example, the correct operation of equipment can be the point of interest while raw sampled data is of minor importance. Classiﬁcation algorithms can be used to make state cla
Energy Technology Data Exchange (ETDEWEB)
Karpinski, M. [Univ. of Bonn (Germany); Larmore, L.L. [Univ. of Nevada, Las Vegas, NV (United States); Rytter, W. [Warsaw Univ. (Poland)
1996-12-31
A sublinear time subquadratic work parallel algorithm for construction of an optimal binary search tree, in a special case of practical interest, namely where the frequencies of items to be stored are not too small, is given. A sublinear time subquadratic work parallel algorithm for construction of an approximately optimal binary search tree in the general case is also given. Sub-quadratic work and sublinear time are achieved using a fast parallel algorithm for the column minima problem for Monge matrices developed by Atallah and Kosaraju. The algorithms given in this paper take O(n{sup 0.6}) time with n processors in the CREW PRAM model. Our 29orithms work well if every subtree of the optimal binary search tree of depth {Omega}(log n) has o(n) leaves. We prove that there is a sequential algorithm with subquadratic average-case complexity, by demonstrating that the {open_quotes}small subtree{close_quotes} condition holds with very high probability for a randomly permuted weight sequence. This solves the conjecture posed in liand breaks the quadratic time {open_quotes}barrier{close_quotes} of Knuth`s algorithm. This algorithm can also be parallelized to run in average sublinear time with n processors.
Jawarneh, Sana; Abdullah, Salwani
2015-01-01
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
Directory of Open Access Journals (Sweden)
A. Belloufi*
2013-01-01
Full Text Available The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.
A Distributed and Deterministic TDMA Algorithm for Write-All-With-Collision Model
Arumugam, Mahesh
2008-01-01
Several self-stabilizing time division multiple access (TDMA) algorithms are proposed for sensor networks. In addition to providing a collision-free communication service, such algorithms enable the transformation of programs written in abstract models considered in distributed computing literature into a model consistent with sensor networks, i.e., write all with collision (WAC) model. Existing TDMA slot assignment algorithms have one or more of the following properties: (i) compute slots using a randomized algorithm, (ii) assume that the topology is known upfront, and/or (iii) assign slots sequentially. If these algorithms are used to transform abstract programs into programs in WAC model then the transformed programs are probabilistically correct, do not allow the addition of new nodes, and/or converge in a sequential fashion. In this paper, we propose a self-stabilizing deterministic TDMA algorithm where a sensor is aware of only its neighbors. We show that the slots are assigned to the sensors in a concu...
Learning theory of distributed spectral algorithms
Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan
2017-07-01
Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms.
Step Size Bound of the Sequential Partial Update LMS Algorithm with Periodic Input Signals
Directory of Open Access Journals (Sweden)
Pedro Ramos
2006-12-01
Full Text Available This paper derives an upper bound for the step size of the sequential partial update (PU LMS adaptive algorithm when the input signal is a periodic reference consisting of several harmonics. The maximum step size is expressed in terms of the gain in step size of the PU algorithm, defined as the ratio between the upper bounds that ensure convergence in the following two cases: firstly, when only a subset of the weights of the filter is updated during every iteration; and secondly, when the whole filter is updated at every cycle. Thus, this gain in step-size determines the factor by which the step size parameter can be increased in order to compensate the inherently slower convergence rate of the sequential PU adaptive algorithm. The theoretical analysis of the strategy developed in this paper excludes the use of certain frequencies corresponding to notches that appear in the gain in step size. This strategy has been successfully applied in the active control of periodic disturbances consisting of several harmonics, so as to reduce the computational complexity of the control system without either slowing down the convergence rate or increasing the residual error. Simulated and experimental results confirm the expected behavior.
Performance of a Distributed Stochastic Approximation Algorithm
Bianchi, Pascal; Hachem, Walid
2012-01-01
In this paper, a distributed stochastic approximation algorithm is studied. Applications of such algorithms include decentralized estimation, optimization, control or computing. The algorithm consists in two steps: a local step, where each node in a network updates a local estimate using a stochastic approximation algorithm with decreasing step size, and a gossip step, where a node computes a local weighted average between its estimates and those of its neighbors. Convergence of the estimates toward a consensus is established under weak assumptions. The approach relies on two main ingredients: the existence of a Lyapunov function for the mean field in the agreement subspace, and a contraction property of the random matrices of weights in the subspace orthogonal to the agreement subspace. A second order analysis of the algorithm is also performed under the form of a Central Limit Theorem. The Polyak-averaged version of the algorithm is also considered.
Synthesis of sequential control algorithms for pneumatic drives controlled by monostable valves
Directory of Open Access Journals (Sweden)
Ł. Dworzak
2009-07-01
Full Text Available Application of the Grafpol method [1] for synthesising sequential control algorithms for pneumatic drives controlled by monostable valves is presented. The developed principles simplify the MTS method of programming production processes in the scope of the memory realisation [2]. Thanks to this, time for synthesising the schematic equation can be significantly reduced in comparison to the network transformation method [3]. The designed schematic equation makes a ground for writing an application program of a PLC using any language defined in IEC 61131-3.
Distributed Algorithms in Wireless Sensor Networks
Nieberg, Tim; Broersma, Hajo; Faigle, Ulrich; Hurink, Johann; Pickl, Stefan; Woeginger, Gerhard
2003-01-01
Wireless sensor networks (WSNs) are an emerging field of research which combines many challenges in distributed computing and network optimization. One important goal is to improve the functional lifetime of the sensor network using energy-efficient distributed algorithms, networking and routing tec
Advanced algorithms for distributed fusion
Gelfand, A.; Smith, C.; Colony, M.; Bowman, C.; Pei, R.; Huynh, T.; Brown, C.
2008-03-01
The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.
Kirk, M S; Jackiewicz, J; McNamara, B J; McAteer, R T J
2011-01-01
We present a new automated algorithm to identify, track, and characterize small-scale brightening associated with solar eruptive phenomena observed in H{\\alpha}. The temporal spatially-localized changes in chromospheric intensities can be separated into two categories: flare ribbons and sequential chromospheric brightenings (SCBs). Within each category of brightening we determine the smallest resolvable locus of pixels, a kernel, and track the temporal evolution of the position and intensity of each kernel. This tracking is accomplished by isolating the eruptive features, identifying kernels, and linking detections between frames into trajectories of kernels. We fully characterize the evolving intensity and morphology of the flare ribbons by observing the tracked flare kernels in aggregate. With the location of SCB and flare kernels identified, they can easily be overlaid on top of complementary data sets to extract Doppler velocities and magnetic field intensities underlying the kernels. This algorithm is ad...
Iterative quantum algorithm for distributed clock synchronization
Institute of Scientific and Technical Information of China (English)
Wang Hong-Fu; Zhang Shou
2012-01-01
Clock synchronization is a well-studied problem with many practical and scientific applications.We propose an arbitrary accuracy iterative quantum algorithm for distributed clock synchronization using only three qubits.The n bits of the time difference △ between two spatially separated clocks can be deterministically extracted by communicating only O(n) messages and executing the quantum iteration process n times based on the classical feedback and measurement operations.Finally,we also give the algorithm using only two qubits and discuss the success probability of the algorithm.
Distribution agnostic structured sparsity recovery algorithms
Al-Naffouri, Tareq Y.
2013-05-01
We present an algorithm and its variants for sparse signal recovery from a small number of its measurements in a distribution agnostic manner. The proposed algorithm finds Bayesian estimate of a sparse signal to be recovered and at the same time is indifferent to the actual distribution of its non-zero elements. Termed Support Agnostic Bayesian Matching Pursuit (SABMP), the algorithm also has the capability of refining the estimates of signal and required parameters in the absence of the exact parameter values. The inherent feature of the algorithm of being agnostic to the distribution of the data grants it the flexibility to adapt itself to several related problems. Specifically, we present two important extensions to this algorithm. One extension handles the problem of recovering sparse signals having block structures while the other handles multiple measurement vectors to jointly estimate the related unknown signals. We conduct extensive experiments to show that SABMP and its variants have superior performance to most of the state-of-the-art algorithms and that too at low-computational expense. © 2013 IEEE.
Mining The Data From Distributed Database Using An Improved Mining Algorithm
Renjit, J Arokia
2010-01-01
Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an Improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a centre site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in d...
Precise algorithm to generate random sequential addition of hard hyperspheres at saturation.
Zhang, G; Torquato, S
2013-11-01
The study of the packing of hard hyperspheres in d-dimensional Euclidean space R^{d} has been a topic of great interest in statistical mechanics and condensed matter theory. While the densest known packings are ordered in sufficiently low dimensions, it has been suggested that in sufficiently large dimensions, the densest packings might be disordered. The random sequential addition (RSA) time-dependent packing process, in which congruent hard hyperspheres are randomly and sequentially placed into a system without interparticle overlap, is a useful packing model to study disorder in high dimensions. Of particular interest is the infinite-time saturation limit in which the available space for another sphere tends to zero. However, the associated saturation density has been determined in all previous investigations by extrapolating the density results for nearly saturated configurations to the saturation limit, which necessarily introduces numerical uncertainties. We have refined an algorithm devised by us [S. Torquato, O. U. Uche, and F. H. Stillinger, Phys. Rev. E 74, 061308 (2006)] to generate RSA packings of identical hyperspheres. The improved algorithm produce such packings that are guaranteed to contain no available space in a large simulation box using finite computational time with heretofore unattained precision and across the widest range of dimensions (2≤d≤8). We have also calculated the packing and covering densities, pair correlation function g(2)(r), and structure factor S(k) of the saturated RSA configurations. As the space dimension increases, we find that pair correlations markedly diminish, consistent with a recently proposed "decorrelation" principle, and the degree of "hyperuniformity" (suppression of infinite-wavelength density fluctuations) increases. We have also calculated the void exclusion probability in order to compute the so-called quantizer error of the RSA packings, which is related to the second moment of inertia of the average
A general algorithm for distributing information in a graph
Aji, Srinivas M.; McEliece, Robert J.
1997-01-01
We present a general “message-passing” algorithm for distributing information in a graph. This algorithm may help us to understand the approximate correctness of both the Gallager-Tanner-Wiberg algorithm, and the turbo-decoding algorithm.
Liu, Wei; Ma, Shunjian; Sun, Mingwei; Yi, Haidong; Wang, Zenghui; Chen, Zengqiang
2016-08-01
Path planning plays an important role in aircraft guided systems. Multiple no-fly zones in the flight area make path planning a constrained nonlinear optimization problem. It is necessary to obtain a feasible optimal solution in real time. In this article, the flight path is specified to be composed of alternate line segments and circular arcs, in order to reformulate the problem into a static optimization one in terms of the waypoints. For the commonly used circular and polygonal no-fly zones, geometric conditions are established to determine whether or not the path intersects with them, and these can be readily programmed. Then, the original problem is transformed into a form that can be solved by the sequential quadratic programming method. The solution can be obtained quickly using the Sparse Nonlinear OPTimizer (SNOPT) package. Mathematical simulations are used to verify the effectiveness and rapidity of the proposed algorithm.
Institute of Scientific and Technical Information of China (English)
YANG Jian-qiu; WANG Yan-rong
2011-01-01
Several structural design parameters for the description of the geometric features of a hollow fan blade were determined. A structural design optimization model of a hollow fan blade which based on the strength constraint and minimum mass was established based on the finite element method through these parameters. Then, the sequential quadratic programming algorithm was employed to search the optimal solutions. Several groups of value for initial design variables were chosen, for the purpose of not only finding much more local optimal results but also analyzing which discipline that the variables according to could be benefit for the convergence and robustness. Response surface method and Monte Carlo simulations were used to analyze whether the objective function and constraint function are sensitive to the variation of variables or not. Then the robust results could be found among a group of different local optimal solutions.
A Survey of Distributed Data Aggregation Algorithms
Jesus, Paulo; Almeida, Paulo Sérgio
2011-01-01
Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizin...
Quantum algorithms for testing properties of distributions
Bravyi, Sergey; Hassidim, Avinatan
2009-01-01
Suppose one has access to oracles generating samples from two unknown probability distributions P and Q on some N-element set. How many samples does one need to test whether the two distributions are close or far from each other in the L_1-norm ? This and related questions have been extensively studied during the last years in the field of property testing. In the present paper we study quantum algorithms for testing properties of distributions. It is shown that the L_1-distance between P and Q can be estimated with a constant precision using approximately N^{1/2} queries in the quantum settings, whereas classical computers need \\Omega(N) queries. We also describe quantum algorithms for testing Uniformity and Orthogonality with query complexity O(N^{1/3}). The classical query complexity of these problems is known to be \\Omega(N^{1/2}).
Cooperated Bayesian algorithm for distributed scheduling problem
Institute of Scientific and Technical Information of China (English)
QIANG Lei; XIAO Tian-yuan
2006-01-01
This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving NP scheduling problems.The proposed approach integrates BOA into the co-evolutionary schema,which builds up a concurrent computing environment.A new search strategy is also introduced for local optimization process.It integrates the reinforcement learning(RL) mechanism into the BOA search processes,and then uses the mixed probability information from BOA (post-probability) and RL (pre-probability) to enhance the cooperation between different local controllers,which improves the optimization ability of the algorithm.The experiment shows that the new algorithm does better in both optimization (2.2%) and convergence (11.7%),compared with classic BOA.
Gossip Algorithms for Distributed Signal Processing
Dimakis, Alexandros G; Moura, Jose M F; Rabbat, Michael G; Scaglione, Anna
2010-01-01
Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
Pruning Neural Networks with Distribution Estimation Algorithms
Energy Technology Data Exchange (ETDEWEB)
Cantu-Paz, E
2003-01-15
This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.
Parsons, T.; Blakely, R.J.; Brocher, T.M.
2001-01-01
The geologic structure of the Earth's upper crust can be revealed by modeling variation in seismic arrival times and in potential field measurements. We demonstrate a simple method for sequentially satisfying seismic traveltime and observed gravity residuals in an iterative 3-D inversion. The algorithm is portable to any seismic analysis method that uses a gridded representation of velocity structure. Our technique calculates the gravity anomaly resulting from a velocity model by converting to density with Gardner's rule. The residual between calculated and observed gravity is minimized by weighted adjustments to the model velocity-depth gradient where the gradient is steepest and where seismic coverage is least. The adjustments are scaled by the sign and magnitude of the gravity residuals, and a smoothing step is performed to minimize vertical streaking. The adjusted model is then used as a starting model in the next seismic traveltime iteration. The process is repeated until one velocity model can simultaneously satisfy both the gravity anomaly and seismic traveltime observations within acceptable misfits. We test our algorithm with data gathered in the Puget Lowland of Washington state, USA (Seismic Hazards Investigation in Puget Sound [SHIPS] experiment). We perform resolution tests with synthetic traveltime and gravity observations calculated with a checkerboard velocity model using the SHIPS experiment geometry, and show that the addition of gravity significantly enhances resolution. We calculate a new velocity model for the region using SHIPS traveltimes and observed gravity, and show examples where correlation between surface geology and modeled subsurface velocity structure is enhanced.
Uniform wire segmentation algorithm of distributed interconnects
Institute of Scientific and Technical Information of China (English)
Yin Guoli; Lin Zhenghui
2007-01-01
A uniform wire segmentation algorithm for performance optimization of distributed RLC interconnects was proposed in this paper. The optimal wire length for identical segments and buffer size for buffer insertion are obtained through computation and derivation, based on a 2-pole approximation model of distributed RLC interconnect. For typical inductance value and long wires under 180nm technology, experiments show that the uniform wire segmentation technique proposed in the paper can reduce delay by about 27% ～ 56% , while requires 34%～69% less total buffer usage and thus 29% to 58% less power consumption. It is suitable for long RLC interconnect performance optimization.
An algorithm for sequential tail value at risk for path-independent payoffs in a binomial tree
Roorda, Berend
2010-01-01
We present an algorithm that determines Sequential Tail Value at Risk (STVaR) for path-independent payoffs in a binomial tree. STVaR is a dynamic version of Tail-Value-at-Risk (TVaR) characterized by the property that risk levels at any moment must be in the range of risk levels later on. The algori
REVIEW OF CHECKPOINTING ALGORITHMS IN DISTRIBUTED SYSTEMS
Directory of Open Access Journals (Sweden)
Poonam Gahlan
2010-06-01
Full Text Available Checkpointing is the process of saving the status information. Checkpoint is defined as a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. Mobile computing raises many new issues such as lack of stablestorage, low bandwidth of wireless channel, high mobility, and limited battery life. Coordinated checkpointing is an attractive approach for transparently adding fault tolerance to distributed applications since it avoids domino effects and minimizes the stable storage requirement. This paper presents the review of the algorithms,which have been reported in the literature for checkpointing. This paper also covers backward error recovery techniques for distributed systems specially the distributed mobile systems.
An Efficient Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...
A Scalable Local Algorithm for Distributed Multivariate Regression
National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...
Dynamic Topology Re-Configuration in Multihop Cellular Networks Using Sequential Genetic Algorithm
Directory of Open Access Journals (Sweden)
B.Shantha Kumari
2014-10-01
Full Text Available Cellular communications has experienced explosive growth in the past two decades. Today millions of people around the world use cellular phones. Cellular phones allow a person to make or receive a call from almost anywhere. Likewise, a person is allowed to continue the phone conversation while on the move. Cellular communications is supported by an infrastructure called a cellular network, which integrates cellular phones into the public switched telephone network. The cellular network has gone through three generations.The first generation of cellular networks is analog in nature. To accommodate more cellular phone subscribers, digital TDMA (time division multiple access and CDMA (code division multiple access technologies are used in the second generation (2G to increase the network capacity. With digital technologies, digitized voice can be coded and encrypted. Therefore, the 2G cellular network is also more secure. The third generation (3G integrates cellular phones into the Internet world by providing highspeed packet-switching data transmission in addition to circuit-switching voice transmission. The 3G cellular networks have been deployed in some parts of Asia, Europe, and the United States since 2002 and will be widely deployed in the coming years. The high increase in traffic and data rate for future generations of mobile communication systems, with simultaneous requirement for reduced power consumption, makes Multihop Cellular Networks (MCNs an attractive technology. To exploit the potentials of MCNs a new network paradigm is proposed in this paper. In addition, a novel sequential genetic algorithm (SGA is proposed as a heuristic approximation to reconfigure the optimum relaying topology as the network traffic changes. Network coding is used to combine the uplink and downlink transmissions, and incorporate it into the optimum bidirectional relaying with ICI awareness. Numerical results have shown that the algorithms suggested in this
Vuković, Najdan; Miljković, Zoran
2013-10-01
Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
New Algorithms and Lower Bounds for Sequential-Access Data Compression
Gagie, Travis
2009-02-01
This thesis concerns sequential-access data compression, i.e., by algorithms that read the input one or more times from beginning to end. In one chapter we consider adaptive prefix coding, for which we must read the input character by character, outputting each character's self-delimiting codeword before reading the next one. We show how to encode and decode each character in constant worst-case time while producing an encoding whose length is worst-case optimal. In another chapter we consider one-pass compression with memory bounded in terms of the alphabet size and context length, and prove a nearly tight tradeoff between the amount of memory we can use and the quality of the compression we can achieve. In a third chapter we consider compression in the read/write streams model, which allows us passes and memory both polylogarithmic in the size of the input. We first show how to achieve universal compression using only one pass over one stream. We then show that one stream is not sufficient for achieving good grammar-based compression. Finally, we show that two streams are necessary and sufficient for achieving entropy-only bounds.
Hand, Troy; Feron, Eric
2010-01-01
This paper discusses the effect of sequential conflict resolution maneuvers of an infinite aircraft flow through a finite control volume. Aircraft flow models are utilized to simulate traffic flows and determine stability. Pseudo-random flow geometry is considered to determine airspace stability in a more random airspace, where aircraft flows are spread over a given positive width. The use of this aircraft flow model generates a more realistic flow geometry. A set of upper bounds on the maximal aircraft deviation during conflict resolution is derived. Also with this flow geometry it is proven that these bounds are not symmetric, unlike the symmetric bounds derived in previous papers for simpler flow configurations. Stability is preserved under sequential conflict resolution algorithms for all flow geometries discussed in this paper.
Energy Technology Data Exchange (ETDEWEB)
Wohletz, K.H. (Earth and Space Science Division Los Alamos National Laboratory, New Mexico (USA)); Sheridan, M.F. (Department of Geology, Arizona State University, Tempe (USA)); Brown, W.K. (Math/Science Division, Lassen College, Susanville, California (USA))
1989-11-10
The assumption that distributions of mass versus size interval for fragmented materials fit the log normal distribution is empirically based and has historical roots in the late 19th century. Other often used distributions (e.g., Rosin-Rammler, Weibull) are also empirical and have the general form for mass per size interval: {ital n}({ital l})={ital kl}{sup {alpha}} exp(-{ital l}{beta}), where {ital n}({ital l}) represents the number of particles of diameter {ital l}, {ital l} is the normalized particle diameter, and {ital k}, {alpha}, and {beta} are constants. We describe and extend the sequential fragmentation distribution to include transport effects upon observed volcanic ash size distributions. The sequential fragmentation/transport (SFT) distribution is also of the above mathematical form, but it has a physical basis rather than empirical. The SFT model applies to a particle-mass distribution formed by a sequence of fragmentation (comminution) and transport (size sorting) events acting upon an initial mass {ital m}{prime}: {ital n}({ital x}, {ital m})={ital C} {integral}{integral} {ital n}({ital x}{prime}, {ital m}{prime}){ital p}({xi}) {ital dx}{prime} {ital dm}{prime}, where {ital x}{prime} denotes spatial location along a linear axis, {ital C} is a constant, and integration is performed over distance from an origin to the sample location and mass limits from 0 to {ital m}.
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel
Shekhovtsov, Alexander
2011-01-01
We develop a novel distributed algorithm for the minimum cut problem. We primarily aim at solving large sparse problems. Assuming vertices of the graph are partitioned into several regions, the algorithm performs path augmentations inside the regions and updates of the push-relabel style between the regions. The interaction between regions is considered expensive (regions are loaded into the memory one-by-one or located on separate machines in a network). The algorithm works in sweeps - passes over all regions. Let $B$ be the set of vertices incident to inter-region edges of the graph. We present a sequential and parallel versions of the algorithm which terminate in at most $2|B|^2+1$ sweeps. The competing algorithm by Delong and Boykov uses push-relabel updates inside regions. In the case of a fixed partition we prove that this algorithm has a tight $O(n^2)$ bound on the number of sweeps, where $n$ is the number of vertices. We tested sequential versions of the algorithms on instances of maxflow problems in ...
An Improved Harmony Search Algorithm for Power Distribution Network Planning
Directory of Open Access Journals (Sweden)
Wei Sun
2015-01-01
Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
A Flexible Multi-Agent Algorithm for Fast Restoration of Distribution Systems, Compatible with GIS
Directory of Open Access Journals (Sweden)
Vahid Motaghi
2014-07-01
Full Text Available Power distribution network configuration must be restructured fast after occurring a fault in order to restoring healthy parts. A logical algorithm is needed to do such restoration. In recent years, by developing GIS systems in distribution networks, data entering and structuring in computers has been changed and dissimilar to conventional softwares, computer cannot view the network configuration. In this paper a proper definition of network agents are suggested in matrix format. By such definition fast detection of fault location and deenergized sections located at its downstream parts is done. Also curtailed load is calculated. Finally with digital data available in the GIS databases, optimal restoration strategy is selected and applied based on available routs. The proposed algorithm is applied on a complicated three feeder distribution network to test its effectiveness and speed. Simulations in MATLAB shows that the proposed algorithm is very fast and robust to any complicated network configuration as well as any number and location on sequential faults.
Energy Technology Data Exchange (ETDEWEB)
Huppertz, A. [Charite Universitaetsklinikum Berlin, Campus Mitte (Germany). Dept. of Radiology; Charite Universitaetsklinikum Berlin (Germany). Imaging Science Inst.; Schmidt, M.; Schoeffski, O. [Erlangen-Nuernberg Univ. (Germany). Inst. for Health Management; Wagner, M.; Asbach, P.; Maurer, M.H. [Charite Universitaetsklinikum Berlin, Campus Mitte (Germany). Dept. of Radiology; Puettcher, O. [Vivantes Klinikum im Friedrichshain, Berlin (Germany). Dept. of Radiology; Strassburg, J. [Vivantes Klinikum im Friedrichshain, Berlin (Germany). Dept. of Surgery; Stoeckmann, F. [Vivantes Klinikum im Friedrichshain, Berlin (Germany). Dept. of Gastroenterology
2010-09-15
Purpose: To compare the direct costs of two diagnostic algorithms for pretherapeutic TNM staging of rectal cancer. Materials and Methods: In a study including 33 patients (mean age: 62.5 years), the direct fixed and variable costs of a sequential multimodal algorithm (rectoscopy, endoscopic and abdominal ultrasound, chest X-ray, thoracic/abdominal CT in the case of positive findings in abdominal ultrasound or chest X-ray) were compared to those of a novel algorithm of rectoscopy followed by MRI using a whole-body scanner. MRI included T 2w sequences of the rectum, 3D T 1w sequences of the liver and chest after bolus injection of gadoxetic acid, and delayed phases of the liver. The personnel work times, material items, and work processes were tracked to the nearest minute by interviewing those responsible for the process (surgeon, gastroenterologist, two radiologists). The costs of labor and materials were determined from personnel reimbursement data and hospital accounting records. Fixed costs were determined from vendor pricing. Results: The mean MRI time was 55 min. CT was performed in 19 / 33 patients (57 %) causing an additional day of hospitalization (costs 374 Euro). The costs for equipment and material were higher for MRI compared to sequential algorithm (equipment 116 vs. 30 Euro; material 159 vs. 60 Euro per patient). The personnel costs were markedly lower for MRI (436 vs. 732 Euro per patient). Altogether, the absolute cost advantage of MRI was 31.3 % (711 vs. 1035 Euro for sequential algorithm). Conclusion: Substantial savings are achievable with the use of whole-body MRI for the preoperative TNM staging of patients with rectal cancer. (orig.)
Wohletz, K. H.; Sheridan, M. F.; Brown, W. K.
1989-11-01
The assumption that distributions of mass versus size interval for fragmented materials fit the log normal distribution is empirically based and has historical roots in the late 19th century. Other often used distributions (e.g., Rosin-Rammler, Weibull) are also empirical and have the general form for mass per size interval: n(l) = klα exp (-lβ), where n(l) represents the number of particles of diameter l, l is the normalized particle diameter, and k, α, and β are constants. We describe and extend the sequential fragmentation distribution to include transport effects upon observed volcanic ash size distributions. The sequential fragmentation/transport (SFT) distribution is also of the above mathematical form, but it has a physical basis rather than empirical. The SFT model applies to a particle-mass distribution formed by a sequence of fragmentation (comminution) and transport (size sorting) events acting upon an initial mass m': n(x, m) = C ∫∫ n(x', m')p(ξ)dx' dm', where x' denotes spatial location along a linear axis, C is a constant, and integration is performed over distance from an origin to the sample location and mass limits from 0 to m. We show that the probability function that models the production of particles of different size from an initial mass and sorts that distribution, p(ξ), is related to mg, where g (noted as γ for fragmentation processes) is a free parameter that determines the location, breadth, and skewness of the distribution; g(γ) must be greater than -1, and it increases from that value as the distribution matures with greater number of sequential steps in the fragmentation or transport process; γ is expected to be near -1 for "sudden" fragmentation mechanisms such as single-event explosions and transport mechanisms that are functionally dependent upon particle mass. This free parameter will be more positive for evolved fragmentation mechanisms such as ball milling and complex transport processes such as saltation. The SFT
Directory of Open Access Journals (Sweden)
S. J. Noh
2011-10-01
Full Text Available Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP, is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF and the sequential importance resampling (SIR particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2009-01-01
This paper addresses the unit commitment (UC) in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which implies that it is difficult to determine the relative cost...... efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce...... the dimension of the UC problem and dynamic regrouping is used to improve the solution quality. Numerical results based on real-life data sets show that this algorithm is efficient and optimal or near-optimal solutions with very small optimality gap are obtained....
Lee, Jin-Ho; Kim, Dong-Jin; Ahn, Byung-Koo
2015-06-01
The objectives of this study were to investigate the distribution of thallium in soils collected near suspected areas such as cement plants, active and closed mines, and smelters and to examine the extraction of thallium in the soils using 19 single chemical and sequential chemical extraction procedures. Thallium concentrations in soils near cement plants were distributed between 1.20 and 12.91 mg kg(-1). However, soils near mines and smelters contained relatively low thallium concentrations ranging from 0.18 to 1.09 mg kg(-1). Thallium extractability with 19 single chemical extractants from selected soils near cement plants ranged from 0.10% to 8.20% of the total thallium concentration. In particular, 1.0 M NH4Cl, 1.0 M (NH4)2SO4, and 1.0 M CH3COONH4 extracted more thallium than other extractants. Sequential fractionation results of thallium from different soils such as industrially and artificially contaminated soils varied with the soil properties, especially soil pH and the duration of thallium contamination.
Cooperative distributed target tracking algorithm in mobile wireless sensor networks
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
The paper proposes a cooperative distributed target tracking algorithm in mobile wireless sensor networks.There are two main components in the algorithm:distributed sensor-target assignment and sensor motion control.In the key idea of the sensor-target assignment,sensors are considered as autonomous agents and the defined objective function of each sensor concentrates on two fundamental factors:the tracking accuracy and the tracking cost.Compared with the centralized algorithm and the noncooperative distrib...
A Multiple Pattern Matching Algorithm Based on Sequential Binary Tree%基于有序二叉树的多模式匹配算法
Institute of Scientific and Technical Information of China (English)
胡佩华; 王永成; 刘功申
2002-01-01
By analyzing the multiple pattern matching algorithm based on tree structure, a multiple pattern matching algorithm based on sequential binary tree is proposed in this paper. It is proved by experiment that the algorithm has three features: its constructing process is quick. Its cost of memory is small. At the same time, its searching process is as quickly as the traditional algorithm. The algorithm proposed in this paper is suit for the application whose pattern set is changing dynamically, that is to say, it is suit for the application whose automata must be constructed dynamically. So, the algorithm has a good application prospect.
Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network
Directory of Open Access Journals (Sweden)
T. Panigrahi
2012-01-01
Full Text Available In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS and block incremental least mean square (BILMS by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.
Directory of Open Access Journals (Sweden)
S. J. Noh
2011-04-01
Full Text Available Applications of data assimilation techniques have been widely used to improve hydrologic prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC methods, known as "particle filters", provide the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response time of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on Markov chain Monte Carlo (MCMC is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, WEP is implemented for the sequential data assimilation through the updating of state variables. Particle filtering is parallelized and implemented in the multi-core computing environment via open message passing interface (MPI. We compare performance results of particle filters in terms of model efficiency, predictive QQ plots and particle diversity. The improvement of model efficiency and the preservation of particle diversity are found in the lagged regularized particle filter.
Nguyen, Robert; Masani, Kei; Micera, Silvestro; Morari, Manfred; Popovic, Milos R
2011-12-01
Functional electrical stimulation (FES) is limited by the rapid onset of muscle fatigue caused by localized nerve excitation repeatedly activating only a subset of motor units. The purpose of this study was to investigate reducing fatigue by sequentially changing, pulse by pulse, the area of stimulation using multiple surface electrodes that cover the same area as one electrode during conventional stimulation. Paralyzed triceps surae muscles of an individual with complete spinal cord injury were stimulated, via the tibial nerve, through four active electrodes using spatially distributed sequential stimulation (SDSS) that was delivered by sending a stimulation pulse to each electrode one after another with 90° phase shift between successive electrodes. For comparison, single electrode stimulation was delivered through one active electrode. For both modes of stimulation, the resultant frequency to the muscle as a whole was 40 Hz. Isometric ankle torque was measured during fatiguing stimulations lasting 2 min. Each mode of stimulation was delivered a total of six times over 12 separate days. Three fatigue measures were used for comparison: fatigue index (final torque normalized to maximum torque), fatigue time (time for torque to drop by 3 dB), and torque-time integral (over the entire trial). The measures were all higher during SDSS (P < 0.001), by 234, 280, and 171%, respectively. The results are an encouraging first step toward addressing muscle fatigue, which is one of the greatest problems for FES.
Black-Box Algorithms for Sampling from Continuous Distributions
Hörmann, Wolfgang; Leydold, Josef
2006-01-01
For generating non-uniform random variates, black-box algorithms are powerful tools that allow drawing samples from large classes of distributions. We give an overview of the design principles of such methods and show that they have advantages compared to specialized algorithms even for standard distributions, e.g., the marginal generation times are fast and depend mainly on the chosen method and not on the distribution. Moreover these methods are suitable for specialized tasks like sampling ...
Logistics distribution centers location problem and algorithm under fuzzy environment
Yang, Lixing; Ji, Xiaoyu; Gao, Ziyou; Li, Keping
2007-11-01
Distribution centers location problem is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. This paper mainly investigates this problem under fuzzy environment. Consequentially, chance-constrained programming model for the problem is designed and some properties of the model are investigated. Tabu search algorithm, genetic algorithm and fuzzy simulation algorithm are integrated to seek the approximate best solution of the model. A numerical example is also given to show the application of the algorithm.
Distributed multicast routing algorithm with dynamic performance in multimedia networks
Institute of Scientific and Technical Information of China (English)
Zhu Baoping; Zhang Kun
2009-01-01
Tbe delay and DVBMT problem is known to be NP-complete. In this paper, an efficient distributed dynamic multicast muting algorithm was proposed to produce muting trees with delay and delay variation constraints. The pro-posed algorithm is fully distributed, and supports the dynamic reorganizing of the muhicast tree in response to changes for the destination. Simulations demonstrate that our algorithm is better in terms of tree delay and muting success ratio as compared with other existing algorithms, and performs excellently in delay variation performance under lower time complexity, which ensures it to support the requirements of real-time multimedia communications more effectively.
A distributed deadlock detection algorithm for mobile computing system
Institute of Scientific and Technical Information of China (English)
CHENG Xin; LIU Hong-wei; ZUO De-cheng; JIN Feng; YANG Xiao-zong
2005-01-01
The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1 ) the blocked nodes inform their predecessors and successors simultaneously; 2 ) the detection messages ( agents )hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n - 2 steps and with ( n2 - n - 2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.
Institute of Scientific and Technical Information of China (English)
Giada Sebastiani; Alessandro Vario; Maria Guido; Alfredo Alberti
2007-01-01
AIM: To assess the performance of several noninvasive markers and of our recently proposed stepwise combination algorithms to diagnose significant fibrosis (F ≥ 2 by METAVIR) and cirrhosis (F4 by METAVIR) in chronic hepatitis B (CHB).METHODS: One hundred and ten consecutive patients (80 males, 30 females, mean age: 42.6 ± 11.3) with CHB undergoing diagnostic liver biopsy were included. ASTto-Platelet ratio (APRI), Forns' index, AST-to-ALT Ratio,Goteborg University Cirrhosis Index (GUCI), Hui's model and Fibrotest were measured on the day of liver biopsy.The performance of these methods and of sequential algorithms combining Fibrotest, APRI and biopsy was defined by positive (PPV) and negative (NPV) predictive values, accuracy and area under the curve (AUC).RESULTS: PPV for significant fibrosis was excellent (100%) with Forns and high (＞ 92%) with APRI, GUCI,Fibrotest and Hui. However, significant fibrosis could not be excluded by any marker (NPV ＜ 65%). Fibrotest had the best PPV and NPV for cirrhosis (87% and 90%, respectively). Fibrotest showed the best AUC for both significant fibrosis and cirrhosis (0.85 and 0.76,respectively). Stepwise combination algorithms of APRI,Fibrotest and biopsy showed excellent performance (0.96 AUC, 100% NPV) for significant fibrosis and 0.95 AUC,98% NPV for cirrhosis, with 50%-80% reduced need for liver biopsy.CONCLUSION: In CHB sequential combination of APRI,Fibrotest and liver biopsy greatly improves the diagnostic performance of the single non-invasive markers. Need for liver biopsy is reduced by 50%-80% but cannot be completely avoided. Non-invasive markers and biopsy should be considered as agonists and not antagonists towards the common goal of estimating liver fibrosis.
Distribution network planning algorithm based on Hopfield neural network
Institute of Scientific and Technical Information of China (English)
GAO Wei-xin; LUO Xian-jue
2005-01-01
This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.
Novel algorithm for distributed replicas management based on dynamic programming
Institute of Scientific and Technical Information of China (English)
Wang Tao; Lu Xianliang; Hou Mengshu
2006-01-01
Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove the feasibility of the algorithm.
A hybrid evolutionary algorithm for distribution feeder reconfiguration
Institute of Scientific and Technical Information of China (English)
Taher; NIKNAM; Ehsan; AZAD; FARSANI
2010-01-01
This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In SAPSO the learning factors of PSO coevolve with the particles.SFLA is combined with the SAPSO algorithm to improve its performance.The proposed algorithm is tested on two distribution test networks.The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.
Hearing the clusters in a graph: A distributed algorithm
Sahai, Tuhin; Banaszuk, Andrzej
2009-01-01
We propose a novel distributed algorithm to decompose graphs or cluster data. The algorithm recovers the solution obtained from spectral clustering without need for expensive eigenvalue/ eigenvector computations. We demonstrate that by solving the wave equation on the graph, every node can assign itself to a cluster by performing a local fast Fourier transform. We prove the equivalence of our algorithm to spectral clustering, derive convergence rates and demonstrate it on examples.
Parallel Algorithm Design on Some Distributed Systems
Institute of Scientific and Technical Information of China (English)
孙家昶; 张林波; 等
1997-01-01
Some testing results on DAWINING-1000,Paragon and workstation cluster are described in this paper.On the home-made parallel system DAWNING-1000 with 32 computational processors,the practical performance of 1.1777 Gflops and 1.58 Gflops has been measured in solving a dense linear system and doing matrix multiplication,respectively .The scalability is also investigated.The importance of designing efficient parallel algorithms for evaluating parallel systems is emphasized.
Software Model Checking for Verifying Distributed Algorithms
2014-10-28
Verification procedure is an intelligent exhaustive search of the state space of the design Model Checking 6 Verifying Synchronous Distributed App...Distributed App Sagar Chaki, June 11, 2014 © 2014 Carnegie Mellon University Tool Usage Project webpage (http://mcda.googlecode.com) • Tutorial
Economic Models and Algorithms for Distributed Systems
Neumann, Dirk; Altmann, Jorn; Rana, Omer F
2009-01-01
Distributed computing models for sharing resources such as Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. This book intends to discover fresh avenues of research and amendments to existing technologies, aiming at the successful deployment of commercial distributed systems
Distributed algorithm for controlling scaled-free polygonal formations
Garcia de Marina Peinado, Hector; Jayawardhana, Bayu; Cao, Ming
2017-01-01
This paper presents a distributed algorithm for controlling the deployment of a team of agents in order to form a broad class of polygons, including regular ones, where each agent occupies a corner of the polygon. The algorithm shares the properties from the popular distance- based rigid formation c
Sheehan, Sara; Harris, Kelley; Song, Yun S
2013-07-01
Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional sampling distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.
Du, Tingsong; Hu, Yang; Ke, Xianting
2015-01-01
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.
Agent-based Algorithm for Spatial Distribution of Objects
Collier, Nathan
2012-06-02
In this paper we present an agent-based algorithm for the spatial distribution of objects. The algorithm is a generalization of the bubble mesh algorithm, initially created for the point insertion stage of the meshing process of the finite element method. The bubble mesh algorithm treats objects in space as bubbles, which repel and attract each other. The dynamics of each bubble are approximated by solving a series of ordinary differential equations. We present numerical results for a meshing application as well as a graph visualization application.
Directory of Open Access Journals (Sweden)
Thakral B
2006-01-01
Full Text Available Background: HIV/AIDS pandemic brought into focus the importance of safe blood donor pool. Aims: To analyze true seroprevalence of HIV infection in our blood donors and devise an algorithm for donor recall avoiding unnecessary referrals to voluntary counseling and testing centre (VCTC. Materials and Methods: 39,784 blood units were screened for anti-HIV 1/2 using ELISA immunoassay (IA-1. Samples which were repeat reactive on IA-1 were further tested using two different immunoassays (IA-2 and IA-3 and Western blot (WB. Based on results of these sequential IAs and WB, an algorithm for recall of true HIV seroreactive blood donors is suggested for countries like India where nucleic acid testing or p24 antigen assays are not mandatory and given the limited resources may not be feasible. Results: The anti-HIV seroreactivity by repeat IA-1, IA-2, IA-3 and WB were 0.16%, 0.11%, 0.098% and 0.07% respectively. Of the 44 IA-1 reactive samples, 95.2% (20/21 of the seroreactive samples by both IA-2 and IA-3 were also WB positive and 100% (6/6 of the non-reactive samples by these IAs were WB negative. IA signal/cutoff ratio was significantly low in biological false reactive donors. WB indeterminate results were largely due to non-specific reactivity to gag protein (p55. Conclusions: HIV seroreactivity by sequential immunoassays (IA-1, IA-2 and IA-3; comparable to WHO Strategy-III prior to donor recall results in decreased referral to VCTC as compared to single IA (WHO Strategy-I being followed currently in India. Moreover, this strategy will repose donor confidence in our blood transfusion services and strengthen voluntary blood donation program.
A distributed scheduling algorithm for heterogeneous real-time systems
Zeineldine, Osman; El-Toweissy, Mohamed; Mukkamala, Ravi
1991-01-01
Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.
MDSA: Modified Distributed Storage Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Mohamed Labib Borham
2012-10-01
Full Text Available In this paper, we propose a Modified distributed storage algorithm for wireless sensor networks (MDSA. Wireless Sensor Networks, as it is well known, suffer of power limitation, small memory capacity,and limited processing capabilities. Therefore, every node may disappear temporarily or permanently from the network due to many different reasons such as battery failure or physical damage. Since every node collects significant data about its region, it is important to find a methodology to recover these data in case of failure of the source node. Distributed storage algorithms provide reliable access to data through the redundancy spread over individual unreliable nodes. The proposed algorithm uses flooding to spread data over the network and unicasting to provide controlled data redundancy through the network. We evaluate the performance of the proposed algorithm through implementation and simulation. We show the results and the performance evaluation of the proposed algorithm
An Algorithm to Compute the Character Access Count Distribution for Pattern Matching Algorithms
Marschall, T.; Rahmann, S.
2011-01-01
We propose a framework for the exact probabilistic analysis of window-based pattern matching algorithms, such as Boyer--Moore, Horspool, Backward DAWG Matching, Backward Oracle Matching, and more. In particular, we develop an algorithm that efficiently computes the distribution of a pattern matching
A Distributed Mutual Exclusion Algorithm for Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Orhan Dagdeviren
2012-04-01
Full Text Available We propose a distributed mutual exclusion algorithm for mobile ad hoc networks. This algorithm requires a ring of cluster coordinators as the underlying topology. The topology is built by first providing clusters of mobile nodes in the first step and then forming a backbone consisting of the cluster heads in a ring as the second step. The modified version of the Ricart-Agrawala Algorithm on top of this topologyprovides analytically and experimentally an order of decrease in message complexity with respect to the original algorithm. We analyze the algorithm, provide performance results of the implementation, discuss the fault tolerance and the other algorithmic extensions, and show that this architecture can be used for other middleware functions in mobile networks.
Energy Technology Data Exchange (ETDEWEB)
Ying Chen; Shao-Jing Dong; Terrence Draper; Ivan Horvath; Keh-Fei Liu; Nilmani Mathur; Sonali Tamhankar; Cidambi Srinivasan; Frank X. Lee; Jianbo Zhang
2004-05-01
We introduce the ''Sequential Empirical Bayes Method'', an adaptive constrained-curve fitting procedure for extracting reliable priors. These are then used in standard augmented-{chi}{sup 2} fits on separate data. This better stabilizes fits to lattice QCD overlap-fermion data at very low quark mass where a priori values are not otherwise known. Lessons learned (including caveats limiting the scope of the method) from studying artificial data are presented. As an illustration, from local-local two-point correlation functions, we obtain masses and spectral weights for ground and first-excited states of the pion, give preliminary fits for the a{sub 0} where ghost states (a quenched artifact) must be dealt with, and elaborate on the details of fits of the Roper resonance and S{sub 11}(N{sup 1/2-}) previously presented elsewhere. The data are from overlap fermions on a quenched 16{sup 3} x 28 lattice with spatial size La = 3.2 fm and pion mass as low as {approx}180 MeV.
Comparative Study of Mutual Exclusion Algorithms in Distributed Systems
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Jijnasa Patil
2014-07-01
Full Text Available Mutual Exclusion is an important phenomenon in distributed systems. In this paper, we analyze and compare various mutual exclusion algorithms in distributed systems. In permission based mutual exclusion process waits for permission from other processes to enter into a critical section. In token based mutual exclusion, a special message called token is passed over the system and process holding the token can enter into the critical section. We present a comparative study of quorum based, token ring token asking and multiple token algorithms for mutual exclusion in distributed systems.
Parallel matrix transpose algorithms on distributed memory concurrent computers
Energy Technology Data Exchange (ETDEWEB)
Choi, J.; Walker, D.W. [Oak Ridge National Lab., TN (United States); Dongarra, J.J. [Oak Ridge National Lab., TN (United States)]|[Univ. of Tennessee, Knoxville, TN (United States). Dept. of Computer Science
1993-10-01
This paper describes parallel matrix transpose algorithms on distributed memory concurrent processors. It is assumed that the matrix is distributed over a P x Q processor template with a block scattered data distribution. P, Q, and the block size can be arbitrary, so the algorithms have wide applicability. The communication schemes of the algorithms are determined by the greatest common divisor (GCD) of P and Q. If P and Q are relatively prime, the matrix transpose algorithm involves complete exchange communication. If P and Q are not relatively prime, processors are divided into GCD groups and the communication operations are overlapped for different groups of processors. Processors transpose GCD wrapped diagonal blocks simultaneously, and the matrix can be transposed with LCM/GCD steps, where LCM is the least common multiple of P and Q. The algorithms make use of non-blocking, point-to-point communication between processors. The use of nonblocking communication allows a processor to overlap the messages that it sends to different processors, thereby avoiding unnecessary synchronization. Combined with the matrix multiplication routine, C = A{center_dot}B, the algorithms are used to compute parallel multiplications of transposed matrices, C = A{sup T}{center_dot}B{sup T}, in the PUMMA package. Details of the parallel implementation of the algorithms are given, and results are presented for runs on the Intel Touchstone Delta computer.
Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers.
Church, Philip C; Goscinski, Andrzej; Holt, Kathryn; Inouye, Michael; Ghoting, Amol; Makarychev, Konstantin; Reumann, Matthias
2011-01-01
The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.
Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems
Energy Technology Data Exchange (ETDEWEB)
Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias
2017-04-24
The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.
New Heuristic Distributed Parallel Algorithms for Searching and Planning
Institute of Scientific and Technical Information of China (English)
无
1995-01-01
This paper proposes new heuristic distributed parallel algorithms for searching and planning,which are based on the concepts of wave concurrent propagations and competitive activation mechanisms.These algorithms are characterized by simplicity and clearness of control strategies for earching,and distinguished abilities in many aspects,such as high speed processing,wide suitability for searching AND/OR implicit graphs,and ease in hardware implementation.
Combining soft decision algorithms and scale-sequential hypotheses pruning for object recognition
Energy Technology Data Exchange (ETDEWEB)
Kumar, V.P.; Manolakos, E.S. [Northeastern Univ., Boston, MA (United States)
1996-12-31
This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.
Directory of Open Access Journals (Sweden)
Bokyung Goo
2016-12-01
Full Text Available When a blackout occurs, it is important to reduce the time for power system restoration to minimize damage. For fast restoration, it is important to reduce taking time for the selection of generators, transmission lines and transformers. In addition, it is essential that a determination of a generator start-up sequence (GSS be made to restore the power system. In this paper, we propose the optimal selection of black start units through the generator start-up sequence (GSS to minimize the restoration time using generator characteristic data and the enhanced Dijkstra algorithm. For each restoration step, the sequence selected for the next start unit is recalculated to reflect the system conditions. The proposed method is verified by the empirical Korean power systems.
Real-world experimentation of distributed DSA network algorithms
DEFF Research Database (Denmark)
Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão;
2013-01-01
such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design...... of the available spectrum by nodes in a network, without centralized coordination. While proof-of-concept and statistical validation of such algorithms is typically achieved by using system level simulations, experimental activities are valuable contributions for the investigation of particular aspects...... to the setup of experiments. A practical example of experimentation process with a DSA algorithm is also provided....
Feature Subset Selection by Estimation of Distribution Algorithms
Energy Technology Data Exchange (ETDEWEB)
Cantu-Paz, E
2002-01-17
This paper describes the application of four evolutionary algorithms to the identification of feature subsets for classification problems. Besides a simple GA, the paper considers three estimation of distribution algorithms (EDAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the EDAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. In contrast with previous studies, we did not find evidence to support or reject the use of EDAs for this problem.
A Tunable Checkpointing Algorithm for Distributed Mobile Applications
Directory of Open Access Journals (Sweden)
Sungchae Lim
2011-11-01
Full Text Available The aim of a distributed checkpointing algorithm is to efficiently restore the execution state of distributed applications in face of hardware or software failures. Originally, such algorithms were devised for fixed networking systems, of which computing components communicate with each other via wired networks. Therefore, those algorithms usually suffer from heavy networking costs coming from frequent data transits over wireless networks, if they are used in the wireless computing environment. In this paper, to reduce usage of wireless communications, our checkpointing algorithm allows the distributed mobile application to tune the level of its checkpointing strictness. The strictness is defined by the maximum rollback distance (MRD that says how many recent local checkpoints can be rolled back in the worst case. Since our algorithm have more flexibility in checkpointing schedule due to the use of MRD, it is possible to reduce the number of enforced local checkpointing. In particular, the amount of data transited on wirelesses networks becomes smaller than in earlier methods; thus, our algorithm provides less communication cost and shortened blocking time.
Detection of outliers in reference distributions: performance of Horn's algorithm.
Solberg, Helge Erik; Lahti, Ari
2005-12-01
Medical laboratory reference data may be contaminated with outliers that should be eliminated before estimation of the reference interval. A statistical test for outliers has been proposed by Paul S. Horn and coworkers (Clin Chem 2001;47:2137-45). The algorithm operates in 2 steps: (a) mathematically transform the original data to approximate a gaussian distribution; and (b) establish detection limits (Tukey fences) based on the central part of the transformed distribution. We studied the specificity of Horn's test algorithm (probability of false detection of outliers), using Monte Carlo computer simulations performed on 13 types of probability distributions covering a wide range of positive and negative skewness. Distributions with 3% of the original observations replaced by random outliers were used to also examine the sensitivity of the test (probability of detection of true outliers). Three data transformations were used: the Box and Cox function (used in the original Horn's test), the Manly exponential function, and the John and Draper modulus function. For many of the probability distributions, the specificity of Horn's algorithm was rather poor compared with the theoretical expectation. The cause for such poor performance was at least partially related to remaining nongaussian kurtosis (peakedness). The sensitivity showed great variation, dependent on both the type of underlying distribution and the location of the outliers (upper and/or lower tail). Although Horn's algorithm undoubtedly is an improvement compared with older methods for outlier detection, reliable statistical identification of outliers in reference data remains a challenge.
Distributed query plan generation using multiobjective genetic algorithm.
Panicker, Shina; Kumar, T V Vijay
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Directory of Open Access Journals (Sweden)
Shina Panicker
2014-01-01
Full Text Available A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC and the site-to-site communication cost (CC. In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.
Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution
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Hongxue Yang
2014-04-01
Full Text Available Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distribution is proposed in this paper. Taboo search is a metaheuristic search method to perform local search used for logistic optimization. The taboo search is employed to accelerate convergence and the aspiration criterion is combined with the heuristics algorithm to solve routing optimization. Simulation experimental results demonstrate that the optimal routing in the logistic distribution can be quickly obtained by the taboo search algorithm
Institute of Scientific and Technical Information of China (English)
ZHOU Pei; TIAN Wen-Dong; MA Yu-Gang; CAI Xiang-Zhou; FANG De-Qing; WANG Hong-Wei
2011-01-01
Extensive calculations on isoscaling behavior with the sequential-decay model GEMINI are performed for the mediate-heavy nuclei in the mass range A = 110 and at excitation energies of up to 3 MeV per nucleon. Isoscaling can still be observed after entire-step decays are considered for the light products as in the only first-step decay process case. Comparison between the products after the first-step decay and the ones after entire-step decay demonstrates that multi-step secondary sequential decay strongly influences the isoscaling parameters a, 0 as well as the fragment isospin distribution. After entire-step decays, the isoscaling parameters a and /? Are decreased and the fragment isospin distribution can better reproduce the isospin distribution shape as the experimental data.%@@ Extensive calculations on isoscaling behavior with the sequential-decay model GEMINI are performed for the mediate-heavy nuclei in the mass range A = 110 and at excitation energies of up to 3 MeV per nucleon.Isoscaling can still be observed after entire-step decays are considered for the light products as in the only first-step decay process case.Comparison between the products after the first-step decay and the ones after entire-step decay demonstrates that multi-step secondary sequential decay strongly influences the isoscaling parameters α,β as well as the fragment isospin distribution.After entire-step decays, the isoscaling parameters α and β are decreased and the fragment isospin distribution can better reproduce the isospin distribution shape as the experimental data.
Improvement on Conventional Load Distribution Algorithm in Hot Tandem Mills
Institute of Scientific and Technical Information of China (English)
LI Hai-jun; XU Jian-zhong; WANG Guo-dong; LIU Xiang-hua
2007-01-01
Load distribution is a key technology in strip hot rolling process, which influences the coil′s microstructure and performance. Currently, Newton-Raphson algorithm is applied to load distribution of hot tandem mills in many hot rolling plants and has some serious defects such as having a strict restriction on initial iterative calculation value and requiring coefficient matrix of nonlinear equations to be nonsingular. To eliminate these defects and improve the online performance of the process control computer, Newton descendent numeric algorithm is introduced to this field to widen the initial value range and a new model named error conversion algorithm is put forth to deal with special conditions when the coefficient matrix is singular. Furthermore, considering the characteristics of load distribution, a condition of strip thickness distribution abnormality and corresponding solutions are provided which ensure that rolling parameters can be calculated normally. Simulation results show that the improved algorithm has overcome the defects of the Newton-Raphson algorithm and is suitable for online application.
Mesh Algorithms for PDE with Sieve I: Mesh Distribution
Directory of Open Access Journals (Sweden)
Matthew G. Knepley
2009-01-01
Full Text Available We have developed a new programming framework, called Sieve, to support parallel numerical partial differential equation(s (PDE algorithms operating over distributed meshes. We have also developed a reference implementation of Sieve in C++ as a library of generic algorithms operating on distributed containers conforming to the Sieve interface. Sieve makes instances of the incidence relation, or arrows, the conceptual first-class objects represented in the containers. Further, generic algorithms acting on this arrow container are systematically used to provide natural geometric operations on the topology and also, through duality, on the data. Finally, coverings and duality are used to encode not only individual meshes, but all types of hierarchies underlying PDE data structures, including multigrid and mesh partitions. In order to demonstrate the usefulness of the framework, we show how the mesh partition data can be represented and manipulated using the same fundamental mechanisms used to represent meshes. We present the complete description of an algorithm to encode a mesh partition and then distribute a mesh, which is independent of the mesh dimension, element shape, or embedding. Moreover, data associated with the mesh can be similarly distributed with exactly the same algorithm. The use of a high level of abstraction within the Sieve leads to several benefits in terms of code reuse, simplicity, and extensibility. We discuss these benefits and compare our approach to other existing mesh libraries.
National Aeronautics and Space Administration — This paper discusses the effect of sequential conflict resolution maneuvers of an infinite aircraft flow through a finite control volume. Aircraft flow models are...
Algorithm describing pressure distribution of non-contact TNT explosion
Directory of Open Access Journals (Sweden)
Radosław Kiciński
2014-12-01
Full Text Available [b]Abstract[/b]. The aim of this study is to develop a computational algorithm, describing the shock wave pressure distribution in the space induced by non-contact TNT explosion. The procedure describes pressure distribution on a damp surface of the hull. Simulations have been carried out using Abaqus/CAE. The study also shows the pressure waveform descriptions provided by various authors and presents them in charts. The formulated conclusions convince efficiency of the algorithm application.[b]Keywords:[/b] Underwater explosion, shock wave, CAE, TNT, Kobben class submarine
Optimal Network Reconfiguration with Distributed Generation Using NSGA II Algorithm
Directory of Open Access Journals (Sweden)
Jasna Hivziefendic
2016-10-01
Full Text Available This paper presents a method to solve electrical network reconfiguration problem in the presence of distributed generation (DG with an objective of minimizing real power loss and energy not supplied function in distribution system. A method based on NSGA II multi-objective algorithm is used to simultaneously minimize two objective functions and to identify the optimal distribution network topology. The constraints of voltage and branch current carrying capacity are included in the evaluation of the objective function. The method has been tested on radial electrical distribution network with 213 nodes, 248 lines and 72 switches. Numerical results are presented to demonstrate the performance and effectiveness of the proposed methodology.
A DISTRIBUTED RING ALGORITHM FOR COORDINATOR ELECTION IN DISTRIBUTED SYSTEMS
Directory of Open Access Journals (Sweden)
Shaik Naseera
2016-09-01
Full Text Available In distributed systems, nodes are connected at different geographical locations. As a part of effective resource utilization, the data and resources are shared among these nodes. A leader or pioneer is necessary to take care of this resource sharing process by eliminating conflicting among the nodes. The shared resources are to be accessed in a fair and optimal manner among all the nodes in the network. This makes the importance of electing a leader which can coordinate with all the nodes and make fair use of resources among the nodes. As nodes are distributed in different geographical locations and factors influencing its operation make it inevitable that a leader may go down temporarily or permanently. In such case a new leader has to be elected for coordination. The time taken to elect a new leader is one of the crucial factors in improving the performance of the system. In this paper, we propose a new approach for leader election to optimize the time taken for the nodes to elect the leader.
Minami, Shintaro; Sawada, Kengo; Chikenji, George
2013-01-18
Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed. We have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here. MICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non
2013-01-01
Background Protein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed. Results We have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here. Conclusions MICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool
Efficient Algorithms for Generating Truncated Multivariate Normal Distributions
Institute of Scientific and Technical Information of China (English)
Jun-wu YU; Guo-liang TIAN
2011-01-01
Sampling from a truncated multivariate normal distribution (TMVND) constitutes the core computational module in fitting many statistical and econometric models.We propose two efficient methods,an iterative data augmentation (DA) algorithm and a non-iterative inverse Bayes formulae (IBF) sampler,to simulate TMVND and generalize them to multivariate normal distributions with linear inequality constraints.By creating a Bayesian incomplete-data structure,the posterior step of the DA algorithm directly generates random vector draws as opposed to single element draws,resulting obvious computational advantage and easy coding with common statistical software packages such as S-PLUS,MATLAB and GAUSS.Furthermore,the DA provides a ready structure for implementing a fast EM algorithm to identify the mode of TMVND,which has many potential applications in statistical inference of constrained parameter problems.In addition,utilizing this mode as an intermediate result,the IBF sampling provides a novel alternative to Gibbs sampling and eliminates problems with convergence and possible slow convergence due to the high correlation between components of a TMVND.The DA algorithm is applied to a linear regression model with constrained parameters and is illustrated with a published data set.Numerical comparisons show that the propoeed DA algorithm and IBF sampler are more efficient than the Gibbs sampler and the accept-reject algorithm.
Intelligent decision support algorithm for distribution system restoration.
Singh, Reetu; Mehfuz, Shabana; Kumar, Parmod
2016-01-01
Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network.
Generalized quantum counting algorithm for non-uniform amplitude distribution
Tan, Jianing; Ruan, Yue; Li, Xi; Chen, Hanwu
2017-03-01
We give generalized quantum counting algorithm to increase universality of quantum counting algorithm. Non-uniform initial amplitude distribution is possible due to the diversity of situations on counting problems or external noise in the amplitude initialization procedure. We give the reason why quantum counting algorithm is invalid on this situation. By modeling in three-dimensional space spanned by unmarked state, marked state and free state to the entire Hilbert space of n qubits, we find Grover iteration can be regarded as improper rotation in the space. This allows us to give formula to solve counting problem. Furthermore, we express initial amplitude distribution in the eigenvector basis of improper rotation matrix. This is necessary to obtain mathematical analysis of counting problem on various situations. Finally, we design four simulation experiments, the results of which show that compared with original quantum counting algorithm, generalized quantum counting algorithm wins great satisfaction from three aspects: (1) Whether initial amplitude distribution is uniform; (2) the diversity of situations on counting problems; and (3) whether phase estimation technique can get phase exactly.
A Token Based Algorithm to Distributed Computation in Sensor Networks
Saligrama, Venkatesh
2011-01-01
We consider distributed algorithms for data aggregation and function computation in sensor networks. The algorithms perform pairwise computations along edges of an underlying communication graph. A token is associated with each sensor node, which acts as a transmission permit. Nodes with active tokens have transmission permits; they generate messages at a constant rate and send each message to a randomly selected neighbor. By using different strategies to control the transmission permits we can obtain tradeoffs between message and time complexity. Gossip corresponds to the case when all nodes have permits all the time. We study algorithms where permits are revoked after transmission and restored upon reception. Examples of such algorithms include Simple-Random Walk(SRW), Coalescent-Random-Walk(CRW) and Controlled Flooding(CFLD) and their hybrid variants. SRW has a single node permit, which is passed on in the network. CRW, initially initially has a permit for each node but these permits are revoked gradually....
Combinations of Estimation of Distribution Algorithms and Other Techniques
Institute of Scientific and Technical Information of China (English)
Qingfu Zhang; Jianyong Sun; Edward Tsang
2007-01-01
This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem, b) evolutionary algorithms refining a heuristic, we advocate a strategy for solving a hard optimization problem with complicated data structure, and c) combination of two different local search techniques and EDA for numerical global optimization problems, its basic idea is that not all the new generated points are needed to be improved by an expensive local search.
Sumin, M. I.
2015-06-01
A parametric nonlinear programming problem in a metric space with an operator equality constraint in a Hilbert space is studied assuming that its lower semicontinuous value function at a chosen individual parameter value has certain subdifferentiability properties in the sense of nonlinear (nonsmooth) analysis. Such subdifferentiability can be understood as the existence of a proximal subgradient or a Fréchet subdifferential. In other words, an individual problem has a corresponding generalized Kuhn-Tucker vector. Under this assumption, a stable sequential Kuhn-Tucker theorem in nondifferential iterative form is proved and discussed in terms of minimizing sequences on the basis of the dual regularization method. This theorem provides necessary and sufficient conditions for the stable construction of a minimizing approximate solution in the sense of Warga in the considered problem, whose initial data can be approximately specified. A substantial difference of the proved theorem from its classical same-named analogue is that the former takes into account the possible instability of the problem in the case of perturbed initial data and, as a consequence, allows for the inherited instability of classical optimality conditions. This theorem can be treated as a regularized generalization of the classical Uzawa algorithm to nonlinear programming problems. Finally, the theorem is applied to the "simplest" nonlinear optimal control problem, namely, to a time-optimal control problem.
A hybrid evolutionary algorithm for distribution feeder reconﬁguration
Indian Academy of Sciences (India)
Taher Niknam; Reza Khorshidi; Bahman Bahmani Firouzi
2010-04-01
Distribution feeder reconﬁguration (DFR) is formulated as a multiobjective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance ($\\lambda_2$ norm) between the vectorvalued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.
Real-world experimentation of distributed DSA network algorithms
DEFF Research Database (Denmark)
Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão
2013-01-01
of the available spectrum by nodes in a network, without centralized coordination. While proof-of-concept and statistical validation of such algorithms is typically achieved by using system level simulations, experimental activities are valuable contributions for the investigation of particular aspects...... such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design......The problem of spectrum scarcity in uncoordinated and/or heterogeneous wireless networks is the key aspect driving the research in the field of flexible management of frequency resources. In particular, distributed dynamic spectrum access (DSA) algorithms enable an efficient sharing...
Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Meenakshi Panda
2014-01-01
Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.
Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Ying Tian
2014-01-01
Full Text Available This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targets γ-covered.
Mesh Algorithms for PDE with Sieve I: Mesh Distribution
Knepley, Matthew G
2009-01-01
We have developed a new programming framework, called Sieve, to support parallel numerical PDE algorithms operating over distributed meshes. We have also developed a reference implementation of Sieve in C++ as a library of generic algorithms operating on distributed containers conforming to the Sieve interface. Sieve makes instances of the incidence relation, or \\emph{arrows}, the conceptual first-class objects represented in the containers. Further, generic algorithms acting on this arrow container are systematically used to provide natural geometric operations on the topology and also, through duality, on the data. Finally, coverings and duality are used to encode not only individual meshes, but all types of hierarchies underlying PDE data structures, including multigrid and mesh partitions. In order to demonstrate the usefulness of the framework, we show how the mesh partition data can be represented and manipulated using the same fundamental mechanisms used to represent meshes. We present the complete des...
Angular Distributions for ψ' Sequential Decays into 2(π+π-)p(p-)γ via XcJ
Institute of Scientific and Technical Information of China (English)
PING Rong-Gang; YUAN Chang-Zheng
2005-01-01
Amplitudes for ψ(2S) sequential decays into 2(π+π-)p(p-)γ via XcJ are constructed in effective coupling scheme. A Mote-Carlo simulation is carried out to study angular distributions of the decayed particles in laboratory system. The results can be taken as a reference for measuring the decay of XcJ into Ξ-Ξ+ at BESII/BEPC in the near future.
An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network.
Liu, Jing; Huang, Kaiyu; Zhang, Guoxian
2017-04-20
We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP
A Decomposition Algorithm for Optimal Control of Distributed Energy System
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Standardi, Laura
2013-01-01
In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems...
A survey of checkpointing algorithms for parallel and distributed computers
Indian Academy of Sciences (India)
S Kalaiselvi; V Rajaraman
2000-10-01
Checkpoint is defined as a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. Checkpointing is the process of saving the status information. This paper surveysthe algorithms which have been reported in the literature for checkpointing parallel/distributed systems. It has been observed that most of the algorithms published for checkpointing in message passing systems are based on the seminal article by Chandy and Lamport. A large number of articles have been published in this area by relaxing the assumptions made in this paper and by extending it to minimise the overheads of coordination and context saving. Checkpointing for sharedmemory systems primarily extend cache coherence protocolstomaintain a consistent memory. All of them assume that the main memory is safe for storing the context. Recently algorithms have been published for distributed shared memory systems, which extend the cache coherence protocols used in shared memory systems. They however also include methods for storing the status of distributed memory in stable storage. Most of the algorithms assume that there is no knowledge about the programs being executed.It is howeverfelt that in development of parallel programs the user has to do a fair amount of work in distributing tasks and this information can be effectively used to simplify checkpointing and rollback recovery.
A distributed Canny edge detector: algorithm and FPGA implementation.
Xu, Qian; Varadarajan, Srenivas; Chakrabarti, Chaitali; Karam, Lina J
2014-07-01
The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100
Iterative selection algorithm for service composition in distributed environments
Institute of Scientific and Technical Information of China (English)
SU Sen; LI Fei; YANG FangChun
2008-01-01
In service oriented architecture (SOA), service composition is a promising way to create new services. However, some technical challenges are hindering the application of service composition. One of the greatest challenges for composite service provider is to select a set of services to instantiate composite service with end to-end quality of service (QoS) assurance across different autonomous networks and business regions. This paper presents an iterative service selection algorithm for quality driven service composition. The algorithm runs on a peer-to-peer (P2P) service execution environment - distributed intelligent service execution (DISE),which provides scalable QoS registry, dynamic service selection and service execution services. The most significant feature of our iterative service selection algorithm is that it can work on a centralized QoS registry as well as cross decentralized ones. Network status is an optional factor in our QoS model and selection algorithm. The algorithm iteratively selects services following service execution order, so it can be applied either before service execution or at service run-time without any modification. We test our algorithm with a series of experiments on DISE. Experimental results illustrated its excellent selection and outstanding performance.
An Analysis of Checkpointing Algorithms for Distributed Mobile Systems
Directory of Open Access Journals (Sweden)
Ajay Khunteta
2010-07-01
Full Text Available Distributed snapshots are an important building block for distributed systems, and are useful for constructing efficient checkpointing protocols, among other uses. Direct application of these algorithms to mobile systems is not easible, however, due to differences in the environment in which mobile systems operate, relative to general distributed systems. The mobile computing environment introduces newchallenges in the area of fault-tolerant computing. Compared to traditional distributed environments, wireless networks are typically slower, providing lower throughput and latency, comparing to wireline networks. In addition, the mobile hosts have limited computation esources, are often exposed to harsh operating environment that makes them more likely to fail, and can roam while operating. Over the past two decades, intensive research work has been carried out on providing efficient checkpointing protocols in traditional distributed computing. Recently, more attention has been paid to providing checkpointing protocols for mobile systems. Some of these protocols have been adapted from the traditional distributed environment; others have been created from scratch for mobile systems. Checkpoint is defined as a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at alater time. Checkpointing is the process of saving the status information. This paper surveys the algorithms which have been reported in the literature for checkpointing in Mobile Distributed systems.
Online algorithms for optimal energy distribution in microgrids
Wang, Yu; Nelms, R Mark
2015-01-01
Presenting an optimal energy distribution strategy for microgrids in a smart grid environment, and featuring a detailed analysis of the mathematical techniques of convex optimization and online algorithms, this book provides readers with essential content on how to achieve multi-objective optimization that takes into consideration power subscribers, energy providers and grid smoothing in microgrids. Featuring detailed theoretical proofs and simulation results that demonstrate and evaluate the correctness and effectiveness of the algorithm, this text explains step-by-step how the problem can b
Directory of Open Access Journals (Sweden)
Tingsong Du
2015-01-01
Full Text Available An improved quantum artificial fish swarm algorithm (IQAFSA for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA, the basic artificial fish swarm algorithm (BAFSA, and the global edition artificial fish swarm algorithm (GAFSA to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.
Schwarz waveform relaxation algorithm for heat equations with distributed delay
Directory of Open Access Journals (Sweden)
Wu Shu-Lin
2016-01-01
Full Text Available Heat equations with distributed delay are a class of mathematic models that has wide applications in many fields. Numerical computation plays an important role in the investigation of these equations, because the analytic solutions of partial differential equations with time delay are usually unavailable. On the other hand, duo to the delay property, numerical computation of these equations is time-consuming. To reduce the computation time, we analyze in this paper the Schwarz waveform relaxation algorithm with Robin transmission conditions. The Robin transmission conditions contain a free parameter, which has a significant effect on the convergence rate of the Schwarz waveform relaxation algorithm. Determining the Robin parameter is therefore one of the top-priority matters for the study of the Schwarz waveform relaxation algorithm. We provide new formula to fix the Robin parameter and we show numerically that the new Robin parameter is more efficient than the one proposed previously in the literature.
Parallel Variable Distribution Algorithm for Constrained Optimization with Nonmonotone Technique
Directory of Open Access Journals (Sweden)
Congying Han
2013-01-01
Full Text Available A modified parallel variable distribution (PVD algorithm for solving large-scale constrained optimization problems is developed, which modifies quadratic subproblem QPl at each iteration instead of the QPl0 of the SQP-type PVD algorithm proposed by C. A. Sagastizábal and M. V. Solodov in 2002. The algorithm can circumvent the difficulties associated with the possible inconsistency of QPl0 subproblem of the original SQP method. Moreover, we introduce a nonmonotone technique instead of the penalty function to carry out the line search procedure with more flexibly. Under appropriate conditions, the global convergence of the method is established. In the final part, parallel numerical experiments are implemented on CUDA based on GPU (Graphics Processing unit.
Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications
Masood, Mudassir
2015-05-01
Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c
Improving permafrost distribution modelling using feature selection algorithms
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
is distributed in the sense that all calculations are implemented where the necessary information is available, including both parameters and measurements. A communication network between the pumps is implemented for global optimization. The local implementation of the algorithm means that the system becomes......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....
Opportunistic Scheduling in Heterogeneous Networks: Distributed Algorithms and System Capacity
Kampeas, Dor-Joseph; Gurewitz, Omer
2012-01-01
In this work, we design and analyze novel distributed scheduling algorithms for multi-user MIMO systems. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they require communication between the users themselves, yet, we prove their performance closely approximates that of a centrally-controlled system, which is able to schedule the strongest user in each time-slot. Possible application include, but are not limited to, modern 4G networks such as 3GPP LTE, or random access protocols. The analysis is based on a novel application of the Point-Process approximation, enabling the examination of non-homogeneous cases, such as non-identically distributed users, or handling various QoS considerations, which to date had been open.
Distributed Spectrum Sensing with Sequential Ordered Transmissions to a Cognitive Fusion Center
Hesham, Laila; Nafie, Mohammed
2011-01-01
Cooperative spectrum sensing is a robust strategy that enhances the detection probability of primary licensed users. However, a large number of detectors reporting to a fusion center for a final decision causes significant delay and also presumes the availability of unreasonable communication resources at the disposal of a network searching for spectral opportunities. In this work, we employ the idea of sequential detection to obtain a quick, yet reliable, decision regarding primary activity. Local detectors take measurements, and only a few of them transmit the log likelihood ratios (LLR) to a fusion center in descending order of LLR magnitude. The fusion center runs a sequential test with a maximum imposed on the number of sensors that can report their LLR measurements. We calculate the detection thresholds using two methods. The first achieves the same probability of error as the optimal block detector. In the second, an objective function is constructed and decision thresholds are obtained via backward in...
DEFF Research Database (Denmark)
Hansen, Thomas Mejer; Mosegaard, Klaus; Cordua, Knud Skou
2010-01-01
Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample the solutions to non-linear inverse problems. In principle these methods allow incorporation of arbitrarily complex a priori information, but current methods allow only relatively simple...... this algorithm with the Metropolis algorithm to obtain an efficient method for sampling posterior probability densities for nonlinear inverse problems....
An Improved Genetic Algorithm for Allocation Optimization of Distribution Centers
Institute of Scientific and Technical Information of China (English)
钱晶; 庞小红; 吴智铭
2004-01-01
This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.
Distribution System Optimization Planning Based on Plant Growth Simulation Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Chun; CHENG Hao-zhong; HU Ze-chun; WANG Yi
2008-01-01
An approach for the integrated optimization of the construction/expansion capacity of high-voltage/medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.
Tsai, Ming-Chi; Tsui, Fu-Chiang; Wagner, Michael M
2007-10-11
Performing fast data analysis to detect disease outbreaks plays a critical role in real-time biosurveillance. In this paper, we described and evaluated an Algorithm Distribution Manager Service (ADMS) based on grid technologies, which dynamically partition and distribute detection algorithms across multiple computers. We compared the execution time to perform the analysis on a single computer and on a grid network (3 computing nodes) with and without using dynamic algorithm distribution. We found that algorithms with long runtime completed approximately three times earlier in distributed environment than in a single computer while short runtime algorithms performed worse in distributed environment. A dynamic algorithm distribution approach also performed better than static algorithm distribution approach. This pilot study shows a great potential to reduce lengthy analysis time through dynamic algorithm partitioning and parallel processing, and provides the opportunity of distributing algorithms from a client to remote computers in a grid network.
Françoise Benz
2004-01-01
ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...
Françoise Benz
2004-01-01
ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...
An Improved Leader Election Algorithm for Distributed Systems
Directory of Open Access Journals (Sweden)
P BeaulahSoundarabai
2013-04-01
Full Text Available Leader Election Algorithm , not only in distributed systems but in any communication network, is anessential matter for discussion. Tremendous amount of work are happening in the research community onthis Election, because many network protocols are in need of a coordinator process for the smooth runningof the system. These socalled Leader or Coordinator processes are responsible for the synchronization ofthe system. If there is no synchronization, then the entire system would become inconsistent which internmakes the system to lose its reliability. Since all the processes need to interact with the leader process, theyall must agree upon who the present leader is. Furthermore, if the leader process crashes, the new leaderprocess should take the charge as early as possible. New leader is one among the currently runningprocesses with the highest process id. In this paper we have presented a modified version of ring algorithm.Our work involves substantial modifications of the existing ring election algorithm and the comparison ofmessage complexity with the original algorithm. Simulation results show that our algorithmminimizes thenumber of messages being exchanged in electing the coordinator
Analysis of an algorithm for distributed recognition and accountability
Energy Technology Data Exchange (ETDEWEB)
Ko, C.; Frincke, D.A.; Goan, T. Jr.; Heberlein, L.T.; Levitt, K.; Mukherjee, B.; Wee, C. [California Univ., Davis, CA (United States). Dept. of Computer Science
1993-08-01
Computer and network systems are available to attacks. Abandoning the existing huge infrastructure of possibly-insecure computer and network systems is impossible, and replacing them by totally secure systems may not be feasible or cost effective. A common element in many attacks is that a single user will often attempt to intrude upon multiple resources throughout a network. Detecting the attack can become significantly easier by compiling and integrating evidence of such intrusion attempts across the network rather than attempting to assess the situation from the vantage point of only a single host. To solve this problem, we suggest an approach for distributed recognition and accountability (DRA), which consists of algorithms which ``process,`` at a central location, distributed and asynchronous ``reports`` generated by computers (or a subset thereof) throughout the network. Our highest-priority objectives are to observe ways by which an individual moves around in a network of computers, including changing user names to possibly hide his/her true identity, and to associate all activities of multiple instance of the same individual to the same network-wide user. We present the DRA algorithm and a sketch of its proof under an initial set of simplifying albeit realistic assumptions. Later, we relax these assumptions to accommodate pragmatic aspects such as missing or delayed ``reports,`` clock slew, tampered ``reports,`` etc. We believe that such algorithms will have widespread applications in the future, particularly in intrusion-detection system.
An Estimation of Distribution Algorithm for Nurse Scheduling
Aickelin, Uwe
2008-01-01
Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (eda) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (ga) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The eda is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, ...
An Efficient Diffusion Load Balancing Algorithm in Distributed System
Directory of Open Access Journals (Sweden)
Rafiqul Z. Khan
2014-07-01
Full Text Available In distributed computing system some nodes are very fast and some are slow and during the computation many fast nodes become idle or under loaded while the slow nodes become over loaded due to the uneven distribution of load in the system. In distributed system, the most common important factor is the information collection about loads on different nodes. The success of load balancing algorithm depends on how quickly the information about the load in the system is collected by a node willing to transfer or accept load. In this paper we have shown that the number of communication overheads depends on the number of overloaded nodes present in the domain of an under loaded nodes and vice-versa. We have also shown that communication overhead for load balancing is always fairly less than KN but in worst case our algorithm’s complexity becomes equal to KN.
Directory of Open Access Journals (Sweden)
Khalil Ibrahim Mohammad Abuzanouneh
2016-05-01
Full Text Available In this paper, we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increases exponentially and directly proportional to the size of the problem, The construction of timetable is most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA is unable to provide satisfactory results, a distributed EA (dEA, which deploys the population on distributed systems ,it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distributed models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
Distribution and sequential extraction of some heavy metals in urban soils of Guiyang City, China
Institute of Scientific and Technical Information of China (English)
WU Yongfeng; LIU Congqiang; TU Chenglong
2008-01-01
Sixty-two soil samples collected from different functional zones of Guiyang were analyzed for total concentrations and sequential extraction of Cr, Cu, Pb, Zn and Cd by ICP spectrometry. The average total concentrations of Cr, Cu, Pb, Zn and Cd in the soils of Guiyang were 92.9, 51.6, 44.1, 139.3 and 0.28 mg/kg, respectively. The soils have been polluted by Cr, Cu, Pb, Zn and Cd to some extent in comparison with the background values of Guiyang. Significant differences were recognized in the concentrations of Cr, Cu, Pb, Zn and Cd in different functional zones. As for the sequential extraction, Cr, Cu and Zn were present mainly in the residual fraction, and Pb was present mainly in the oxidizable fraction. The reducible fraction of Cd accounts for 47.5%, and the residual fraction is lowest. The mobility and bioavailability of heavy metals follow the order of Cd>Pb>Cu>Cr>Zn.
Solving Packing Problems by a Distributed Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
Nian-Ze Hu
2012-01-01
Full Text Available Packing optimization problems aim to seek the best way of placing a given set of rectangular boxes within a minimum volume rectangular box. Current packing optimization methods either find it difficult to obtain an optimal solution or require too many extra 0-1 variables in the solution process. This study develops a novel method to convert the nonlinear objective function in a packing program into an increasing function with single variable and two fixed parameters. The original packing program then becomes a linear program promising to obtain a global optimum. Such a linear program is decomposed into several subproblems by specifying various parameter values, which is solvable simultaneously by a distributed computation algorithm. A reference solution obtained by applying a genetic algorithm is used as an upper bound of the optimal solution, used to reduce the entire search region.
Cluster-Based Distributed Algorithms for Very Large Linear Equations
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In many applications such as computational fluid dynamics and weather prediction, as well as image processing and state of Markov chain etc., the grade of matrix n is often very large, and any serial algorithm cannot solve the problems. A distributed cluster-based solution for very large linear equations is discussed, it includes the definitions of notations, partition of matrix, communication mechanism, and a master-slaver algorithm etc., the computing cost is O(n3/N), the memory cost is O(n2/N), the I/O cost is O(n2/N), and the communication cost is O(Nn), here, N is the number of computing nodes or processes. Some tests show that the solution could solve the double type of matrix under 106×106 effectively.
Optimal Design Of Existng Water Distribution Network Using Genetics Algorithms.
Directory of Open Access Journals (Sweden)
A Saminu
2016-07-01
Full Text Available In this study EPANET, a widely used water distribution package was linked to OptiGa, a Visual Basic ActiveX control for implementation of genetic algorithm, through Visual Basic programming technique, to modify the computer software called OptiNetwork. OptiNetwork in its modifications, introduced means of selecting options for advanced genetic algorithm parameters (Top mate; Roulette cost; Random; Tournament methods; and one point crossover; two points crossover; uniform crossover methods and random seed number. Using Badarawa/Malali existing water distribution network consisting of 96 pipes of different materials, 75junctions, two tanks, and one overhead reservoir, and a source reservoir (i.e treatment plant from which water is pumped through a pumping main to the overhead reservoir and later distributed to the network by gravity .The modified software optiNetwork was applied to Badarawa / Malali networks distribution designs. The results obtained were compared with those obtained using commercial software package (OptiDesigner, The modified software has been able to obtained almost equal result with OptiDesigner software for the first optimization i.e before the application of advance GA, after the application of Advance GA It was observed that the least-cost design of $195,200.00 that satisfies the constraints requirements was obtained using optiNetwork, which is much lower than $435,118.00 obtained from OptiDesigner software. The results obtained show that the introduction of the advanced genetic parameters of OptiNetwork is justified. This is because, it has been able to improve the search method in terms of achieving the “least-cost” designed water distribution system that will supply sufficient water quantities at adequate pressure to the consumers.
Directory of Open Access Journals (Sweden)
P. Dinakara Prasad Reddy
2016-05-01
Full Text Available Distributed generator (DG resources are small, self contained electric generating plants that can provide power to homes, businesses or industrial facilities in distribution feeders. By optimal placement of DG we can reduce power loss and improve the voltage profile. However, the values of DGs are largely dependent on their types, sizes and locations as they were installed in distribution feeders. The main contribution of the paper is to find the optimal locations of DG units and sizes. Index vector method is used for optimal DG locations. In this paper new optimization algorithm i.e. flower pollination algorithm is proposed to determine the optimal DG size. This paper uses three different types of DG units for compensation. The proposed methods have been tested on 15-bus, 34-bus, and 69-bus radial distribution systems. MATLAB, version 8.3 software is used for simulation.
Multi-objective quantum genetic algorithm in WSNs distribution optimization
Wen, Hao; Ren, Hong-liang
2013-03-01
To achieve lower energy and higher detection coverage simultaneously in scattering distribution wireless sensor networks (WSNs), a multi-objective optimization function combined with area coverage and node-communication energy is constructed. Based on the multi-objective quantum genetic algorithm (Mo-QGA) proposed by Li Bin and Zhuang-zhen Quan et al, we have obtained optimum solutions close to Pareto front. Experimental results indicate that the Mo-QGA has advantages both on efficiency and coverage, as well as low energy.
3D Hail Size Distribution Interpolation/Extrapolation Algorithm
Lane, John
2013-01-01
Radar data can usually detect hail; however, it is difficult for present day radar to accurately discriminate between hail and rain. Local ground-based hail sensors are much better at detecting hail against a rain background, and when incorporated with radar data, provide a much better local picture of a severe rain or hail event. The previous disdrometer interpolation/ extrapolation algorithm described a method to interpolate horizontally between multiple ground sensors (a minimum of three) and extrapolate vertically. This work is a modification to that approach that generates a purely extrapolated 3D spatial distribution when using a single sensor.
Room Acoustical Simulation Algorithm Based on the Free Path Distribution
VORLÄNDER, M.
2000-04-01
A new algorithm is presented which provides estimates of impulse responses in rooms. It is applicable to arbitrary shaped rooms, thus including non-diffuse spaces like workrooms or offices. In the latter cases, for instance, sound propagation curves are of interest to be applied in noise control. In the case of concert halls and opera houses, the method enables very fast predictions of room acoustical criteria like reverberation time, strength or clarity. The method is based on a low-resolved ray tracing and recording of the free paths. Estimates of impulse responses are derived from evaluation of the free path distribution and of the free path transition probabilities.
Optimal allocation of solar based distributed generators in distribution system using Bat algorithm
Directory of Open Access Journals (Sweden)
Suresh Kumar Sudabattula
2016-09-01
Full Text Available With increased demand of electrical energy, limited availability of fossil fuels and environmental concerns, it is necessary to consider renewable energy based generation in a power system network. Optimal allocation of renewable based distributed generators in the distribution system is a challenging task in the recent years. In this paper an effective technique is proposed for optimal allocation of solar based distributed generators in the distribution network using a Bat algorithm (BA is presented. The objective is to minimize power loss of radial distribution system. Different operating constraints related to the distribution network are considered. The stochastic nature of solar irradiance is modeled by using suitable probability distribution function (PDF. The proposed method is tested and validated on IEEE 33 bus test system.
Combustion distribution control using the extremum seeking algorithm
Marjanovic, A.; Krstic, M.; Djurovic, Z.; Kvascev, G.; Papic, V.
2014-12-01
Quality regulation of the combustion process inside the furnace is the basis of high demands for increasing robustness, safety and efficiency of thermal power plants. The paper considers the possibility of spatial temperature distribution control inside the boiler, based on the correction of distribution of coal over the mills. Such control system ensures the maintenance of the flame focus away from the walls of the boiler, and thus preserves the equipment and reduces the possibility of ash slugging. At the same time, uniform heat dissipation over mills enhances the energy efficiency of the boiler, while reducing the pollution of the system. A constrained multivariable extremum seeking algorithm is proposed as a tool for combustion process optimization with the main objective of centralizing the flame in the furnace. Simulations are conducted on a model corresponding to the 350MW boiler of the Nikola Tesla Power Plant, in Obrenovac, Serbia.
ZAP: a distributed channel assignment algorithm for cognitive radio networks
Directory of Open Access Journals (Sweden)
Munaretto Anelise
2011-01-01
Full Text Available Abstract We propose ZAP, an algorithm for the distributed channel assignment in cognitive radio (CR networks. CRs are capable of identifying underutilized licensed bands of the spectrum, allowing their reuse by secondary users without interfering with primary users. In this context, efficient channel assignment is challenging as ideally it must be simple, incur acceptable communication overhead, provide timely response, and be adaptive to accommodate frequent changes in the network. Another challenge is the optimization of network capacity through interference minimization. In contrast to related work, ZAP addresses these challenges with a fully distributed approach based only on local (neighborhood knowledge, while significantly reducing computational costs and the number of messages required for channel assignment. Simulations confirm the efficiency of ZAP in terms of (i the performance tradeoff between different metrics and (ii the fast achievement of a suitable assignment solution regardless of network size and density.
Rahman, Mst Farhana; Ripon, Kazi Shah Nawaz; Suvo, Md Iqbal Hossain
2010-01-01
In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic Algorithm (GA). It is an evolutionary approach, and used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and time. The implementation of proposed method is compared with Genetic ZRP, i.e., GZRP and the result demonstrates better performance for the proposed method. Since the method provides a set of paths to the destination, it results in load balance to the network. As both EDA and GA use random search method to reach the optimal point, the searching cost reduced significantly, especially when the number of data is large.
Sujni Paul
2010-01-01
Many current data mining tasks can be accomplished successfully only in a distributed setting. The field of distributed data mining has therefore gained increasing importance in the last decade. The Apriori algorithm by Rakesh Agarwal has emerged as one of the best Association Rule mining algorithms. Ii also serves as the base algorithm for most parallel algorithms. The enormity and high dimensionality of datasets typically available as input to problem of association rule discovery, makes it...
Directory of Open Access Journals (Sweden)
I. Moradi
2014-03-01
Full Text Available Distributed feeder reconfiguration (DFR is an operation processand a very important methodfor saving electrical energy and loss reduction in distribution systems. This process is carried out by changingdistribution system topology by opening and/or closing of circuit breakers. Status of the circuit breakers is optimally determined to have an improved system operation and reduced power losses. This paper proposes a multi-objective evolutionary method for distribution feeder reconfiguration. The multi-objectives optimization minimizes power losses and improves reliability of the system. For this purpose a particle swarm optimization algorithm is used for solving the problem. Simulation results show the efficiency of the proposed method for DFR
Rice, J.K.; Verhaegen, M.
2009-01-01
We consider the problem of designing controllers for spatially-varying interconnected systems distributed in one spatial dimension. The matrix structure of such systems can be exploited to allow fast analysis and design of centralized controllers with simple distributed implementations. Iterative al
Liqiang Liu; Yuntao Dai; Jinyu Gao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...
Shieh, Shin-Lin; Han, Yunghsiang S
2007-01-01
A common problem on sequential-type decoding is that at the signal-to-noise ratio (SNR) below the one corresponding to the cutoff rate, the average decoding complexity per information bit and the required stack size grow rapidly with the information length. In order to alleviate the problem in the maximum-likelihood sequential decoding algorithm (MLSDA), we propose to directly eliminate the top path whose end node is $\\Delta$-trellis-level prior to the farthest one among all nodes that have been expanded thus far by the sequential search. Following random coding argument, we analyze the early-elimination window $\\Delta$ that results in negligible performance degradation for the MLSDA. Our analytical results indicate that the required early elimination window for negligible performance degradation is just twice of the constraint length for rate one-half convolutional codes. For rate one-third convolutional codes, the required early-elimination window even reduces to the constraint length. The suggestive theore...
Token Ring Algorithm To Achieve Mutual Exclusion In Distributed System - A Centralized Approach
Directory of Open Access Journals (Sweden)
Sandipan Basu
2011-01-01
Full Text Available This paper presents an algorithm for achieving mutual exclusion in Distributed System. The proposed algorithm is a betterment of the already existing Token Ring Algorithm, used to handle mutual exclusion in Distributed system. In the already existing algorithm, there are few problems, which, if occur during process execution, then the distributed system will not be able to ensure mutual exclusion among the processes and consequently unwanted situations may arise. The proposed algorithm will overcome all the problems in the existing algorithm, and ensures mutual exclusion among the processes, when they want to execute in their critical section of code.
A Request Distribution Algorithm for Web Server Cluster
Directory of Open Access Journals (Sweden)
Wei Zhang
2011-12-01
Full Text Available With the explosively increasing of web-based applications’ workloads, Web server cluster encounters challenge in response time for requests. Request distribution among servers in web server cluster is the key to address such challenge, especially under heavy workloads. In this paper, we propose a new request distribution algorithm named llac (least load active cache for load balancing switch in web server cluster. The goal of llac is to improve the cache hit rate and reduce response time. Packets are parsed in IP level, and back-end servers are notified to cache hot files using link change technology, neither changing URL information nor modifying the service program. This avoids switching overhead between user mode and kernel mode. The load balancing switch directly creates connection with the selected server, avoiding migrating connection overhead. This policy estimates the current composited load of each server and selects the server with the least load to serve the request. It also improves the resource utilization of web servers. Experimental results show that llac achieves better performance for web applications than wrr (weight round robin which is a popular request distribution.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Sierra-Hernandez, Juan Manuel; Avila-Garcia, Maria Susana; Rojas-Laguna, Roberto
2017-01-01
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. PMID:28321264
Cooperative Cognitive Networks: Optimal, Distributed and Low-Complexity Algorithms
Zheng, Gan; Wong, Kai-Kit; Ottersten, Bjorn
2012-01-01
This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unit rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, wh...
Fast Parabola Detection Using Estimation of Distribution Algorithms.
Guerrero-Turrubiates, Jose de Jesus; Cruz-Aceves, Ivan; Ledesma, Sergio; Sierra-Hernandez, Juan Manuel; Velasco, Jonas; Avina-Cervantes, Juan Gabriel; Avila-Garcia, Maria Susana; Rostro-Gonzalez, Horacio; Rojas-Laguna, Roberto
2017-01-01
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Directory of Open Access Journals (Sweden)
Jose de Jesus Guerrero-Turrubiates
2017-01-01
Full Text Available This paper presents a new method based on Estimation of Distribution Algorithms (EDAs to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
DEFF Research Database (Denmark)
Kordheili, Reza Ahmadi; Bak-Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna
2015-01-01
for distribution system operators (DSOs). Two smart charging algorithms are proposed in this study. The proposed algorithms are applied to a part of the Danish distribution grid as a case study. As a comparison, a dumb charging scenario, i.e. charging EVs without any specific order or algorithm, is also simulated...
Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm
Directory of Open Access Journals (Sweden)
W. M. Bukhari
2011-01-01
Full Text Available Problem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of wavelet transform was to distribute the signal energy with the change of time in different frequency bands. This will showed the characteristic of the signals since energy was an important physical variable in signal analysis. The EOG signals were captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features used to determine the characteristic of eye movement waveform. This technique adopted because it was a non-invasive, inexpensive and accurate. The new technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200. The recorded data was composed of an eye movement toward four directions, i.e., downward, upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT with energy algorithm and by comparing the energy distribution with the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to display the different percentages of energy for each wavelet coefficient towards different movement. Conclusion: From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding scale such as leftward with scale 6 (8- 16Hz, rightward with scale 8 (2-4Hz, downward with scale 9 (1-2Hz and upward with level 7 (4-8Hz. Statistically, the results in this study indicate that there are 93% (averages significance differences in the extracted features of wavelet Scalogram analysis.
Directory of Open Access Journals (Sweden)
Danial Rahdari
2015-02-01
Full Text Available Distributed systems consist of several management sites which have different resource sharing levels. Resources can be shared among inner site and outer site processes at first and second level respectively. Global coordinator should exist in order to coordinate access to multi site’s shared resources. Moreover; some other coordinators should manage access to inner site’s shared resources so that exerting appropriate coordinator election algorithms in each level is crucial to achieve most efficient system. In this paper a hierarchical distributed election algorithm is proposed which eliminates single point of failure of election launcher. Meanwhile traffic is applied to network at different times and the number of election messages is extremely decreased as well which applies more efficiency especially in high traffic networks. A standby system between coordinators and their first alternative is considered to induct less wait time to processes which want to communicate with coordinator
A Leveled Dag Critical Task Firstschedule Algorithm in Distributed Computing Systems
Directory of Open Access Journals (Sweden)
Amal EL-NATTAT
2016-01-01
Full Text Available In distributed computing environment, efficient task scheduling is essential to obtain high performance. A vital role of designing and development of task scheduling algorithms is to achieve better makes pan. Several task scheduling algorithms have been developed for homogeneous and heterogeneous distributed computing systems. In this paper, a new static task scheduling algorithm is proposed namely; Leveled DAG Critical Task First (LDCTF that optimizes the performance of Leveled DAG Prioritized Task (LDPT algorithm to efficiently schedule tasks on homogeneous distributed computing systems. LDPT was compared to B-level algorithm which is the most famous algorithm in homogeneous distributed systems and it provided better results. LDCTF is a list based scheduling algorithm which depends on sorting tasks into a list according to their priority then scheduling one by one on the suitable processor. LDCTF aims to improve the performance of the system by minimizing the schedule length than LDPT and B-level algorithms.
The derivation of distributed termination detection algorithms from garbage collection schemes
Tel, G.; Mattern, F.
2001-01-01
It is shown that the termination detection problem for distributed computations can be modelled as an instance of the garbage collection problem. Consequently, algorithms for the termination detection problem are obtained by applying transformations to garbage collection algorithms. The transformati
The derivation of distributed termination detection algorithms from garbage collection schemes
Tel, G.; Mattern, F.
1990-01-01
It is shown that the termination detection problem for distributed computations can be modelled as an instance of the garbage collection problem. Consequently, algorithms for the termination detection problem are obtained by applying transformations to garbage collection algorithms. The
Algorithm-dependent fault tolerance for distributed computing
Energy Technology Data Exchange (ETDEWEB)
P. D. Hough; M. e. Goldsby; E. J. Walsh
2000-02-01
Large-scale distributed systems assembled from commodity parts, like CPlant, have become common tools in the distributed computing world. Because of their size and diversity of parts, these systems are prone to failures. Applications that are being run on these systems have not been equipped to efficiently deal with failures, nor is there vendor support for fault tolerance. Thus, when a failure occurs, the application crashes. While most programmers make use of checkpoints to allow for restarting of their applications, this is cumbersome and incurs substantial overhead. In many cases, there are more efficient and more elegant ways in which to address failures. The goal of this project is to develop a software architecture for the detection of and recovery from faults in a cluster computing environment. The detection phase relies on the latest techniques developed in the fault tolerance community. Recovery is being addressed in an application-dependent manner, thus allowing the programmer to take advantage of algorithmic characteristics to reduce the overhead of fault tolerance. This architecture will allow large-scale applications to be more robust in high-performance computing environments that are comprised of clusters of commodity computers such as CPlant and SMP clusters.
Liu, Hua-Long; Liu, Hua-Dong
2014-10-01
Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And
Directory of Open Access Journals (Sweden)
Šime Ukić
2013-01-01
Full Text Available Gradient ion chromatography was used for the separation of eight sugars: arabitol, cellobiose, fructose, fucose, lactulose, melibiose, N-acetyl-D-glucosamine, and raffinose. The separation method was optimized using a combination of simplex or genetic algorithm with the isocratic-to-gradient retention modeling. Both the simplex and genetic algorithms provided well separated chromatograms in a similar analysis time. However, the simplex methodology showed severe drawbacks when dealing with local minima. Thus the genetic algorithm methodology proved as a method of choice for gradient optimization in this case. All the calculated/predicted chromatograms were compared with the real sample data, showing more than a satisfactory agreement.
Directory of Open Access Journals (Sweden)
Richveisová Barbora Micháleková
2014-06-01
Full Text Available Heavy metals are taken up by the vascular plant root system from water solutions in cationic forms. Subsequently, during both short and long distance transport to other plant tissues, cation forms are incorporated to many bioorganic compounds differing in stability, ionic character and physico-chemical properties such as solubility in lipid structures and mobility across cell membrane systems. Many sequential and single step extraction methods have been elaborated for characterization of the role of individual components of plant cells components in transport and detoxication of heavy metals. In our study, dry biomass of giant reed (Arundo donax L. grown in hydroponic media spiked with 65ZnCl2 and 109CdCl2 was treated with dithizone solutions as complexing ligand in order to convert free Zn2+ and Cd2+ ions to corresponding dithizonates. Treatment with dithizone showed that up to 67 % of the total plant Cd and 56 % of the total plant Zn were transformed to dithizonate complexes extracted with chloroform. Extraction of biomass with Folch reagent showed that up to 48 % of the total root cadmium and up to 18 % of the total shoot cadmium is bound in lipid fraction. Zinc was not found in lipid fraction of root and shoot. Derivatization of the dried root and shoot lipid fraction by dithizone showed that two third of Cd in root and practically all Cd in shoot lipid fraction could be transformed to Cd-dithizonate. Methods of biomass treating with complexing ligands and a method of sequential extraction procedures with non-polar organic solvents and radiotracer methodology seem to be useful methods for the study of metal speciation and distribution in vascular plants
Keim, Sophia; Zoernig, Inka; Spille, Anna; Lahrmann, Bernd; Brand, Karsten; Herpel, Esther; Grabe, Niels; Jäger, Dirk; Halama, Niels
2012-08-01
The role of the immune system in the course of colorectal cancer has been elucidated in the last decade. While quantification of immune cell infiltrates within the resected specimen at diagnosis has a clear power to estimate the prognosis of the patient, the role of infiltrating immune cells within the metastatic situation and especially within the metastatic lesion itself requires further detailed analyses. Recent analyses of infiltrates in colorectal cancer liver metastases revealed a role for the infiltrate density not only for prognosis but also in the prediction of treatment response. This not only broadens the view on these infiltrates and indicates a systematic role of the local immunological microenvironment, but also raises the question how these infiltrates change during repeated courses of treatment (i.e., resection, chemotherapy, etc.). To address this question, sequential lung or sequential liver metastases of colorectal cancer patients were analyzed using whole slide image quantification after immunohistochemical staining against CD3, CD8, FOXP3, CD68 and Granzyme B. The clinical data and interventions were associated with each individual patient and the metastatic lesions. The resulting cell densities reveal a heterogeneous profile: after successful treatment of a metastatic lesion, the recurrent lesion can still have the same immunophenotype with similar cell distributions. In a situation of a favorable immune cell profile, this profile can return and apparently convey a similar favorable course throughout the disease. But also the opposite was found: the recurrent metastatic lesion could have a different profile with alterations in specific immune cell subsets over time. Further analyses are required to elucidate the different patterns and their associations to the treatment, the tumor cell phenotype and other dynamic factors. However, it is clear from this data however, that there is an immune cell plasticity that needs to be analyzed for
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
Directory of Open Access Journals (Sweden)
Jing Chen
2015-06-01
Full Text Available This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.
Distributed Random Access Algorithm: Scheduling and Congesion Control
Jiang, Libin; Shin, Jinwoo; Walrand, Jean
2009-01-01
This paper provides proofs of the rate stability, Harris recurrence, and epsilon-optimality of CSMA algorithms where the backoff parameter of each node is based on its backlog. These algorithms require only local information and are easy to implement. The setup is a network of wireless nodes with a fixed conflict graph that identifies pairs of nodes whose simultaneous transmissions conflict. The paper studies two algorithms. The first algorithm schedules transmissions to keep up with given arrival rates of packets. The second algorithm controls the arrivals in addition to the scheduling and attempts to maximize the sum of the utilities of the flows of packets at the different nodes. For the first algorithm, the paper proves rate stability for strictly feasible arrival rates and also Harris recurrence of the queues. For the second algorithm, the paper proves the epsilon-optimality. Both algorithms operate with strictly local information in the case of decreasing step sizes, and operate with the additional info...
Directory of Open Access Journals (Sweden)
Shahrokh Shojaeian
2014-01-01
Full Text Available There are always some uncertainties in prediction and estimation of distribution systems loads. These uncertainties impose some undesirable impacts and deviations on power flow of the system which may cause reduction in accuracy of the results obtained by system analysis. Thus, probabilistic analysis of distribution system is very important. This paper proposes a probabilistic power flow technique by applying a normal probabilistic distribution in seven standard deviations on forward-backward algorithm. The losses and voltage of IEEE 33-bus test distribution network is investigated by our new algorithm and the results are compared with the conventional algorithm i.e., based on deterministic methods.
Institute of Scientific and Technical Information of China (English)
王晖; 刘大有; 等
1994-01-01
In this paper we consider the problem of sequential processing and present a sequential model based on the back-propagation algorithm.This model is intended to deal with intrinsically sequential problems,such as word recognition,speech recognition,natural language understanding.This model can be used to train a network to learn the sequence of input patterns,in a fixed order or a random order.Besides,this model is open- and partial-associative,characterized as “resognizing while accumulating”, which, as we argue, is mental cognition process oriented.
A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems
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Moharram Challenger
2013-01-01
Full Text Available Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs, and distributed databases. Reducing key metrics like message count per any critical section (CS and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algorithm needs only O(log2(N messages per CS and Suzuki-Kasami's algorithm needs just one message delivery time between two CS entrances. Nevertheless, both algorithms are weak in the other metric, synchronization delay and message complexity correspondingly. In this work, a new hybrid algorithm is proposed which gains from powerful aspects of both algorithms. Raysuz's algorithm (the proposed algorithm uses a clustered graph and executes Suzuki-Kasami's algorithm intraclusters and Raymond's algorithm interclusters. This leads to have better message complexity than that of pure Suzuki-Kasami's algorithm and better synchronization delay than that of pure Raymond's algorithm, resulting in an overall efficient DMX algorithm pure algorithm.
Directory of Open Access Journals (Sweden)
Lau Nguyen Dinh
2016-01-01
Full Text Available The problem of finding maximum flow in network graph is extremely interesting and practically applicable in many fields in our daily life, especially in transportation. Therefore, a lot of researchers have been studying this problem in various methods. Especially in 2013, we has developed a new algorithm namely, postflow-pull algorithm to find the maximum flow on traditional networks. In this paper, we revised postflow-push methods to solve this problem of finding maximum flow on extended mixed network. In addition, to take more advantage of multi-core architecture of the parallel computing system, we build this parallel algorithm. This is a completely new method not being announced in the world. The results of this paper are basically systematized and proven. The idea of this algorithm is using multi processors to work in parallel by postflow_push algorithm. Among these processors, there is one main processor managing data, sending data to the sub processors, receiving data from the sub-processors. The sub-processors simultaneously execute their work and send their data to the main processor until the job is finished, the main processor will show the results of the problem.
National Aeronautics and Space Administration — This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines...
Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding
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Yongjian Nian
2013-01-01
Full Text Available A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.
Directory of Open Access Journals (Sweden)
Niti Ashish Kumar Desai
2015-12-01
Full Text Available Business Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user’s interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of six additional constraints is as well to ensure that all patterns are recently active (Recency, active for certain time span (Compactness, profitable and indicative of next timeline for purchase (Length―Item―Gap. The article also attempts to throw light on how proposed Constraint-based Prefix Span algorithm is helpful to understand buying behaviour of customer which is in formative stage.
A Volume Rendering Algorithm for Sequential 2D Medical Images%序列二维医学图象的体绘制法
Institute of Scientific and Technical Information of China (English)
吕忆松; 陈亚珠
2002-01-01
Volume rendering of 3D data sets composed of sequential 2D medical images has become an important branch in image processing and computer graphics. To help physicians fully understand deep-seated human organs and focuses (e. g. a tumnout) as 3D structures, in this paper, we present a modified volume rendering algorithm to render volumetric data. Using this method, the projection images of structures of interest from different viewing directions can be obtained satisfactorily. By rotating the light source and the observer eyepoint, this method avoids rotates the whole volumetric data in main memory and thus reduces computational complexity and rendering time. Experiments on CT images suggest that the proposed method is useful and efficient for rendering 3D data sets.
DISTRIBUTED VERTEX COVER ALGORITHMS FOR WIRELESS SENSOR NETWORKS
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Vedat Kavalci
2014-01-01
Full Text Available Vertex covering has important applications for wireless sensor networks such as monitoring link failures, facility location, clustering, and data aggregation. In this study, we designed three algorithms for constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM environment. Finally we have implemented, compared and assessed all these approaches. The transmitted message count of the first algorithm is smallest among other algorithms where the third algorithm has turned out to be presenting the best results in vertex cover approximation ratio.
A Distributed Election and Spanning Tree Algorithm Based on Depth First Search Traversals
DEFF Research Database (Denmark)
Skyum, Sven
The existence of an effective distributed traversal algorithm for a class of graphs has proven useful in connection with election problems for those classes. In this paper we show how a general traversal algorithm, such as depth first search, can be turned into an effective election algorithm using...
Li, Xiaowei; Mei, Qingqing; Dai, Xiaohu; Ding, Guoji
2017-03-01
Thermogravimetric analysis, Gaussian-fit-peak model (GFPM), and distributed activation energy model (DAEM) were firstly used to explore the effect of anaerobic digestion on sequential pyrolysis kinetic of four organic solid wastes (OSW). Results showed that the OSW weight loss mainly occurred in the second pyrolysis stage relating to organic matter decomposition. Compared with raw substrate, the weight loss of corresponding digestate was lower in the range of 180-550°C, but was higher in 550-900°C. GFPM analysis revealed that organic components volatized at peak temperatures of 188-263, 373-401 and 420-462°C had a faster degradation rate than those at 274-327°C during anaerobic digestion. DAEM analysis showed that anaerobic digestion had discrepant effects on activation energy for four OSW pyrolysis, possibly because of their different organic composition. It requires further investigation for the special organic matter, i.e., protein-like and carbohydrate-like groups, to confirm the assumption. Copyright © 2016 Elsevier Ltd. All rights reserved.
Page, Andrew J; Keane, Thomas M; Naughton, Thomas J
2010-07-01
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Liqiang Liu
2014-01-01
Full Text Available Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Reconciling fault-tolerant distributed algorithms and real-time computing.
Moser, Heinrich; Schmid, Ulrich
We present generic transformations, which allow to translate classic fault-tolerant distributed algorithms and their correctness proofs into a real-time distributed computing model (and vice versa). Owing to the non-zero-time, non-preemptible state transitions employed in our real-time model, scheduling and queuing effects (which are inherently abstracted away in classic zero step-time models, sometimes leading to overly optimistic time complexity results) can be accurately modeled. Our results thus make fault-tolerant distributed algorithms amenable to a sound real-time analysis, without sacrificing the wealth of algorithms and correctness proofs established in classic distributed computing research. By means of an example, we demonstrate that real-time algorithms generated by transforming classic algorithms can be competitive even w.r.t. optimal real-time algorithms, despite their comparatively simple real-time analysis.
On distribution reduction and algorithm implementation in inconsistent ordered information systems.
Zhang, Yanqin
2014-01-01
As one part of our work in ordered information systems, distribution reduction is studied in inconsistent ordered information systems (OISs). Some important properties on distribution reduction are studied and discussed. The dominance matrix is restated for reduction acquisition in dominance relations based information systems. Matrix algorithm for distribution reduction acquisition is stepped. And program is implemented by the algorithm. The approach provides an effective tool for the theoretical research and the applications for ordered information systems in practices. For more detailed and valid illustrations, cases are employed to explain and verify the algorithm and the program which shows the effectiveness of the algorithm in complicated information systems.
Grahl, J.; Minner, S.; Bosman, P.A.N.; Michalewicz, Z.; Siarry, P.
2008-01-01
This chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit certain structur
J. Grahl; S. Minner; P.A.N. Bosman (Peter); Z. Michalewicz; P. Siarry
2008-01-01
htmlabstractThis chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit
Institute of Scientific and Technical Information of China (English)
YANGGuo-Sheng; WENCheng-Lin; TANMin
2004-01-01
A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.
Sopov, E.; Semenkina, O.
2015-01-01
Genetic and distribution building algorithms with binary representation are analyzed. A property of convergence to the optimal solution is discussed. A novel convergence prediction method is proposed and investigated. The method is based on analysis of gene value probabilities distribution dynamics, thus it can predict gene values of the optimal solution to which the algorithm converges. The results of investigations for the optimal prediction algorithm performance are presented.
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher [Electrical and Electronics Engineering Department, Shiraz University of Technology, Modars Blvd. P.O. 71555-313, Shiraz (Iran, Islamic Republic of)
2011-03-15
In recent years, Distributed Generators (DGs) connected to the distribution network have received increasing attention. The connection of enormous DGs into existing distribution network changes the operation of distribution systems. Because of the small X/R ratio and radial structure of distribution systems, DGs affect the daily Volt/Var control. This paper presents a new algorithm for multiobjective daily Volt/Var control in distribution systems including Distributed Generators (DGs). The objectives are costs of energy generation by DGs and distribution companies, electrical energy losses and the voltage deviations for the next day. A new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. Since objectives are not the same, a fuzzy system is used to calculate the best solution. The plausibility of the proposed algorithm is demonstrated and its performance is compared with other methods on a 69-bus distribution feeder. Simulation results illustrate that the proposed algorithm has better outperforms the other algorithms. (author)
Survivable algorithms and redundancy management in NASA's distributed computing systems
Malek, Miroslaw
1992-01-01
The design of survivable algorithms requires a solid foundation for executing them. While hardware techniques for fault-tolerant computing are relatively well understood, fault-tolerant operating systems, as well as fault-tolerant applications (survivable algorithms), are, by contrast, little understood, and much more work in this field is required. We outline some of our work that contributes to the foundation of ultrareliable operating systems and fault-tolerant algorithm design. We introduce our consensus-based framework for fault-tolerant system design. This is followed by a description of a hierarchical partitioning method for efficient consensus. A scheduler for redundancy management is introduced, and application-specific fault tolerance is described. We give an overview of our hybrid algorithm technique, which is an alternative to the formal approach given.
T-Algorithm-Based Logic Simulation on Distributed Systems
Sundaram, S; Patnaik, LM
1992-01-01
Increase in the complexity of VLSI digital circuit it sign demands faster logic simulation techniques than those currently available. One of the ways of speeding up existing logic simulataon algorithms is by exploiting the inherent parallelism an the sequentaal versaon. In this paper, we explore the possibility of mapping a T-algoriihm based logac samulataon algorithm onto a cluster of workstation interconnected by an ethernet. The set of gates at a particular level as partitioned by the hias...
Distributed Algorithm Simulation Using Input/Output Automata
1990-09-01
include: " leader election : Choose exactly one distinguished "leader" process (to coordinate some computation). " mutual exclusion: Grant permission for...mechanisms for defining I/O automaton types. Section 3.4 contains an example of an automaton type for LeLann’s leader election algorithm [39]. Finally...e. status ,"announced") Figure 3-1: Automaton types for LeLann’s leader election algorithm. 3.5. SUPPORT FOR VERIFICATION, ANALYSIS, AND
Performance of Concurrency Control Algorithms in Distributed Systems
1989-08-01
these processes then discover that they are chosen is a dilemma shared with mutual exclusion algorithms. Leader election is a special case of mutual...exclusion. In both leader election and mutual exclusion algorithms, some single process is chosen from among the other processes in the system. This...process is then granted spe- cial status: in mutual exclusion, the chosen process enters the critical section; in leader election , the chosen process
Algorithms and ordering heuristics for distributed constraint satisfaction problems
Wahbi , Mohamed
2013-01-01
DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence.A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several applications in multi-agent coordination that are of a distributed nature. In this type of application, the knowledge about the problem, that is, variables and constraints, may be logically or geographically distributed among physical distributed agents. This distribution is mainly due to p
Laubacher, Marco; Aksöz, Efe A; Binder-Macleod, Stuart; Hunt, Kenneth J
2016-06-13
Spatially distributed sequential stimulation (SDSS) has demonstrated substantial power output and fatigue benefits compared to single electrode stimulation (SES) in the application of functional electrical stimulation (FES). This asymmetric electrode setup brings new possibilities but also new questions since precise placement of the electrodes is one critical factor for good muscle activation. The aim of this study was to compare the power output, fatigue and activation properties of proximally versus distally placed SDSS electrodes in an isokinetic knee extension task simulating knee movement during recumbent cycling. M. vastus lateralis and medialis of seven able-bodied subjects were stimulated with rectangular bi-phasic pulses of constant amplitude of 40 mA and at an SDSS frequency of 35 Hz for 6 min on both legs with both setups (i.e. n=14). Torque was measured during knee-extension movement by a dynamometer at an angular velocity of 110 deg/s. Mean power, peak power and activation time were calculated and compared for the initial and final stimulation phases, together with an overall fatigue index. Power output values (Pmean, Ppeak) were scaled to a standardised reference input pulse width of 100 μs (Pmean,s, Ppeak,s). The initial evaluation phase showed no significant differences between the two setups for all outcome measures. Ppeak and Ppeak,s were both significantly higher in the final phase for the distal setup (25.4 ± 8.1 W vs. 28.2 ± 6.2 W, p=0.0062 and 34.8 ± 9.5 W vs. 38.9 ± 6.7 W, p=0.021, respectively). With distal SDSS, there was modest evidence of higher Pmean and Pmean,s (p=0.071, p=0.14, respectively) but of longer activation time (p=0.096). The rate of fatigue was similar for both setups. For practical FES applications, distal placement of the SDSS electrodes is preferable.
Metadata distribution algorithm based on directory hash in mass storage system
Wu, Wei; Luo, Dong-jian; Pei, Can-hao
2008-12-01
The distribution of metadata is very important in mass storage system. Many storage systems use subtree partition or hash algorithm to distribute the metadata among metadata server cluster. Although the system access performance is improved, the scalability problem is remarkable in most of these algorithms. This paper proposes a new directory hash (DH) algorithm. It treats directory as hash key value, implements a concentrated storage of metadata, and take a dynamic load balance strategy. It improves the efficiency of metadata distribution and access in mass storage system by hashing to directory and placing metadata together with directory granularity. DH algorithm has solved the scalable problems existing in file hash algorithm such as changing directory name or permission, adding or removing MDS from the cluster, and so on. DH algorithm reduces the additional request amount and the scale of each data migration in scalable operations. It enhances the scalability of mass storage system remarkably.
SMCTC: Sequential Monte Carlo in C++
Directory of Open Access Journals (Sweden)
Adam M. Johansen
2009-04-01
Full Text Available Sequential Monte Carlo methods are a very general class of Monte Carlo methodsfor sampling from sequences of distributions. Simple examples of these algorithms areused very widely in the tracking and signal processing literature. Recent developmentsillustrate that these techniques have much more general applicability, and can be appliedvery eectively to statistical inference problems. Unfortunately, these methods are oftenperceived as being computationally expensive and dicult to implement. This articleseeks to address both of these problems.A C++ template class library for the ecient and convenient implementation of verygeneral Sequential Monte Carlo algorithms is presented. Two example applications areprovided: a simple particle lter for illustrative purposes and a state-of-the-art algorithmfor rare event estimation.
Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming
Peng-Yeng Yin; Hsi-Li Wu
2013-01-01
The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The suc...
Document distribution algorithm for load balancing on an extensible Web server architecture
Ng, CP; Wang, CL
2001-01-01
Access latency and load balancing are the two main issues in the design of clustered Web server architecture for achieving high performance. We propose a novel document distribution algorithm for load balancing on a cluster of distributed Web servers. We group Web pages that are likely to be accessed during a request session into a migrating unit, which is used as the basic unit of document placement. A modified binning algorithm is developed to distribute the migrating units among the Web se...
The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller
Institute of Scientific and Technical Information of China (English)
CHENQing-Geng; WANGNing; HUANGShao-Feng
2005-01-01
Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.
Energy Technology Data Exchange (ETDEWEB)
D' Azevedo, E.F.; Romine, C.H.
1992-09-01
The standard formulation of the conjugate gradient algorithm involves two inner product computations. The results of these two inner products are needed to update the search direction and the computed solution. In a distributed memory parallel environment, the computation and subsequent distribution of these two values requires two separate communication and synchronization phases. In this paper, we present a mathematically equivalent rearrangement of the standard algorithm that reduces the number of communication phases. We give a second derivation of the modified conjugate gradient algorithm in terms of the natural relationship with the underlying Lanczos process. We also present empirical evidence of the stability of this modified algorithm.
Directory of Open Access Journals (Sweden)
Wanxing Sheng
2014-01-01
Full Text Available To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS. The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application.
DEFF Research Database (Denmark)
Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar
2017-01-01
, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...... and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm...... optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality....
Target distribution in cooperative combat based on Bayesian optimization algorithm
Institute of Scientific and Technical Information of China (English)
Shi Zhifu; Zhang An; Wang Anli
2006-01-01
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
RELOCATION ALGORITHM FOR NON-UNIFORM DISTRIBUTION IN MOBILE SENSOR NETWORK
Institute of Scientific and Technical Information of China (English)
Pei Zhiqiang; Xu Changqing; Teng Jing
2009-01-01
Energy is the determinant factor for the survival of Mobile Sensor Networks (MSN). Based on the analysis of the energy distribution in this paper, a two-phase relocation algorithm is proposed based on the balance between the energy provision and energy consumption distribution. Our main objectives are to maximize the coverage percentage and to minimize the total distance of node movements. This algorithm is designed to meet the requirement of non-uniform distribution network applications, to extend the lifetime of MSN and to simplify the design of the routing protocol. In addition, test results show the feasibility of our proposed relocation algorithm.
Secure Computation, I/O-Efficient Algorithms and Distributed Signatures
DEFF Research Database (Denmark)
Damgård, Ivan Bjerre; Kölker, Jonas; Toft, Tomas
2012-01-01
adversary corrupting a constant fraction of the players and servers. Using packed secret sharing, the data can be stored in a compact way but will only be accessible in a block-wise fashion. We explore the possibility of using I/O-efficient algorithms to nevertheless compute on the data as efficiently...
Numerical algorithm of distributed TOPKAPI model and its application
Directory of Open Access Journals (Sweden)
Deng Peng
2008-12-01
Full Text Available The TOPKAPI (TOPographic Kinematic APproximation and Integration model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model’s computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km2, using a DEM (digital elevation model grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.
Numerical algorithm of distributed TOPKAPI model and its application
Institute of Scientific and Technical Information of China (English)
Deng Peng; Li Zhijia; Liu Zhiyu
2008-01-01
The TOPKAPI (TOPographic Kinematic APproximation and Integration) model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model's computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km2, using a DEM (digital elevation model) grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.
Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms
Bosman, P.A.N.; Thierens, D.; Thierens, D.
2007-01-01
Recent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs) has shown that when maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we argue that th
A Data Mining Algorithm Based on Distributed Decision-Tree in Grid Computing Environments
Institute of Scientific and Technical Information of China (English)
Zhongda Lin; Yanfeng Hong; Kun Deng
2006-01-01
Recently, researches on distributed data mining by making use of grid are in trend. This paper introduces a data mining algorithm by means of distributed decision-tree, which has taken the advantage of conveniences and services supplied by the computing platform-grid, and can perform a data mining of distributed classification on grid.
Li, Wenhao
2011-01-01
Distributed workflow technology has been widely used in modern education and e-business systems. Distributed web applications have shown cross-domain and cooperative characteristics to meet the need of current distributed workflow applications. In this paper, the author proposes a dynamic and adaptive scheduling algorithm PCSA (Pre-Calculated…
Li, Wenhao
2011-01-01
Distributed workflow technology has been widely used in modern education and e-business systems. Distributed web applications have shown cross-domain and cooperative characteristics to meet the need of current distributed workflow applications. In this paper, the author proposes a dynamic and adaptive scheduling algorithm PCSA (Pre-Calculated…
Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms
P.A.N. Bosman (Peter); D. Thierens (Dirk); D. Thierens (Dirk)
2007-01-01
htmlabstractRecent research into single-objective continuous Estimation-of-Distribution Algorithms (EDAs) has shown that when maximum-likelihood estimations are used for parametric distributions such as the normal distribution, the EDA can easily suffer from premature convergence. In this paper we
Routing Optimization Based on Taboo Search Algorithm for Logistic Distribution
Hongxue Yang; Lingling Xuan
2014-01-01
Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distr...
A distributed routing algorithm for data aggregation in wireless sensor networks
Institute of Scientific and Technical Information of China (English)
Hong LUO; Fangchun YANG; Yonghe LIU
2008-01-01
Considering the impact of aggregation cost on the performance of aggregation routes in wireless sensor networks, an aggregation-decision-based distributed rout-ing algorithm for data aggregation is proposed. When source nodes arrive or leave, the algorithm can calculate the aggregation benefit according to data correlation, aggregation cost and transmission cost. Then the algo-rithm will adaptively make aggregation and routing decisions based on aggregation benefit. Therefore, it can jointly optimize the aggregation and transmission costs and reduce the energy consumption for data gathering. This distributed algorithm makes all the decisions only relying on the local information. Hence, the routing maintenance cost is limited. Simulation results show that the energy consumption difference between this distrib-uted online algorithm and the previous offilne one is within 17% under any network conditions.
Laubacher, Marco; Aksöz, Efe A.; Binder-Macleod, Stuart; Hunt, Kenneth J.
2016-01-01
Spatially distributed sequential stimulation (SDSS) has demonstrated substantial power output and fatigue benefits compared to single electrode stimulation (SES) in the application of functional electrical stimulation (FES). This asymmetric electrode setup brings new possibilities but also new questions since precise placement of the electrodes is one critical factor for good muscle activation. The aim of this study was to compare the power output, fatigue and activation properties of proximally versus distally placed SDSS electrodes in an isokinetic knee extension task simulating knee movement during recumbent cycling. M. vastus lateralis and medialis of seven able-bodied subjects were stimulated with rectangular bi-phasic pulses of constant amplitude of 40 mA and at an SDSS frequency of 35 Hz for 6 min on both legs with both setups (i.e. n=14). Torque was measured during knee-extension movement by a dynamometer at an angular velocity of 110 deg/s. Mean power, peak power and activation time were calculated and compared for the initial and final stimulation phases, together with an overall fatigue index. Power output values (Pmean, Ppeak) were scaled to a standardised reference input pulse width of 100 μs (Pmean,s, Ppeak,s). The initial evaluation phase showed no significant differences between the two setups for all outcome measures. Ppeak and Ppeak,s were both significantly higher in the final phase for the distal setup (25.4 ± 8.1 W vs. 28.2 ± 6.2 W, p=0.0062 and 34.8 ± 9.5 W vs. 38.9 ± 6.7 W, p=0.021, respectively). With distal SDSS, there was modest evidence of higher Pmean and Pmean,s (p=0.071, p=0.14, respectively) but of longer activation time (p=0.096). The rate of fatigue was similar for both setups. For practical FES applications, distal placement of the SDSS electrodes is preferable. PMID:27478563
Directory of Open Access Journals (Sweden)
Marco Laubacher
2016-06-01
Full Text Available Spatially distributed sequential stimulation (SDSS has demonstrated substantial power output and fatigue benefits compared to single electrode stimulation (SES in the application of functional electrical stimulation (FES. This asymmetric electrode setup brings new possibilities but also new questions since precise placement of the electrodes is one critical factor for good muscle activation. The aim of this study was to compare the power output, fatigue and activation properties of proximally versus distally placed SDSS electrodes in an isokinetic knee extension task simulating knee movement during recumbent cycling. M. vastus lateralis and medialis of seven able-bodied subjects were stimulated with rectangular bi-phasic pulses of constant amplitude of 40 mA and at an SDSS frequency of 35 Hz for 6 min on both legs with both setups (i.e. n=14. Torque was measured during knee-extension movement by a dynamometer at an angular velocity of 110 deg/s. Mean power, peak power and activation time were calculated and compared for the initial and final stimulation phases, together with an overall fatigue index. Power output values (Pmean, Ppeak were scaled to a standardised reference input pulse width of 100 μs (Pmean,s, Ppeak,s. The initial evaluation phase showed no significant differences between the two setups for all outcome measures. Ppeak and Ppeak,s were both significantly higher in the final phase for the distal setup (25.4 ± 8.1 W vs. 28.2 ± 6.2 W, p=0.0062 and 34.8 ± 9.5 W vs. 38.9 ± 6.7 W, p=0.021, respectively. With distal SDSS, there was modest evidence of higher Pmean and Pmean,s (p=0.071, p=0.14, respectively but of longer activation time (p=0.096. The rate of fatigue was similar for both setups. For practical FES applications, distal placement of the SDSS electrodes is preferable.
Directory of Open Access Journals (Sweden)
Nirmeen A. Bahnasawy
2011-11-01
Full Text Available In distributed computing, the schedule by which tasks are assigned to processors is critical to minimizing the execution time of the application. However, the problem of discovering the schedule that gives the minimum execution time is NP-complete. In this paper, a new task scheduling algorithm called Sorted Nodes in Leveled DAG Division (SNLDD is introduced and developed for HeDCSs with consider a bounded number of processors. The main principle of the developed algorithm is to divide the Directed Acyclic Graph (DAG into levels and sort the tasks in each level according to their computation size in descending order. To evaluate the performance of the developed SNLDD algorithm, a comparative study has been done between the developed SNLDD algorithm and the Longest Dynamic Critical Path (LDCP algorithm which is considered the most efficient existing algorithm. According to the comparative results, it is found that the performance of the developed algorithm provides better performance than the LDCP algorithm in terms of speedup, efficiency, complexity, and quality. Also, a new procedure called Superior Performance Optimization Procedure (SPOP has been introduced and implemented in the developed SNLDD algorithm and the LDCP algorithm to minimize the sleek time of the processors in the system. Again, the performance of the SNLDD algorithm outperforms the existing LDCP algorithm after adding the SPOP procedure.
Bio-Inspired Distributed Decision Algorithms for Anomaly Detection
2017-03-01
NUMBER RU 5f. WORK UNIT NUMBER TG 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Rutgers University New Brunswick, NJ 08901 8. PERFORMING... ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME( S ) AND ADDRESS(ES) Air Force Research Laboratory/RIG 525 Brooks Road Rome NY 13441-4505...algorithm ia with thresholds tist , . We then defined four different test functions tic , for all nodes in order to explore the impact of different
Liang, Faming; Jin, Ick-Hoon
2013-08-01
Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and exponential random graph models. Unlike other auxiliary variable Markov chain Monte Carlo (MCMC) algorithms, such as the Møller and exchange algorithms, the MCMH algorithm avoids the requirement for perfect sampling, and thus can be applied to many statistical models for which perfect sampling is not available or very expensive. The MCMH algorithm can also be applied to Bayesian inference for random effect models and missing data problems that involve simulations from a distribution with intractable integrals.
Analysis of Distributed and Adaptive Genetic Algorithm for Mining Interesting Classification Rules
Institute of Scientific and Technical Information of China (English)
YI Yunfei; LIN Fang; QIN Jun
2008-01-01
Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules. The paper gives the method to encode for the rules, the fitness function, the selecting, crossover, mutation and migration operator for the DAGA at the same time are designed.
A Harmony Search Based Algorithm for Detecting Distributed Predicates
Directory of Open Access Journals (Sweden)
Eslam Al Maghayreh
2012-10-01
Full Text Available Detection of distributed predicates (also referred to as runtime verification can be used to verify that a particular run of a given distributed program satisfies certain properties (represented as predicates. Consequently, distributed predicates detection techniques can be used to effectively improve the dependability of a given distributed application. Due to concurrency, the detection of distributed predicates can incur significant overhead. Most of the effective techniques developed to solve this problem work efficiently for certain classes of predicates, like conjunctive predicates. In this paper, we have presented a technique based on harmony search to efficiently detect the satisfaction of a predicate under the possibly modality. We have implemented the proposed technique and we have conducted several experiments to demonstrate its effectiveness.
Appraisal of jump distributions in ensemble-based sampling algorithms
Dejanic, Sanda; Scheidegger, Andreas; Rieckermann, Jörg; Albert, Carlo
2017-04-01
Sampling Bayesian posteriors of model parameters is often required for making model-based probabilistic predictions. For complex environmental models, standard Monte Carlo Markov Chain (MCMC) methods are often infeasible because they require too many sequential model runs. Therefore, we focused on ensemble methods that use many Markov chains in parallel, since they can be run on modern cluster architectures. Little is known about how to choose the best performing sampler, for a given application. A poor choice can lead to an inappropriate representation of posterior knowledge. We assessed two different jump moves, the stretch and the differential evolution move, underlying, respectively, the software packages EMCEE and DREAM, which are popular in different scientific communities. For the assessment, we used analytical posteriors with features as they often occur in real posteriors, namely high dimensionality, strong non-linear correlations or multimodality. For posteriors with non-linear features, standard convergence diagnostics based on sample means can be insufficient. Therefore, we resorted to an entropy-based convergence measure. We assessed the samplers by means of their convergence speed, robustness and effective sample sizes. For posteriors with strongly non-linear features, we found that the stretch move outperforms the differential evolution move, w.r.t. all three aspects.
Sequential Monte Carlo on large binary sampling spaces
Schäfer, Christian
2011-01-01
A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for a good performance. In this paper, we present such a parametric family for adaptive sampling on high-dimensional binary spaces. A practical motivation for this problem is variable selection in a linear regression context. We want to sample from a Bayesian posterior distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions of Sequential Monte Carlo are easily implemented using binary vectors with independent components. For high-dimensional problems, however, these simple proposals do not yield satisfactory results. The key to an efficient adaptive algorithm are binary parametric families which take correlations into account, analogously to the multivariate normal distribution on continuous spaces. We provide a review of models for binar...
National Aeronautics and Space Administration — Recent work on distributed multi-spacecraft systems has resulted in a number of architectures and algorithms for accurate estimation of spacecraft and formation...
National Aeronautics and Space Administration — In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used...
A Local Scalable Distributed EM Algorithm for Large P2P Networks
National Aeronautics and Space Administration — his paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
National Aeronautics and Space Administration — This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...
Directory of Open Access Journals (Sweden)
Kaifeng Yang
2014-01-01
Full Text Available A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics.
EDMC: An enhanced distributed multi-channel anti-collision algorithm for RFID reader system
Zhang, YuJing; Cui, Yinghua
2017-05-01
In this paper, we proposes an enhanced distributed multi-channel reader anti-collision algorithm for RFID environments which is based on the distributed multi-channel reader anti-collision algorithm for RFID environments (called DiMCA). We proposes a monitor method to decide whether reader receive the latest control news after it selected the data channel. The simulation result shows that it improves interrogation delay.
对数正态分布寿命型序贯验证试验方法%Sequential compliance test method for lognormal distribution
Institute of Scientific and Technical Information of China (English)
邓清; 袁宏杰
2012-01-01
Using the experience of sequential verification test program in exponential distribution for reference,the method of making the sequential verification test program in lognormal distribution was discussed,which takes the average life as an indicator.The test procedure of sequential test was provided,and the upper limit value of the producer and consumer risks were studied under censoring.According to the sampling method in practical engineering,the simulation method was proposed to evaluate the above mentioned test program.Evaluation results indicate that the proposed sequential verification test program can meet the requirements of controlling both sides of risk on the premise of satisfying the requirement of sample and censored size.And the consumer's risk is lower than the expected value.%借鉴指数分布寿命型序贯验证试验方案的思想,讨论了以平均寿命为指标的对数正态分布寿命型产品序贯验证试验的制定方法,给出了序贯试验的试验程序,研究了截尾状态下序贯试验的生产方风险和使用方风险的上限.基于工程实际的抽样方法,给出了计算机仿真评价方法,对给出的序贯试验方案进行评价.评价结果表明,在样本量和截尾数满足要求的前提下,所提出的序贯验证试验方法能够满足对双方风险的控制要求,且对使用方风险提供了更大的保护.
Dynamic Consensus Algorithm based Distributed Unbalance Compensation in Islanded Microgrids
DEFF Research Database (Denmark)
Meng, Lexuan; Zhao, Xin; Firoozabadi, Mehdi Savaghebi;
2015-01-01
In islanded microgrids (MG), distributed generators (DG) can be employed as distributed compensators for improving the power quality (voltage unbalance and harmonics) in consumer side. Hierarchical control is usually applied with different control levels differentiated. In case of voltage unbalance...... compensation, droop control and virtual impedance can be applied in primary control to help the positive sequence active and reactive power sharing. Secondary control is used to assist voltage unbalance compensation. However, if transmission line differences are considered, the negative sequence current cannot...
Smail, Linda
2016-06-01
The basic task of any probabilistic inference system in Bayesian networks is computing the posterior probability distribution for a subset or subsets of random variables, given values or evidence for some other variables from the same Bayesian network. Many methods and algorithms have been developed to exact and approximate inference in Bayesian networks. This work compares two exact inference methods in Bayesian networks-Lauritzen-Spiegelhalter and the successive restrictions algorithm-from the perspective of computational efficiency. The two methods were applied for comparison to a Chest Clinic Bayesian Network. Results indicate that the successive restrictions algorithm shows more computational efficiency than the Lauritzen-Spiegelhalter algorithm.
Generalized Analysis of a Distributed Energy Efficient Algorithm for Change Detection
Banerjee, Taposh
2009-01-01
An energy efficient distributed Change Detection scheme based on Page's CUSUM algorithm was presented in \\cite{icassp}. In this paper we consider a nonparametric version of this algorithm. In the algorithm in \\cite{icassp}, each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. In this paper, we generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis.
A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing
Institute of Scientific and Technical Information of China (English)
徐勇; 张玉洁; 邢婧; 李宏伟
2015-01-01
A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero (significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.
Enhanced Bully Algorithm for Leader Node Election in Synchronous Distributed Systems
Directory of Open Access Journals (Sweden)
Md. Golam Murshed
2012-06-01
Full Text Available In distributed computing systems, if an elected leader node fails, the other nodes of the system need to elect another leader. The bully algorithm is a classical approach for electing a leader in a synchronous distributed computing system. This paper presents an enhancement of the bully algorithm, requiring less time complexity and minimum message passing. This significant gain has been achieved by introducing node sets and tie breaker time. The latter provides a possible solution to simultaneous elections initiated by different nodes. In comparison with the classical algorithm and its existing modifications, this proposal generates minimum messages, stops redundant elections, and maintains fault-tolerant behaviour of the system.
Experiments with the auction algorithm for the shortest path problem
DEFF Research Database (Denmark)
Larsen, Jesper; Pedersen, Ib
1999-01-01
The auction approach for the shortest path problem (SPP) as introduced by Bertsekas is tested experimentally. Parallel algorithms using the auction approach are developed and tested. Both the sequential and parallel auction algorithms perform significantly worse than a state-of-the-art Dijkstra......-like reference algorithm. Experiments are run on a distributed-memory MIMD class Meiko parallel computer....
Liang, Faming
2013-08-01
Simulating from distributions with intractable normalizing constants has been a long-standing problem inmachine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and exponential random graph models. Unlike other auxiliary variable Markov chain Monte Carlo (MCMC) algorithms, such as the Møller and exchange algorithms, the MCMH algorithm avoids the requirement for perfect sampling, and thus can be applied to many statistical models for which perfect sampling is not available or very expensive. TheMCMHalgorithm can also be applied to Bayesian inference for random effect models and missing data problems that involve simulations from a distribution with intractable integrals. © 2013 Massachusetts Institute of Technology.
Institute of Scientific and Technical Information of China (English)
ZHENG Qing; YANG Zhen
2005-01-01
Based on the Multi-Packet Reception(MPR)capability at the physical layer and the Distributed Coordination Function(DCF)of the IEEE 802.11 MAC protocol,we propose a modified new solution about WAITING mechanism to make full use of the MPR capability in this paper,which is named as modified distributed medium access control algorithm.We describe the details of each step of the algorithm after introducing the WAITING mechanism.Then,we also analyze how the waiting-time affects the throughput performance of the network.The network simulator NS-2 is used to evaluate the throughput performance of the new WAITING algorithm and we compare it with IEEE 802.11 MAC protocol and the old WAITING algorithm.The experimental results show that our new algorithm has the best performance.
Distributed edge detection algorithm based on wavelet transform for wireless video sensor network
Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng
2011-05-01
Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.
Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references
Janapati, Ravi Chander; Balaswamy, Ch.; Soundararajan, K.
2016-08-01
Localization is the key research area in Wireless Sensor Networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao Bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization algorithm PSO with CRB is proposed. Proposed method shows better results in terms of position accuracy, latency and complexity.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In order to resolve the multisensor multiplied maneuvering target tracking problem, this paper presents a distributed interacted multiple model multisensor joint probabilistic data association algorithm (DIMM-MSJPDA). First of all, the interacted multiple model joint probabilistic data association algorithm is applied to each sensor, and then the state estimation, estimation covariance, model probability, combined innovation, innovation covariance are delivered to the fusion center. Then, the tracks from each sensor are correlated and the D-S evidence theory is used to gain the model probability of an identical target. Finally, the ultimate state estimation of each target is calculated according to the new model probability, and the state estimation is transmitted to each sensor. Simulations are designed to test the tracking performance of DIMM-MSJPDA algorithm. The results show that the use of DIMM-MSJPDA algorithm enables the distributed multisensor system to track multiplied maneuvering targets and its tracking performance is much better than that of IMMJPDA algorithm.
Location and Size of Distributed Generation Using a Modified Water Cycle Algorithm
Directory of Open Access Journals (Sweden)
John Edwin Candelo Becerra
2015-06-01
Full Text Available This paper presents a modified water cycle algorithm (WCA adapted to the problem of finding the location and size of distributed generation (DG. Power losses minimization was used as an objective function to compare the proposed algorithm with particle swarm optimization (PSO, the batinspired Algorithm (BA, and harmony search (HS. The test scenarios consisted of locating five to seven generators with a maximum real and reactive power in the 33-node and 69-node radial distribution networks. The experiment was designed to start iterations from the same initial population to identify the algorithms’ performance when searching for the best solutions. The results demonstrate that the modified WCA found the minimum power losses after locating and sizing distributed generators for most of the test scenarios. The algorithm converged quickly to the best solution and the solutions for all repetitions tested were close to the best for each case simulated.
Directory of Open Access Journals (Sweden)
N. H. Shamsudin
2014-05-01
Full Text Available Power losses issues persevered over few decades in the high demand utilization of energy electricity in developing countries. Thus, the radial structure of distribution network configuration is extensively used in high populated areas to ensure continuity of power supply in the event of fault. This paper proposes heuristic Genetic Algorithm known as SIGA (Selection Improvement in Genetic Algorithm in consideration of genetic operator probabilities likewise the progression of switch adjustment in Distribution Network Reconfiguration (DNR while satisfying the parameters constraints. The SIGA algorithm was embodied throughout the process in IEEE 33-bus distribution system in selection of five tie switches. As a consequence, the power losses were ranked in accordance to the minimum values and voltage profile improvement obtainable by the proposed algorithm. The results show that the SIGA performs better than GA by giving the minimized value of power losses.
An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering
Uwe, Aickelin; Jingpeng, Li
2007-01-01
This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is ...
Generalization of the MOACS algorithm for Many Objectives. An application to motorcycle distribution
Directory of Open Access Journals (Sweden)
Benjamin Baran
2015-08-01
Full Text Available To solve many-objective routing problems, this paper generalizes the Multi-Objective Ant Colony System (MOACS algorithm, a well-known Multi-Objective Ant Colony Optimization (MOACO metaheuristic proposed in 2003. This Generalized MOACS algorithm is used to solve a Split-Delivery/Mixed-Fleet Vehicle Routing Problem (SD/MF-VRP under different constraints, resulting from the mathematical modeling of a logistic problem: the distribution of motorcycles by a Paraguayan factory, considering several objective functions as: (1 total distribution cost, (2 total traveled distance, (3 total traveled time, and (4 nsatisfied demand. Experimental results using the proposed algorithm in weekly operations of the motorcycle factory prove the advantages of using the proposed algorithm, facilitating the work of the logistic planner, reducing the distribution cost and minimizing the time needed to satisfy customers.
A sequential tree approach for incremental sequential pattern mining
Indian Academy of Sciences (India)
RAJESH KUMAR BOGHEY; SHAILENDRA SINGH
2016-12-01
‘‘Sequential pattern mining’’ is a prominent and significant method to explore the knowledge and innovation from the large database. Common sequential pattern mining algorithms handle static databases.Pragmatically, looking into the functional and actual execution, the database grows exponentially thereby leading to the necessity and requirement of such innovation, research, and development culminating into the designing of mining algorithm. Once the database is updated, the previous mining result will be incorrect, and we need to restart and trigger the entire mining process for the new updated sequential database. To overcome and avoid the process of rescanning of the entire database, this unique system of incremental mining of sequential pattern is available. The previous approaches, system, and techniques are a priori-based frameworks but mine patterns is an advanced and sophisticated technique giving the desired solution. We propose and incorporate an algorithm called STISPM for incremental mining of sequential patterns using the sequence treespace structure. STISPM uses the depth-first approach along with backward tracking and the dynamic lookahead pruning strategy that removes infrequent and irregular patterns. The process and approach from the root node to any leaf node depict a sequential pattern in the database. The structural characteristic of the sequence tree makes it convenient and appropriate for incremental sequential pattern mining. The sequence tree also stores all the sequential patterns with its count and statistics, so whenever the support system is withdrawn or changed, our algorithm using frequent sequence tree as the storage structure can find and detect all the sequential patternswithout mining the database once again.
Chandrasekhar equations and computational algorithms for distributed parameter systems
Burns, J. A.; Ito, K.; Powers, R. K.
1984-01-01
The Chandrasekhar equations arising in optimal control problems for linear distributed parameter systems are considered. The equations are derived via approximation theory. This approach is used to obtain existence, uniqueness, and strong differentiability of the solutions and provides the basis for a convergent computation scheme for approximating feedback gain operators. A numerical example is presented to illustrate these ideas.
Randomized algorithms for tracking distributed count, frequencies, and ranks
DEFF Research Database (Denmark)
Zengfeng, Huang; Ke, Yi; Zhang, Qin
2012-01-01
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k players, each holding a counter ni that gets incremented over time, and the goal is to track an ∑-approximation...
Distributed topology control algorithm for multihop wireless netoworks
Borbash, S. A.; Jennings, E. H.
2002-01-01
We present a network initialization algorithmfor wireless networks with distributed intelligence. Each node (agent) has only local, incomplete knowledge and it must make local decisions to meet a predefined global objective. Our objective is to use power control to establish a topology based onthe relative neighborhood graph which has good overall performance in terms of power usage, low interference, and reliability.
Distributed Spectrum Detection Based on Sequential Markov Chain%基于时序马尔可夫链的分布式频谱检测
Institute of Scientific and Technical Information of China (English)
罗银辉; 华漫
2011-01-01
针对无线传感网络中的合作谱检测问题,提出一种基于时序马尔可夫链的分布式频谱检测算法.假定单节点对频谱的感知是一个马尔可夫过程,本地序列检测采用序列概率比测试进行频谱探测,得到本地序列检测值.各个感知节点将检测结果发送到数据融合中心,根据设定门限确定最终检测结果.通过Matlab仿真验证了该算法的时序检测性能.%Aiming at the cooperation spectrum detection in Wireless Sensor Network(WSN), this paper presents a distributed spectrum detection based on sequential Markov chain. It assumes the detection of the single node is a Markov process, and the result value of local sequence detection is obtained by the Sequential Probability Ratio Test(SPRT). After the measurements result of each node's own is sent to the data fusion center, the final results can be determined in line with the threshold. Matlab simulation validates the sequential detection performance.
A new grid-associated algorithm in the distributed hydrological model simulations
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper presents a new grid-associated algorithm to improve the performance of a D8 algorithm based distributed hydrological model computation.The algorithm is based on the well known single-flow D8 algorithm of grid flow.This algorithm allocates calculation priorities according to the distance between the units and the outlet,then carries out the ergodic computations of the hydrological units according to the priority division.For the parallelized algorithm,a standard thread-level shared memory system for parallel programming(OpenMP-Open specifications for Multi Processing) was introduced,and the parallel coding was implemented in C lan-guage.A case study showed that the absolute speed-up ratio of the grid-associated algorithm is 1.64 over the original D8 algorithm,and the linear speed-up ratio of the parallel associated algorithm is 2.42 under 4 cores.The parallel grid-associated algorithm can be applied to a variety of research fields that use the grid method.
Mitavskiy, Boris; Cannings, Chris
2009-01-01
The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.
A Distributed Algorithm for Determining Minimal Covers of Acyclic Database Schemes
Institute of Scientific and Technical Information of China (English)
叶新铭
1994-01-01
Acyclic databases possess several desirable properties for their design and use.A distributed algorithm is proposed for determining a minimal cover of an alpha-,beta-,gamma-,or Berge-acyclic database scheme over a set of attributes in a distributed environment.
Probabilistic Analyses and Algorithms for Three-Level Distribution Systems
1998-01-01
We consider the problem of integrating inventory control and vehicle routing into a cost-effective strategy for a distribution system consisting of a single outside vendor, a fixed number of warehouses and many geographically dispersed retailers. Each retailer faces a constant, retailer specific, demand rate and inventory holding cost is charged at the retailers and the warehouses. We show that, in an effective strategy which minimizes the asymptotic long run average cost, each warehouse rece...
New algorithm and system for measuring size distribution of blood cells
Institute of Scientific and Technical Information of China (English)
Cuiping Yao(姚翠萍); Zheng Li(李政); Zhenxi Zhang(张镇西)
2004-01-01
In optical scattering particle sizing, a numerical transform is sought so that a particle size distribution can be determined from angular measurements of near forward scattering, which has been adopted in the measurement of blood cells. In this paper a new method of counting and classification of blood cell, laser light scattering method from stationary suspensions, is presented. The genetic algorithm combined with nonnegative least squared algorithm is employed to inverse the size distribution of blood cells. Numerical tests show that these techniques can be successfully applied to measuring size distribution of blood cell with high stability.
A distributed multi-agent linear biobjective algorithm for energy flow optimization in microgrids
DEFF Research Database (Denmark)
Brehm, Robert; Top, Søren; Mátéfi-Tempfli, Stefan
2016-01-01
A distributed linear bi-objective optimization algorithm for management of energy flow in a microgrid with individual agents is introduced. In contrast to widely used centralized approaches for energy management a distributed multi-agent scheme for optimization of energy flow within a microgrid...... as a classical transportation problem. This allows applying an auction algorithm scheme in a distributed way where each energy supply system node is either a source or a sink and is represented by an individual acting agent. The single-objective approach is extended towards bi-objectivity to build a framework...
A New Algorithm for Distributed Control Problem with Shortest-Distance Constraints
Directory of Open Access Journals (Sweden)
Yu Zhou
2016-01-01
Full Text Available This paper investigates the distributed shortest-distance problem of multiagent systems where agents satisfy the same continuous-time dynamics. The objective of multiagent systems is to find a common point for all agents to minimize the sum of the distances from each agent to its corresponding convex region. A distributed consensus algorithm is proposed based on local information. A sufficient condition also is given to guarantee the consensus. The simulation example shows that the distributed shortest-distance consensus algorithm is effective for our theoretical results.
Sequential triangulation of orbital photography
Rajan, M.; Junkins, J. L.; Turner, J. D.
1979-01-01
The feasibility of structuring the satellite photogrammetric triangulation as an iterative Extended Kalman estimation algorithm is demonstrated. Comparative numerical results of the sequential against batch estimation algorithm are presented. Difficulty of accurately modeling of the attitude motion is overcome by utilizing the on-board angular rate measurements. Solutions of the differential equations and the evaluation of state transition matrix are carried out numerically.
Sequential triangulation of orbital photography
Rajan, M.; Junkins, J. L.; Turner, J. D.
1979-01-01
The feasibility of structuring the satellite photogrammetric triangulation as an iterative Extended Kalman estimation algorithm is demonstrated. Comparative numerical results of the sequential against batch estimation algorithm are presented. Difficulty of accurately modeling of the attitude motion is overcome by utilizing the on-board angular rate measurements. Solutions of the differential equations and the evaluation of state transition matrix are carried out numerically.
A Novel Distributed Clustering Algorithm for Mobile Ad-hoc Networks
Directory of Open Access Journals (Sweden)
Sahar Adabi
2008-01-01
Full Text Available This paper proposed a new Distributed Score Based Clustering Algorithm (DSBCA for Mobile Ad-hoc Networks (MANETs.In MANETs, select suitable nodes in clusters as cluster heads are so important. The proposed Clustering Algorithm considers the Battery Remaining, Number of Neighbors, Number of Members, and Stability in order to calculate the node's score with a linear algorithm. After each node calculates its score independently, the neighbors of the node must be notified about it. Also each node selects one of its neighbors with the highest score to be its cluster head and, therefore the selection of cluster heads is performed in a distributed manner with most recent information about current status of neighbor nodes. The proposed algorithm was compared with Weighted Clustering Algorithm and Distributed Weighted Clustering Algorithm in terms of number of clusters, number of re-affiliations, lifespan of nodes in the system, end-to-end throughput and overhead. The simulation results proved that the proposed algorithm has achieved the goals.
Kabinejadian, Foad; Ghista, Dhanjoo N
2012-09-01
We have recently developed a novel design for coronary arterial bypass surgical grafting, consisting of coupled sequential side-to-side and end-to-side anastomoses. This design has been shown to have beneficial blood flow patterns and wall shear stress distributions which may improve the patency of the CABG, as compared to the conventional end-to-side anastomosis. In our preliminary computational simulation of blood flow of this coupled sequential anastomoses design, the graft and the artery were adopted to be rigid vessels and the blood was assumed to be a Newtonian fluid. Therefore, the present study has been carried out in order to (i) investigate the effects of wall compliance and non-Newtonian rheology on the local flow field and hemodynamic parameters distribution, and (ii) verify the advantages of the CABG coupled sequential anastomoses design over the conventional end-to-side configuration in a more realistic bio-mechanical condition. For this purpose, a two-way fluid-structure interaction analysis has been carried out. A finite volume method is applied to solve the three-dimensional, time-dependent, laminar flow of the incompressible, non-Newtonian fluid; the vessel wall is modeled as a linearly elastic, geometrically non-linear shell structure. In an iteratively coupled approach the transient shell equations and the governing fluid equations are solved numerically. The simulation results indicate a diameter variation ratio of up to 4% and 5% in the graft and the coronary artery, respectively. The velocity patterns and qualitative distribution of wall shear stress parameters in the distensible model do not change significantly compared to the rigid-wall model, despite quite large side-wall deformations in the anastomotic regions. However, less flow separation and reversed flow is observed in the distensible models. The wall compliance reduces the time-averaged wall shear stress up to 32% (on the heel of the conventional end-to-side model) and somewhat
Wald, Abraham
2013-01-01
In 1943, while in charge of Columbia University's Statistical Research Group, Abraham Wald devised Sequential Design, an innovative statistical inference system. Because the decision to terminate an experiment is not predetermined, sequential analysis can arrive at a decision much sooner and with substantially fewer observations than equally reliable test procedures based on a predetermined number of observations. The system's immense value was immediately recognized, and its use was restricted to wartime research and procedures. In 1945, it was released to the public and has since revolutio
Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks
DEFF Research Database (Denmark)
Huang, Zengfeng; Yi, Ke; Zhang, Qin
2011-01-01
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the {\\em count-tracking} problem, where there are $k$ players, each holding a counter $n_i$ that gets incremented over time, and the goal is to track an $\\eps......$-approximation of their sum $n=\\sum_i n_i$ continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is $\\Theta(k/\\eps \\cdot \\log N)$, where $N$ is the final value of $n$ when the tracking finishes, we show that with randomization, the communication cost can...
Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns.
Pan, Shaoming; Li, Yongkai; Xu, Zhengquan; Chong, Yanwen
2015-01-01
Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10-15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance.
Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns.
Directory of Open Access Journals (Sweden)
Shaoming Pan
Full Text Available Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10-15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance.
Energy Technology Data Exchange (ETDEWEB)
Fukuyama, Y.; Ueki, Y. (Fuji Electric Co. R and D Ltd., Tokyo (Japan))
1994-04-10
This paper describes genetic algorithms to decision on power supply feeders for each load in service restoration in a distribution system. Service restoration is a power supply distribution operation that calls for a system switching operation for retransmission when a non-service restoration section has occurred due to feeder failures. The operation determines the on-off condition of switches and decides the power supply feeders. The power supply feeder decision formulates as an objective function that minimization of the total sum of the load amount in load nodes that may cause a power failure under such conditions as radiation-formed loads, power supply capacity restriction, and transport pass restriction, and averaging of the reserve supply power. The genetic algorithm that derives the optimal solution thereof has the advantage that it does not use the gradient of the evaluation function and increase the calculation speed by means of parallel processing. The formulation by means of the genetic algorithm uses a string expression method, which reduces the string length and decaying strings. The method derives solutions by evaluating the set strings and repeating such processes as the crossing (partial replacement of service restoration conditions), the modified operator (modifies the strings to meet the restrictions) and sudden change (change in power supply direction in one load). 19 refs., 12 figs.
A solution for automatic parallelization of sequential assembly code
Directory of Open Access Journals (Sweden)
Kovačević Đorđe
2013-01-01
Full Text Available Since modern multicore processors can execute existing sequential programs only on a single core, there is a strong need for automatic parallelization of program code. Relying on existing algorithms, this paper describes one new software solution tool for parallelization of sequential assembly code. The main goal of this paper is to develop the parallelizator which reads sequential assembler code and at the output provides parallelized code for MIPS processor with multiple cores. The idea is the following: the parser translates assembler input file to program objects suitable for further processing. After that the static single assignment is done. Based on the data flow graph, the parallelization algorithm separates instructions on different cores. Once sequential code is parallelized by the parallelization algorithm, registers are allocated with the algorithm for linear allocation, and the result at the end of the program is distributed assembler code on each of the cores. In the paper we evaluate the speedup of the matrix multiplication example, which was processed by the parallelizator of assembly code. The result is almost linear speedup of code execution, which increases with the number of cores. The speed up on the two cores is 1.99, while on 16 cores the speed up is 13.88.
Institute of Scientific and Technical Information of China (English)
周炳海
2016-01-01
In order to improve the scheduling efficiency of photolithography, bottleneck process of wafer fabrications in the semiconductor industry, an effective estimation of distribution algorithm is pro-posed for scheduling problems of parallel litho machines with reticle constraints, where multiple reti-cles are available for each reticle type.First, the scheduling problem domain of parallel litho ma-chines is described with reticle constraints and mathematical programming formulations are put for-ward with the objective of minimizing total weighted completion time.Second, estimation of distribu-tion algorithm is developed with a decoding scheme specially designed to deal with the reticle con-straints.Third, an insert-based local search with the first move strategy is introduced to enhance the local exploitation ability of the algorithm.Finally, simulation experiments and analysis demonstrate the effectiveness of the proposed algorithm.
Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint
Energy Technology Data Exchange (ETDEWEB)
Wang, Dexin; Yang, Liuqing; Florita, Anthony; Alam, S.M. Shafiul; Elgindy, Tarek; Hodge, Bri-Mathias
2016-08-01
The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.
Improved mine blast algorithm for optimal cost design of water distribution systems
Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon
2015-12-01
The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.
A swarm intelligence based memetic algorithm for task allocation in distributed systems
Sarvizadeh, Raheleh; Haghi Kashani, Mostafa
2012-01-01
This paper proposes a Swarm Intelligence based Memetic algorithm for Task Allocation and scheduling in distributed systems. The tasks scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperformed the existing GA-based method in terms of CPU utilization.
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
Directory of Open Access Journals (Sweden)
Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
A Bayesian sequential design with binary outcome.
Zhu, Han; Yu, Qingzhao; Mercante, Donald E
2017-03-02
Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha-spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha-spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.
Voltage Profile Improvement in Distribution System Using Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
V.Veera Nagireddy
2016-06-01
Full Text Available The traditional method in electric power distribution is to have centralized plants distributing electricity through an extensive distribution network. Distributed generation (DG provides electric power at a site closer to the customer which reduces the transmission and distribution costs, reduces fossil fuel emissions, capital cost, reduce maintenance costs and improve the distribution feeder voltage profiles. In the case of small generation systems, the locations of DG and penetration level of DG are usually not priori known. In this paper, Particle Swarm Optimization (PSO algorithm attempts to calculate the boundaries of the randomly placed distributed generators in a distribution network. simulations are performed using MATLAB, and overall better improvements are determined with estimated DG size and location. The proposed PSO approach is compared with conventional method on IEEE 34 bus distribution feeder network
A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution
Directory of Open Access Journals (Sweden)
Huanxin Zou
2016-07-01
Full Text Available The simple linear iterative clustering (SLIC method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD. Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.
A voltage resonance-based single-ended online fault location algorithm for DC distribution networks
Institute of Scientific and Technical Information of China (English)
JIA Ke; LI Meng; BI TianShu; YANG QiXun
2016-01-01
A novel single-ended online fault location algorithm is investigated for DC distribution networks.The proposed algorithm calculates the fault distance based on the characteristics of the voltage resonance.The Prony's method is introduced to extract the characteristics.A novel method is proposed to solve the pseudo dual-root problem in the calculation process.The multiple data windows are adopted to enhance the robustness of the proposed algorithm.An index is proposed to evaluate the accuracy and validity of the results derived from the various data windows.The performances of the proposed algorithm in different fault scenarios were evaluated using the PSCAD/EMTDC simulations.The results show that the algorithm can locate the faults with transient resistance using the 1.6 ms data of the DC-side voltage after a fault inception and offers a good precision.
Van Otterlo, M
2009-01-01
Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.
Exploring Design Tradeoffs Of A Distributed Algorithm For Cosmic Ray Event Detection
Yousaf, Suhail; van Steen, Maarten; Voulgaris, Spyros; Kelley, John L
2012-01-01
Many sensor networks, including large particle detector arrays measuring high-energy cosmic-ray air showers, traditionally rely on centralised trigger algorithms to find spatial and temporal coincidences of individual nodes. Such schemes suffer from scalability problems, especially if the nodes communicate wirelessly or have bandwidth limitations. However, nodes which instead communicate with each other can, in principle, use a distributed algorithm to find coincident events themselves without communication with a central node. We present such an algorithm and consider various design tradeoffs involved, in the context of a potential trigger for the Auger Engineering Radio Array (AERA).
A DISTRIBUTED QOS ROUTING BASED ON ANT ALGORITHM FOR LEO SATELLITE NETWORK
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Low Earth Orbit (LEO) satellites provide short round-trip delays and are becoming increasingly important. One of the challenges in LEO satellite networks is the development of specialized and efficient routing algorithms. To satisfy the QoS requirements of multimedia applications, satellite routing protocols should consider handovers and minimize their effect on the active connections. A distributed QoS routing scheme based on heuristic ant algorithm is proposed for satisfying delay bound and avoiding link congestion. Simulation results show that the call blocking probabilities of this algorithm are less than that of Shortest Path First (SPF) with different delay bound.
A simple consensus algorithm for distributed averaging in random geographical networks
Indian Academy of Sciences (India)
Mahdi Jalili
2012-09-01
Random geographical networks are realistic models for wireless sensor networks which are used in many applications. Achieving average consensus is very important in sensor networks and the faster the consensus is, the durable the sensors’ life, and thus, the better the performance of the network. In this paper we compared the performance of a number of linear consensus algorithms with application to distributed averaging in random geographical networks. Interestingly, the simplest algorithm – where only the degree of receiving nodes is needed for the averaging – had the best performance in terms of the consensus time. Furthermore, we proved that the network has guaranteed convergence with this simple algorithm.
Using frame correlation algorithm in a duration distribution based hidden Markov model
Institute of Scientific and Technical Information of China (English)
王作英; 崔小东
2000-01-01
The assumption of frame independence is a widely known weakness of traditional hidden Markov model (HMM). In this paper, a frame correlation algorithm based on the duration distribution based hidden Markov model (DDBHMM) is proposed. In the algorithm, an AR model is used to depict the low pass effect of vocal tract from which stems the inertia leading to frame correlation. In the preliminary experiment of middle vocabulary speaker dependent isolated word recognition, our frame correlation algorithm outperforms the frame independent one. The average error reduction is about 20% .
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
Bhaduri, Kanishka; Srivastava, Ashok N.
2009-01-01
This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.
Logeswaran, Rajasvaran; Chen, Li-Choo
2008-12-01
Service architectures are necessary for providing value-added services in telecommunications networks, including those in medical institutions. Separation of service logic and control from the actual call switching is the main idea of these service architectures, examples include Intelligent Network (IN), Telecommunications Information Network Architectures (TINA), and Open Service Access (OSA). In the Distributed Service Architectures (DSA), instances of the same object type can be placed on different physical nodes. Hence, the network performance can be enhanced by introducing load balancing algorithms to efficiently distribute the traffic between object instances, such that the overall throughput and network performance can be optimised. In this paper, we propose a new load balancing algorithm called "Node Status Algorithm" for DSA infrastructure applicable to electronic-based medical institutions. The simulation results illustrate that this proposed algorithm is able to outperform the benchmark load balancing algorithms-Random Algorithm and Shortest Queue Algorithm, especially under medium and heavily loaded network conditions, which are typical of the increasing bandwidth utilization and processing requirements at paperless hospitals and in the telemedicine environment.
Sequential operators in computability logic
Japaridze, Giorgi
2007-01-01
Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html) is a semantical platform and research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for (interactive) computational problems, understood as games between a machine and its environment; logical operators represent operations on such entities; and "truth" is understood as existence of an effective solution, i.e., of an algorithmic winning strategy. The formalism of CL is open-ended, and may undergo series of extensions as the study of the subject advances. The main groups of operators on which CL has been focused so far are the parallel, choice, branching, and blind operators. The present paper introduces a new important group of operators, called sequential. The latter come in the form of sequential conjunction and disjunction, sequential quantifiers, and sequential recurrences. As the name may suggest, the algorithmic ...
Multilevel sequential Monte-Carlo samplers
Jasra, Ajay
2016-01-05
Multilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.
Efficiency analysis of control algorithms in spatially distributed systems with chaotic behavior
Directory of Open Access Journals (Sweden)
Korus Łukasz
2014-12-01
Full Text Available The paper presents results of examination of control algorithms for the purpose of controlling chaos in spatially distributed systems like the coupled map lattice (CML. The mathematical definition of the CML, stability analysis as well as some basic results of numerical simulation exposing complex, spatiotemporal and chaotic behavior of the CML were already presented in another paper. The main purpose of this article is to compare the efficiency of controlling chaos by simple classical algorithms in spatially distributed systems like CMLs. This comparison is made based on qualitative and quantitative evaluation methods proposed in the previous paper such as the indirect Lyapunov method, Lyapunov exponents and the net direction phase indicator. As a summary of this paper, some conclusions which can be useful for creating a more efficient algorithm of controlling chaos in spatially distributed systems are made.
Directory of Open Access Journals (Sweden)
J. L. Guardado
2014-01-01
Full Text Available Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2 and the Nondominated Sorting Genetic Algorithm II (NSGA-II. The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
Guardado, J L; Rivas-Davalos, F; Torres, J; Maximov, S; Melgoza, E
2014-01-01
Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
Karjee, Jyotirmoy
2011-01-01
Objective: The main objective of this paper is to construct a distributed clustering algorithm based upon spatial data correlation among sensor nodes and perform data accuracy for each distributed cluster at their respective cluster head node. Design Procedure/Approach: We investigate that due to deployment of high density of sensor nodes in the sensor field, spatial data are highly correlated among sensor nodes in spatial domain. Based on high data correlation among sensor nodes, we propose a non -overlapping irregular distributed clustering algorithm with different sizes to collect most accurate or precise data at the cluster head node for each respective distributed cluster. To collect the most accurate data at the cluster head node for each distributed cluster in sensor field, we propose a Data accuracy model and compare the results with Information accuracy model. Finding: Simulation results shows that our propose Data accuracy model collects more accurate data and gives better performance than Informati...
Directory of Open Access Journals (Sweden)
Mahdi Mozaffari Legha
2013-06-01
Full Text Available Development of distribution systems result in higher system losses and poor voltage regulation. Consequently, an efficient and effective distribution system has become more urgent and important. Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line. This paper examines the use of different evolutionary algorithms, genetic algorithm (GA, to optimal branch conductor selection in planning radial distribution systems with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors and reliability in order to improve productivity. Furthermore, The Backward-Forward sweep iterative method was adopted to solve the radial load flow analysis. Simulations are carried out on 69-bus radial distribution network using GA approach in order to show the accuracy as well as the efficiency of the proposed solution technique.
Placement and Sizing of DG Using PSO&HBMO Algorithms in Radial Distribution Networks
Directory of Open Access Journals (Sweden)
M. A.Taghikhani
2012-09-01
Full Text Available Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG in radial distri-bution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduction is obtained in distribution system.
Equalization Algorithm for Distributed Energy Storage Systems in Islanded AC Microgrids
DEFF Research Database (Denmark)
Aldana, Nelson Leonardo Diaz; Hernández, Adriana Carolina Luna; Quintero, Juan Carlos Vasquez
2015-01-01
This paper presents a centralized strategy for equalizing the state of charge of distributed energy storage systems in an islanded ac microgrid. The strategy is based on a simple algorithm denoted as equalization algorithm, which modifies the charge or discharge ratio on the time, for distributed...... energy storage systems, within a determined period of time in order to equalize the state of charge. The proposed approach has been tested in a MATLAB/Simulink model of the microgrid where the performance of the proposed strategy was verified....
Distributed genetic algorithm for optimal planar arrays of aperture synthesis telescope
Institute of Scientific and Technical Information of China (English)
贺小箭; 唐新怀; 尤晋元; 文建国
2004-01-01
Sparse arrays of telescopes have a limited ( u, v)-plane coverage. In this paper, an optimization method for designing planar arrays of an aperture synthesis telescope is proposed that is based on distributed genetic algorithm. This distributed genetic algorithm is implemented on a network of workstations using community communication model. Such an aperture synthesis system performs with imperfection of (u, v) components caused by deviations and(or) some missing baselines. With the maximum ( u, v)-plane coverage of this rotation-optimized array, the image of the source reconstructed by inverse Fourier transform is satisfactory.
An efficient algorithm for generating random number pairs drawn from a bivariate normal distribution
Campbell, C. W.
1983-01-01
An efficient algorithm for generating random number pairs from a bivariate normal distribution was developed. Any desired value of the two means, two standard deviations, and correlation coefficient can be selected. Theoretically the technique is exact and in practice its accuracy is limited only by the quality of the uniform distribution random number generator, inaccuracies in computer function evaluation, and arithmetic. A FORTRAN routine was written to check the algorithm and good accuracy was obtained. Some small errors in the correlation coefficient were observed to vary in a surprisingly regular manner. A simple model was developed which explained the qualities aspects of the errors.
A Dantzig-Wolfe Decomposition Algorithm for Economic MPC of Distributed Energy Systems
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian
2013-01-01
In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems of this t......In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems...
Institute of Scientific and Technical Information of China (English)
Haixing Liu,Jing Lu,Ming Zhao∗; Yixing Yuan
2016-01-01
In order to compare two advanced multi⁃objective evolutionary algorithms, a multi⁃objective water distribution problem is formulated in this paper. The multi⁃objective optimization has received more attention in the water distribution system design. On the one hand the cost of water distribution system including capital, operational, and maintenance cost is mostly concerned issue by the utilities all the time; on the other hand improving the performance of water distribution systems is of equivalent importance, which is often conflicting with the previous goal. Many performance metrics of water networks are developed in recent years, including total or maximum pressure deficit, resilience, inequity, probabilistic robustness, and risk measure. In this paper, a new resilience metric based on the energy analysis of water distribution systems is proposed. Two optimization objectives are comprised of capital cost and the new resilience index. A heuristic algorithm, speed⁃constrained multi⁃objective particle swarm optimization ( SMPSO) extended on the basis of the multi⁃objective particle swarm algorithm, is introduced to compare with another state⁃of⁃the⁃art heuristic algorithm, NSGA⁃II. The solutions are evaluated by two metrics, namely spread and hypervolume. To illustrate the capability of SMPSO to efficiently identify good designs, two benchmark problems ( two⁃loop network and Hanoi network) are employed. From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.
Zhang, Yanjun; Yu, Chunjuan; Fu, Xinghu; Liu, Wenzhe; Bi, Weihong
2015-12-01
In the distributed optical fiber sensing system based on Brillouin scattering, strain and temperature are the main measuring parameters which can be obtained by analyzing the Brillouin center frequency shift. The novel algorithm which combines the cuckoo search algorithm (CS) with the improved differential evolution (IDE) algorithm is proposed for the Brillouin scattering parameter estimation. The CS-IDE algorithm is compared with CS algorithm and analyzed in different situation. The results show that both the CS and CS-IDE algorithm have very good convergence. The analysis reveals that the CS-IDE algorithm can extract the scattering spectrum features with different linear weight ratio, linewidth combination and SNR. Moreover, the BOTDR temperature measuring system based on electron optical frequency shift is set up to verify the effectiveness of the CS-IDE algorithm. Experimental results show that there is a good linear relationship between the Brillouin center frequency shift and temperature changes.
Institute of Scientific and Technical Information of China (English)
Reza Sirjani; Melkamu Gamene Bade
2015-01-01
Shunt capacitors are broadly applied in distribution systems to scale down power losses, improve voltage profile and boost system capacity. The amount of capacitors added and location of deployment in the system highly determine the advantage of compensation. A novel global harmony search (GHS) algorithm in parallel with the backward/ forward sweep power flow technique and radial harmonic power flow was used to investigate the optimal placement and sizing of capacitors in radial distribution networks for minimizing power loss and total cost by taking account load unbalancing, mutual coupling and harmonics. The optimal capacitor placement outcomes show that the GHS algorithm can reduce total power losses up to 60 kW and leads to more than 18% of cost saving. The results also demonstrate that the GHS algorithm is more effective in minimization of power loss and total costs compared with genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) algorithm. Moreover, the proposed algorithm converges within 800 iterations and is faster in terms of computational time and gives better performance in finding optimal capacitor location and size compared with other optimization techniques.
Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming
Directory of Open Access Journals (Sweden)
Peng-Yeng Yin
2013-01-01
Full Text Available The estimation of distribution algorithm (EDA aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs, EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.
A Bio-Inspired Robust Adaptive Random Search Algorithm for Distributed Beamforming
Tseng, Chia-Shiang; Lin, Che
2010-01-01
A bio-inspired robust adaptive random search algorithm (BioRARSA), designed for distributed beamforming for sensor and relay networks, is proposed in this work. It has been shown via a systematic framework that BioRARSA converges in probability and its convergence time scales linearly with the number of distributed transmitters. More importantly, extensive simulation results demonstrate that the proposed BioRARSA outperforms existing adaptive distributed beamforming schemes by as large as 29.8% on average. This increase in performance results from the fact that BioRARSA can adaptively adjust its sampling stepsize via the "swim" behavior inspired by the bacterial foraging mechanism. Hence, the convergence time of BioRARSA is insensitive to the initial sampling stepsize of the algorithm, which makes it robust against the dynamic nature of distributed wireless networks.
Song, Kai-Sheng
2008-08-01
Many applications in real-time signal, image, and video processing require automatic algorithms for rapid characterizations of signals and images through fast estimation of their underlying statistical distributions. We present fast and globally convergent algorithms for estimating the three-parameter generalized gamma distribution (G Gamma D). The proposed method is based on novel scale-independent shape estimation (SISE) equations. We show that the SISE equations have a unique global root in their semi-infinite domains and the probability that the sample SISE equations have a unique global root tends to one. The consistency of the global root, its scale, and index shape estimators is obtained. Furthermore, we establish that, with probability tending to one, Newton-Raphson (NR) algorithms for solving the sample SISE equations converge globally to the unique root from any initial value in its given domain. In contrast to existing methods, another remarkable novelty is that the sample SISE equations are completely independent of gamma and polygamma functions and involve only elementary mathematical operations, making the algorithms well suited for real-time both hardware and software implementations. The SISE estimators also allow the maximum likelihood (ML) ratio procedure to be carried out for testing the generalized Gaussian distribution (GGD) versus the G Gamma D. Finally, the fast global convergence and accuracy of our algorithms for finite samples are demonstrated by both simulation studies and real image analysis.
Directory of Open Access Journals (Sweden)
Hua Xu
2013-01-01
Full Text Available Estimation of distribution algorithms (EDAs, as an extension of genetic algorithms, samples new solutions from the probabilistic model, which characterizes the distribution of promising solutions in the search space at each generation. This paper introduces and evaluates a novel estimation of a distribution algorithm, called L1-regularized Bayesian optimization algorithm, L1BOA. In L1BOA, Bayesian networks as probabilistic models are learned in two steps. First, candidate parents of each variable in Bayesian networks are detected by means of L1-regularized logistic regression, with the aim of leading a sparse but nearly optimized network structure. Second, the greedy search, which is restricted to the candidate parent-child pairs, is deployed to identify the final structure. Compared with the Bayesian optimization algorithm (BOA, L1BOA improves the efficiency of structure learning due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems show that L1BOA not only outperforms BOA when no prior knowledge about problem structure is available, but also achieves and even exceeds the best performance of BOA that applies explicit controls on network complexity. Furthermore, Bayesian networks built by L1BOA and BOA during evolution are analysed and compared, which demonstrates that L1BOA is able to build simpler, yet more accurate probabilistic models.
Institute of Scientific and Technical Information of China (English)
TANG Cheng-long; WANG Shi-gang; LIANG Qin-hua; XU Wei
2009-01-01
Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include: (1) the initial clustering points are preselected according to the density queue of data objects; and (2) Mahatanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common k-means algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns' recognition of transversal distribution of steel strip thickness and it will be useful in on-line quality control system.
GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-II FOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Vehicle routing problem in distribution (VRPD) is a widely used type of vehicle routing problem (VRP), which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm (GA), non-dominated sorting in genetic algorithm-Ⅱ(NSGA-Ⅱ) also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ (GNSGA-Ⅱ) is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-II is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-II, premature convergence is better avoided and search efficiency has been improved sharply.
A Distributed Algorithm for Parallel Multi-task Allocation Based on Profit Sharing Learning
Institute of Scientific and Technical Information of China (English)
SU Zhao-Pin; JIANG Jian-Guo; LIANG Chang-Yong; ZHANG Guo-Fu
2011-01-01
Task allocation via coalition formation is a fundanental research challenge in several application domains of multi-agent systems (MAS),such as resource allocation,disaster response management,and so on.It mainly deals with how to allocate many unresolved tasks to groups of agents in a distributed manner.In this paper,we propose a distributed parallel multi-task allocation algorithm among self-organizing and self-learning agents.To tackle the situation,we disperse agents and tanks geographically in two-dimensional cells,and then introduce profit sharing learning (PSL) for a single agent to search its tasks by continual self-learuing.We also present strategies for communication and negotiation among agents to allocate real workload to every tasked agent.Finally,to evaluate the effectiveness of the proposed algorithm,we compare it with Shehory and Kraus' distributed task allocation algorithm which were discussed by many researchers in recent years.Experimental results show that the proposed algorithm can quickly form a solving coalition for every task.Moreover,the proposed algorithm can specifically tell us the real workload of every tasked agent,and thus can provide a specific and significant reference for practical control tasks.
Institute of Scientific and Technical Information of China (English)
Shan CHENG; Min-you CHEN; Rong-jong WAI; Fang-zong WANG
2014-01-01
This paper deals with the optimal placement of distributed generation (DG) units in distribution systems via an enhanced multi-objective particle swarm optimization (EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational con-straints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been inte-grated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is em-ployed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage sta-bility. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and loca-tions of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.
Directory of Open Access Journals (Sweden)
Godfrey Chagwiza
2016-01-01
Full Text Available A modified soccer league algorithm is presented in this paper. The effect of stubborn fixed players is investigated and the algorithm is implemented to three benchmark water distribution networks. The modified algorithm is compared to several algorithms. The results show that the modified algorithm performs better than the soccer league competition algorithm, in particular, on the average number of evaluations required to find the optimal cost. Computational results show that the utility benefit of both the individual player and team is essential. The algorithm becomes more reliable when utility benefits are high and as the number of fixed players increases.
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher [Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz (Iran)
2009-08-15
This paper introduces a robust searching hybrid evolutionary algorithm to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The main objective of the DFR is to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and balance the loads on the feeders. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. This paper presents a new approach based on norm3 for the DFR problem. In the proposed method, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. Since the proposed DFR is a multi objective and non-differentiable optimization problem, a new hybrid evolutionary algorithm (EA) based on the combination of the Honey Bee Mating Optimization (HBMO) and the Discrete Particle Swarm Optimization (DPSO), called DPSO-HBMO, is implied to solve it. The results of the proposed reconfiguration method are compared with the solutions obtained by other approaches, the original DPSO and HBMO over different distribution test systems. (author)
Research and application of genetic algorithm in path planning of logistics distribution vehicle
Wang, Yong; Zhou, Heng; Wang, Ying
2017-08-01
The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.
An Efficient Algorithm for Resource Allocation in Parallel and Distributed Computing Systems
Directory of Open Access Journals (Sweden)
S.F. El-Zoghdy
2013-03-01
Full Text Available Resource allocation in heterogeneous parallel and distributed computing systems is the process of allocating user tasks to processing elements for execution such that some performance objective is optimized. In this paper, a new resource allocation algorithm for the computing grid environment is proposed. It takes into account the heterogeneity of the computational resources. It resolves the single point of failure problem which many of the current algorithms suffer from. In this algorithm, any site manager receives two kinds of tasks namely, remote tasks arriving from its associated local grid manager, and local tasks submitted directly to the site manager by local users in its domain. It allocates the grid workload based on the resources occupation ratio and the communication cost. The grid overall mean task response time is considered as the main performance metric that need to be minimized. The simulation results show that the proposed resource allocation algorithm improves the grid overall mean task response time. (Abstract
Institute of Scientific and Technical Information of China (English)
Lei Zhang; Xue Feng; Wei Zhang; Xiaoming Liu
2009-01-01
The deconvolution algorithm is adopted on the fiber Raman distributed temperature sensor (FRDTS) to improve the spatial resolution without reducing the pulse width of the light source. Numerical simulation shows that the spatial resolution is enhanced by four times using the frequency-domain deconvolution algorithm with high temperature accuracy. In experiment, a spatial resolution of 15 m is realized using a master oscillator power amplifier light source with 300-ns pulse width. In addition, the dispersion-induced limitation of the minimum spatial resolution achieved by deconvolution algorithm is analyzed. The results indicate that the deconvolution algorithm is a beneficial complement for the FRDTS to realize accurate locating and temperature monitoring for sharp temperature variations.
Efficient heuristic algorithm used for optimal capacitor placement in distribution systems
Energy Technology Data Exchange (ETDEWEB)
Segura, Silvio; Rider, Marcos J. [Department of Electric Energy Systems, University of Campinas, Campinas, Sao Paulo (Brazil); Romero, Ruben [Faculty of Engineering of Ilha Solteira, Paulista State University, Ilha Solteira, Sao Paulo (Brazil)
2010-01-15
An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods. (author)
Kanematsu, Nobuyuki
2007-01-01
A simple and efficient variant of the pencil-beam algorithm for dose distribution calculation is proposed. Compared to the conventional pencil-beam algorithms, the new algorithm is intrinsically faster due to minimized computation within the convolution integral. Namely, computation for physical interaction is decoupled from the convolution integral and the convolution kernel is approximated by simple grid-to-grid correlation. Implementation to a treatment planning system for carbon-ion radiotherapy has enabled realistic beam blurring with marginal speed decrease from the broad-beam calculation. Evaluation of a modeled proton pencil beam exhibits inaccuracy within its spread at the Bragg peak when the beam incidence is angled to all the dose grid axes, which will be minimized in broad-beam formation and may be acceptable depending on its relative significance to the other sources of errors. The new algorithm will provide balanced accuracy and speed without technical difficulty for high-resolution dose distrib...
Institute of Scientific and Technical Information of China (English)
TIAN Ye; SHENG Min; LI Jiandong
2007-01-01
This Paper presents a novel distributed media access control(MAC)address assignment algorithm,namely virtual grid spatial reusing(VGSR),for wireless sensor networks,which reduces the size of the MAC address efficiently on the basis of both the spatial reuse of MAC address and the mapping of geographical position.By adjusting the communication range of sensor nodes,VGSR algorithm can minimize the size of MAC address and meanwhile guarantee the connectivity of the sensor network.Theoretical analysis and experimental results show that VGSR algorithm is not only of low energy cost,but also scales well with the network ize,with its performance superior to that of other existing algorithms.
Directory of Open Access Journals (Sweden)
Wenbo Wu
2014-01-01
Full Text Available This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality.
Study of Power Flow Algorithm of AC/DC Distribution System including VSC-MTDC
Directory of Open Access Journals (Sweden)
Haifeng Liang
2015-08-01
Full Text Available In recent years, distributed generation and a large number of sensitive AC and DC loads have been connected to distribution networks, which introduce a series of challenges to distribution network operators (DNOs. In addition, the advantages of DC distribution networks, such as the energy conservation and emission reduction, mean that the voltage source converter based multi-terminal direct current (VSC-MTDC for AC/DC distribution systems demonstrates a great potential, hence drawing growing research interest. In this paper, considering losses of the reactor, the filter and the converter, a mathematical model of VSC-HVDC for the load flow analysis is derived. An AC/DC distribution network architecture has been built, based on which the differences in modified equations of the VSC-MTDC-based network under different control modes are analyzed. In addition, corresponding interface functions under five control modes are provided, and a back/forward iterative algorithm which is applied to power flow calculation of the AC/DC distribution system including VSC-MTDC is proposed. Finally, by calculating the power flow of the modified IEEE14 AC/DC distribution network, the efficiency and validity of the model and algorithm are evaluated. With various distributed generations connected to the network at appropriate locations, power flow results show that network losses and utilization of transmission networks are effectively reduced.
An improved distributed routing algorithm for Benes based optical NoC
Zhang, Jing; Yang, Yintang
2011-01-01
Integrated optical interconnect is believed to be one of the main technologies to replace electrical wires. Optical Network-on-Chip (ONoC) has attracted more attentions nowadays. Benes topology is a good choice for ONoC for its rearrangeable non-blocking character, multistage feature and easy scalability. Routing algorithm plays an important role in determining the performance of ONoC. But traditional routing algorithms for Benes network are not suitable for ONoC communication, we developed a new distributed routing algorithm for Benes ONoC in this paper. Our algorithm selected the routing path dynamically according to network condition and enables more path choices for the message traveling in the network. We used OPNET to evaluate the performance of our routing algorithm and also compared it with a well-known bit-controlled routing algorithm. ETE delay and throughput were showed under different packet length and network sizes. Simulation results show that our routing algorithm can provide better performance...
A simple algorithm for measuring particle size distributions on an uneven background from TEM images
Energy Technology Data Exchange (ETDEWEB)
Cervera Gontard, Lionel, E-mail: lionelcg@gmail.com [Center for Electron Nanoscopy, Technical University of Denmark, DK-2800 Kgs. Lyngby (Denmark); Ozkaya, Dogan [Johnson Matthey Technology Centre, Blount' s Court, Sonning Common, Reading RG4 9NH (United Kingdom); Dunin-Borkowski, Rafal E. [Center for Electron Nanoscopy, Technical University of Denmark, DK-2800 Kgs. Lyngby (Denmark)
2011-01-15
Nanoparticles have a wide range of applications in science and technology. Their sizes are often measured using transmission electron microscopy (TEM) or X-ray diffraction. Here, we describe a simple computer algorithm for measuring particle size distributions from TEM images in the presence of an uneven background. The approach is based on adaptive thresholding, making use of local threshold values that change with spatial coordinate. The algorithm allows particles to be detected and characterized with greater accuracy than using more conventional methods, in which a global threshold is used. Its application to images of heterogeneous catalysts is presented. -- Research Highlights: {yields}The paper describes a novel algorithm for segmenting TEM images of nanoparticles which is simple but robust. {yields}A Graphical User Interface allows interactivity during the processing of images. This allows maximise the success of local thresholding. {yields}The method described can be used to provide more accurate measurements of particle size distributions.
A Distributed Authentication Algorithm Based on GQ Signature for Mobile Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
YAO Jun; ZENG Gui-hua
2006-01-01
Identity authentication plays an important role in ad hoc networks as a part of the secure mechanism. On the basis of GQ signature scheme, a new GQ threshold group signature scheme was presented, by which a novel distributed algorithm was proposed to achieve the multi-hop authentication for mobile ad hoc networks. In addition, a protocol verifying the identity with zero knowledge proofs was designed so that the reuse of certificates comes into truth. Moreover, the security of this algorithm was proved through the random oracle model. With the lower cost of computation and communication, this algorithm is efficient, secure and especially suitable for mobile ad hoc networks characterized by distributed computing, dynamic topology and multi-hop communications.
Institute of Scientific and Technical Information of China (English)
吴颖颖; 吴耀华; 沈长鹏
2012-01-01
To shorten the length of the virtual window and reduce the order picking time,the picking device was improved and a compressible dynamic virtual window algorithm was proposed.A gravity buffer and a flashboard were added to each dispenser.The items were launched to the gravity buffer in which the gap between each other was at fiost compressed,and then merged to the conveyor from the gravity buffer.Therefore the items were close to each other on the conveyor and the length of the virtual window was shortened.A model of the compressible dynamic virtual window algorithm was built based on the sequential picking strategy.The simulation with 3 sets of data collected from a tobacco distribution center showed that the picking time could be reduced by 87.45%~87.77%,and the picking time was decreased when the launching time of the dispenser and the merging time of the gravity buffer increased.%为缩短虚拟容器长度、减少订单拣选总时间,改进了拣选设备,提出了压缩动态虚拟视窗算法。该方法为每台拣选机设置一个重力缓存区及挡板,拣选时先将货物弹射至到重力缓存区,在缓存区内压缩货物间距;再把货物从重力缓存区合并至皮带输送机,实现货物的密集排列和虚拟容器长度的缩短。基于货物的顺序拣选策略,建立了压缩动态虚拟视窗算法的数学模型。以某地市卷烟配送中心的3批订单数据为例进行了仿真,结果表明该方法将拣选时间缩短了87.45%～87.77%,并且拣选时间随着拣选机弹射速度和重力缓存区货物合并速度的增加而减少。
Chen, Yao; Xia, Weiguo; Cao, Ming; Lu, Jinhu
2016-01-01
Given a distributed coordination algorithm (DCA) for agents coupled by a network, which can be characterized by a stochastic matrix, we say that the DCA can be asynchronously implemented if the consensus property is preserved when the agents are activated to update their states according to their ow
A simple algorithm for measuring particle size distributions on an uneven background from TEM images
DEFF Research Database (Denmark)
Gontard, Lionel Cervera; Ozkaya, Dogan; Dunin-Borkowski, Rafal E.
2011-01-01
Nanoparticles have a wide range of applications in science and technology. Their sizes are often measured using transmission electron microscopy (TEM) or X-ray diffraction. Here, we describe a simple computer algorithm for measuring particle size distributions from TEM images in the presence of a...
A Dantzig-Wolfe Decomposition Algorithm for Economic MPC of Distributed Energy Systems
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian
2013-01-01
In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems...
Gonoskov, Arkady
2016-01-01
We propose an algorithm for reducing the number of macro-particles in PIC simulations in such a way that an arbitrary number of conservation laws can be preserved exactly and all the distribution functions are not modified in any other way than due to the statistical noise.
Directory of Open Access Journals (Sweden)
Xuehua Shen
2015-01-01
Full Text Available Temperature, especially temperature distribution, is one of the most fundamental and vital parameters for theoretical study and control of various industrial applications. In this paper, ultrasonic thermometry to reconstruct temperature distribution is investigated, referring to the dependence of ultrasound velocity on temperature. In practical applications of this ultrasonic technique, reconstruction algorithm based on least square method is commonly used. However, it has a limitation that the amount of divided blocks of measure area cannot exceed the amount of effective travel paths, which eventually leads to its inability to offer sufficient temperature information. To make up for this defect, an improved reconstruction algorithm based on least square method and multiquadric interpolation is presented. And then, its reconstruction performance is validated via numerical studies using four temperature distribution models with different complexity and is compared with that of algorithm based on least square method. Comparison and analysis indicate that the algorithm presented in this paper has more excellent reconstruction performance, as the reconstructed temperature distributions will not lose information near the edge of area while with small errors, and its mean reconstruction time is short enough that can meet the real-time demand.
A practical algorithm for distribution state estimation including renewable energy sources
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher [Electronic and Electrical Department, Shiraz University of Technology, Modares Blvd., P.O. 71555-313, Shiraz (Iran); Firouzi, Bahman Bahmani [Islamic Azad University Marvdasht Branch, Marvdasht (Iran)
2009-11-15
Renewable energy is energy that is in continuous supply over time. These kinds of energy sources are divided into five principal renewable sources of energy: the sun, the wind, flowing water, biomass and heat from within the earth. According to some studies carried out by the research institutes, about 25% of the new generation will be generated by Renewable Energy Sources (RESs) in the near future. Therefore, it is necessary to study the impact of RESs on the power systems, especially on the distribution networks. This paper presents a practical Distribution State Estimation (DSE) including RESs and some practical consideration. The proposed algorithm is based on the combination of Nelder-Mead simplex search and Particle Swarm Optimization (PSO) algorithms, called PSO-NM. The proposed algorithm can estimate load and RES output values by Weighted Least-Square (WLS) approach. Some practical considerations are var compensators, Voltage Regulators (VRs), Under Load Tap Changer (ULTC) transformer modeling, which usually have nonlinear and discrete characteristics, and unbalanced three-phase power flow equations. The comparison results with other evolutionary optimization algorithms such as original PSO, Honey Bee Mating Optimization (HBMO), Neural Networks (NNs), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) for a test system demonstrate that PSO-NM is extremely effective and efficient for the DSE problems. (author)
Ad hoc distributed mutual exclusion algorithm based on token-asking
Institute of Scientific and Technical Information of China (English)
Wang Zheng; Liu Xin song; Li Mei'an
2007-01-01
The solution of distributed mutual exclusion is difficult in Ad hoc networks owing to dynamic topologies and mobility.Based on the analysis of the properties of Ad hoc networks and the disadvantages of the traditional algorithms, an improved Ad hoc system model was given and a novel algorithm was presented as AHDME (Ad Hoc Distributed Mutual Exclusion); it was based on the token-asking algorithms.It utilized broadcast to search for the token and to decrease the message complexity of multi-hop Ad hoc networks.Lamport's timestamp was improved to ensure the time sequence and to prevent nodes from starvation.When compared to traditional algorithms, AHDME does not require the fixed size of request queues and the global system information, which adapts itself to the frequent arrival/departures and the limited computing capability of nodes in Ad hoc networks.Performance analysis and simulation results show that the AHDME algorithm has low message complexity, small space complexity, and short response delay.
Directory of Open Access Journals (Sweden)
Hui He
2013-01-01
Full Text Available It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.
Energy Technology Data Exchange (ETDEWEB)
Lober, R.R.; Tautges, T.J.; Vaughan, C.T.
1997-03-01
Paving is an automated mesh generation algorithm which produces all-quadrilateral elements. It can additionally generate these elements in varying sizes such that the resulting mesh adapts to a function distribution, such as an error function. While powerful, conventional paving is a very serial algorithm in its operation. Parallel paving is the extension of serial paving into parallel environments to perform the same meshing functions as conventional paving only on distributed, discretized models. This extension allows large, adaptive, parallel finite element simulations to take advantage of paving`s meshing capabilities for h-remap remeshing. A significantly modified version of the CUBIT mesh generation code has been developed to host the parallel paving algorithm and demonstrate its capabilities on both two dimensional and three dimensional surface geometries and compare the resulting parallel produced meshes to conventionally paved meshes for mesh quality and algorithm performance. Sandia`s {open_quotes}tiling{close_quotes} dynamic load balancing code has also been extended to work with the paving algorithm to retain parallel efficiency as subdomains undergo iterative mesh refinement.
He, Hui; Fan, Guotao; Ye, Jianwei; Zhang, Weizhe
2013-01-01
It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system's emergency response capabilities, alleviate the cyber attacks' damage, and strengthen the system's counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system's plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks' topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.
Execution time supports for adaptive scientific algorithms on distributed memory machines
Berryman, Harry; Saltz, Joel; Scroggs, Jeffrey
1990-01-01
Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated.
A Reputation-based Distributed District Scheduling Algorithm for Smart Grids
Directory of Open Access Journals (Sweden)
D. Borra
2015-05-01
Full Text Available In this paper we develop and test a distributed algorithm providing Energy Consumption Schedules (ECS in smart grids for a residential district. The goal is to achieve a given aggregate load prole. The NP-hard constrained optimization problem reduces to a distributed unconstrained formulation by means of Lagrangian Relaxation technique, and a meta-heuristic algorithm based on a Quantum inspired Particle Swarm with Levy flights. A centralized iterative reputation-reward mechanism is proposed for end-users to cooperate to avoid power peaks and reduce global overload, based on random distributions simulating human behaviors and penalties on the eective ECS diering from the suggested ECS. Numerical results show the protocols eectiveness.
A randomised primal-dual algorithm for distributed radio-interferometric imaging
Onose, Alexandru; McEwen, Jason D; Wiaux, Yves
2016-01-01
Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state...
Shao, Yuxiang; Chen, Qing; Wei, Zhenhua
Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.
A Low Overhead Minimum Process Global Snapshop Collection Algorithm for Mobile Distributed System
Kumar, Surender; Kumar, Parveen; 10.5121/ijma.2010.2202
2010-01-01
Coordinated checkpointing is an effective fault tolerant technique in distributed system as it avoids the domino effect and require minimum storage requirement. Most of the earlier coordinated checkpoint algorithms block their computation during checkpointing and forces minimum-process or non-blocking but forces all nodes to takes checkpoint even though many of them may not be necessary or non-blocking minimum-process but takes useless checkpoints or reduced useless checkpoint but has higher synchronization message overhead or has high checkpoint request propagation time. Hence in mobile distributed systems there is a great need of minimizing the number of communication message and checkpointing overhead as it raise new issues such as mobility, low bandwidth of wireless channels, frequently disconnections, limited battery power and lack of reliable stable storage on mobile nodes. In this paper, we propose a minimum-process coordinated checkpointing algorithm for mobile distributed system where no useless chec...
Directory of Open Access Journals (Sweden)
Chu-Liangyong
2013-06-01
Full Text Available The network of Chinese Waterborne Petroleum Logistics (CWPL is so complex that reasonably disposing and choosing Chinese Waterborne Petroleum Logistics Distribution Center (CWPLDC take on the important theory value and the practical significance. In the study, the network construct of CWPL distribution is provided and the corresponding mathematical model for locating CWPLDC is established, which is a nonlinear mixed interger model. In view of the nonlinerar programming characteristic of model, the genetic algorithm as the solution strategy is put forward here, the strategies of hybrid coding, constraint elimination , fitness function and genetic operator are given followed the algorithm. The result indicates that this model is effective and reliable. This method could also be applicable for other types of large-scale logistics distribution center optimization.
A Genetic Algorithm-Based Approach for Process Scheduling In Distributed Operating Systems
Directory of Open Access Journals (Sweden)
2012-01-01
Full Text Available A Distributed Computing System comprising networked heterogeneous processors requires efficient process allocation algorithms to achieve minimum turnaround time and highest possible throughput. To efficiently execute processes on a distributed system, processes must be correctly assigned to processors and determine the execution order of processes so that the overall execution time is minimized. Even when target processors are fully connected and the communication among processors is fast and no dependencies exist among processes the scheduling problem is NP-complete. Complexity of scheduling problem dependent of number of processors, process execution time and the processor network topology. As distributed systems exist in kinds of homogeneous and heterogeneous, in heterogeneous systems the difference between processors leads to different execution time for an individual process on different processors and makes scheduling problem more complex. Our proposed genetic algorithm is applicable for both homogeneous and heterogeneous kinds.
Lesser, V R; 10.1613/jair.1786
2011-01-01
Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques.
Microphysical properties and distribution retrieval with a variable base point algorithm
Osterloh, Lukas; Böckmann, Christine
2009-09-01
We present a new algorithm for the retrieval of the volume distribution - and thus, other relevant microphysical properties such as the effective radius - of stratospheric and tropospheric aerosols from multiwavelength lidar data. We consider the basic equation as a linear ill-posed problem and solve the linear system derived from spline collocation. Starting from here, algorithmical improvements for the inversion process are proposed. While a standard approach consisting of spline collocation and a regularization method such as truncated singular value decomposition or Tikhonov-Philips regularization proves sufficient in some cases, that kind of algorithm is not suitable for a more general case; the base points of the spline collocation take a key role here. Indeed, there is a direct connection between the number and position of the base points on the solution, as the problem of the correct regularization parameter - which is represented here by both location and number of base points - and its implications on over- or underregularization of the solution have to be investigated. Here, we present an algorithm that makes use of the fact that smoother areas of the solution require less base points in the vicinity for a proper reconstruction, combined with a Padé-type iterative regularization method. The algorithm starts with equidistant base points, then moves these base points during the calculation away from the smoother areas of the solution. This algorithm proved to work very well in many different simulation cases. Different weight functions for the base point shift are investigated, leading to slightly different results. Also, an improvement on this algoritm is proposed which, in addition to the position of the base points, also actively controls the actual number of base points, as solutions that more smooth in a global sense require less base points. Finally, we also take a look at how this new algorithm can also help us in simultaneously retrieving the
Institute of Scientific and Technical Information of China (English)
Xu-Zhi Lai; Simon X. Yang; Gui-Xiu Zeng; Jin-Hua She; Min Wu
2007-01-01
This paper presents a new distributed positioning algorithm for unknown nodes in a wireless sensor network. The algorithm is based exclusively on connectivity. First, assuming that the positions of the anchor nodes are already known, a circular belt containing an unknown node is obtained using information about the anchor nodes that are in radio range of the unknown node, based on the geometric relationships and communication constraints among the unknown node and the anchor nodes. Then, the centroid of the circular belt is taken to be the estimated position of the unknown node. Since the algorithm is very simple and since the only communication needed is between the anchor nodes and the unknown node, the communication and computational loads are very small. Furthermore, the algorithm is robust because neither the failure of old unknown nodes nor the addition of new unknown nodes influences the positioning of unknown nodes to be located. A theoretical analysis and simulation results show that the algorithm does not produce any cumulative error and is insensitive to range error, and that a change in the number of sensor nodes does not affect the communication or computational load. These features make this algorithm suitable for all sizes of low-power wireless sensor networks.
An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Rosana Lachowski
2015-01-01
Full Text Available Monitoring and data collection are the two main functions in wireless sensor networks (WSNs. Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.
An efficient distributed algorithm for constructing spanning trees in wireless sensor networks.
Lachowski, Rosana; Pellenz, Marcelo E; Penna, Manoel C; Jamhour, Edgard; Souza, Richard D
2015-01-14
Monitoring and data collection are the two main functions in wireless sensor networks (WSNs). Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.
An Efficient Estimation of Distribution Algorithm for Job Shop Scheduling Problem
He, Xiao-Juan; Zeng, Jian-Chao; Xue, Song-Dong; Wang, Li-Fang
An estimation of distribution algorithm with probability model based on permutation information of neighboring operations for job shop scheduling problem was proposed. The probability model was given using frequency information of pair-wise operations neighboring. Then the structure of optimal individual was marked and the operations of optimal individual were partitioned to some independent sub-blocks. To avoid repeating search in same area and improve search speed, each sub-block was taken as a whole to be adjusted. Also, stochastic adjustment to the operations within each sub-block was introduced to enhance the local search ability. The experimental results show that the proposed algorithm is more robust and efficient.
A Novel Algorithm of Forecasting the Potential Development of Generation in the Distribution Grid
Directory of Open Access Journals (Sweden)
Michał Bajor
2014-06-01
Full Text Available The paper presents a novel method of forecasting the potential for the development of various types of generation, including renewable, connecting to the distribution grid. The proposed algorithm is based on the idea of identifying different factors influencing the possibility of developing various types of generation in different time horizons. Descriptions of subsequent stages of the forecasting procedure, used terms and the software implementing the algorithm, developed by the authors, are also included in the paper. Finally, comments regarding the reliability of the results obtained using the method are described.
Semi-Distributed Relay Selection Algorithm for Multi-User Cooperative Wireless Networks
Directory of Open Access Journals (Sweden)
Yafeng Wang
2011-06-01
Full Text Available In this paper, we propose a novel semi-distributed algorithm with low overhead and complexity for multi-user cooperative wireless networks with opportunistic relaying. The source is required to satisfy its minimum rate requirement with a feasible relay and help another source as a relay. The optimal solution can be obtained by exhaustive search with intractable computational complexity. Simulation results suggest that the proposed relay selection algorithm has the similar outage probability as the exhaustive search approach but with much less computational burden.
Multi-agent coordination algorithms for control of distributed energy resources in smart grids
Cortes, Andres
Sustainable energy is a top-priority for researchers these days, since electricity and transportation are pillars of modern society. Integration of clean energy technologies such as wind, solar, and plug-in electric vehicles (PEVs), is a major engineering challenge in operation and management of power systems. This is due to the uncertain nature of renewable energy technologies and the large amount of extra load that PEVs would add to the power grid. Given the networked structure of a power system, multi-agent control and optimization strategies are natural approaches to address the various problems of interest for the safe and reliable operation of the power grid. The distributed computation in multi-agent algorithms addresses three problems at the same time: i) it allows for the handling of problems with millions of variables that a single processor cannot compute, ii) it allows certain independence and privacy to electricity customers by not requiring any usage information, and iii) it is robust to localized failures in the communication network, being able to solve problems by simply neglecting the failing section of the system. We propose various algorithms to coordinate storage, generation, and demand resources in a power grid using multi-agent computation and decentralized decision making. First, we introduce a hierarchical vehicle-one-grid (V1G) algorithm for coordination of PEVs under usage constraints, where energy only flows from the grid in to the batteries of PEVs. We then present a hierarchical vehicle-to-grid (V2G) algorithm for PEV coordination that takes into consideration line capacity constraints in the distribution grid, and where energy flows both ways, from the grid in to the batteries, and from the batteries to the grid. Next, we develop a greedy-like hierarchical algorithm for management of demand response events with on/off loads. Finally, we introduce distributed algorithms for the optimal control of distributed energy resources, i
Distributed power control algorithm based on game theory for wireless sensor networks
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network.Because the character of wireless sensor networks is restrictive energy,this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime.The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.
Sheck, L E; Muirhead, K A; Horan, P K
1980-09-01
Cell sorting and tritiated thymidine autoradiography were used to define the distribution of S phase cells in flow cytometric DNA histograms obtained from exponential mouse lymphoma cells (L5178Y). The numbers of labeled S phase cells, autoradiographically determined from cells sorted at 2-channel intervals in the G1/early S and late S/G2M regions of the histogram, were compared with the numbers of computed S phase cells in comparable 2-channel intervals as predicted by several computer algorithms used to extract cell cycle phase distributions from DNA histograms. Polynomial and multirectangle algorithms gave computed estimates of total %S in close agreement with the tritiated thymidine labeling index for the cell population, while multi-Gaussian algorithms underestimated %S. Interval autoradiographic and algorithm studies confirmed these results in that no significant differences were found between the autoradiographic S phase distribution and S phase distributions calculated by the polynomial and multirectangle models. However, S phase cells were significantly underestimated in G1/early S by a constrained multi-Gaussian model and in both G1/early S and late S/G2 by an unconstrained multi-Gaussian model. For the particular cell line (L5178Y), staining protocol (mithramycin following ethanol fixation) and instrumentation (Coulter TPS-2 cell sorter) used in this study, close agreement between computed %S and tritiated thymidine labeling index was found to be a reliable indicator of an algorithm's success in resolving S phase cells in the G1/S and S/G2 transition regions of the DNA histograms.
Ogawa, Masakatsu; Hiraguri, Takefumi; Nishimori, Kentaro; Takaya, Kazuhiro; Murakawa, Kazuo
This paper proposes and investigates a distributed adaptive contention window adjustment algorithm based on the transmission history for wireless LANs called the transmission-history-based distributed adaptive contention window adjustment (THAW) algorithm. The objective of this paper is to reduce the transmission delay and improve the channel throughput compared to conventional algorithms. The feature of THAW is that it adaptively adjusts the initial contention window (CWinit) size in the binary exponential backoff (BEB) algorithm used in the IEEE 802.11 standard according to the transmission history and the automatic rate fallback (ARF) algorithm, which is the most basic algorithm in automatic rate controls. This effect is to keep CWinit at a high value in a congested state. Simulation results show that the THAW algorithm outperforms the conventional algorithms in terms of the channel throughput and delay, even if the timer in the ARF is changed.
1981-12-01
the variance of point estimators are given by Mendenhall and Scheaffer (Ref 17:269), for both biased and unbiased estimations. In addition to this...Weibull Distribution. Thesis, Wright-Patterson AFB, Ohio: Air Force Institute of Technology, December 1980. 17. Mendenhall, W. and R. L. Scheaffer
Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm
Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven
2010-05-01
Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.
Distribution Grid Reactive Power Optimization Based on Improved Cloud Particle Swarm Algorithm
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Hongsheng Su
2013-01-01
Full Text Available To resolve the problems that cloud particle swarm optimization(CPSO was easily trapped in local minimum and possessed slow convergence speed and early-maturing during distribution grid reactive power optimization, CPSO algorithm was improved based on cloud digital features in this paper. The method firstly combined Local search with global search together based on solution space transform, where the crossover and mutation operation of the particles were implemented based on normal cloud operator. And then the dramatic achievements were acquired in time-consuming and storage-cost using the improved algorithm. Finally, applied in bus IEEE30 system, the simulation results show that the better global solution is attained using the improved CPSO algorithm, and its convergence speed and accuracy possesses a dramatic improvement.
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M. Mohammadi
2015-01-01
Full Text Available This paper presents the optimal planning of harmonic passive filters in distribution system using three intelligent methods including genetic algorithm (GA, particle swarm optimization (PSO, artificial bee colony (ABC and as a new research is compared with biogeography based optimization (BBO algorithm. In this work, the considered objective function is to minimize the value of investment cost of filters and total harmonic distortion of three-phase current. It is shown that through an economical placement and sizing of LC passive filters the total voltage harmonic distortion and cost could be minimized simultaneously. BBO is a novel evolutionary algorithm that is based on the mathematics of biogeography. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. The simulation results show that the proposed method is efficient for solving the presented problem.
Vehicle Routing Optimization in Logistics Distribution Using Hybrid Ant Colony Algorithm
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Chengming Qi
2013-09-01
Full Text Available The Vehicle Routing Problem (VRP is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitated Vehicle Routing Problem (CVRP constrained by the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm to address CVRP. It takes the advantages of Simulated Annealing (SA and ant colony optimization for solving the capacitated vehicle routing problem. In the first phase of proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. In the second phase, Iterative Local Search (ILS method is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle. Experimental results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on partial benchmark problems.
Energy Technology Data Exchange (ETDEWEB)
Ndoye, Mandoye [Lawrence Livermore National Laboratory (LLNL); Barker, Alan M [ORNL; Krogmeier, James [Purdue University; Bullock, Darcy [Purdue University
2011-01-01
A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.
An algorithm for automatic unfolding of one-dimensional data distributions
Energy Technology Data Exchange (ETDEWEB)
Dembinski, Hans P., E-mail: hans.dembinski@kit.edu; Roth, Markus
2013-11-21
We discuss a non-parametric algorithm to unfold detector effects from one-dimensional data distributions. Unfolding is performed by fitting a flexible spline model to the data using an unbinned maximum-likelihood method while employing a smooth regularisation that maximises the relative entropy of the solution with respect to an a priori guess. A regularisation weight is picked automatically such that it minimises the mean integrated squared error of the fit. The algorithm scales to large data sets by employing an adaptive binning scheme in regions of high density. An estimate of the uncertainty of the solution is provided and shown to be accurate by studying the frequentist properties of the algorithm in Monte-Carlo simulations. The simulations show that the regularisation bias decreases as the sample size increases.
An algorithm for automatic unfolding of one-dimensional data distributions
Dembinski, Hans P.; Roth, Markus
2013-11-01
We discuss a non-parametric algorithm to unfold detector effects from one-dimensional data distributions. Unfolding is performed by fitting a flexible spline model to the data using an unbinned maximum-likelihood method while employing a smooth regularisation that maximises the relative entropy of the solution with respect to an a priori guess. A regularisation weight is picked automatically such that it minimises the mean integrated squared error of the fit. The algorithm scales to large data sets by employing an adaptive binning scheme in regions of high density. An estimate of the uncertainty of the solution is provided and shown to be accurate by studying the frequentist properties of the algorithm in Monte-Carlo simulations. The simulations show that the regularisation bias decreases as the sample size increases.
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)
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S.NEELIMA
2011-08-01
Full Text Available A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with appropriate size is always a challenge. Thus the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 69 bus system and compared with other methods in the literature.
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.
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Ramin Mansouri
2014-06-01
Full Text Available Iran, has caused most of the water used and as much as possible to avoid losses. One of the important parameters in agriculture is water distribution uniformity coefficient (CU in sprinkler irrigation. CU amount of water sprinkler operating depends on different pressure heads (P, riser height (RH, distance between sprinklers on lateral pipes (Sl and the distance between lateral pipes (Sm. The best combination of the above parameters for maximum CU, is still unknown for applicators. In this research, CU quantities of zb model sprinkler (made in Iran were measured at Hashemabad cotton research station of Gorgan under 3 different pressure heads (2.5, 3 and 3.5 atm, 2 riser heads (60 and 100 cm and 7 sprinkler (Sl×Sm including 9×12, 9×15, 12×12, 15×12, 12×18, 15×15, 15×18m arrangements. By using differential evolution algorithm (DE, CU equation was optimized and the best optimized coefficients obtained. In this algorithm, the coefficients F and CR equal to 2 and 0.5, respectively, with a population of 100 members and 1000 number of generations (iterations, provides the best results. Absolute error between the results of this algorithm with the measured results is 2.2%. As well as values Wilmot (d and the root-mean square error (RMSE, equal to 0.919 and 2.126, respectively. This results show that this algorithm has high accuracy to estimate water distribution uniformity.
Distributed Matching Algorithms: Maximizing Secrecy in the Presence of Untrusted Relay
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B. Ali
2017-06-01
Full Text Available In this paper, we propose a secrecy sum-rate maximization based matching algorithm between primary transmitters and secondary cooperative jammers in the presence of an eavesdropper. More explicitly, we consider an untrusted relay scenario, where the relay is a potential eavesdropper. We first show the achievable secrecy regions employing a friendly jammer in a cooperative scenario with employing an untrusted relay. Then, we provide results for the secrecy regions for two scenarios, where in the first case we consider no direct transmission between the source and the destination, while in the second case we include a source to destination direct link in our communication system. Furthermore, a friendly jammer helps to send a noise signal during the first phase of the cooperative transmission, for securing the information transmitted from the source. In our matching algorithm, the selected cooperative jammer or the secondary user, is rewarded with the spectrum allocation for a fraction of time slot from the source which is the primary user. The Conventional Distributed Algorithm (CDA and the Pragmatic Distributed Algorithm (PDA, which were originally designed for maximising the user’s sum rate, are modified and adapted for maximizing the secrecy sum-rate for the primary user. Instead of assuming perfect modulation and/or perfect channel coding, we have also investigated our proposed schemes when practical channel coding and modulation schemes are invoked.
A secure quantum key distribution scheme based on variable quantum encoding algorithms
Institute of Scientific and Technical Information of China (English)
Zhiwen Zhao; Yi Luo; Zhangji Zhao; Haiming Long
2011-01-01
The security of the quantum secret key plays a critical role in quantum communications. Thus far, one problem that still exists in existing protocols is the leakage of the length of the secret key. In this letter, based on variable quantum encoding algorithms, we propose a secure quantum key distribution scheme, which can overcome the security problem involving the leakage of the secret key. Security analysis shows that the proposed scheme is both secure and effective.%@@ The security of the quantum secret key plays a critical role in quantum communications.Thus far, one problem that still exists in existing protocols is the leakage of the length of the secret key.In this letter,based on variable quantum encoding algorithms, we propose a secure quantum key distribution scheme,which can overcome the security problem involving the leakage of the secret key.Security analysis shows that the proposed scheme is both secure and effective.
The production-distribution problem with order acceptance and package delivery: models and algorithm
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Khalili Majid
2016-01-01
Full Text Available The production planning and distribution are among the most important decisions in the supply chain. Classically, in this problem, it is assumed that all orders have to produced and separately delivered; while, in practice, an order may be rejected if the cost that it brings to the supply chain exceeds its revenue. Moreover, orders can be delivered in a batch to reduce the related costs. This paper considers the production planning and distribution problem with order acceptance and package delivery to maximize the profit. At first, a new mathematical model based on mixed integer linear programming is developed. Using commercial optimization software, the model can optimally solve small or even medium sized instances. For large instances, a solution method, based on imperialist competitive algorithms, is also proposed. Using numerical experiments, the proposed model and algorithm are evaluated.
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower's functions are separable with respect to the follower's variables, and the other is that the follower's functions are convex if the follower's variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader's variable x, it is proved that the optimal solution y of the follower's mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower's programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter A, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.
Belief Consensus Algorithms for Distributed Target Tracking in Wireless Sensor Networks
Savic, Vladimir; Zazo, Santiago
2012-01-01
In distributed target tracking in wireless sensor networks (WSN), agreement on the target state is usually achieved by the construction and maintenance of a communication path. Such an approach lack robustness to failures, and is not applicable to asynchronous networks. Recently, methods have been proposed that can solve these problems using consensus algorithms. However, these methods suffer from at least one of the following problems: i) they do not use fastest consensus methods, and ii) they cannot handle all parametric and nonparametric likelihood functions. In this paper, we propose a general framework for target tracking using distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Since DPF can be also solved (without consensus) by exchanging the observed data, we determine under which conditions BC-based methods are preferred. Finally, we perform extensive simulations to anal...
MULTI-LEVEL KEY DISTRIBUTION ALGORITHM FOR SECRET KEY RECOVERY SYSTEM
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TANAPAT MAHAVEERAWAT
2015-02-01
Full Text Available Most of Multi Agent Key Recovery Systems are proposed from the assumption that Key Recovery Agents in the system have same availability of security service levelagreement and trust. Which mean, secret key will be shared to each Key Recovery Agent in equal secret’s portion. Practically, each Key Recovery Agent may have their own limitation in terms of securityservice level agreement according to economic cost, complexity and risks. This paper proposedMulti Level Key Distribution Algorithm,which the secret key can be managed into portionsharing and assignto each Key Recovery Agent (KRA according to user’s trust. Withproposed algorithm, the experimental result had shown the advantage in secret sharing size and the system had improved initssecurity from the advantage of multilevel secret key distribution capability.
Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm
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Seyed Abbas Taher
2012-01-01
Full Text Available Differential evolution (DE algorithm is used to determine optimal location of unified power quality conditioner (UPQC considering its size in the radial distribution systems. The problem is formulated to find the optimum location of UPQC based on an objective function (OF defined for improving of voltage and current profiles, reducing power loss and minimizing the investment costs considering the OF's weighting factors. Hence, a steady-state model of UPQC is derived to set in forward/backward sweep load flow. Studies are performed on two IEEE 33-bus and 69-bus standard distribution networks. Accuracy was evaluated by reapplying the procedures using both genetic (GA and immune algorithms (IA. Comparative results indicate that DE is capable of offering a nearer global optimal in minimizing the OF and reaching all the desired conditions than GA and IA.
Distributed Algorithms for Improving Search Efficiency in P2P Overlays
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Chittaranjan Hota
2012-04-01
Full Text Available Peer-to-peer (P2P overlay is a distributed application architecture in which peers share their resources. Peers are equally privileged, equipotent participants in the application. Several algorithms for enhancing P2P file searching have been proposed in the literature. In this paper, we have proposed a unique approach of reducing the P2P search complexity and improving search efficiency by using distributed algorithms. In our approach a peer mounts other popular peer’s files and also replicates other popular files or critical files identified using a threshold value. Once a file is mounted, file access requests can be serviced by transparently retrieving the file and sending it to the requesting peer. Replication used in this work improves the file retrieval time by allowing parallel transfer. We have shown the performance analysis of our proposed approach which shows improvement in the search efficiency.
Multilayer PV-storage Microgrids Algorithm for the Dispatch of Distributed Network
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Yang Ping
2016-01-01
Full Text Available In recent years, due to the support of our country, PV-storage microgrid develops rapidly. However, the flexible network operation modes of PV-storage microgrid change flexibly and the operating characteristics with a large amout of sources is highly complicated. Based on the existing microgrid coordinate control methods, this paper proposes multilayer PV-storage microgrid algorithm for fitting dispatch of distributed network, which achieves maximum output of renewable energy when meeting the scheduling requirements of network, by building PV-storage microgrid type dynamic simulation system in a variety of conditions in PSCAD. Simulation results show that the heuristic algorithm proposed can achieve microgrid stable operation and satisfy the demands of the dispatch in distributed network.
A Token-Based Fair Algorithm for Group Mutual Exclusion in Distributed Systems
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Abhishek Swaroop
2007-01-01
Full Text Available The group mutual exclusion (GME problem is a generalization of the mutual exclusion problem. In group mutual exclusion, a process requests a session before entering its critical section (CS. Processes requesting the same session are allowed to be in their CS simultaneously, however, processes requesting different sessions must execute their CS in mutually exclusive way. The paper presents a token-based distributed algorithm for the GME problem in asynchronous message passing systems. The algorithm uses the concept of dynamic request sets. The algorithm does not use any message to be exchanged in the best case and uses n+1 messages in the worst case, where n is the number of processes in the system. The maximum concurrency of the algorithm is n and synchronization delay under heavy load (worst case is 2T, where T is the maximum message propagation delay. The algorithm uses first come first serve approach in selecting the next session type and satisfies the concurrent occupancy property. The static performance analysis and correctness proof is also included in the present exposition.
Reconstruction of strain distribution in fiber Bragg grat-ings with differential evolution algorithm
Institute of Scientific and Technical Information of China (English)
WEN Xiao-yan; YU Qoan
2008-01-01
Differential evolution algorithm is used to solve the inverse problem of strain distribution in tibet Bragg grating (FBG).Linear and nonlinear strain profiles are reconstructed based on the reflection spectra. An approximate solution could beobtained within only 50 rounds of evolutions. Numerical examples show good agreements between target strain profilesand reconstructed ones. Online performance analysis illuminates the efficiency and practicality of differential evolutionalgorithm in solving the inverse problem of FBG.
Institute of Scientific and Technical Information of China (English)
LIU Zi-ping; LI Li-xin
2013-01-01
Based on the niche genetic algorithm, the intelligent and optimizing model for the rolling force distribution in hot strip mills was put forward. The research showed that the model had many advantages such as fast searching speed, high calculating pre-cision and suiting for on-line calculation. A good strip shape could be achieved by using the model and it is appropriate and practica-ble for rolling producing.
Karimi, Mohammad
2011-01-01
Many loads in power systems are inductive loads then consume reactive power, this fact lead to drop voltage and in worst case blackout and collapse voltage. Best option in distribution networks for avoid of this problem is installation of capacitor bank. In capacitor installation, finding optimal location and size of capacitor have special importance. In this paper, Differential Evolutionary (DE) algorithm is proposed for optimal placement and sizing of capacitor. Our objective funct...
Mi Jeong Kim; Sung Joon Maeng; Yong Soo Cho
2015-01-01
In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity...
Wenbo Wu; Jiahong Liang; Xinyu Yao; Baohong Liu
2014-01-01
This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improv...
Abduljabbar, Mustafa
2017-05-11
Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical N-Body algorithms like Fast Multipole Method (FMM). In the present work, we propose three independent strategies to improve partitioning and reduce communication. First, we show that the conventional wisdom of using space-filling curve partitioning may not work well for boundary integral problems, which constitute a significant portion of FMM’s application user base. We propose an alternative method that modifies orthogonal recursive bisection to relieve the cell-partition misalignment that has kept it from scaling previously. Secondly, we optimize the granularity of communication to find the optimal balance between a bulk-synchronous collective communication of the local essential tree and an RDMA per task per cell. Finally, we take the dynamic sparse data exchange proposed by Hoefler et al. [1] and extend it to a hierarchical sparse data exchange, which is demonstrated at scale to be faster than the MPI library’s MPI_Alltoallv that is commonly used.
Directory of Open Access Journals (Sweden)
Geoff McLachlan
2013-11-01
The usefulness of the proposed algorithm is demonstrated in three applications to real datasets. The first example illustrates the use of the main function fmmst in the package by fitting a MST distribution to a bivariate unimodal flow cytometric sample. The second example fits a mixture of MST distributions to the Australian Institute of Sport (AIS data, and demonstrates that EMMIXuskew can provide better clustering results than mixtures with restricted MST components. In the third example, EMMIXuskew is applied to classify cells in a trivariate flow cytometric dataset. Comparisons with some other available methods suggest that EMMIXuskew achieves a lower misclassification rate with respect to the labels given by benchmark gating analysis.
Sanyal, Soumya; Jain, Amit; Das, Sajal K.; Biswas, Rupak
2003-01-01
In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.
Zheng, Yan
2015-03-01
Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.
WANG, Qingrong; ZHU, Changfeng
2017-06-01
Integration of distributed heterogeneous data sources is the key issues under the big data applications. In this paper the strategy of variable precision is introduced to the concept lattice, and the one-to-one mapping mode of variable precision concept lattice and ontology concept lattice is constructed to produce the local ontology by constructing the variable precision concept lattice for each subsystem, and the distributed generation algorithm of variable precision concept lattice based on ontology heterogeneous database is proposed to draw support from the special relationship between concept lattice and ontology construction. Finally, based on the standard of main concept lattice of the existing heterogeneous database generated, a case study has been carried out in order to testify the feasibility and validity of this algorithm, and the differences between the main concept lattice and the standard concept lattice are compared. Analysis results show that this algorithm above-mentioned can automatically process the construction process of distributed concept lattice under the heterogeneous data sources.
Application of the LSQR algorithm in non-parametric estimation of aerosol size distribution
He, Zhenzong; Qi, Hong; Lew, Zhongyuan; Ruan, Liming; Tan, Heping; Luo, Kun
2016-05-01
Based on the Least Squares QR decomposition (LSQR) algorithm, the aerosol size distribution (ASD) is retrieved in non-parametric approach. The direct problem is solved by the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law. An optimal wavelength selection method is developed to improve the retrieval accuracy of the ASD. The proposed optimal wavelength set is selected by the method which can make the measurement signals sensitive to wavelength and decrease the degree of the ill-condition of coefficient matrix of linear systems effectively to enhance the anti-interference ability of retrieval results. Two common kinds of monomodal and bimodal ASDs, log-normal (L-N) and Gamma distributions, are estimated, respectively. Numerical tests show that the LSQR algorithm can be successfully applied to retrieve the ASD with high stability in the presence of random noise and low susceptibility to the shape of distributions. Finally, the experimental measurement ASD over Harbin in China is recovered reasonably. All the results confirm that the LSQR algorithm combined with the optimal wavelength selection method is an effective and reliable technique in non-parametric estimation of ASD.
Ameli, Kazem; Alfi, Alireza; Aghaebrahimi, Mohammadreza
2016-09-01
Similarly to other optimization algorithms, harmony search (HS) is quite sensitive to the tuning parameters. Several variants of the HS algorithm have been developed to decrease the parameter-dependency character of HS. This article proposes a novel version of the discrete harmony search (DHS) algorithm, namely fuzzy discrete harmony search (FDHS), for optimizing capacitor placement in distribution systems. In the FDHS, a fuzzy system is employed to dynamically adjust two parameter values, i.e. harmony memory considering rate and pitch adjusting rate, with respect to normalized mean fitness of the harmony memory. The key aspect of FDHS is that it needs substantially fewer iterations to reach convergence in comparison with classical discrete harmony search (CDHS). To the authors' knowledge, this is the first application of DHS to specify appropriate capacitor locations and their best amounts in the distribution systems. Simulations are provided for 10-, 34-, 85- and 141-bus distribution systems using CDHS and FDHS. The results show the effectiveness of FDHS over previous related studies.
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-01-01
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks. PMID:28671641
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-07-03
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Dijkstra algorithm is a basic algorithm to analyze the vehicle routing problem (VRP) in the terminal distribution of logistics center. According to the actual client demands of service speed and quality, the conceptions of economical distance of delivery and the best routing algorithm were given on the base of the Dijkstra algorithm with consideration of a coefficient of the road hustle degree. Economical distance of delivery is the shortest physical distance between two customers. It is the value of goods delivery in shortest distance when concerning factors such as the road length, the hustle degree, the driveway quantity, and the type of the road. The improved algorithm is being used in the development and application of a distribution path information system in the terminal distribution of logistics center. The simulation and practical case prove that the algorithm is effective and reasonable.
Dynamic Consensus Algorithm based Distributed Voltage Harmonic Compensation in Islanded Microgrids
DEFF Research Database (Denmark)
Meng, Lexuan; Tang, Fen; Firoozabadi, Mehdi Savaghebi
2015-01-01
In islanded microgrids, the existence of nonlinear electric loads may cause voltage distortion and affect the performance of power quality sensitive equipment. Thanks to the prevalent utilization of interfacing power electronic devices and information/communication technologies, distributed...... generators can be employed as compensators to enhance the power quality on consumer side. However, conventional centralized control is facing obstacles because of the distributed fashion of generation and consumption. Accordingly, this paper proposes a consensus algorithm based distributed hierarchical...... control to realize voltage harmonic compensation and accurate current sharing in multi-bus islanded microgrids. Low order harmonic components are considered as examples in this paper. Harmonic current sharing is also realized among distributed generators by applying the proposed methods. Plug...
Optimum Distribution Generator Placement in Power Distribution System Using Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Mehdi Mahdavi
2009-03-01
Full Text Available The recent development in renewable energy systems and the high demand for having clean and low cost energy sources encourage people to use distributed generator (DG systems. Proper addition and placement of DG units can increase reliability and reduce the loss and production cost. In this paper using Ant Colony method, we developed an optimum placing scheme for DGs. The proposed method is tested on an IEEE 34-shinhe system. Results show that if DGs are able to generate active power, their effectiveness will increase.
DEFF Research Database (Denmark)
Sun, Qiuye; Han, Renke; Zhang, Huaguang
2015-01-01
which is applied to distributed generators in the Energy Internet. Then, the decomposed tasks, models, and information flow of the proposed method are analyzed. The proposed coordinated controller installed between the Energy Internet and the Main-Grid keeps voltage angles and amplitudes consensus while......With the bidirectional power flow provided by the Energy Internet, various methods are promoted to improve and increase the energy utilization between Energy Internet and Main-Grid. This paper proposes a novel distributed coordinated controller combined with a multi-agent-based consensus algorithm...... providing accurate power-sharing and minimizing circulating currents. Finally, the Energy Internet can be integrated into the Main-Grid seamlessly if necessary. Hence the Energy Internet can be operated as a spinning reserve system. Simulation results are provided to show the effectiveness of the proposed...
A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Peng Jiang
2017-01-01
Full Text Available For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA. After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes’ being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS. The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network’s best service quality and lifetime.
A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks.
Jiang, Peng; Xu, Yiming; Liu, Jun
2017-01-19
For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes' being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network's best service quality and lifetime.
On the taxonomy of optimization problems under estimation of distribution algorithms.
Echegoyen, Carlos; Mendiburu, Alexander; Santana, Roberto; Lozano, Jose A
2013-01-01
Understanding the relationship between a search algorithm and the space of problems is a fundamental issue in the optimization field. In this paper, we lay the foundations to elaborate taxonomies of problems under estimation of distribution algorithms (EDAs). By using an infinite population model and assuming that the selection operator is based on the rank of the solutions, we group optimization problems according to the behavior of the EDA. Throughout the definition of an equivalence relation between functions it is possible to partition the space of problems in equivalence classes in which the algorithm has the same behavior. We show that only the probabilistic model is able to generate different partitions of the set of possible problems and hence, it predetermines the number of different behaviors that the algorithm can exhibit. As a natural consequence of our definitions, all the objective functions are in the same equivalence class when the algorithm does not impose restrictions to the probabilistic model. The taxonomy of problems, which is also valid for finite populations, is studied in depth for a simple EDA that considers independence among the variables of the problem. We provide the sufficient and necessary condition to decide the equivalence between functions and then we develop the operators to describe and count the members of a class. In addition, we show the intrinsic relation between univariate EDAs and the neighborhood system induced by the Hamming distance by proving that all the functions in the same class have the same number of local optima and that they are in the same ranking positions. Finally, we carry out numerical simulations in order to analyze the different behaviors that the algorithm can exhibit for the functions defined over the search space [Formula: see text].
An Algorithm for Optimized Time, Cost, and Reliability in a Distributed Computing System
Directory of Open Access Journals (Sweden)
Pankaj Saxena
2013-03-01
Full Text Available Distributed Computing System (DCS refers to multiple computer systems working on a single problem. A distributed system consists of a collection of autonomous computers, connected through a network which enables computers to coordinate their activities and to share the resources of the system. In distributed computing, a single problem is divided into many parts, and each part is solved by different computers. As long as the computers are networked, they can communicate with each other to solve the problem. DCS consists of multiple software components that are on multiple computers, but run as a single system. The computers that are in a distributed system can be physically close together and connected by a local network, or they can be geographically distant and connected by a wide area network. The ultimate goal of distributed computing is to maximize performance in a time effective, cost-effective, and reliability effective manner. In DCS the whole workload is divided into small and independent units, called tasks and it allocates onto the available processors. It also ensures fault tolerance and enables resource accessibility in the event that one of the components fails. The problem is addressed of assigning a task to a distributed computing system. The assignment of the modules of tasks is done statically. We have to give an algorithm to solve the problem of static task assignment in DCS, i.e. given a set of communicating tasks to be executed on a distributed system on a set of processors, to which processor should each task be assigned to get the more reliable results in lesser time and cost. In this paper an efficient algorithm for task allocation in terms of optimum time or optimum cost or optimum reliability is presented where numbers of tasks are more then the number of processors.
Zucker, S W; Zucker, Shay; Mazeh, Tsevi
2001-01-01
We construct a maximum-likelihood algorithm - MAXLIMA, to derive the mass distribution of the extrasolar planets when only the minimum masses are observed. The algorithm derives the distribution by solving a numerically stable set of equations, and does not need any iteration or smoothing. Based on 50 minimum masses, MAXLIMA yields a distribution which is approximately flat in log M, and might rise slightly towards lower masses. The frequency drops off very sharply when going to masses higher than 10 Jupiter masses, although we suspect there is still a higher mass tail that extends up to probably 20 Jupiter masses. We estimate that 5% of the G stars in the solar neighborhood have planets in the range of 1-10 Jupiter masses with periods shorter than 1500 days. For comparison we present the mass distribution of stellar companions in the range of 100--1000 Jupiter masses, which is also approximately flat in log M. The two populations are separated by the "brown-dwarf desert", a fact that strongly supports the id...
Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid
Directory of Open Access Journals (Sweden)
Omowunmi Mary Longe
2017-06-01
Full Text Available The contributions of Distributed Energy Generation (DEG and Distributed Energy Storage (DES for Demand Side Management (DSM purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s. A grid-connected smart microgrid comprising heterogeneous (active and passive smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.
Capataz: a framework for distributing algorithms via the World Wide Web
Directory of Open Access Journals (Sweden)
Gonzalo J. Martínez
2015-08-01
Full Text Available In recent years, some scientists have embraced the distributed computing paradigm. As experiments and simulations demand ever more computing power, coordinating the efforts of many different processors is often the only reasonable resort. We developed an open-source distributed computing framework based on web technologies, and named it Capataz. Acting as an HTTP server, web browsers running on many different devices can connect to it to contribute in the execution of distributed algorithms written in Javascript. Capataz takes advantage of architectures with many cores using web workers. This paper presents an improvement in Capataz´ usability and why it was needed. In previous experiments the total time of distributed algorithms proved to be susceptible to changes in the execution time of the jobs. The system now adapts by bundling jobs together if they are too simple. The computational experiment to test the solution is a brute force estimation of pi. The benchmark results show that by bundling jobs, the overall perfomance is greatly increased.
Optimal Power Flow in Islanded Microgrids Using a Simple Distributed Algorithm
DEFF Research Database (Denmark)
Sanseverino, Eleonora Riva; Di Silvestre, Maria Luisa; Badalamenti, Romina
2015-01-01
In this paper, the problem of distributed power losses minimization in islanded distribution systems is dealt with. The problem is formulated in a very simple manner and a solution is reached after a few iterations. The considered distribution system, a microgrid, will not need large bandwidth co...... results of the proposed method on an islanded microgrid. Simulation results of the distributed algorithm are compared to a centralized Optimal Power Flow approach and very small errors can be observed.......In this paper, the problem of distributed power losses minimization in islanded distribution systems is dealt with. The problem is formulated in a very simple manner and a solution is reached after a few iterations. The considered distribution system, a microgrid, will not need large bandwidth...... communication channels, since only closeby nodes will exchange information. The correction of generated active powers is possible by means of the active power losses partition concept that attributes a portion of the overall power losses in each branch to each generator. The experimental part shows the first...
An estimation of distribution algorithm (EDA) variant with QGA for Flowshop scheduling problem
Latif, Muhammad Shahid; Hong, Zhou; Ali, Amir
2014-04-01
In this research article, a hybrid approach is presented which based on well-known meta-heuristics algorithms. This study based on integration of Quantum Genetic Algorithm (QGA) and Estimation of Distribution Algorithm, EDA, (for simplicity we use Q-EDA) for flowshop scheduling, a well-known NP hard Problem, while focusing on the total flow time minimization criterion. A relatively new method has been adopted for the encoding of jobs sequence in flowshop known as angel rotations instead of random keys, so QGA become more efficient. Further, EDA has been integrated to update the population of QGA by making a probability model. This probabilistic model is built and used to generate new candidate solutions which comprised on best individuals, obtained after several repetitions of proposed (Q-EDA) approach. As both heuristics based on probabilistic characteristics, so exhibits excellent learning capability and have minimum chances of being trapped in local optima. The results obtained during this study are presented and compared with contemporary approaches in literature. The current hybrid Q-EDA has implemented on different benchmark problems. The experiments has showed better convergence and results. It is concluded that hybrid Q-EDA algorithm can generally produce better results while implemented for Flowshop Scheduling Problem (FSSP).
Distributed and Location-Based Multicast Routing Algorithms for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Hakki Bagci
2009-01-01
Full Text Available Multicast routing protocols in wireless sensor networks are required for sending the same message to multiple different destinations. In this paper, we propose two different distributed algorithms for multicast routing in wireless sensor networks which make use of location information of sensor nodes. Our first algorithm groups the destination nodes according to their angular positions and forwards the multicast message toward each group in order to reduce the number of total branches in multicast tree which also reduces the number of messages transmitted. Our second algorithm calculates an Euclidean minimum spanning tree at the source node by using the positions of the destination nodes. The multicast message is forwarded to destination nodes according to the calculated MST. This helps in reducing the total energy consumed for delivering the message to all destinations by decreasing the number of total transmissions. Evaluation results show that the algorithms we propose are scalable and energy efficient, so they are good candidates to be used for multicasting in wireless sensor networks.
Wei, Lin-Yang; Qi, Hong; Ren, Ya-Tao; Ruan, Li-Ming
2016-11-01
Inverse estimation of the refractive index distribution in one-dimensional participating media with graded refractive index (GRI) is investigated. The forward radiative transfer problem is solved by the Chebyshev collocation spectral method. The stochastic particle swarm optimization (SPSO) algorithm is employed to retrieve three kinds of GRI distribution, i.e. the linear, sinusoidal and quadratic GRI distribution. The retrieval accuracy of GRI distribution with different wall emissivity, optical thickness, absorption coefficients and scattering coefficients are discussed thoroughly. To improve the retrieval accuracy of quadratic GRI distribution, a double-layer model is proposed to supply more measurement information. The influence of measurement errors upon the precision of estimated results is also investigated. Considering the GRI distribution is unknown beforehand in practice, a quadratic function is employed to retrieve the linear GRI by SPSO algorithm. All the results show that the SPSO algorithm is applicable to retrieve different GRI distributions in participating media accurately even with noisy data.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Energy Technology Data Exchange (ETDEWEB)
Li, Weixuan [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Lin, Guang, E-mail: guanglin@purdue.edu [Department of Mathematics and School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States)
2015-08-01
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
A new algorithm for importance analysis of the inputs with distribution parameter uncertainty
Li, Luyi; Lu, Zhenzhou
2016-10-01
Importance analysis is aimed at finding the contributions by the inputs to the uncertainty in a model output. For structural systems involving inputs with distribution parameter uncertainty, the contributions by the inputs to the output uncertainty are governed by both the variability and parameter uncertainty in their probability distributions. A natural and consistent way to arrive at importance analysis results in such cases would be a three-loop nested Monte Carlo (MC) sampling strategy, in which the parameters are sampled in the outer loop and the inputs are sampled in the inner nested double-loop. However, the computational effort of this procedure is often prohibitive for engineering problem. This paper, therefore, proposes a newly efficient algorithm for importance analysis of the inputs in the presence of parameter uncertainty. By introducing a 'surrogate sampling probability density function (SS-PDF)' and incorporating the single-loop MC theory into the computation, the proposed algorithm can reduce the original three-loop nested MC computation into a single-loop one in terms of model evaluation, which requires substantially less computational effort. Methods for choosing proper SS-PDF are also discussed in the paper. The efficiency and robustness of the proposed algorithm have been demonstrated by results of several examples.
Chatterjee, A.; Ghoshal, S. P.; Mukherjee, V.
In this paper, a conventional thermal power system equipped with automatic voltage regulator, IEEE type dual input power system stabilizer (PSS) PSS3B and integral controlled automatic generation control loop is considered. A distributed generation (DG) system consisting of aqua electrolyzer, photovoltaic cells, diesel engine generator, and some other energy storage devices like flywheel energy storage system and battery energy storage system is modeled. This hybrid distributed system is connected to the grid. While integrating this DG with the onventional thermal power system, improved transient performance is noticed. Further improvement in the transient performance of this grid connected DG is observed with the usage of superconducting magnetic energy storage device. The different tunable parameters of the proposed hybrid power system model are optimized by artificial bee colony (ABC) algorithm. The optimal solutions offered by the ABC algorithm are compared with those offered by genetic algorithm (GA). It is also revealed that the optimizing performance of the ABC is better than the GA for this specific application.
Refinement verification of the lazy caching algorithm
Hesselink, Wim H.
2006-01-01
The lazy caching algorithm of Afek et al. (ACM Trans. Program. Lang. Syst. 15, 182-206, 1993) is a protocol that allows the use of local caches with delayed updates. It results in a memory model that is not atomic (linearizable) but only sequentially consistent as defined by Lamport. In Distributed
Distributed Clustering Algorithm to Explore Selection Diversity in Wireless Sensor Networks
Kong, Hyung-Yun; Asaduzzaman, Hyung-Yun
This paper presents a novel cross-layer approach to explore selection diversity for distributed clustering based wireless sensor networks (WSNs) by selecting a proper cluster-head. We develop and analyze an instantaneous channel state information (CSI) based cluster-head selection algorithm for a distributed, dynamic and randomized clustering based WSN. The proposed cluster-head selection scheme is also random and capable to distribute the energy uses among the nodes in the network. We present an analytical approach to evaluate the energy efficiency and system lifetime of our proposal. Analysis shows that the proposed scheme outperforms the performance of additive white Gaussian noise (AWGN) channel under Rayleigh fading environment. This proposal also outperforms the existing cooperative diversity protocols in terms of system lifetime and implementation complexity.
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness......In a DC microgrid, several paralleled conversion systems are installed in distributed substations for transferring power from external grid to a DC microgrid. Droop control is used for the distributed load sharing among all the DC/DC converters. Considering the typical efficiency feature of power...... electronic converters, optimization method can be implemented in tertiary level for improving the overall system efficiency. However, optimization purposes usually require centralized communication, data acquisition and computation which might be either impractical or costly for dispersed systems...
Derivation of plotting position formula for GEV distribution using genetic algorithm
Kim, S.; Kim, T.; Heo, J.
2008-12-01
Probability plotting positions are used for the graphical display of annual maximum rainfall or flood series and the estimation of exceedance probability of those values. In addition, plotting positions allow a visual examination of the fitness of probability distribution provided by flood frequency analysis. Therefore, the graphical approach using plotting position has been applied to many fields of hydrology and water resources planning. Various plotting position formulas were developed for probability distributions in many researches and plotting position formulas by Gringorten(1963) and Cunnane(1978) were applied to the Gumbel and GEV distributions generally. Especially, Cunnane(1978) defined the plotting position that related with the mean of data and proposed the general formula that can be applied to various probability distributions. The definition of the plotting position by Cunnane(1978) have influenced on the plotting position of probability distribution contained shape parameter. In this study, the plotting position formula for the GEV distribution is derived by using the combination of the order statistics and the probability weight moment of the GEV distribution for various sample sizes and shape parameters. In addition, the parameters of plotting position formula for the GEV distribution are estimated by using genetic algorithm considering the range of a coefficient of skewness related with the shape parameters of the GEV distribution. The accuracy of derived plotting position formula for the GEV distribution is examined by the comparison of root mean square errors between theoretical reduced variates and those calculated from the derived and existing plotting position formulas such as Gringorten(1963) and Cunnane(1978).
Institute of Scientific and Technical Information of China (English)
Yanmin Wang; Guobiao Liang; Zhidong Pan
2010-01-01
A modified regularization algorithm with a more proper operator was proposed for the inversion of partide size distribution(PSD)from light-scattering data in a laser particle sizer based on the Mie scattering principle.The Generalized Cross-Validation(GCV)method and the L-curve method were used for determining the regularization parameter.The Successive Over-Relaxation(SOR)iterative method was used to increase the exactness and stability of the converged result.The simulated results based on the modified algorithm are in a good agreement with the experimental data measured for nine standard particulate samples,their mixtures as well as three natural particulate materials with irregular shapes,indicating that this modified regularization method is not only feasible but also effective for the simulation of PSD from corresponding light-scattering data.
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Routing and wavelength assignment for online real-time multicast connection setup is a difficulttask due to the dynamic change of availabilities of wavelengths on links and the consideration of wave-length conversion delay in WDM networks. This paper presents a distributed routing and wavelength as-signment scheme for the setup of real-time multicast connections. It integrates routing and wavelength as-signment as a single process, which greatly reduces the connection setup time. The proposed routingmethod is based on the Prim's MST (Minimum Spanning Tree) algorithm and the K-restricted breadth-first search method, which can produce a sub-minimal cost tree under a given delay bound. The wave-length assignment uses the least-conversion and load balancing strategies. Simulation results show that theproposed algorithm is suitable for online multicast connection establishment in WDM networks.
Energy Technology Data Exchange (ETDEWEB)
Loring, Burlen; Karimabadi, Homa; Rortershteyn, Vadim
2014-07-01
The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.
QoS-enabled ANFIS Dead Reckoning Algorithm for Distributed Interactive Simulation
Hakiri, Akram
2010-01-01
Dead Reckoning mechanisms are usually used to estimate the position of simulated entity in virtual environment. However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favor of low computational complexity. A novel extension of Dead Reckoning is suggested in this paper to increase the network availability and fulfill the required Quality of Service in large scale distributed simulation application. The proposed algorithm is referred to as ANFIS Dead Reckoning, which stands for Adaptive Neuro-based Fuzzy Inference System Dead Reckoning is based on a fuzzy inference system which is trained by the learning algorithm derived from the neuronal networks and fuzzy inference theory. The proposed mechanism takes its based on the optimization approach to calculate the error threshold violation in networking games. Our model shows it primary benefits especially in the decision making of the behavior of simulated e...
Directory of Open Access Journals (Sweden)
Hehua Li
2013-09-01
Full Text Available Sensor network adopts the lossy compression techniques to collect long-term data, analyze the data tendency and the interested specific data model. In these applications, the sensor is established to collect large numbers of continuous data, and allow the access to the lossy and untimely data. In addition, the neighbor sensor data is correlated both in time and in space. Therefore, the data sensed by the sensor itself in the intermediate node and the data will be lossy compressed for prolonging the system operation lifetime. To study the optimal distributed problem of the bite-rate and the lossy degree. When the optimal distribution problem between the bit-rate and the lossy degree is discussed, how to make the optimal decision distributes the compressibility of all sensors in the satisfaction of the acceptable data distorted condition. For adopting the minimum transmittal bit-rate to collect the top-quality data. The optimal solution is introduced for the distribution problem, and the decentralized distribution algorithm is introduced in terms of the optimal solution. Compared with the average distribution strategy, the simulation result shows that the optimal solution and the decentralized actually can reduce large numbers of the network transmittal data volume
A partition enhanced mining algorithm for distributed association rule mining systems
Directory of Open Access Journals (Sweden)
A.O. Ogunde
2015-11-01
Full Text Available The extraction of patterns and rules from large distributed databases through existing Distributed Association Rule Mining (DARM systems is still faced with enormous challenges such as high response times, high communication costs and inability to adapt to the constantly changing databases. In this work, a Partition Enhanced Mining Algorithm (PEMA is presented to address these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process is divided into two stages. In the first stage, the data agents horizontally segment the databases with small average transaction length into relatively smaller partitions based on the number of available sites and the available memory. On the other hand, databases with relatively large average transaction length were vertically partitioned. After this, Mobile Agent-Based Association Rule Mining-Agents, which are the mining agents, carry out the discovery of the local frequent itemsets. At the second stage, the local frequent itemsets were incrementally integrated by the from one data site to another to get the global frequent itemsets. This reduced the response time and communication cost in the system. Results from experiments conducted on real datasets showed that the average response time of PEMA showed an improvement over existing algorithms. Similarly, PEMA incurred lower communication costs with average size of messages exchanged lower when compared with benchmark DARM systems. This result showed that PEMA could be efficiently deployed for efficient discovery of valuable knowledge in distributed databases.
Optimal design of unit hydrographs using probability distribution and genetic algorithms
Indian Academy of Sciences (India)
Rajib Kumar Bhattacharjya
2004-10-01
A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.
A Power Load Distribution Algorithm to Optimize Data Center Electrical Flow
Directory of Open Access Journals (Sweden)
Paulo Maciel
2013-07-01
Full Text Available Energy consumption is a matter of common concern in the world today. Research demonstrates that as a consequence of the constantly evolving and expanding field of information technology, data centers are now major consumers of electrical energy. Such high electrical energy consumption emphasizes the issues of sustainability and cost. Against this background, the present paper proposes a power load distribution algorithm (PLDA to optimize energy distribution of data center power infrastructures. The PLDA, which is based on the Ford-Fulkerson algorithm, is supported by an environment called ASTRO, capable of performing the integrated evaluation of dependability, cost and sustainability. More specifically, the PLDA optimizes the flow distribution of the energy flow model (EFM. EFMs are responsible for estimating sustainability and cost issues of data center infrastructures without crossing the restrictions of the power capacity that each device can provide (power system or extract (cooling system. Additionally, a case study is presented that analyzed seven data center power architectures. Significant results were observed, achieving a reduction in power consumption of up to 15.5%.
Directory of Open Access Journals (Sweden)
Álvaro Gutiérrez
2011-11-01
Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Jevtić, Aleksandar; Gutiérrez, Alvaro
2011-01-01
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the distributed bees algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a genetic algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.
Directory of Open Access Journals (Sweden)
V. A. Sednin
2010-01-01
Full Text Available The paper presents an algorithm for optimization of thermal load distribution among heat-sources in the system of centralized heat supply. The algorithm can be used while elaborating plans for development of heat supply systems in cities and settlements.
Kropacheva, Marya; Melgunov, Mikhail; Makarova, Irina
2017-02-01
The study of migration pathways of artificial isotopes in the flood-plain biogeocoenoses, impacted by the nuclear fuel cycle plants, requires determination of isotope speciations in the biomass of higher terrestrial plants. The optimal method for their determination is the sequential elution technique (SET). The technique was originally developed to study atmospheric pollution by metals and has been applied to lichens, terrestrial and aquatic bryophytes. Due to morphological and physiological differences, it was necessary to adapt SET for new objects: coastal macrophytes growing on the banks of the Yenisei flood-plain islands in the near impact zone of Krasnoyarsk Mining and Chemical Combine (KMCC). In the first version of SET, 20 mM Na2EDTA was used as a reagent at the first stage; in the second version of SET, it was 1 M CH3COONH4. Four fractions were extracted. Fraction I included elements from the intercellular space and those connected with the outer side of the cell wall. Fraction II contained intracellular elements; fraction III contained elements firmly bound in the cell wall and associated structures; fraction IV contained insoluble residue. Adaptation of SET has shown that the first stage should be performed immediately after sampling. Separation of fractions III and IV can be neglected, since the output of isotopes into the IV fraction is at the level of error detection. The most adequate version of SET for terrestrial vascular plants is the version using 20 mM Na2EDTA at the first stage. Isotope (90)Sr is most sensitive to the technique changes. Its distribution depends strongly on both the extractant used at stage 1 and duration of the first stage. Distribution of artificial radionuclides in the biomass of terrestrial vascular plants can vary from year to year and depends significantly on the age of the plant.
National Research Council Canada - National Science Library
N. Nishimoto; S. Terae; M. Uesugi; K. Ogasawara; T. Sakurai
2008-01-01
Objectives: The objectives of this study were to investigate the transitional probability distribution of medical term boundaries between characters and to develop a parsing algorithm specifically for medical texts. Methods...
物流配送车辆的干扰管理序贯决策方法研究%Sequential Decision Methods for Disruption Management in Distribution
Institute of Scientific and Technical Information of China (English)
胡祥培; 于楠; 丁秋雷
2011-01-01
针对物流配送系统中车辆由于干扰事件而产生的时间延迟,运用干扰管理的思想,首先对干扰事件演进过程进行分析,提出了时间延迟的干扰管理问题的多阶段划分方法,然后通过对偏离成本的探讨,建立了兼顾物流供应商和客户两个主体的时间延迟度量模型,进而形成了处理时间延迟干扰问题的序贯决策方法.最后采用一个具体实例,验证了上述方法的可行性.%Distribution plays a key role in the logistics management because satisfactory delivery services can increase customer satisfaction. However, many factors,such as vehicle breakdown, traffic jam, and inclement weather conditions, can disrupt delivery services. This study aims to improve customer satisfaction by improving the quality of delivery services. Current literature on disruption management primarily focuses on strategies to cope with disruption events after they occur.Very few studies focus on managing disruption events during the occurrence. In addition, there is a need to evaluate the effect of delayed delivery on the quality of delivery services. Understanding this effect can enable logistics managers to make effective decisions on improving the quality of delivery services.This study is primarily based on organizational research and behavioral science theories. In the first part of the study, we adopted the stage division method of disruption management to thoroughly analyze the delayed delivery and define key factors.This understanding enabled us to derive a time-delayed measurement model incorporating those multi-stage factors. We further measured the system deviation costs as the function of future lost costs and extra transportation costa. Based on the stage division method, the dlayed delivery was divided into sequential and correlated stages. This sequential decision making method can enable decision makers to choose a coping strategy in each stage according to the conditions of present
Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm
DEFF Research Database (Denmark)
Zhang, Chunyu; Ding, Yi; Wu, Qiuwei;
2013-01-01
This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...
A Novel Robust Communication Algorithm for Distributed Secondary Control of Islanded MicroGrids
DEFF Research Database (Denmark)
Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos;
2013-01-01
Distributed secondary control (DSC) is a new approach for MicroGrids (MGs) such that frequency, voltage and power regulation is made in each unit locally to avoid using a central controller. Due to the constrained traffic pattern required by the secondary control, it is viable to implement...... dedicated local area communication functionality among the local controllers. This paper presents a new, wireless-based robust communication algorithm for DSC of MGs designed to avoid communication bottlenecks and enable the plug-and-play capability of new DGs. Real-time simulation and experimental results...
A Filter-Based Uniform Algorithm for Optimizing Top-k Query in Distributed Networks
Institute of Scientific and Technical Information of China (English)
ZHAO Zhibin; YAO Lan; YANG Xiaochun; LI Binyang; YU Ge
2006-01-01
In this paper we propose a Filter-based Uniform Algorithm (FbUA) for optimizing top-k query in distributed networks, which has been a topic of much recent interest.The basic idea of FbUA is to set a filter at each node to prevent it from sending out the data with little chance to contribute to the top-k result.FbUA can gain exact answers to top-k query through two phrases of round-trip communications between query station and participant nodes.The experiment results show that FbUA reduces network bandwidth consumption dramatically.
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
Directory of Open Access Journals (Sweden)
Zhijun Luo
2014-01-01
Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Directory of Open Access Journals (Sweden)
Arnaud Dufays
2016-03-01
Full Text Available Sequential Monte Carlo (SMC methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT algorithm, developed in this paper, combines (off-line tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
Directory of Open Access Journals (Sweden)
Han Liwei
2014-07-01
Full Text Available Monitoring data on an earth-rockfill dam constitutes a form of spatial data. Such data include much uncertainty owing to the limitation of measurement information, material parameters, load, geometry size, initial conditions, boundary conditions and the calculation model. So the cloud probability density of the monitoring data must be addressed. In this paper, the cloud theory model was used to address the uncertainty transition between the qualitative concept and the quantitative description. Then an improved algorithm of cloud probability distribution density based on a backward cloud generator was proposed. This was used to effectively convert certain parcels of accurate data into concepts which can be described by proper qualitative linguistic values. Such qualitative description was addressed as cloud numerical characteristics-- {Ex, En, He}, which could represent the characteristics of all cloud drops. The algorithm was then applied to analyze the observation data of a piezometric tube in an earth-rockfill dam. And experiment results proved that the proposed algorithm was feasible, through which, we could reveal the changing regularity of piezometric tube’s water level. And the damage of the seepage in the body was able to be found out.
A Unified Algorithm for Virtual Desktops Placement in Distributed Cloud Computing
Directory of Open Access Journals (Sweden)
Jiangtao Zhang
2016-01-01
Full Text Available Distributed cloud has been widely adopted to support service requests from dispersed regions, especially for large enterprise which requests virtual desktops for multiple geodistributed branch companies. The cloud service provider (CSP aims to deliver satisfactory services at the least cost. CSP selects proper data centers (DCs closer to the branch companies so as to shorten the response time to user request. At the same time, it also strives to cut cost considering both DC level and server level. At DC level, the expensive long distance inter-DC bandwidth consumption should be reduced and lower electricity price is sought. Inside each tree-like DC, servers are trying to be used as little as possible so as to save equipment cost and power. In nature, there is a noncooperative relation between the DC level and server level in the selection. To attain these objectives and capture the noncooperative relation, multiobjective bilevel programming is used to formulate the problem. Then a unified genetic algorithm is proposed to solve the problem which realizes the selection of DC and server simultaneously. The extensive simulation shows that the proposed algorithm outperforms baseline algorithm in both quality of service guaranteeing and cost saving.
Khatchikian, C.; Sangermano, F.; Kendell, D.; Livdahl, T.
2010-01-01
The present work evaluates the use of species distribution model (SDM) algorithms to classify high density of small container Aedes mosquitoes at a fine scale, in the Bermuda islands. Weekly ovitrap data collected by the Health Department of Bermuda (UK) for the years 2006 and 2007 were used for the models. The models evaluated included the following algorithms: Bioclim, Domain, GARP, logistic regression, and MaxEnt. Models were evaluated according to performance and robustness. The area Receiver Operating Characteristic (ROC) curve was used to evaluate each model’s performance, and robustness was assessed considering the spatial correlation between classification risks for the two datasets. Relative to the other algorithms, logistic regression was the best model for classifying high risk areas, and the maximum entropy approach (MaxEnt) presented the second best performance. We report the importance of covariables for these two models, and discuss the utility of SDMs for vector control efforts and the potential for the development of scripts that automate the task of creating risk assessment maps. PMID:21198711
Naeem, Muhammad; Pareek, Udit; Lee, Daniel C; Anpalagan, Alagan
2013-04-12
Due to the rapid increase in the usage and demand of wireless sensor networks (WSN), the limited frequency spectrum available for WSN applications will be extremely crowded in the near future. More sensor devices also mean more recharging/replacement of batteries, which will cause significant impact on the global carbon footprint. In this paper, we propose a relay-assisted cognitive radio sensor network (CRSN) that allocates communication resources in an environmentally friendly manner. We use shared band amplify and forward relaying for cooperative communication in the proposed CRSN. We present a multi-objective optimization architecture for resource allocation in a green cooperative cognitive radio sensor network (GC-CRSN). The proposed multi-objective framework jointly performs relay assignment and power allocation in GC-CRSN, while optimizing two conflicting objectives. The first objective is to maximize the total throughput, and the second objective is to minimize the total transmission power of CRSN. The proposed relay assignment and power allocation problem is a non-convex mixed-integer non-linear optimization problem (NC-MINLP), which is generally non-deterministic polynomial-time (NP)-hard. We introduce a hybrid heuristic algorithm for this problem. The hybrid heuristic includes an estimation-of-distribution algorithm (EDA) for performing power allocation and iterative greedy schemes for constraint satisfaction and relay assignment. We analyze the throughput and power consumption tradeoff in GC-CRSN. A detailed analysis of the performance of the proposed algorithm is presented with the simulation results.
Energy Technology Data Exchange (ETDEWEB)
Schatz, Martin D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kolda, Tamara G. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); van de Geijn, Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-09-01
Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized im- plementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.
Zhuang, H. M.; Jiang, X. J.
2016-08-01
This paper presents an active and reactive power dynamic optimization model for active distribution network (ADN), whose control variables include the output of distributed generations (DGs), charge or discharge power of energy storage system (ESS) and reactive power from capacitor banks. To solve the high-dimension nonlinear optimization model, a new heuristic swarm intelligent method, namely wolf pack algorithm (WPA) with better global convergence and computational robustness, is adapted so that the network loss minimization can be achieved. In this paper, the IEEE33-bus system is used to show the effectiveness of WPA technique compared with other techniques. Numerical tests on the modified IEEE 33-bus system show that WPA for active and reactive multi-period optimization of ADN is exact and effective.
Energy Technology Data Exchange (ETDEWEB)
Choi, Jaeyoung; Walker, D.W. [Oak Ridge National Lab., TN (US); Dongarra, J.J. [Oak Ridge National Lab., TN (US)]|[Univ. of Tennessee, Knoxville, TN (US). Dept. of Computer Science
1993-08-01
This paper describes the Parallel Universal Matrix Multiplication Algorithms (PUMMA) on distributed memory concurrent computers. The PUMMA package includes not only the non-transposed matrix multiplication routine C = A{center_dot}B, but also transposed multiplication routines C = A{sup T}{center_dot}B, C = A{center_dot}B{sup T}, and C = A{sup T}{center_dot}B{sup T}, for a block scattered data distribution. The routines perform efficiently for a wide range of processor configurations and block sizes. The PUMMA together provide the same functionality as the Level 3 BLAS routine xGEMM. Details of the parallel implementation of the routines are given, and results are presented for runs on the Intel Touchstone Delta computer.
Jafarizadeh, Saber
2010-01-01
Providing an analytical solution for the problem of finding Fastest Distributed Consensus (FDC) is one of the challenging problems in the field of sensor networks. Most of the methods proposed so far deal with the FDC averaging algorithm problem by numerical convex optimization methods and in general no closed-form solution for finding FDC has been offered up to now except in [3] where the conjectured answer for path has been proved. Here in this work we present an analytical solution for the problem of Fastest Distributed Consensus for the Path network using semidefinite programming particularly solving the slackness conditions, where the optimal weights are obtained by inductive comparing of the characteristic polynomials initiated by slackness conditions.
Upgraded Algorithm for Calculating the Turbo-Expander of Gas Distribution Stations
Directory of Open Access Journals (Sweden)
Chekardovskiy Mikhail
2016-01-01
Full Text Available The article deals with the urgency of adapting computational turbo-expander unit parameters techniques to the conditions of their application at gas distribution stations. Existing computational methods based on the use of air as the working medium yield incorrect data to determine the actual design parameters for operating conditions where the working medium is natural gas. A modernized algorithm of thermogasdynamic calculation of turbo-expanders in order to form the correct initial data for design calculations has been proposed. The objective of calculating turbo-expanders is to identify thermogasdynamic parameters and dimensions of the flow channel, rotational speed, and shaft power. Procedure of thermogasdynamic calculations is shown on the example of a turbo-expander running on natural gas. The result will simplify the process of selecting or designing turbo-expander units for gas distribution stations.
Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo
2015-07-28
In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.
Directory of Open Access Journals (Sweden)
Mi Jeong Kim
2015-07-01
Full Text Available In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA-based wireless mesh network (WMN with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.
Mali, P.; Mukhopadhyay, A.; Manna, S. K.; Haldar, P. K.; Singh, G.
2017-03-01
Horizontal visibility graphs (HVGs) and the sandbox (SB) algorithm usually applied for multifractal characterization of complex network systems that are converted from time series measurements, are used to characterize the fluctuations in pseudorapidity densities of singly charged particles produced in high-energy nucleus-nucleus collisions. Besides obtaining the degree distribution associated with event-wise pseudorapidity distributions, the common set of observables, typical of any multifractality measurement, are studied in 16O-Ag/Br and 32S-Ag/Br interactions, each at an incident laboratory energy of 200 GeV/nucleon. For a better understanding, we systematically compare the experiment with a Monte Carlo model simulation based on the Ultra-relativistic Quantum Molecular Dynamics (UrQMD). Our results suggest that the HVG-SB technique is an efficient tool that can characterize multifractality in multiparticle emission data, and in some cases, it is even superior to other methods more commonly used in this regard.