Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks.

Science.gov (United States)

Devi, D Chitra; Uthariaraj, V Rhymend

2016-01-01

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.

Load Balancing of Parallel Monte Carlo Transport Calculations

International Nuclear Information System (INIS)

Procassini, R J; O'Brien, M J; Taylor, J M

2005-01-01

The performance of parallel Monte Carlo transport calculations which use both spatial and particle parallelism is increased by dynamically assigning processors to the most worked domains. Since he particle work load varies over the course of the simulation, this algorithm determines each cycle if dynamic load balancing would speed up the calculation. If load balancing is required, a small number of particle communications are initiated in order to achieve load balance. This method has decreased the parallel run time by more than a factor of three for certain criticality calculations

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

Science.gov (United States)

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

2016-01-01

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

Directory of Open Access Journals (Sweden)

D. Chitra Devi

2016-01-01

Full Text Available Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM’s multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM, the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.

A Dynamic Model for Load Balancing in Cloud Infrastructure

Directory of Open Access Journals (Sweden)

Jitendra Bhagwandas Bhatia

2015-08-01

Full Text Available This paper analysis various challenges faced in optimizing computing resource utilization via load balancing and presents a platform-independent model for load balancing which targets high availability of resources, low SLA (Service Level agreement violations and saves power. To achieve this, incoming requests are monitored for sudden burst, a prediction model is employed to maintain high availability and a power-aware algorithm is applied for choosing a suitable physical node for a virtual host. The proposed dynamic load balancing model provides a way to conflicting goals of saving power and maintaining high resource availability.For anyone building a private, public or hybrid IaaS cloud infrastructure, load balancing of virtual hosts on a limited number of physical nodes, becomes a crucial aspect. This paper analysis various challenges faced in optimizing computing resource utilization via load balancing and presents a platform independent model for load balancing which targets high availability of resources, low SLA (Service Level agreement violations and saves power. To achieve this, incoming requests are monitored for sudden burst, prediction model is employed to maintain high availability and power aware algorithm is applied for choosing a suitable physical node for virtual host. The proposed dynamic load balancing model provides a way to conflicting goals of saving power and maintaining high resource availability.

Dynamic Load Balancing of Parallel Monte Carlo Transport Calculations

International Nuclear Information System (INIS)

O'Brien, M; Taylor, J; Procassini, R

2004-01-01

The performance of parallel Monte Carlo transport calculations which use both spatial and particle parallelism is increased by dynamically assigning processors to the most worked domains. Since the particle work load varies over the course of the simulation, this algorithm determines each cycle if dynamic load balancing would speed up the calculation. If load balancing is required, a small number of particle communications are initiated in order to achieve load balance. This method has decreased the parallel run time by more than a factor of three for certain criticality calculations

Automatic mesh refinement and parallel load balancing for Fokker-Planck-DSMC algorithm

Science.gov (United States)

Küchlin, Stephan; Jenny, Patrick

2018-06-01

Recently, a parallel Fokker-Planck-DSMC algorithm for rarefied gas flow simulation in complex domains at all Knudsen numbers was developed by the authors. Fokker-Planck-DSMC (FP-DSMC) is an augmentation of the classical DSMC algorithm, which mitigates the near-continuum deficiencies in terms of computational cost of pure DSMC. At each time step, based on a local Knudsen number criterion, the discrete DSMC collision operator is dynamically switched to the Fokker-Planck operator, which is based on the integration of continuous stochastic processes in time, and has fixed computational cost per particle, rather than per collision. In this contribution, we present an extension of the previous implementation with automatic local mesh refinement and parallel load-balancing. In particular, we show how the properties of discrete approximations to space-filling curves enable an efficient implementation. Exemplary numerical studies highlight the capabilities of the new code.

Studies on load metric and communication for a load balancing algorithm in a distributed data acquisition system

International Nuclear Information System (INIS)

Simon, M; Kozielski, S; Sakulin, H

2011-01-01

The proposed method is designed for a data acquisition system acquiring data from n independent sources. The data sources are supposed to produce fragments that together constitute some logical wholeness. These fragments are produced with the same frequency and in the same sequence. The discussed algorithm aims to balance the data dynamically between m logically autonomous processing units (consisting of computing nodes) in case of variation in their processing power which could be caused by some faults like failing computing nodes, or broken network connections. As a case study we consider the Data Acquisition System of the Compact Muon Solenoid Experiment at CERN's new Large Hadron Collider. The system acquires data from about 500 sources and combines them into full events. Each data source is expected to deliver event fragments of an average size of 2 kB with 100 kHz frequency. In this paper we present the results of applying proposed load metric and load communication pattern. Moreover, we discuss their impact on the algorithm's overall efficiency and scalability, as well as on fault tolerance of the whole system. We also propose a general concept of an algorithm that allows for choosing the destination processing unit in all source nodes asynchronously and asserts that all fragments of same logical data always go to same unit.

Research on a Method of Geographical Information Service Load Balancing

Science.gov (United States)

Li, Heyuan; Li, Yongxing; Xue, Zhiyong; Feng, Tao

2018-05-01

With the development of geographical information service technologies, how to achieve the intelligent scheduling and high concurrent access of geographical information service resources based on load balancing is a focal point of current study. This paper presents an algorithm of dynamic load balancing. In the algorithm, types of geographical information service are matched with the corresponding server group, then the RED algorithm is combined with the method of double threshold effectively to judge the load state of serve node, finally the service is scheduled based on weighted probabilistic in a certain period. At the last, an experiment system is built based on cluster server, which proves the effectiveness of the method presented in this paper.

Dynamic Load Balancing for PIC code using Eulerian/Lagrangian partitioning

OpenAIRE

Sauget, Marc; Latu, Guillaume

2017-01-01

This document presents an analysis of different load balance strategies for a Plasma physics code that models high energy particle beams with PIC method. A comparison of different load balancing algorithms is given: static or dynamic ones. Lagrangian and Eulerian partitioning techniques have been investigated.

Node Load Balance Multi-flow Opportunistic Routing in Wireless Mesh Networks

Directory of Open Access Journals (Sweden)

Wang Tao

2014-04-01

Full Text Available Opportunistic routing (OR has been proposed to improve the performance of wireless networks by exploiting the multi-user diversity and broadcast nature of the wireless medium. It involves multiple candidate forwarders to relay packets every hop. The existing OR doesn’t take account of the traffic load and load balance, therefore some nodes may be overloaded while the others may not, leading to network performance decline. In this paper, we focus on opportunities routing selection with node load balance which is described as a convex optimization problem. To solve the problem, by combining primal-dual and sub-gradient methods, a fully distributed Node load balance Multi-flow Opportunistic Routing algorithm (NMOR is proposed. With node load balance constraint, NMOR allocates the flow rate iteratively and the rate allocation decides the candidate forwarder selection of opportunities routing. The simulation results show that NMOR algorithm improves 100 %, 62 % of the aggregative throughput than ETX and EAX, respectively.

Static and dynamic load-balancing strategies for parallel reservoir simulation

International Nuclear Information System (INIS)

Anguille, L.; Killough, J.E.; Li, T.M.C.; Toepfer, J.L.

1995-01-01

Accurate simulation of the complex phenomena that occur in flow in porous media can tax even the most powerful serial computers. Emergence of new parallel computer architectures as a future efficient tool in reservoir simulation may overcome this difficulty. Unfortunately, major problems remain to be solved before using parallel computers commercially: production serial programs must be rewritten to be efficient in parallel environments and load balancing methods must be explored to evenly distribute the workload on each processor during the simulation. This study implements both a static load-balancing algorithm and a receiver-initiated dynamic load-sharing algorithm to achieve high parallel efficiencies on both the IBM SP2 and Intel IPSC/860 parallel computers. Significant speedup improvement was recorded for both methods. Further optimization of these algorithms yielded a technique with efficiencies as high as 90% and 70% on 8 and 32 nodes, respectively. The increased performance was the result of the minimization of message-passing overhead

On delay adjustment for dynamic load balancing in distributed virtual environments.

Science.gov (United States)

Deng, Yunhua; Lau, Rynson W H

2012-04-01

Distributed virtual environments (DVEs) are becoming very popular in recent years, due to the rapid growing of applications, such as massive multiplayer online games (MMOGs). As the number of concurrent users increases, scalability becomes one of the major challenges in designing an interactive DVE system. One solution to address this scalability problem is to adopt a multi-server architecture. While some methods focus on the quality of partitioning the load among the servers, others focus on the efficiency of the partitioning process itself. However, all these methods neglect the effect of network delay among the servers on the accuracy of the load balancing solutions. As we show in this paper, the change in the load of the servers due to network delay would affect the performance of the load balancing algorithm. In this work, we conduct a formal analysis of this problem and discuss two efficient delay adjustment schemes to address the problem. Our experimental results show that our proposed schemes can significantly improve the performance of the load balancing algorithm with neglectable computation overhead.

Simulation model of load balancing in distributed computing systems

Science.gov (United States)

Botygin, I. A.; Popov, V. N.; Frolov, S. G.

2017-02-01

The availability of high-performance computing, high speed data transfer over the network and widespread of software for the design and pre-production in mechanical engineering have led to the fact that at the present time the large industrial enterprises and small engineering companies implement complex computer systems for efficient solutions of production and management tasks. Such computer systems are generally built on the basis of distributed heterogeneous computer systems. The analytical problems solved by such systems are the key models of research, but the system-wide problems of efficient distribution (balancing) of the computational load and accommodation input, intermediate and output databases are no less important. The main tasks of this balancing system are load and condition monitoring of compute nodes, and the selection of a node for transition of the user’s request in accordance with a predetermined algorithm. The load balancing is one of the most used methods of increasing productivity of distributed computing systems through the optimal allocation of tasks between the computer system nodes. Therefore, the development of methods and algorithms for computing optimal scheduling in a distributed system, dynamically changing its infrastructure, is an important task.

Green IGP Link Weights for Energy-efficiency and Load-balancing in IP Backbone Networks

OpenAIRE

Francois, Frederic; Wang, Ning; Moessner, Klaus; Georgoulas, Stylianos; Xu, Ke

2013-01-01

The energy consumption of backbone networks has become a primary concern for network operators and regulators due to the pervasive deployment of wired backbone networks to meet the requirements of bandwidth-hungry applications. While traditional optimization of IGP link weights has been used in IP based load-balancing operations, in this paper we introduce a novel link weight setting algorithm, the Green Load-balancing Algorithm (GLA), which is able to jointly optimize both energy efficiency ...

A novel grooming algorithm with the adaptive weight and load balancing for dynamic holding-time-aware traffic in optical networks

Science.gov (United States)

Xu, Zhanqi; Huang, Jiangjiang; Zhou, Zhiqiang; Ding, Zhe; Ma, Tao; Wang, Junping

2013-10-01

To maximize the resource utilization of optical networks, the dynamic traffic grooming, which could efficiently multiplex many low-speed services arriving dynamically onto high-capacity optical channels, has been studied extensively and used widely. However, the link weights in the existing research works can be improved since they do not adapt to the network status and load well. By exploiting the information on the holding times of the preexisting and new lightpaths, and the requested bandwidth of a user service, this paper proposes a grooming algorithm using Adaptively Weighted Links for Holding-Time-Aware (HTA) (abbreviated as AWL-HTA) traffic, especially in the setup process of new lightpath(s). Therefore, the proposed algorithm can not only establish a lightpath that uses network resource efficiently, but also achieve load balancing. In this paper, the key issues on the link weight assignment and procedure within the AWL-HTA are addressed in detail. Comprehensive simulation and experimental results show that the proposed algorithm has a much lower blocking ratio and latency than other existing algorithms.

RPL LOAD BALANCING IN INTERNET OF THINGS

Directory of Open Access Journals (Sweden)

Mohammad Reza Parsaei

2017-12-01

Full Text Available ABSTRACT:  The wide address space provided by Internet Protocol version 6 (IPv6 lets any thing to be identified uniquely. consistency of the modified version of IPv6 protocol stack with smart objects, facilitated the Internet interconnection of the networks of smart objects and introduced Internet of things. A smart object is a small micro-electronic device that consists of a communication device, a small microprocessor and a sensor or an actuator. A network made of such devices is called low-power and lossy network. RPL routing protocol that is consistent to IPv6, is designed to be used in these kinds of networks. Load balancing is not considered in the RPL design process. Whenever RPL is used in large scale low-power and lossy networks some nodes will suffer from congestion and this problem severely degrades network performance. In this paper, we consider solutions provided to tackle RPL load balancing problems. Load balancing algorithms and protoclos are evaluated through simulation. We evaluate IETF RPL implementation and LB-RPL method with Contiki OS Java (COOJA simulator. They are assessed comprehensively through metrics such as Packet delivery Ratio, Average End to End delay, and Gateway Throughput. LB-RPL improves RPL in terms of Packet delivery Ratio and throughput but increases Average End to End delay. Simulations results show that RPL load balancing needs extensive works to be performed yet.

Two Stage Secure Dynamic Load Balancing Architecture for SIP Server Clusters

Directory of Open Access Journals (Sweden)

G. Vennila

2014-08-01

Full Text Available Session Initiation Protocol (SIP is a signaling protocol emerged with an aim to enhance the IP network capabilities in terms of complex service provision. SIP server scalability with load balancing has a greater concern due to the dramatic increase in SIP service demand. Load balancing of session method (request/response and security measures optimizes the SIP server to regulate of network traffic in Voice over Internet Protocol (VoIP. Establishing a honeywall prior to the load balancer significantly reduces SIP traffic and drops inbound malicious load. In this paper, we propose Active Least Call in SIP Server (ALC_Server algorithm fulfills objectives like congestion avoidance, improved response times, throughput, resource utilization, reducing server faults, scalability and protection of SIP call from DoS attacks. From the test bed, the proposed two-tier architecture demonstrates that the ALC_Server method dynamically controls the overload and provides robust security, uniform load distribution for SIP servers.

Load Balancing in Hypergraphs

Science.gov (United States)

Delgosha, Payam; Anantharam, Venkat

2018-03-01

Consider a simple locally finite hypergraph on a countable vertex set, where each edge represents one unit of load which should be distributed among the vertices defining the edge. An allocation of load is called balanced if load cannot be moved from a vertex to another that is carrying less load. We analyze the properties of balanced allocations of load. We extend the concept of balancedness from finite hypergraphs to their local weak limits in the sense of Benjamini and Schramm (Electron J Probab 6(23):13, 2001) and Aldous and Steele (in: Probability on discrete structures. Springer, Berlin, pp 1-72, 2004). To do this, we define a notion of unimodularity for hypergraphs which could be considered an extension of unimodularity in graphs. We give a variational formula for the balanced load distribution and, in particular, we characterize it in the special case of unimodular hypergraph Galton-Watson processes. Moreover, we prove the convergence of the maximum load under some conditions. Our work is an extension to hypergraphs of Anantharam and Salez (Ann Appl Probab 26(1):305-327, 2016), which considered load balancing in graphs, and is aimed at more comprehensively resolving conjectures of Hajek (IEEE Trans Inf Theory 36(6):1398-1414, 1990).

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

Science.gov (United States)

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Sustainable Load-Balancing Scheme for Inter-Sensor Convergence Processing of Routing Cooperation Topology

Directory of Open Access Journals (Sweden)

Hyun-Woo Kim

2016-05-01

Full Text Available Recent advancements in Information Technology (IT have sparked the creation of numerous and diverse types of devices and services. Manual data collection measurement methods have been automated through the use of various wireless or wired sensors. Single sensor devices are included in smart devices such as smartphones. Data transmission is critical for big data collected from sensor nodes, such as Mobile Sensor Nodes (MSNs, where sensors move dynamically according to sensor mobility, or Fixed Sensor Nodes (FSNs, where sensor locations are decided by the users. False data transfer processing of big data results in topology lifespan reduction and data transfer delays. Hence, a variety of simulators and diverse load-balancing algorithms have been developed as protocol verification tools for topology lifespan maximization and effective data transfer processing. However, those previously developed simulators have limited functions, such as an event function for a specific sensor or a battery consumption rate test for sensor deployment. Moreover, since the previous load-balancing algorithms consider only the general traffic distribution and the number of connected nodes without considering the current topology condition, the sustainable load-balancing technique that takes into account the battery consumption rate of the dispersed sensor nodes is required. Therefore, this paper proposes the Sustainable Load-balancing Scheme (SLS, which maximizes the overall topology lifespan through effective and sustainable load-balancing of data transfer among the sensors. SLS is capable of maintaining an effective topology as it considers both the battery consumption rate of the sensors and the data transfer delay.

I/O load balancing for big data HPC applications

Energy Technology Data Exchange (ETDEWEB)

Paul, Arnab K. [Virginia Polytechnic Institute and State University; Goyal, Arpit [Virginia Polytechnic Institute and State University; Wang, Feiyi [ORNL; Oral, H Sarp [ORNL; Butt, Ali R. [Virginia Tech, Blacksburg, VA; Brim, Michael J. [ORNL; Srinivasa, Sangeetha B. [Virginia Polytechnic Institute and State University

2018-01-01

High Performance Computing (HPC) big data problems require efficient distributed storage systems. However, at scale, such storage systems often experience load imbalance and resource contention due to two factors: the bursty nature of scientific application I/O; and the complex I/O path that is without centralized arbitration and control. For example, the extant Lustre parallel file system-that supports many HPC centers-comprises numerous components connected via custom network topologies, and serves varying demands of a large number of users and applications. Consequently, some storage servers can be more loaded than others, which creates bottlenecks and reduces overall application I/O performance. Existing solutions typically focus on per application load balancing, and thus are not as effective given their lack of a global view of the system. In this paper, we propose a data-driven approach to load balance the I/O servers at scale, targeted at Lustre deployments. To this end, we design a global mapper on Lustre Metadata Server, which gathers runtime statistics from key storage components on the I/O path, and applies Markov chain modeling and a minimum-cost maximum-flow algorithm to decide where data should be placed. Evaluation using a realistic system simulator and a real setup shows that our approach yields better load balancing, which in turn can improve end-to-end performance.

Partitioning of unstructured meshes for load balancing

International Nuclear Information System (INIS)

Martin, O.C.; Otto, S.W.

1994-01-01

Many large-scale engineering and scientific calculations involve repeated updating of variables on an unstructured mesh. To do these types of computations on distributed memory parallel computers, it is necessary to partition the mesh among the processors so that the load balance is maximized and inter-processor communication time is minimized. This can be approximated by the problem, of partitioning a graph so as to obtain a minimum cut, a well-studied combinatorial optimization problem. Graph partitioning algorithms are discussed that give good but not necessarily optimum solutions. These algorithms include local search methods recursive spectral bisection, and more general purpose methods such as simulated annealing. It is shown that a general procedure enables to combine simulated annealing with Kernighan-Lin. The resulting algorithm is both very fast and extremely effective. (authors) 23 refs., 3 figs., 1 tab

Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

Directory of Open Access Journals (Sweden)

Johan Parent

2004-01-01

Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

A high performance load balance strategy for real-time multicore systems.

Science.gov (United States)

Cho, Keng-Mao; Tsai, Chun-Wei; Chiu, Yi-Shiuan; Yang, Chu-Sing

2014-01-01

Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper.

A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

Directory of Open Access Journals (Sweden)

Jia Zhao

2013-01-01

Full Text Available Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA. This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A location selection policy of live virtual machine migration for power saving and load balancing.

Science.gov (United States)

Zhao, Jia; Ding, Yan; Xu, Gaochao; Hu, Liang; Dong, Yushuang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

K. Mohaideen Pitchai

2017-07-01

Full Text Available Wireless Sensor Network (WSN consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP. In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

A High Performance Load Balance Strategy for Real-Time Multicore Systems

Directory of Open Access Journals (Sweden)

Keng-Mao Cho

2014-01-01

Full Text Available Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS. Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

OpenAIRE

Sliwko, L.; Getov, Vladimir

2015-01-01

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded ...

Joint Load Balancing and Power Allocation for Hybrid VLC/RF Networks

KAUST Repository

Obeed, Mohanad

2018-01-15

In this paper, we propose and study a new joint load balancing (LB) and power allocation (PA) scheme for a hybrid visible light communication (VLC) and radio frequency (RF) system consisting of one RF\\\\access point (AP) and multiple VLC\\\\APs. An iterative algorithm is proposed to distribute the users on the APs and distribute the powers of these APs on their users. In PA subproblem, an optimization problem is formulated to allocate the power of each AP to the connected users for the total achievable data rates maximization. It is proved that the PA optimization problem is concave but not easy to tackle. Therefore, we provide a new algorithm to obtain the optimal dual variables after formulating them in terms of each other. Then, the users that are connected to the overloaded APs and receive less data rates start seeking for other APs that offer higher data rates. Users with lower data rates continue re-connecting from AP to other to balance the load only if this travel increases the summation of the achievable data rates and enhances the system fairness. The numerical results demonstrate that the proposed algorithms improve the system capacity and system fairness with fast convergence.

Joint Load Balancing and Power Allocation for Hybrid VLC/RF Networks

KAUST Repository

Obeed, Mohanad; Salhab, Anas M.; Zummo, Salam A.; Alouini, Mohamed-Slim

2018-01-01

In this paper, we propose and study a new joint load balancing (LB) and power allocation (PA) scheme for a hybrid visible light communication (VLC) and radio frequency (RF) system consisting of one RF\\access point (AP) and multiple VLC\\APs. An iterative algorithm is proposed to distribute the users on the APs and distribute the powers of these APs on their users. In PA subproblem, an optimization problem is formulated to allocate the power of each AP to the connected users for the total achievable data rates maximization. It is proved that the PA optimization problem is concave but not easy to tackle. Therefore, we provide a new algorithm to obtain the optimal dual variables after formulating them in terms of each other. Then, the users that are connected to the overloaded APs and receive less data rates start seeking for other APs that offer higher data rates. Users with lower data rates continue re-connecting from AP to other to balance the load only if this travel increases the summation of the achievable data rates and enhances the system fairness. The numerical results demonstrate that the proposed algorithms improve the system capacity and system fairness with fast convergence.

A network flow model for load balancing in circuit-switched multicomputers

Science.gov (United States)

Bokhari, Shahid H.

1990-01-01

In multicomputers that utilize circuit switching or wormhole routing, communication overhead depends largely on link contention - the variation due to distance between nodes is negligible. This has a major impact on the load balancing problem. In this case, there are some nodes with excess load (sources) and others with deficit load (sinks) and it is required to find a matching of sources to sinks that avoids contention. The problem is made complex by the hardwired routing on currently available machines: the user can control only which nodes communicate but not how the messages are routed. Network flow models of message flow in the mesh and the hypercube were developed to solve this problem. The crucial property of these models is the correspondence between minimum cost flows and correctly routed messages. To solve a given load balancing problem, a minimum cost flow algorithm is applied to the network. This permits one to determine efficiently a maximum contention free matching of sources to sinks which, in turn, tells one how much of the given imbalance can be eliminated without contention.

Software defined networks reactive flow programming and load balance switching

OpenAIRE

Καλλιανιώτης, Νικόλαος; Kallianiotis, Nikolaos

2017-01-01

This project serves as a Master Thesis as the requirements of the master’s programme Master of Digital Communications and Networks. It proposes load balancing algorithms applied to Software-Defined Networks to achieve the best possible resource utilisation of each of the links present in a network. The open-sources Opendaylight project and Floodlight project are used as SDN controllers, and the network is emulated using Mininet software

Power Balance AODV Routing Algorithm of WSN in Precision Agriculture Environment Monitoring

Directory of Open Access Journals (Sweden)

Xiaoqin Qin

2013-11-01

Full Text Available As one of important technologies of IOT (Internet of Things, WSN (Wireless Sensor Networks has been widely used in precision agriculture environment monitoring. WSN is a kind of energy-constrained network, but power balance is not taken into account in traditional routing protocols. A novel routing algorithm, named Power Balance Ad hoc On-Demand Distance Vector (PB-AODV, is proposed on cross-layer design. In the route discovery process of PB-AODV, routing path is established by the Received Signal Strength Indication (RSSI value. The optimal transmitting power, which is computed according to RSSI value, power threshold and node’s surplus energy, is encapsulated into Route Reply Packet. Hence, the sender node can adjust its transmission power to save energy according to the Route Reply Packet. Simulation results show that the proposed algorithm is effective for load balancing, and increases the WSN’s lifetime 14.3% consequently.

Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications

Directory of Open Access Journals (Sweden)

M. Lawanyashri

Full Text Available Cloud computing has gained precise attention from the research community and management of IT, due to its scalable and dynamic capabilities. It is evolving as a vibrant technology to modernize and restructure healthcare organization to provide best services to the consumers. The rising demand for healthcare services and applications in cloud computing leads to the imbalance in resource usage and drastically increases the power consumption resulting in high operating cost. To achieve fast execution time and optimum utilization of the virtual machines, we propose a multi-objective hybrid fruitfly optimization technique based on simulated annealing to improve the convergence rate and optimization accuracy. The proposed approach is used to achieve the optimal resource utilization and reduces the energy consumption and cost in cloud computing environment. The result attained in our proposed technique provides an improved solution. The experimental results show that the proposed algorithm efficiently outperforms compared to the existing load balancing algorithms. Keywords: Cloud computing, Electronic Health Records (EHR, Load balancing, Fruitfly Optimization Algorithm (FOA, Simulated Annealing (SA, Energy consumption

Load balancing in integrated optical wireless networks

DEFF Research Database (Denmark)

Yan, Ying; Dittmann, Lars; Wong, S-W.

2010-01-01

In this paper, we tackle the load balancing problem in Integrated Optical Wireless Networks, where cell breathing technique is used to solve congestion by changing the coverage area of a fully loaded cell tower. Our objective is to design a load balancing mechanism which works closely...... with the integrated control scheme so as to maximize overall network throughput in the integrated network architecture. To the best of our knowledge no load balancing mechanisms, especially based on the Multi-Point Control Protocol (MPCP) defined in the IEEE 802.3ah, have been proposed so far. The major research...... issues are outlined and a cost function based optimization model is developed for power management. In particularly, two alternative feedback schemes are proposed to report wireless network status. Simulation results show that our proposed load balancing mechanism improves network performances....

Implementation of GAMMON - An efficient load balancing strategy for a local computer system

Science.gov (United States)

Baumgartner, Katherine M.; Kling, Ralph M.; Wah, Benjamin W.

1989-01-01

GAMMON (Global Allocation from Maximum to Minimum in cONstant time), an efficient load-balancing algorithm, is described. GAMMON uses the available broadcast capability of multiaccess networks to implement an efficient search technique for finding hosts with maximal and minimal loads. The search technique has an average overhead which is independent of the number of participating stations. The transition from the theoretical concept to a practical, reliable, and efficient implementation is described.

DNS load balancing in the CERN cloud

Science.gov (United States)

Reguero Naredo, Ignacio; Lobato Pardavila, Lorena

2017-10-01

Load Balancing is one of the technologies enabling deployment of large-scale applications on cloud resources. A DNS Load Balancer Daemon (LBD) has been developed at CERN as a cost-effective way to balance applications accepting DNS timing dynamics and not requiring persistence. It currently serves over 450 load-balanced aliases with two small VMs acting as master and slave. The aliases are mapped to DNS subdomains. These subdomains are managed with DDNS according to a load metric, which is collected from the alias member nodes with SNMP. During the last years, several improvements were brought to the software, for instance: support for IPv6, parallelization of the status requests, implementing the client in Python to allow for multiple aliases with differentiated states on the same machine or support for application state. The configuration of the Load Balancer is currently managed by a Puppet type. It discovers the alias member nodes and gets the alias definitions from the Ermis REST service. The Aiermis self-service GUI for the management of the LB aliases has been produced and is based on the Ermis service above that implements a form of Load Balancing as a Service (LBaaS). The Ermis REST API has authorisation based in Foreman hostgroups. The CERN DNS LBD is Open Software with Apache 2 license.

Dynamic load balancing in a concurrent plasma PIC code on the JPL/Caltech Mark III hypercube

International Nuclear Information System (INIS)

Liewer, P.C.; Leaver, E.W.; Decyk, V.K.; Dawson, J.M.

1990-01-01

Dynamic load balancing has been implemented in a concurrent one-dimensional electromagnetic plasma particle-in-cell (PIC) simulation code using a method which adds very little overhead to the parallel code. In PIC codes, the orbits of many interacting plasma electrons and ions are followed as an initial value problem as the particles move in electromagnetic fields calculated self-consistently from the particle motions. The code was implemented using the GCPIC algorithm in which the particles are divided among processors by partitioning the spatial domain of the simulation. The problem is load-balanced by partitioning the spatial domain so that each partition has approximately the same number of particles. During the simulation, the partitions are dynamically recreated as the spatial distribution of the particles changes in order to maintain processor load balance

A parallel 3D particle-in-cell code with dynamic load balancing

International Nuclear Information System (INIS)

Wolfheimer, Felix; Gjonaj, Erion; Weiland, Thomas

2006-01-01

A parallel 3D electrostatic Particle-In-Cell (PIC) code including an algorithm for modelling Space Charge Limited (SCL) emission [E. Gjonaj, T. Weiland, 3D-modeling of space-charge-limited electron emission. A charge conserving algorithm, Proceedings of the 11th Biennial IEEE Conference on Electromagnetic Field Computation, 2004] is presented. A domain decomposition technique based on orthogonal recursive bisection is used to parallelize the computation on a distributed memory environment of clustered workstations. For problems with a highly nonuniform and time dependent distribution of particles, e.g., bunch dynamics, a dynamic load balancing between the processes is needed to preserve the parallel performance. The algorithm for the detection of a load imbalance and the redistribution of the tasks among the processes is based on a weight function criterion, where the weight of a cell measures the computational load associated with it. The algorithm is studied with two examples. In the first example, multiple electron bunches as occurring in the S-DALINAC [A. Richter, Operational experience at the S-DALINAC, Proceedings of the Fifth European Particle Accelerator Conference, 1996] accelerator are simulated in the absence of space charge fields. In the second example, the SCL emission and electron trajectories in an electron gun are simulated

A parallel 3D particle-in-cell code with dynamic load balancing

Energy Technology Data Exchange (ETDEWEB)

Wolfheimer, Felix [Technische Universitaet Darmstadt, Institut fuer Theorie Elektromagnetischer Felder, Schlossgartenstr.8, 64283 Darmstadt (Germany)]. E-mail: wolfheimer@temf.de; Gjonaj, Erion [Technische Universitaet Darmstadt, Institut fuer Theorie Elektromagnetischer Felder, Schlossgartenstr.8, 64283 Darmstadt (Germany); Weiland, Thomas [Technische Universitaet Darmstadt, Institut fuer Theorie Elektromagnetischer Felder, Schlossgartenstr.8, 64283 Darmstadt (Germany)

2006-03-01

A parallel 3D electrostatic Particle-In-Cell (PIC) code including an algorithm for modelling Space Charge Limited (SCL) emission [E. Gjonaj, T. Weiland, 3D-modeling of space-charge-limited electron emission. A charge conserving algorithm, Proceedings of the 11th Biennial IEEE Conference on Electromagnetic Field Computation, 2004] is presented. A domain decomposition technique based on orthogonal recursive bisection is used to parallelize the computation on a distributed memory environment of clustered workstations. For problems with a highly nonuniform and time dependent distribution of particles, e.g., bunch dynamics, a dynamic load balancing between the processes is needed to preserve the parallel performance. The algorithm for the detection of a load imbalance and the redistribution of the tasks among the processes is based on a weight function criterion, where the weight of a cell measures the computational load associated with it. The algorithm is studied with two examples. In the first example, multiple electron bunches as occurring in the S-DALINAC [A. Richter, Operational experience at the S-DALINAC, Proceedings of the Fifth European Particle Accelerator Conference, 1996] accelerator are simulated in the absence of space charge fields. In the second example, the SCL emission and electron trajectories in an electron gun are simulated.

Efektifitas Load Balancing Dalam Mengurangi Kemacetan Lalu Lintas

Directory of Open Access Journals (Sweden)

Erwin Harahap

2017-12-01

Full Text Available Abstrak. Kemacetan jalan raya merupakan permasalahan umum di setiap kota yang memerlukan penanganan serius. Pemecahan permasalahan kemacetan jalan raya tidak hanya dapat diselesaikan dengan hanya meningkatkan kualitas dan kuantitas infrastruktur, namun juga manajemen lalu lintas. Pada artikel diusulkan suatu metode untuk mengurangi kemacetan lalu lintas, yaitu dengan menyeimbangkan beban ke berbagai ruas jalan yang disebut dengan load balancing. Melalui metode ini diharapkan beban lalu lintas terbagi secara merata ke berbagai jalur alternatif sedemikian sehingga antrian panjang kendaraan dapat dihindari. Evaluasi efektifitas dari metode load balancing ini dilakukan melalui simulasi dengan mengimplementasikan salah satu bidang ilmu Matematika, yaitu teori Antrian. Simulasi dibuat dengan menggunakan toolbox SimEvents yang dijalankan pada software MATLAB. Kata Kunci: load balancing, kemacetan, lalu lintas, sim-events, matlab Abstract. (the effectiveness of load balancing in reducing the road traffic congestion Road congestion is a common problem in any city that needs serious handling. The solution of the road congestion problems can not only be solved by simply improving the quality and quantity of infrastructure, but also the traffic management. In this article, we proposed a method to reduce the traffic congestion by balancing the vehicle loads to a various road segments, called as load balancing. Through this method, it is expected that the traffic load is evenly distributed to various alternative routes, such that, long queues and traffic jam can be avoided. Evaluation of the load balancing’s effectiveness is performed through a simulation by implementing the Queueing Theory. Simulations are created using the SimEvents toolbox that runs on MATLAB software. Keywords: load balancing, road congestion, traffic, simevents, matlab.

Load Balancing Routing with Bounded Stretch

Directory of Open Access Journals (Sweden)

Chen Siyuan

2010-01-01

Full Text Available Routing in wireless networks has been heavily studied in the last decade. Many routing protocols are based on classic shortest path algorithms. However, shortest path-based routing protocols suffer from uneven load distribution in the network, such as crowed center effect where the center nodes have more load than the nodes in the periphery. Aiming to balance the load, we propose a novel routing method, called Circular Sailing Routing (CSR, which can distribute the traffic more evenly in the network. The proposed method first maps the network onto a sphere via a simple stereographic projection, and then the route decision is made by a newly defined "circular distance" on the sphere instead of the Euclidean distance in the plane. We theoretically prove that for a network, the distance traveled by the packets using CSR is no more than a small constant factor of the minimum (the distance of the shortest path. We also extend CSR to a localized version, Localized CSR, by modifying greedy routing without any additional communication overhead. In addition, we investigate how to design CSR routing for 3D networks. For all proposed methods, we conduct extensive simulations to study their performances and compare them with global shortest path routing or greedy routing in 2D and 3D wireless networks.

An Efficient SDN Load Balancing Scheme Based on Variance Analysis for Massive Mobile Users

Directory of Open Access Journals (Sweden)

Hong Zhong

2015-01-01

Full Text Available In a traditional network, server load balancing is used to satisfy the demand for high data volumes. The technique requires large capital investment while offering poor scalability and flexibility, which difficultly supports highly dynamic workload demands from massive mobile users. To solve these problems, this paper analyses the principle of software-defined networking (SDN and presents a new probabilistic method of load balancing based on variance analysis. The method can be used to dynamically manage traffic flows for supporting massive mobile users in SDN networks. The paper proposes a solution using the OpenFlow virtual switching technology instead of the traditional hardware switching technology. A SDN controller monitors data traffic of each port by means of variance analysis and provides a probability-based selection algorithm to redirect traffic dynamically with the OpenFlow technology. Compared with the existing load balancing methods which were designed to support traditional networks, this solution has lower cost, higher reliability, and greater scalability which satisfy the needs of mobile users.

SYMMETRICAL (AND GENERIC) ALGORITHMS FOR HEIGHT BALANCED TREES

NARCIS (Netherlands)

BRON, C

1990-01-01

Most algorithms for height balanced trees (or AVL-trees, after Adelson-Velskii and Landis [1]) suffer from a lack of exploitation of the symmetry of balance restoring operations when insertions and deletions in such tree structures are being made. Either we find algorithms in duplicate (i.e.,

Peningkatan Kinerja Siakad Menggunakan Metode Load Balancing dan Fault Tolerance Di Jaringan Kampus Universitas Halu Oleo

Directory of Open Access Journals (Sweden)

Alimuddin Alimuddin

2016-01-01

Full Text Available The application of academic information system (siakad a web-based college is essential to improve the academic services. Siakad the application has many obstacles, especially in dealing with a high amount of access that caused the overload. Moreover in case of hardware or software failure caused siakad inaccessible. The solution of this problem is the use of many existing servers where the load is distributed in the respective server. Need a method of distributing the load evenly in the respective server load balancing is the method by round robin algorithm so high siakad scalability. As for dealing with the failure of a server need fault tolerance for the availability siakad be high. This research is to develop methods of load balancing and fault tolerance using software linux virtual server and some additional programs such as ipvsadm and heartbeat that has the ability to increase scalability and availability siakad. The results showed that with load balancing to minimize the response time to 5,7%, increase throughput by 37% or 1,6 times and maximize resource utilization or utilization of 1,6 times increased, and avoid overload. While high availability is obtained from the server's ability to perform failover or move another server in the event of failure. Thus implementing load balancing and fault tolerance can improve the service performance of siakad and avoid mistakes.

Line Balancing Using Largest Candidate Rule Algorithm In A Garment Industry: A Case Study

Directory of Open Access Journals (Sweden)

V. P.Jaganathan

2014-12-01

Full Text Available The emergence of fast changes in fashion has given rise to the need to shorten production cycle times in the garment industry. As effective usage of resources has a significant effect on the productivity and efficiency of production operations, garment manufacturers are urged to utilize their resources effectively in order to meet dynamic customer demand. This paper focuses specifically on line balancing and layout modification. The aim of assembly line balance in sewing lines is to assign tasks to the workstations, so that the machines of the workstation can perform the assigned tasks with a balanced loading. Largest Candidate Rule Algorithm (LCR has been deployed in this paper.

Perancangan dan Pengujian Load Balancing dan Failover Menggunakan NginX

Directory of Open Access Journals (Sweden)

Rahmad Dani

2017-06-01

Full Text Available Situs web dengan traffic yang tinggi dapat menyebabkan beban kerja yang berat di sisi server, yang pada gilirannya akan mengakibatkan turunnya kinerja server, bahkan kegagalan sistem secara keseluruhan. Salah satu solusi untuk mengatasi masalah tersebut adalah dengan menerapkan teknik load balancing dan failover. Load balancing merupakan teknologi untuk melakukan pembagian beban kepada beberapa server, memastikan tidak terjadi kelebihan beban pada salah satu server. Sementara itu, failover merupakan kemampuan suatu sistem untuk berpindah ke sistem cadangan jika sistem utama mengalami kegagalan. Dalam penelitian ini load balancing dengan teknik failover akan diimplementasikan pada sistem operasi Ubuntu. Software inti yang digunakan dalam penelitian ini adalah Nginx dan KeepAlived. Nginx akan berfungsi sebagai load balancer, sedangkan KeepAlived untuk mengimplementasikan teknik failover. Beberapa skenario telah disiapkan untuk menguji sistem load balancing yang telah dirancang. Pengujian dilakukan dengan menggunakan perangkat lunak JMeter. Berdasarkan pengujian yang telah dilakukan, sistem yang dirancang berhasil membagikan beban permintaan dan dapat terus bekerja walaupun terjadi kegagalan pada server load balancer ataupun kegagalan pada server backend. Selain itu, dalam beberapa pengujian, penggunaan load balancing terbukti mampu menurunkan waktu respon dan meningkatkan thoughput pada sistem sehingga mampu meningkatkan performa keseluruhan sistem. Mengacu pada hasil penelitian ini, sistem load balancing dan failover menggunakan Nginx dapat dijadikan salah satu solusi pada sistem web server dengan situs web yang memiliki traffic tinggi.

LBMR: Load-Balanced Multipath Routing for Wireless Data-Intensive Transmission in Real-Time Medical Monitoring.

Science.gov (United States)

Tseng, Chinyang Henry

2016-05-31

In wireless networks, low-power Zigbee is an excellent network solution for wireless medical monitoring systems. Medical monitoring generally involves transmission of a large amount of data and easily causes bottleneck problems. Although Zigbee's AODV mesh routing provides extensible multi-hop data transmission to extend network coverage, it originally does not, and needs to support some form of load balancing mechanism to avoid bottlenecks. To guarantee a more reliable multi-hop data transmission for life-critical medical applications, we have developed a multipath solution, called Load-Balanced Multipath Routing (LBMR) to replace Zigbee's routing mechanism. LBMR consists of three main parts: Layer Routing Construction (LRC), a Load Estimation Algorithm (LEA), and a Route Maintenance (RM) mechanism. LRC assigns nodes into different layers based on the node's distance to the medical data gateway. Nodes can have multiple next-hops delivering medical data toward the gateway. All neighboring layer-nodes exchange flow information containing current load, which is the used by the LEA to estimate future load of next-hops to the gateway. With LBMR, nodes can choose the neighbors with the least load as the next-hops and thus can achieve load balancing and avoid bottlenecks. Furthermore, RM can detect route failures in real-time and perform route redirection to ensure routing robustness. Since LRC and LEA prevent bottlenecks while RM ensures routing fault tolerance, LBMR provides a highly reliable routing service for medical monitoring. To evaluate these accomplishments, we compare LBMR with Zigbee's AODV and another multipath protocol, AOMDV. The simulation results demonstrate LBMR achieves better load balancing, less unreachable nodes, and better packet delivery ratio than either AODV or AOMDV.

A robust embedded vision system feasible white balance algorithm

Science.gov (United States)

Wang, Yuan; Yu, Feihong

2018-01-01

White balance is a very important part of the color image processing pipeline. In order to meet the need of efficiency and accuracy in embedded machine vision processing system, an efficient and robust white balance algorithm combining several classical ones is proposed. The proposed algorithm mainly has three parts. Firstly, in order to guarantee higher efficiency, an initial parameter calculated from the statistics of R, G and B components from raw data is used to initialize the following iterative method. After that, the bilinear interpolation algorithm is utilized to implement demosaicing procedure. Finally, an adaptive step adjustable scheme is introduced to ensure the controllability and robustness of the algorithm. In order to verify the proposed algorithm's performance on embedded vision system, a smart camera based on IMX6 DualLite, IMX291 and XC6130 is designed. Extensive experiments on a large amount of images under different color temperatures and exposure conditions illustrate that the proposed white balance algorithm avoids color deviation problem effectively, achieves a good balance between efficiency and quality, and is suitable for embedded machine vision processing system.

Mizan: A system for dynamic load balancing in large-scale graph processing

KAUST Repository

Khayyat, Zuhair

2013-01-01

Pregel [23] was recently introduced as a scalable graph mining system that can provide significant performance improvements over traditional MapReduce implementations. Existing implementations focus primarily on graph partitioning as a preprocessing step to balance computation across compute nodes. In this paper, we examine the runtime characteristics of a Pregel system. We show that graph partitioning alone is insufficient for minimizing end-to-end computation. Especially where data is very large or the runtime behavior of the algorithm is unknown, an adaptive approach is needed. To this end, we introduce Mizan, a Pregel system that achieves efficient load balancing to better adapt to changes in computing needs. Unlike known implementations of Pregel, Mizan does not assume any a priori knowledge of the structure of the graph or behavior of the algorithm. Instead, it monitors the runtime characteristics of the system. Mizan then performs efficient fine-grained vertex migration to balance computation and communication. We have fully implemented Mizan; using extensive evaluation we show that - especially for highly-dynamic workloads - Mizan provides up to 84% improvement over techniques leveraging static graph pre-partitioning. © 2013 ACM.

Load balancing in 5G Networks

Directory of Open Access Journals (Sweden)

Tsirakis Christos

2017-01-01

Full Text Available The expected huge increase of mobile devices and user data demand by 2020 will stress the current mobile network in an unprecedented way. The future mobile networks must meet several strong requirements regarding the data rate, latency, quality of service and experience, mobility, spectrum and energy efficiency. Therefore, efforts for more efficient mobile network solutions have been recently initiated. To this direction, load balancing has attracted much attention as a promising solution for higher resource utilization, improved system performance and decreased operational cost. It is an effective method for balancing the traffic and alleviating the congestion among heterogeneous networks in the upcoming 5G networks. In this paper, we focus on an offloading scenario for load balancing among LTE and Wi-Fi networks. Additionally, network graphs methodology and its abstracted parameters are investigated in order to better manage wireless resource allocation among multiple connections. The COHERENT architectural framework, which consists of two main control components, makes use of such abstracted network graphs for controlling or managing various tasks such as traffic steering, load balancing, spectrum sharing and RAN sharing. As a result, the COHERENT project eventually develops a unified programmable control framework used to efficiently coordinate the underlying heterogeneous mobile networks as a whole.

MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

Directory of Open Access Journals (Sweden)

Yuteng Xiao

2017-01-01

Full Text Available Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response. However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

Joint Optimization of Power Allocation and Load Balancing for Hybrid VLC/RF Networks

KAUST Repository

Obeed, Mohanad; Salhab, Anas; Zummo, Salam A.; Alouini, Mohamed-Slim

2018-01-01

In this paper, we propose and study a new joint load balancing (LB) and power allocation (PA) scheme for a hybrid visible light communication (VLC) and radio frequency (RF) system consisting of one RF access point (AP) and multiple VLC APs. An iterative algorithm is proposed to distribute users on APs and distribute the powers of the APs on their users. In the PA subproblem, an optimization problem is formulated to allocate the power of each AP to the connected users for total achievable data rate maximization. In this subproblem, we propose a new efficient algorithm that finds optimal dual variables after formulating them in terms of each other. This new algorithm provides faster convergence and better performance than the traditional subgradient method. In addition, it does not depend on the step size or the initial values of the variables, which we look for, as the subgradient does. Then, we start with the user of the minimum data rate seeking another AP that offers a higher data rate for that user. Users with lower data rates continue reconnecting from one AP to another to balance the load only if this travel increases the summation of the achievable data rates and enhances the system fairness. Two approaches are proposed to have the joint PA and LB performed: a main approach that considers the exact interference information for all users, and a suboptimal approach that aims to decrease the complexity of the first approach by considering only the approximate interference information of users. The numerical results demonstrate that the proposed algorithms improve the system capacity and system fairness with fast convergence.

Joint Optimization of Power Allocation and Load Balancing for Hybrid VLC/RF Networks

KAUST Repository

Obeed, Mohanad

2018-04-18

In this paper, we propose and study a new joint load balancing (LB) and power allocation (PA) scheme for a hybrid visible light communication (VLC) and radio frequency (RF) system consisting of one RF access point (AP) and multiple VLC APs. An iterative algorithm is proposed to distribute users on APs and distribute the powers of the APs on their users. In the PA subproblem, an optimization problem is formulated to allocate the power of each AP to the connected users for total achievable data rate maximization. In this subproblem, we propose a new efficient algorithm that finds optimal dual variables after formulating them in terms of each other. This new algorithm provides faster convergence and better performance than the traditional subgradient method. In addition, it does not depend on the step size or the initial values of the variables, which we look for, as the subgradient does. Then, we start with the user of the minimum data rate seeking another AP that offers a higher data rate for that user. Users with lower data rates continue reconnecting from one AP to another to balance the load only if this travel increases the summation of the achievable data rates and enhances the system fairness. Two approaches are proposed to have the joint PA and LB performed: a main approach that considers the exact interference information for all users, and a suboptimal approach that aims to decrease the complexity of the first approach by considering only the approximate interference information of users. The numerical results demonstrate that the proposed algorithms improve the system capacity and system fairness with fast convergence.

Application of the distributed genetic algorithm for loading pattern optimization problems

International Nuclear Information System (INIS)

Hashimoto, Hiroshi; Yamamoto, Akio

2000-01-01

The distributed genetic algorithm (DGA) is applied for loading pattern optimization problems of the pressurized water reactors (PWR). Due to stiff nature of the loading pattern optimizations (e.g. multi-modality and non-linearity), stochastic methods like the simulated annealing or the genetic algorithm (GA) are widely applied for these problems. A basic concept of DGA is based on that of GA. However, DGA equally distributes candidates of solutions (i.e. loading patterns) to several independent 'islands' and evolves them in each island. Migrations of some candidates are performed among islands with a certain period. Since candidates of solutions independently evolve in each island with accepting different genes of migrants from other islands, premature convergence in the traditional GA can be prevented. Because many candidate loading patterns should be evaluated in one generation of GA or DGA, the parallelization in these calculations works efficiently. Parallel efficiency was measured using our optimization code and good load balance was attained even in a heterogeneous cluster environment due to dynamic distribution of the calculation load. The optimization code is based on the client/server architecture with the TCP/IP native socket and a client (optimization module) and calculation server modules communicate the objects of loading patterns each other. Throughout the sensitivity study on optimization parameters of DGA, a suitable set of the parameters for a test problem was identified. Finally, optimization capability of DGA and the traditional GA was compared in the test problem and DGA provided better optimization results than the traditional GA. (author)

A Baseline Load Schedule for the Manual Calibration of a Force Balance

Science.gov (United States)

Ulbrich, N.; Gisler, R.

2013-01-01

A baseline load schedule for the manual calibration of a force balance is defined that takes current capabilities at the NASA Ames Balance Calibration Laboratory into account. The chosen load schedule consists of 18 load series with a total of 194 data points. It was designed to satisfy six requirements: (i) positive and negative loadings should be applied for each load component; (ii) at least three loadings should be applied between 0 % and 100 % load capacity; (iii) normal and side force loadings should be applied at the forward gage location, aft gage location, and the balance moment center; (iv) the balance should be used in "up" and "down" orientation to get positive and negative axial force loadings; (v) the constant normal and side force approaches should be used to get the rolling moment loadings; (vi) rolling moment loadings should be obtained for 0, 90, 180, and 270 degrees balance orientation. In addition, three different approaches are discussed in the paper that may be used to independently estimate the natural zeros, i.e., the gage outputs of the absolute load datum of the balance. These three approaches provide gage output differences that can be used to estimate the weight of both the metric and non-metric part of the balance. Data from the calibration of a six-component force balance will be used in the final manuscript of the paper to illustrate characteristics of the proposed baseline load schedule.

Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system

Science.gov (United States)

Choudhary, Alok Nidhi; Leung, Mun K.; Huang, Thomas S.; Patel, Janak H.

1989-01-01

Computer vision systems employ a sequence of vision algorithms in which the output of an algorithm is the input of the next algorithm in the sequence. Algorithms that constitute such systems exhibit vastly different computational characteristics, and therefore, require different data decomposition techniques and efficient load balancing techniques for parallel implementation. However, since the input data for a task is produced as the output data of the previous task, this information can be exploited to perform knowledge based data decomposition and load balancing. Presented here are algorithms for a motion estimation system. The motion estimation is based on the point correspondence between the involved images which are a sequence of stereo image pairs. Researchers propose algorithms to obtain point correspondences by matching feature points among stereo image pairs at any two consecutive time instants. Furthermore, the proposed algorithms employ non-iterative procedures, which results in saving considerable amounts of computation time. The system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from consecutive time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters.

Study of load balancing technology for EAST data management

Energy Technology Data Exchange (ETDEWEB)

Li, Shi, E-mail: lishi@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Wang, Feng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Xiao, Bingjia [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui (China); Yang, Fei [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Department of Computer Science, Anhui Medical University, Hefei, Anhui (China); Sun, Xiaoyang; Wang, Yong [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China)

2014-05-15

Highlights: • The load balancing concept is introduced into the MDSplus data service. • The new data service system based on the LVS framework and heartbeat technologies are described. • The scheduling algorithm “WLC” is used, and a software system is developed for optimizing the weight of node server. - Abstract: With the continuous renewal and increasing number of diagnostics, the EAST tokamak routinely generates ∼3 GB of raw data per pulse of the experiment, which is transferred to a centralized data management system. In order to strengthen international cooperation, all the acquired data has been converted and stored in the MDSplus servers. During the data system operation, there are some problems when a lot of client machines connect to a single MDSplus data server. Because the server process keeps the connection until the client closes its connection, a lot of server processes use a lot of network ports and consume a large amount of memory, so that the speed of access to data is very slow, but the CPU resource is not fully utilized. To improve data management system performance, many MDSplus servers will be installed on the blade server and form a server cluster to realize load balancing and high availability by using LVS and heartbeat technology. This paper will describe the details of the design and the test results.

Bit Loading Algorithms for Cooperative OFDM Systems

Directory of Open Access Journals (Sweden)

Gui Bo

2008-01-01

Full Text Available Abstract We investigate the resource allocation problem for an OFDM cooperative network with a single source-destination pair and multiple relays. Assuming knowledge of the instantaneous channel gains for all links in the entire network, we propose several bit and power allocation schemes aiming at minimizing the total transmission power under a target rate constraint. First, an optimal and efficient bit loading algorithm is proposed when the relay node uses the same subchannel to relay the information transmitted by the source node. To further improve the performance gain, subchannel permutation, in which the subchannels are reallocated at relay nodes, is considered. An optimal subchannel permutation algorithm is first proposed and then an efficient suboptimal algorithm is considered to achieve a better complexity-performance tradeoff. A distributed bit loading algorithm is also proposed for ad hoc networks. Simulation results show that significant performance gains can be achieved by the proposed bit loading algorithms, especially when subchannel permutation is employed.

Bit Loading Algorithms for Cooperative OFDM Systems

Directory of Open Access Journals (Sweden)

Bo Gui

2007-12-01

Full Text Available We investigate the resource allocation problem for an OFDM cooperative network with a single source-destination pair and multiple relays. Assuming knowledge of the instantaneous channel gains for all links in the entire network, we propose several bit and power allocation schemes aiming at minimizing the total transmission power under a target rate constraint. First, an optimal and efficient bit loading algorithm is proposed when the relay node uses the same subchannel to relay the information transmitted by the source node. To further improve the performance gain, subchannel permutation, in which the subchannels are reallocated at relay nodes, is considered. An optimal subchannel permutation algorithm is first proposed and then an efficient suboptimal algorithm is considered to achieve a better complexity-performance tradeoff. A distributed bit loading algorithm is also proposed for ad hoc networks. Simulation results show that significant performance gains can be achieved by the proposed bit loading algorithms, especially when subchannel permutation is employed.

General Influence Coefficient Algorithm in Balancing of Rotating Machinery

Directory of Open Access Journals (Sweden)

Xiaoping Yu

2004-01-01

Full Text Available The General Influence Coefficient Algorithm (GICA, developed in this article, is a new calculation method for Influence Coefficients (ICs with a general formula. Compared to the traditional calculation method, GICA can solve the ICs' calculation task when the group of trial weights are installed on the rotor each time, the trial weights are retained on the rotor systems, or there is redundant trial balancing data, when even part of the ICs is known. GICA is also a powerful tool for refining the ICs from redundant balancing data or historical balancing data and serves as a general algorithm. With the general matrix formula, GICA is ready to be applied in a computer-aided balancing system as the key part of calculation software. Examples in industry are also presented to demonstrate the aplication of this new algorithm.

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management.

Science.gov (United States)

Silva, Bhagya Nathali; Khan, Murad; Han, Kijun

2018-02-25

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism.

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

Science.gov (United States)

Silva, Bhagya Nathali; Khan, Murad; Han, Kijun

2018-01-01

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism. PMID:29495346

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

Directory of Open Access Journals (Sweden)

Bhagya Nathali Silva

2018-02-01

Full Text Available The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism.

Physical load handling and listening comprehension effects on balance control.

Science.gov (United States)

Qu, Xingda

2010-12-01

The purpose of this study was to determine the physical load handling and listening comprehension effects on balance control. A total of 16 young and 16 elderly participants were recruited in this study. The physical load handling task required holding a 5-kg load in each hand with arms at sides. The listening comprehension task involved attentive listening to a short conversation. Three short questions were asked regarding the conversation right after the testing trial to test the participants' attentiveness during the experiment. Balance control was assessed by centre of pressure-based measures, which were calculated from the force platform data when the participants were quietly standing upright on a force platform. Results from this study showed that both physical load handling and listening comprehension adversely affected balance control. Physical load handling had a more deleterious effect on balance control under the listening comprehension condition vs. no-listening comprehension condition. Based on the findings from this study, interventions for the improvement of balance could be focused on avoiding exposures to physically demanding tasks and cognitively demanding tasks simultaneously. STATEMENT OF RELEVANCE: Findings from this study can aid in better understanding how humans maintain balance, especially when physical and cognitive loads are applied. Such information is useful for developing interventions to prevent fall incidents and injuries in occupational settings and daily activities.

Partial Key Grouping: Load-Balanced Partitioning of Distributed Streams

OpenAIRE

Nasir, Muhammad Anis Uddin; Morales, Gianmarco De Francisci; Garcia-Soriano, David; Kourtellis, Nicolas; Serafini, Marco

2015-01-01

We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce PARTIAL KEY GROUPING (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation. In so doing, it achieves better load balancing than key grouping while being more scalable than shuffle grouping. We test PKG on severa...

Balance control of grid currents for UPQC under unbalanced loads based on matching-ratio compensation algorithm

DEFF Research Database (Denmark)

Zhao, Xiaojun; Zhang, Chunjiang; Chai, Xiuhui

2018-01-01

In three-phase four-wire systems, unbalanced loads can cause grid currents to be unbalanced, and this may cause the neutral point potential on the grid side to shift. The neutral point potential shift will worsen the control precision as well as the performance of the threephase four-wire unified...... fluctuations, and elaborates the interaction between unbalanced grid currents and DC bus voltage fluctuations; two control strategies of UPQC under three-phase stationary coordinate based on the MCA are given, and finally, the feasibility and effectiveness of the proposed control strategy are verified...... power quality conditioner (UPQC), and it also leads to unbalanced three-phase output voltage, even causing damage to electric equipment. To deal with unbalanced loads, this paper proposes a matching-ratio compensation algorithm (MCA) for the fundamental active component of load currents...

Implementing peak load reduction algorithms for household electrical appliances

International Nuclear Information System (INIS)

Dlamini, Ndumiso G.; Cromieres, Fabien

2012-01-01

Considering household appliance automation for reduction of household peak power demand, this study explored aspects of the interaction between household automation technology and human behaviour. Given a programmable household appliance switching system, and user-reported appliance use times, we simulated the load reduction effectiveness of three types of algorithms, which were applied at both the single household level and across all 30 households. All three algorithms effected significant load reductions, while the least-to-highest potential user inconvenience ranking was: coordinating the timing of frequent intermittent loads (algorithm 2); moving period-of-day time-flexible loads to off-peak times (algorithm 1); and applying short-term time delays to avoid high peaks (algorithm 3) (least accommodating). Peak reduction was facilitated by load interruptibility, time of use flexibility and the willingness of users to forgo impulsive appliance use. We conclude that a general factor determining the ability to shift the load due to a particular appliance is the time-buffering between the service delivered and the power demand of an appliance. Time-buffering can be ‘technologically inherent’, due to human habits, or realised by managing user expectations. There are implications for the design of appliances and home automation systems. - Highlights: ► We explored the interaction between appliance automation and human behaviour. ► There is potential for considerable load shifting of household appliances. ► Load shifting for load reduction is eased with increased time buffering. ► Design, human habits and user expectations all influence time buffering. ► Certain automation and appliance design features can facilitate load shifting.

A Novel Assembly Line Balancing Method Based on PSO Algorithm

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2014-01-01

Full Text Available Assembly line is widely used in manufacturing system. Assembly line balancing problem is a crucial question during design and management of assembly lines since it directly affects the productivity of the whole manufacturing system. The model of assembly line balancing problem is put forward and a general optimization method is proposed. The key data on assembly line balancing problem is confirmed, and the precedence relations diagram is described. A double objective optimization model based on takt time and smoothness index is built, and balance optimization scheme based on PSO algorithm is proposed. Through the simulation experiments of examples, the feasibility and validity of the assembly line balancing method based on PSO algorithm is proved.

Fuzzy pool balance: An algorithm to achieve a two dimensional balance in distribute storage systems

International Nuclear Information System (INIS)

Wu, Wenjing; Chen, Gang

2014-01-01

The limitation of scheduling modules and the gradual addition of disk pools in distributed storage systems often result in imbalances among their disk pools in terms of both disk usage and file count. This can cause various problems to the storage system such as single point of failure, low system throughput and imbalanced resource utilization and system loads. An algorithm named Fuzzy Pool Balance (FPB) is proposed here to solve this problem. The input of FPB is the current file distribution among disk pools and the output is a file migration plan indicating what files are to be migrated to which pools. FPB uses an array to classify the files by their sizes. The file classification array is dynamically calculated with a defined threshold named T max that defines the allowed pool disk usage deviations. File classification is the basis of file migration. FPB also defines the Immigration Pool (IP) and Emigration Pool (EP) according to the pool disk usage and File Quantity Ratio (FQR) that indicates the percentage of each category of files in each disk pool, so files with higher FQR in an EP will be migrated to IP(s) with a lower FQR of this file category. To verify this algorithm, we implemented FPB on an ATLAS Tier2 dCache production system. The results show that FPB can achieve a very good balance in both free space and file counts, and adjusting the threshold value T max and the correction factor to the average FQR can achieve a tradeoff between free space and file count.

Load Balancing Scheme on the Basis of Huffman Coding for P2P Information Retrieval

Science.gov (United States)

Kurasawa, Hisashi; Takasu, Atsuhiro; Adachi, Jun

Although a distributed index on a distributed hash table (DHT) enables efficient document query processing in Peer-to-Peer information retrieval (P2P IR), the index costs a lot to construct and it tends to be an unfair management because of the unbalanced term frequency distribution. We devised a new distributed index, named Huffman-DHT, for P2P IR. The new index uses an algorithm similar to Huffman coding with a modification to the DHT structure based on the term distribution. In a Huffman-DHT, a frequent term is assigned to a short ID and allocated a large space in the node ID space in DHT. Throuth ID management, the Huffman-DHT balances the index registration accesses among peers and reduces load concentrations. Huffman-DHT is the first approach to adapt concepts of coding theory and term frequency distribution to load balancing. We evaluated this approach in experiments using a document collection and assessed its load balancing capabilities in P2P IR. The experimental results indicated that it is most effective when the P2P system consists of about 30, 000 nodes and contains many documents. Moreover, we proved that we can construct a Huffman-DHT easily by estimating the probability distribution of the term occurrence from a small number of sample documents.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

OpenAIRE

Devi, D. Chitra; Uthariaraj, V. Rhymend

2016-01-01

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM’s mul...

Two-Step Load Balancing Scheme for Fairness Improvement in ...

African Journals Online (AJOL)

ABSTRACT: The problem of load imbalance in HetNets among wireless access technologies is addressed in this article. ... balancing could either mean balancing the transmit power or the radio ..... Weight for Multimedia Transmission.

Loading pattern optimization using ant colony algorithm

International Nuclear Information System (INIS)

Hoareau, Fabrice

2008-01-01

Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

Load Balancing As A Service In Openstack-Liberty

Directory of Open Access Journals (Sweden)

Rashmi T V

2015-08-01

Full Text Available Cloud computing is a technology which provides computing resource on demand over the internet as a service. To meet this many opensource cloud operating system are provided for the tenants in order to get useful services from the cloud. There are many opensource cloud OS like AWS Open Shift HP OpenStack etc. Out of all these OpenStack comes with free of cost and it has got a huge community. It can be installed and deploy in private institution or company with free of cost. This paper provides a model and techniques for the dynamic load balancing in OpenStack for managing the trafficloads among the Virtual Machines. The main purpose is to increase the utilization of computing resources and minimize the traffic. Load Balancing as a Service is one of the main service in OpenStack Networking. OpenStack is an opensource platform which provides Infrastructure as a Service. It allows userstenants tocreate their own private clouds and to deploy Virtual Machines which manages different workloads. In this paper we provide an architecture of openstack LBaaS for dynamic load balancing in open stack cloud deployment.

Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes

Science.gov (United States)

Satymbekov, M. N.; Pak, I. T.; Naizabayeva, L.; Nurzhanov, Ch. A.

2017-12-01

In this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Energy-balanced algorithm for RFID estimation

Science.gov (United States)

Zhao, Jumin; Wang, Fangyuan; Li, Dengao; Yan, Lijuan

2016-10-01

RFID has been widely used in various commercial applications, ranging from inventory control, supply chain management to object tracking. It is necessary for us to estimate the number of RFID tags deployed in a large area periodically and automatically. Most of the prior works use passive tags to estimate and focus on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. But for a RFID reader to access tags in a large area, active tags are likely to be used due to their longer operational ranges. But these tags use their own battery as energy supplier. Hence, conserving energy for active tags becomes critical. Some prior works have studied how to reduce energy expenditure of a RFID reader when it reads tags IDs. In this paper, we study how to reduce the amount of energy consumed by active tags during the process of estimating the number of tags in a system and make the energy every tag consumed balanced approximately. We design energy-balanced estimation algorithm that can achieve our goal we mentioned above.

Algorithms for polynomial spectral factorization and bounded-real balanced state space representations

NARCIS (Netherlands)

Rapisarda, P.; Trentelman, H.L.; Minh, H.B.

We illustrate an algorithm that starting from the image representation of a strictly bounded-real system computes a minimal balanced state variable, from which a minimal balanced state realization is readily obtained. The algorithm stems from an iterative procedure to compute a storage function,

Selecting boundary conditions in physiological strain analysis of the femur: Balanced loads, inertia relief method and follower load.

Science.gov (United States)

Heyland, Mark; Trepczynski, Adam; Duda, Georg N; Zehn, Manfred; Schaser, Klaus-Dieter; Märdian, Sven

2015-12-01

Selection of boundary constraints may influence amount and distribution of loads. The purpose of this study is to analyze the potential of inertia relief and follower load to maintain the effects of musculoskeletal loads even under large deflections in patient specific finite element models of intact or fractured bone compared to empiric boundary constraints which have been shown to lead to physiological displacements and surface strains. The goal is to elucidate the use of boundary conditions in strain analyses of bones. Finite element models of the intact femur and a model of clinically relevant fracture stabilization by locking plate fixation were analyzed with normal walking loading conditions for different boundary conditions, specifically re-balanced loading, inertia relief and follower load. Peak principal cortex surface strains for different boundary conditions are consistent (maximum deviation 13.7%) except for inertia relief without force balancing (maximum deviation 108.4%). Influence of follower load on displacements increases with higher deflection in fracture model (from 3% to 7% for force balanced model). For load balanced models, follower load had only minor influence, though the effect increases strongly with higher deflection. Conventional constraints of fixed nodes in space should be carefully reconsidered because their type and position are challenging to justify and for their potential to introduce relevant non-physiological reaction forces. Inertia relief provides an alternative method which yields physiological strain results. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Characteristic statistic algorithm (CSA) for in-core loading pattern optimization

International Nuclear Information System (INIS)

Liu Zhihong; Hu Yongming; Shi Gong

2007-01-01

To solve the problem of PWR in-core loading pattern optimization, a more suitable global optimization algorithm, i.e., Characteristic statistic algorithm (CSA), is used. The searching process of this algorithm and how to apply it to this problem are presented. Loading pattern optimization code SCYCLE is developed. Two different problems on real PWR models are calculated and the results are compared with other algorithms. It is shown that SCYCLE has high efficiency and good global performance on this problem. (authors)

Vestibular control of standing balance is enhanced with increased cognitive load.

Science.gov (United States)

McGeehan, Michael A; Woollacott, Marjorie H; Dalton, Brian H

2017-04-01

When cognitive load is elevated during a motor task, cortical inhibition and reaction time are increased; yet, standing balance control is often unchanged. This disconnect is likely explained by compensatory mechanisms within the balance system such as increased sensitivity of the vestibulomotor pathway. This study aimed to determine the effects of increased cognitive load on the vestibular control of standing balance. Participants stood blindfolded on a force plate with their head facing left and arms relaxed at their sides for two trials while exposed to continuous electrical vestibular stimulation (EVS). Participants either stood quietly or executed a cognitive task (double-digit arithmetic). Surface electromyography (EMG) and anterior-posterior ground-body forces (APF) were measured in order to evaluate vestibular-evoked balance responses in the frequency (coherence and gain) and time (cumulant density) domains. Total distance traveled for anterior-posterior center of pressure (COP) was assessed as a metric of balance variability. Despite similar distances traveled for COP, EVS-medial gastrocnemius (MG) EMG and EVS-APF coherence and EVS-TA EMG and EVS-MG EMG gain were elevated for multiple frequencies when standing with increased cognitive load. For the time domain, medium-latency peak amplitudes increased by 13-54% for EVS-APF and EVS-EMG relationships with the cognitive task compared to without. Peak short-latency amplitudes were unchanged. These results indicate that reliance on vestibular control of balance is enhanced when cognitive load is elevated. This augmented neural strategy may act to supplement divided cortical processing resources within the balance system and compensate for the acute neuromuscular modifications associated with increased cognitive demand.

PeerFlow: Secure Load Balancing in Tor

Directory of Open Access Journals (Sweden)

Johnson Aaron

2017-04-01

Full Text Available We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

Genetic algorithms in loading pattern optimization

International Nuclear Information System (INIS)

Yilmazbayhan, A.; Tombakoglu, M.; Bekar, K. B.; Erdemli, A. Oe

2001-01-01

Genetic Algorithm (GA) based systems are used for the loading pattern optimization. The use of Genetic Algorithm operators such as regional crossover, crossover and mutation, and selection of initial population size for PWRs are discussed. Antithetic variates are used to generate the initial population. The performance of GA with antithetic variates is compared to traditional GA. The results of multi-cycle optimization are discussed for objective function taking into account cycle burn-up and discharge burn-up

Dynamic load-balancing-extended gradient mechanism: Graphic representation

International Nuclear Information System (INIS)

Muniz, Francisco J.

2017-01-01

Load-balancing methods are quite well described in the open literature (hundreds of articles can be found about this subject). In particularly, about the Dynamic Load-balancing mechanism Extended Gradient (EG), several articles of the author are available. Even though, there are some overlap, each one of them is focused on a particular aspect of the mechanism, in a complementary way. In this article, a graphic representation of the Extended Gradient mechanism is done: this representation way had not yet been explored. However, for an in-depth knowledge of the Extended Gradient mechanism, at least, some other articles should to be read. In the CDTN, Clusters are used, mainly, in deterministic methods (CFD) and non-deterministic methods (Monte Carlo). (author)

Dynamic load-balancing-extended gradient mechanism: Graphic representation

Energy Technology Data Exchange (ETDEWEB)

Muniz, Francisco J., E-mail: muniz@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)

2017-07-01

Load-balancing methods are quite well described in the open literature (hundreds of articles can be found about this subject). In particularly, about the Dynamic Load-balancing mechanism Extended Gradient (EG), several articles of the author are available. Even though, there are some overlap, each one of them is focused on a particular aspect of the mechanism, in a complementary way. In this article, a graphic representation of the Extended Gradient mechanism is done: this representation way had not yet been explored. However, for an in-depth knowledge of the Extended Gradient mechanism, at least, some other articles should to be read. In the CDTN, Clusters are used, mainly, in deterministic methods (CFD) and non-deterministic methods (Monte Carlo). (author)

Implementasi Cluster Server pada Raspberry Pi dengan Menggunakan Metode Load Balancing

Directory of Open Access Journals (Sweden)

Ridho Habi Putra

2016-06-01

Full Text Available Server merupakan bagian penting dalam sebuah layanan didalam jaringan komputer. Peran server dapat menentukan kualitas baik buruknya dari layanan tersebut. Kegagalan dari sebuah server bisa disebabkan oleh beberapa faktor diantaranya kerusakan perangkat keras, sistem jaringan serta aliran listrik. Salah satu solusi untuk mengatasi kegagalan server dalam suatu jaringan komputer adalah dengan melakukan clustering server.  Tujuan dari penelitian ini adalah untuk mengukur kemampuan Raspberry Pi (Raspi digunakan sebagai web server. Raspberry Pi yang digunakan menggunakan Raspberry Pi 2 Model B dengan menggunakan processor ARM Cortex-A7 berjalan pada frekuensi 900MHz dengan memiliki RAM 1GB. Sistem operasi yang digunakan pada Raspberry Pi adalah Linux Debian Wheezy. Konsep penelitian ini menggunakan empat buah perangkat Raspberry Pi dimana dua Raspi digunakan sebagai web server dan dua Raspi lainnya digunakan sebagai penyeimbang beban (Load Balancer serta database server. Metode yang digunakan dalam pembangunan cluster server ini menggunakan metode load balancing, dimana beban server bekerja secara merata di masing-masing node. Pengujian yang diterapkan dengan melakukan perbandingan kinerja dari Raspbery Pi yang menangani lalu lintas data secara tunggal tanpa menggunakan load balancer serta pengujian Raspberry Pi dengan menggunakan load balancer sebagai beban penyeimbang antara anggota cluster server.

Bin-packing problems with load balancing and stability constraints

DEFF Research Database (Denmark)

Trivella, Alessio; Pisinger, David

apper in a wide range of disciplines, including transportation and logistics, computer science, engineering, economics and manufacturing. The problem is well-known to be N P-hard and difficult to solve in practice, especially when dealing with the multi-dimensional cases. Closely connected to the BPP...... realistic constraints related to e.g. load balancing, cargo stability and weight limits, in the multi-dimensional BPP. The BPP poses additional challenges compared to the CLP due to the supplementary objective of minimizing the number of bins. In particular, in section 2 we discuss how to integrate bin......-packing and load balancing of items. The problem has only been considered in the literature in simplified versions, e.g. balancing a single bin or introducing a feasible region for the barycenter. In section 3 we generalize the problem to handle cargo stability and weight constraints....

A comparative experiment in distributed load balancing

KAUST Repository

Randles, Martin

2009-12-01

The anticipated uptake of Cloud computing, built on the well-established research fields of web services, networks, utility computing, distributed computing and virtualisation, will bring many advantages in cost, flexibility and availability for service users. These benefits are expected to further drive the demand for cloud services, increasing both the cloud customer base and the scale of cloud installations. This has implications for many technical issues in such Service Oriented Architectures and Internet of Services (IoS) type applications; fault tolerance, high availability and scalability for examples. Central to these issues is the establishment of effective load balancing techniques. It is clear that the scale and complexity of these systems makes centralized individual assignment of jobs to specific servers infeasible; leading to the need for an effective distributed solution. This paper investigates three possible distributed solutions, which have been proposed for load balancing: An approach inspired by the foraging behaviour of the Honeybee, Biased Random Sampling and Active Clustering. © 2009 IEEE.

Load power device and system for real-time execution of hierarchical load identification algorithms

Science.gov (United States)

Yang, Yi; Madane, Mayura Arun; Zambare, Prachi Suresh

2017-11-14

A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

ANALISIS PENGARUH KONFIGURASI EIGRP EQUAL DAN UNEQUAL COST LOAD BALANCING TERHADAP KINERJA ROUTER

Directory of Open Access Journals (Sweden)

Dian Bagus Saptonugroho

2015-04-01

Full Text Available Routing protocol is tasked with finding the best route to send the packet. Assessed using the metric. If there is more than one route with the same metric value, Routing Information Path (RIP, Open Shortest Path First (OSPF, and Enhanched Interior Gateway Routing Protocol (EIGRP support equal cost load balancing to send packets to the destination. If there is more than one route with a different metric values, EIGRP can do unequal cost load balancing. Research needs to be conducted to determine the effect of the configuration of EIGRP equal and unequal cost load balancing on the performance of the router which can be used as a proof-of-concept testing that is part of the project design document on a network. Research networks using EIGRP as the routing protocol. After the equal and unequal load balancing is enabled by configuring the variance, CEF, per-destination load balancing, per-packet load balancing, or traffic sharing and analyzing its effect on the neighbor table, topology table, routing table, the data transmission, survivability, convergence, throughput, and utilization. This study used an emulator GNS3 as Cisco 2691 Router with Cisco IOS version 12:24 (25 c and advanced enterprise-adventerprisek9 image c2691-mz.124-25c.bin, and OPNET Modeler 14.5 for simulation. The results of the study can be used as a proof-of-concept testing in the design document for later use as contemplated in the manufacture of plan implementation and verification plan.

Replication and load balancing strategy of STAR's relational database management system (RDBM)

International Nuclear Information System (INIS)

DePhillips, M; Lauret, J; Kopytine, M

2008-01-01

Database demand resulting from offline analysis and production of data at the STAR experiment at Brookhaven National Laboratory's Relativistic Heavy-Ion Collider has steadily increased over the last six years of data taking activities. With each year, STAR more than doubles the number of events recorded with an anticipation of reaching a billion event capabilities as early as next year. The challenges faced from producing and analyzing this magnitude of events in parallel have raised issues with regard to the distribution of calibrations and geometry data, via databases, to STAR's growing global collaboration. Rapid distribution, availability, ensured synchronization and load balancing have become paramount considerations. Both conventional technology and novel approaches are used in parallel to realize these goals. This paper discusses how STAR uses load balancing to optimize database usage. It discusses distribution methods via MySQL master slave replication; the synchronization issues that arise from this type of distribution and solutions, mostly homegrown, put forth to overcome these issues. A novel approach toward load balancing between slave nodes that assists in maintaining a high availability rate for a veracious community is discussed in detail. This load balancing addresses both, pools of nodes internal to a given location, as well as balancing the load for remote users between different available locations. Challenges, trade-offs, rationale for decisions and paths forward will be discussed in all cases, presenting a solid production environment with a vision for scalable growth

Optimizing the Loads of multi-player online game Servers using Markov Chains

DEFF Research Database (Denmark)

Saeed, Aamir; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup

2015-01-01

that is created due to the load balancing of servers. Load balancing among servers is sensitive to correct status information. The Markov based load prediction was introduced in this paper to predict load of under-loaded servers, based on arrival (μ) and departure (λ) rates of players. The prediction based...... that need to be considered when developing load balancing algorithm, that is the reliability of the information that is shared. Simulation results show that Markov based prediction of load information performed better from the normal load status information sharing....

Improvement on LiFePO4 Cell Balancing Algorithm

OpenAIRE

Vencislav C. Valchev; Plamen V. Yankov; Dimo D. Stefanov

2018-01-01

The paper presents improvement on operation time of cell balancing algorithm compared to conventional multiple cell LiFePO4 charge methodology. A flowchart is synthesised to explain the main steps of the software design, which afterwards is implemented in a microcontroller. Experimental results are provided to clarify the transition between charge and balance process. Graphical data for a voltage equalization of eight cells is presented to verify the proposed improvement.

A strategy for load balancing in distributed storage systems

CERN Multimedia

CERN. Geneva

2012-01-01

Distributed storage systems are critical to the operation of the WLCG. These systems are not limited to fulfilling the long term storage requirements. They also serve data for computational analysis and other computational jobs. Distributed storage systems provide the ability to aggregate the storage and IO capacity of disks and tapes, but at the end of the day IO rate is still bound by the capabilities of the hardware, in particular the hard drives. Throughput of hard drives has increased dramatically over the decades, however for computational analysis IOPS is typically the limiting factor. To maximize return of investment, balancing IO load over available hardware is crucial. The task is made complicated by the common use of heterogeneous hardware and software environments that results from combining new and old hardware into a single storage system. This paper describes recent advances made in load balancing in the dCache distributed storage system. We describe a set of common requirements for load balan...

Waste Load Allocation Based on Total Maximum Daily Load Approach Using the Charged System Search (CSS Algorithm

Directory of Open Access Journals (Sweden)

Elham Faraji

2016-03-01

Full Text Available In this research, the capability of a charged system search algorithm (CSS in handling water management optimization problems is investigated. First, two complex mathematical problems are solved by CSS and the results are compared with those obtained from other metaheuristic algorithms. In the last step, the optimization model developed by the CSS algorithm is applied to the waste load allocation in rivers based on the total maximum daily load (TMDL concept. The results are presented in Tables and Figures for easy comparison. The study indicates the superiority of the CSS algorithm in terms of its speed and performance over the other metaheuristic algorithms while its precision in water management optimization problems is verified.

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

Science.gov (United States)

Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

Improvement on LiFePO4 Cell Balancing Algorithm

Directory of Open Access Journals (Sweden)

Vencislav C. Valchev

2018-02-01

Full Text Available The paper presents improvement on operation time of cell balancing algorithm compared to conventional multiple cell LiFePO4 charge methodology. A flowchart is synthesised to explain the main steps of the software design, which afterwards is implemented in a microcontroller. Experimental results are provided to clarify the transition between charge and balance process. Graphical data for a voltage equalization of eight cells is presented to verify the proposed improvement.

Neural Network Algorithm for Particle Loading

International Nuclear Information System (INIS)

Lewandowski, J.L.V.

2003-01-01

An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given

Efficient graph-based dynamic load-balancing for parallel large-scale agent-based traffic simulation

NARCIS (Netherlands)

Xu, Y.; Cai, W.; Aydt, H.; Lees, M.; Tolk, A.; Diallo, S.Y.; Ryzhov, I.O.; Yilmaz, L.; Buckley, S.; Miller, J.A.

2014-01-01

One of the issues of parallelizing large-scale agent-based traffic simulations is partitioning and load-balancing. Traffic simulations are dynamic applications where the distribution of workload in the spatial domain constantly changes. Dynamic load-balancing at run-time has shown better efficiency

Design of a Load-Balancing Architecture For Parallel Firewalls

National Research Council Canada - National Science Library

Joyner, William

1999-01-01

.... This thesis proposes a load-balancing firewall architecture to meet the Navy's needs. It first conducts an architectural analysis of the problem and then presents a high-level system design as a solution...

Analisis Kinerja Penerapan Container untuk Load Balancing Web Server

Directory of Open Access Journals (Sweden)

Muhammad Agung Nugroho

2016-12-01

Full Text Available Container merupakan teknologi virtualisasi terbaru. Container memudahkan system administrator dalam mengelola aplikasi pada server. Docker container dapat digunakan untuk membangun, mempersiapkan, dan menjalankan aplikasi. Dapat membuat aplikasi dari bahasa pemrograman yang berbeda pada lapisan apapun. Aplikasi dapat di bungkus dalam container, dan aplikasi dapat berjalan pada lingkungan apapun dimana saja.  Dalam perkembangannya container ini dapat digunakan untuk load balancing, dengan memanfaatkan HA Proxy. Load Balancing dapat digunakan untuk menyelesaikan permasalahan beban kinerja web server yang terlalu berat (overload terhadap permintaan. Load Balancing merupakan salah satu metode untuk meningkatkan skalabilitas web server sekaligus mengurangi beban kerja web server. Ujicoba dilakukan dengan memberikan beban request pada single container dan multi container, dan membandingkan kinerjanya. Analisis kinerja dapat dilakukan dengan menggunakan parameter performance pada processor, memori dan proses layanan. Penerapan ujicoba dilakukan pada raspberry pi. Hasil yang diperoleh multi container dapat digunakan untuk mengembangkan metode load balancing, hasil ujicoba menunjukkan performance raspberry pi dapat optimum karena pembagian beban processor.

Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting

International Nuclear Information System (INIS)

Zhang, Wen Yu; Hong, Wei-Chiang; Dong, Yucheng; Tsai, Gary; Sung, Jing-Tian; Fan, Guo-feng

2012-01-01

The electric load forecasting is complicated, and it sometimes reveals cyclic changes due to cyclic economic activities or climate seasonal nature, such as hourly peak in a working day, weekly peak in a business week, and monthly peak in a demand planned year. Hybridization of support vector regression (SVR) with chaotic sequence and evolutionary algorithms has successfully been applied to improve forecasting accuracy, and to effectively avoid trapping in a local optimum. However, it has not been widely explored to employ SVR-based model to deal with cyclic electric load forecasting. This paper will firstly investigate the potentiality of a novel hybrid algorithm, namely chaotic genetic algorithm-simulated annealing algorithm (CGASA), with an SVR model to improve load forecasting accurate performance. In which, the proposed CGASA employs internal randomness of chaotic iterations to overcome premature local optimum. Secondly, the seasonal mechanism will then be applied to well adjust the cyclic load tendency. Finally, a numerical example from an existed reference is employed to compare the forecasting performance of the proposed SSVRCGASA model. The forecasting results show that the SSVRCGASA model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. -- Highlights: ► Hybridizing the seasonal adjustment mechanism into an SVR model. ► Employing chaotic sequence to improve the premature convergence of genetic algorithm and simulated annealing algorithm. ► Successfully providing significant accurate monthly load demand forecasting.

Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

Directory of Open Access Journals (Sweden)

Muthukkumar R.

2017-04-01

Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

Directory of Open Access Journals (Sweden)

Muthukkumar R.

2016-07-01

Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

Three-phase Power Flow Calculation of Low Voltage Distribution Network Considering Characteristics of Residents Load

Science.gov (United States)

Wang, Yaping; Lin, Shunjiang; Yang, Zhibin

2017-05-01

In the traditional three-phase power flow calculation of the low voltage distribution network, the load model is described as constant power. Since this model cannot reflect the characteristics of actual loads, the result of the traditional calculation is always different from the actual situation. In this paper, the load model in which dynamic load represented by air conditioners parallel with static load represented by lighting loads is used to describe characteristics of residents load, and the three-phase power flow calculation model is proposed. The power flow calculation model includes the power balance equations of three-phase (A,B,C), the current balance equations of phase 0, and the torque balancing equations of induction motors in air conditioners. And then an alternating iterative algorithm of induction motor torque balance equations with each node balance equations is proposed to solve the three-phase power flow model. This method is applied to an actual low voltage distribution network of residents load, and by the calculation of three different operating states of air conditioners, the result demonstrates the effectiveness of the proposed model and the algorithm.

Evaluating algorithms for the Generation of Referring Expressions using a balanced corpus

NARCIS (Netherlands)

Gatt, A.; van der Sluis, Ielka; van Deemter, Kees

2007-01-01

Despite being the focus of intensive research, evaluation of algorithms that generate referring expressions is still in its infancy. We describe a corpusbased evaluation methodology, applied to a number of classic algorithms in this area. The methodology focuses on balance and semantic transparency

A distributed scheduling algorithm for heterogeneous real-time systems

Science.gov (United States)

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.

A Novel Load Balancing Scheme for Multipath Routing Protocol in MANET

Directory of Open Access Journals (Sweden)

Kokilamani Mounagurusamy

2016-09-01

Full Text Available The recent advancements in information and communication technology create a great demand for multipath routing protocols. In MANET, nodes can be arbitrarily located and can move freely at any given time. The topology of MANET can change rapidly and unpredictably. Because wireless link capacities are usually limited, congestion is possible in MANETs. Hence, balancing the load in a MANET is important since nodes with high load will deplete their batteries quickly, thereby increasing the probability of disconnecting or partitioning the network. To overcome these, the multipath protocol should be aware of load at route discovery phase. The main objective of the proposed article is to balance the load on a node and to extend the lifetime of the node due to the congestion, energy depletion and link failures. This article describes a novel load and congestion aware scheme called Path Efficient Ad-hoc On-demand Multipath Distance Vector (PE-AOMDV protocol to increase the performance of routing process in MANET in terms of congestion, end-to-end delay and load balancing. A new threshold value and a counter variable are introduced to limit the number of communication paths passing over a node in route discovery phase. For every new request the counter variable is incremented by one and the threshold value is compared to see whether the maximum number of connections has been reached or not. The proposed method is network simulator ns-2 and it is found that there is a significant improvement in the proposed scheme. It reduces the energy consumption, average end-to-end delay and normalized routing overhead. Also the proposed scheme increases packet delivery ratio, throughput and minimizes routing overheads.

Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game

Science.gov (United States)

Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam

2018-03-01

In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.

Differential harmony search algorithm to optimize PWRs loading pattern

Energy Technology Data Exchange (ETDEWEB)

Poursalehi, N., E-mail: npsalehi@yahoo.com [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of); Zolfaghari, A.; Minuchehr, A. [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of)

2013-04-15

Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms.

Differential harmony search algorithm to optimize PWRs loading pattern

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.

2013-01-01

Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

Science.gov (United States)

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

Directory of Open Access Journals (Sweden)

Yuanfu Mo

Full Text Available In a vehicular ad hoc network (VANET, the periodic exchange of single-hop status information broadcasts (beacon frames produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

Replication and load balancing strategy of STAR's relational database management system (RDBM)

Energy Technology Data Exchange (ETDEWEB)

DePhillips, M; Lauret, J; Kopytine, M [Brookhaven National Laboratory, Upton NY 11973 (United States); Kent State University, Kent Ohio 44242 (United States)], E-mail: jlauret@bnl.gov

2008-07-15

Database demand resulting from offline analysis and production of data at the STAR experiment at Brookhaven National Laboratory's Relativistic Heavy-Ion Collider has steadily increased over the last six years of data taking activities. With each year, STAR more than doubles the number of events recorded with an anticipation of reaching a billion event capabilities as early as next year. The challenges faced from producing and analyzing this magnitude of events in parallel have raised issues with regard to the distribution of calibrations and geometry data, via databases, to STAR's growing global collaboration. Rapid distribution, availability, ensured synchronization and load balancing have become paramount considerations. Both conventional technology and novel approaches are used in parallel to realize these goals. This paper discusses how STAR uses load balancing to optimize database usage. It discusses distribution methods via MySQL master slave replication; the synchronization issues that arise from this type of distribution and solutions, mostly homegrown, put forth to overcome these issues. A novel approach toward load balancing between slave nodes that assists in maintaining a high availability rate for a veracious community is discussed in detail. This load balancing addresses both, pools of nodes internal to a given location, as well as balancing the load for remote users between different available locations. Challenges, trade-offs, rationale for decisions and paths forward will be discussed in all cases, presenting a solid production environment with a vision for scalable growth.

Analisis Perbandingan Load Balancing Web Server Tunggal Dengan Web Server Cluster Menggunakan Linux Virtual Server

OpenAIRE

Lukitasari, Desy; Oklilas, Ahmad Fali

2010-01-01

Virtual server adalah server yang mempunyai skalabilitas dan ketersedian yang tinggi yang dibangun diatas sebuah cluster dari beberapa real server. Real server dan load balancer akan saling terkoneksi baik dalam jaringan lokal kecepatan tinggi atau yang terpisah secara geografis. Load balancer dapat mengirim permintaan-permintaan ke server yang berbeda dan membuat paralel service dari sebuah cluster pada sebuah alamat IP tunggal dan meminta pengiriman dapat menggunakan teknologi IP load...

Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

Directory of Open Access Journals (Sweden)

Ahmad M. Manasrah

2018-01-01

Full Text Available Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

K-means clustering for optimal partitioning and dynamic load balancing of parallel hierarchical N-body simulations

International Nuclear Information System (INIS)

Marzouk, Youssef M.; Ghoniem, Ahmed F.

2005-01-01

A number of complex physical problems can be approached through N-body simulation, from fluid flow at high Reynolds number to gravitational astrophysics and molecular dynamics. In all these applications, direct summation is prohibitively expensive for large N and thus hierarchical methods are employed for fast summation. This work introduces new algorithms, based on k-means clustering, for partitioning parallel hierarchical N-body interactions. We demonstrate that the number of particle-cluster interactions and the order at which they are performed are directly affected by partition geometry. Weighted k-means partitions minimize the sum of clusters' second moments and create well-localized domains, and thus reduce the computational cost of N-body approximations by enabling the use of lower-order approximations and fewer cells. We also introduce compatible techniques for dynamic load balancing, including adaptive scaling of cluster volumes and adaptive redistribution of cluster centroids. We demonstrate the performance of these algorithms by constructing a parallel treecode for vortex particle simulations, based on the serial variable-order Cartesian code developed by Lindsay and Krasny [Journal of Computational Physics 172 (2) (2001) 879-907]. The method is applied to vortex simulations of a transverse jet. Results show outstanding parallel efficiencies even at high concurrencies, with velocity evaluation errors maintained at or below their serial values; on a realistic distribution of 1.2 million vortex particles, we observe a parallel efficiency of 98% on 1024 processors. Excellent load balance is achieved even in the face of several obstacles, such as an irregular, time-evolving particle distribution containing a range of length scales and the continual introduction of new vortex particles throughout the domain. Moreover, results suggest that k-means yields a more efficient partition of the domain than a global oct-tree

A Balancing Algorithm in Wireless Sensor Network Based on the Assistance of Approaching Nodes

Directory of Open Access Journals (Sweden)

Chengpei Tang

2013-03-01

Full Text Available Sensor node in wireless sensor network is a micro-embedded system with limited memory, energy and communication capabilities. Some nodes will run out of energy and exit the network earlier than other nodes because of the uneven energy consumption. This will lead to partial or complete paralysis of the whole wireless sensor network. A balancing algorithm based on the assistance of approaching nodes is proposed. Via the set theory, notes are divided into neighbor nodes set and approaching nodes set. Approaching nodes will help weaker nodes forward part of massages to balance energy consumption. Simulation result has verified the rationality and feasibility of the balancing algorithm.

Load balancing in highly parallel processing of Monte Carlo code for particle transport

International Nuclear Information System (INIS)

Higuchi, Kenji; Takemiya, Hiroshi; Kawasaki, Takuji

1998-01-01

In parallel processing of Monte Carlo (MC) codes for neutron, photon and electron transport problems, particle histories are assigned to processors making use of independency of the calculation for each particle. Although we can easily parallelize main part of a MC code by this method, it is necessary and practically difficult to optimize the code concerning load balancing in order to attain high speedup ratio in highly parallel processing. In fact, the speedup ratio in the case of 128 processors remains in nearly one hundred times when using the test bed for the performance evaluation. Through the parallel processing of the MCNP code, which is widely used in the nuclear field, it is shown that it is difficult to attain high performance by static load balancing in especially neutron transport problems, and a load balancing method, which dynamically changes the number of assigned particles minimizing the sum of the computational and communication costs, overcomes the difficulty, resulting in nearly fifteen percentage of reduction for execution time. (author)

Multicycle Optimization of Advanced Gas-Cooled Reactor Loading Patterns Using Genetic Algorithms

International Nuclear Information System (INIS)

Ziver, A. Kemal; Carter, Jonathan N.; Pain, Christopher C.; Oliveira, Cassiano R.E. de; Goddard, Antony J. H.; Overton, Richard S.

2003-01-01

A genetic algorithm (GA)-based optimizer (GAOPT) has been developed for in-core fuel management of advanced gas-cooled reactors (AGRs) at HINKLEY B and HARTLEPOOL, which employ on-load and off-load refueling, respectively. The optimizer has been linked to the reactor analysis code PANTHER for the automated evaluation of loading patterns in a two-dimensional geometry, which is collapsed from the three-dimensional reactor model. GAOPT uses a directed stochastic (Monte Carlo) algorithm to generate initial population members, within predetermined constraints, for use in GAs, which apply the standard genetic operators: selection by tournament, crossover, and mutation. The GAOPT is able to generate and optimize loading patterns for successive reactor cycles (multicycle) within acceptable CPU times even on single-processor systems. The algorithm allows radial shuffling of fuel assemblies in a multicycle refueling optimization, which is constructed to aid long-term core management planning decisions. This paper presents the application of the GA-based optimization to two AGR stations, which apply different in-core management operational rules. Results obtained from the testing of GAOPT are discussed

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

Directory of Open Access Journals (Sweden)

Ailian Jiang

2018-03-01

Full Text Available Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs. This paper investigates the existing ant colony optimization (ACO-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

Investigation on the improvement of genetic algorithm for PWR loading pattern search and its benchmark verification

International Nuclear Information System (INIS)

Li Qianqian; Jiang Xiaofeng; Zhang Shaohong

2009-01-01

In this study, the age technique, the concepts of relativeness degree and worth function are exploited to improve the performance of genetic algorithm (GA) for PWR loading pattern search. Among them, the age technique endows the algorithm be capable of learning from previous search 'experience' and guides it to do a better search in the vicinity ora local optimal; the introduction of the relativeness degree checks the relativeness of two loading patterns before performing crossover between them, which can significantly reduce the possibility of prematurity of the algorithm; while the application of the worth function makes the algorithm be capable of generating new loading patterns based on the statistics of common features of evaluated good loading patterns. Numerical verification against a loading pattern search benchmark problem ora two-loop reactor demonstrates that the adoption of these techniques is able to significantly enhance the efficiency of the genetic algorithm while improves the quality of the final solution as well. (authors)

Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model

International Nuclear Information System (INIS)

Hong, W.-C.

2009-01-01

Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS)

Energy management based on AM/FM/GIS for phase balancing application on distribution systems

International Nuclear Information System (INIS)

Kuo, C.-C.; Chao, Y.-T.

2010-01-01

Unbalanced feeder systems may deteriorate of power quality, and increase investment and operating costs for distribution systems. The phase swapping method recommends re-phasing circuits of laterals and transformers for making the current and voltage balanced and also reducing feeder loss and voltage drop. In this paper, an application program, based on the automated mapping/facilities management/ geographic information system (AM/FM/GIS) is developed which contains a function of automatically computing Z-bus, load flow and phase balancing of feeders with phase swapping by using easily selecting of feeder on a graphic user interface application. Therefore, the relative data need not be keyed in manually. The presented phase balancing algorithm are based on database and related data structures developed on the AM/FM/GIS and Visual Basic software, which can be integrated into the Tai-Power distribution automation system. Then, the phase balancing algorithm is designed to handle the load patterns and the load of transformers that connect specific feeders when evaluating phase swaps that will result in reduced daily circuit losses over all load points. The proposed application not only can effectively reduce the circuit loss and improves phase balancing, but also enhances power quality efficiently under easily using environment.

Enhanced method of fast re-routing with load balancing in software-defined networks

Science.gov (United States)

Lemeshko, Oleksandr; Yeremenko, Oleksandra

2017-11-01

A two-level method of fast re-routing with load balancing in a software-defined network (SDN) is proposed. The novelty of the method consists, firstly, in the introduction of a two-level hierarchy of calculating the routing variables responsible for the formation of the primary and backup paths, and secondly, in ensuring a balanced load of the communication links of the network, which meets the requirements of the traffic engineering concept. The method provides implementation of link, node, path, and bandwidth protection schemes for fast re-routing in SDN. The separation in accordance with the interaction prediction principle along two hierarchical levels of the calculation functions of the primary (lower level) and backup (upper level) routes allowed to abandon the initial sufficiently large and nonlinear optimization problem by transiting to the iterative solution of linear optimization problems of half the dimension. The analysis of the proposed method confirmed its efficiency and effectiveness in terms of obtaining optimal solutions for ensuring balanced load of communication links and implementing the required network element protection schemes for fast re-routing in SDN.

Comparison of optimization of loading patterns on the basis of SA and PMA algorithms

International Nuclear Information System (INIS)

Beliczai, Botond

2007-01-01

Optimization of loading patterns is a very important task from economical point of view in a nuclear power plant. The optimization algorithms used for this purpose can be categorized basically into two categories: deterministic ones and stochastic ones. In the Paks nuclear power plant a deterministic optimization procedure is used to optimize the loading pattern at BOC, so that the core would have maximal reactivity reserve. To the group of stochastic optimization procedures belong mainly simulated annealing (SA) procedures and genetic algorithms (GA). There are new procedures as well, which try to combine the advantages of SAs and GAs. One of them is called population mutation annealing algorithm (PMA). In the Paks NPP we would like to introduce fuel assemblies including burnable poison (Gd) in the near future. In order to be able to find the optimal loading pattern (or near-optimal loading patterns) in that case, we have to optimize our core not only for objective functions defined at BOC, but at EOC as well. For this purpose I used stochastic algorithms (SA and PMA) to investigate loading pattern optimization results for different objective functions at BOC. (author)

Performance evaluation of Genetic Algorithms on loading pattern optimization of PWRs

International Nuclear Information System (INIS)

Tombakoglu, M.; Bekar, K.B.; Erdemli, A.O.

2001-01-01

Genetic Algorithm (GA) based systems are used for search and optimization problems. There are several applications of GAs in literature successfully applied for loading pattern optimization problems. In this study, we have selected loading pattern optimization problem of Pressurised Water Reactor (PWR). The main objective of this work is to evaluate the performance of Genetic Algorithm operators such as regional crossover, crossover and mutation, and selection and construction of initial population and its size for PWR loading pattern optimization problems. The performance of GA with antithetic variates is compared to traditional GA. Antithetic variates are used to generate the initial population and its use with GA operators are also discussed. Finally, the results of multi-cycle optimization problems are discussed for objective function taking into account cycle burn-up and discharge burn-up.(author)

云计算环境下的DPSO资源负载均衡算法%DPSO resource load balancing in cloud computing

Institute of Scientific and Technical Information of China (English)

冯小靖; 潘郁

2013-01-01

Load balancing problem is one of the hot issues in cloud computing. Discrete particle swarm optimization algoritm is used to research load balancing on cloud computing environment. According to dynamic change of resources demand and low require of servers, each resource management node servers as node of the topological structure, and this paper establishes appropriate resource-task model which is resolved by DPSO. Verification results show that the algorithm enhances the utilization ratio and load balancing of resources.%负载均衡问题是云计算研究的热点问题之一.运用离散粒子群算法对云计算环境下的负载均衡问题进行研究,根据云计算环境下资源需求动态变化,并且对资源节点服务器的要求较低的特点,把各个资源节点当做网络拓扑结构中的各个节点,建立相应的资源-任务分配模型,运用离散粒子群算法实现资源负载均衡.验证表明,该算法提高了资源利用率和云计算资源的负载均衡.

Portable Parallel Programming for the Dynamic Load Balancing of Unstructured Grid Applications

Science.gov (United States)

Biswas, Rupak; Das, Sajal K.; Harvey, Daniel; Oliker, Leonid

1999-01-01

The ability to dynamically adapt an unstructured -rid (or mesh) is a powerful tool for solving computational problems with evolving physical features; however, an efficient parallel implementation is rather difficult, particularly from the view point of portability on various multiprocessor platforms We address this problem by developing PLUM, tin automatic anti architecture-independent framework for adaptive numerical computations in a message-passing environment. Portability is demonstrated by comparing performance on an SP2, an Origin2000, and a T3E, without any code modifications. We also present a general-purpose load balancer that utilizes symmetric broadcast networks (SBN) as the underlying communication pattern, with a goal to providing a global view of system loads across processors. Experiments on, an SP2 and an Origin2000 demonstrate the portability of our approach which achieves superb load balance at the cost of minimal extra overhead.

Multi-Layer Mobility Load Balancing in a Heterogeneous LTE Network

DEFF Research Database (Denmark)

Fotiadis, Panagiotis; Polignano, Michele; Laselva, Daniela

2012-01-01

This paper analyzes the behavior of a distributed Mobility Load Balancing (MLB) scheme in a multi-layer 3GPP (3rd Generation Partnership Project) Long Term Evolution (LTE) deployment with different User Equipment (UE) densities in certain network areas covered with pico cells. Target of the study...

Angular-contact ball-bearing internal load estimation algorithm using runtime adaptive relaxation

Science.gov (United States)

Medina, H.; Mutu, R.

2017-07-01

An algorithm to estimate internal loads for single-row angular contact ball bearings due to externally applied thrust loads and high-operating speeds is presented. A new runtime adaptive relaxation procedure and blending function is proposed which ensures algorithm stability whilst also reducing the number of iterations needed to reach convergence, leading to an average reduction in computation time in excess of approximately 80%. The model is validated based on a 218 angular contact bearing and shows excellent agreement compared to published results.

Generation Following with Thermostatically Controlled Loads via Alternating Direction Method of Multipliers Sharing Algorithm

OpenAIRE

Burger, Eric M.; Moura, Scott J.

2015-01-01

A fundamental requirement of the electric power system is to maintain a continuous and instantaneous balance between generation and load. The intermittency and uncertainty introduced by renewable energy generation requires the expansion of ancillary power system services to maintain such a balance. In this paper, we examine the potential of thermostatically controlled loads (TCLs), such as refrigerators and electric water heaters, to provide generation following services in real-time energy m...

Artificial bee colony algorithm for economic load dispatch with wind power energy

Directory of Open Access Journals (Sweden)

Safari Amin

2016-01-01

Full Text Available This paper presents an efficient Artificial Bee Colony (ABC algorithm for solving large scale economic load dispatch (ELD problems in power networks. To realize the ELD, the valve-point loading effect, system load demand, power losses, ramp rate limits and prohibited operation zones are considered here. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with two forms of power systems, one power system is with a wind power generator and other power system is without a wind power generator. The results of this study reveal that the proposed approach is able to find appreciable ELD solutions than those of previous algorithms.

Load Balanced Mapping of Distributed Objects to Minimize Network Communication

NARCIS (Netherlands)

Stoyenko, Alexander D.; Bosch, J.; Bosch, Jan; Aksit, Mehmet; Marlowe, Thomas J.

1996-01-01

This paper introduces a new load balancing and communica- tion minimizing heuristic used in the Inverse Remote Procedure Call (IRPC) system. While the paper briefly describes the IRPC system, the focus is on the new IRPC assignment heuristic. The IRPC compiler maps a distributed program to a graph

STAR load balancing and tiered-storage infrastructure strategy for ultimate db access

International Nuclear Information System (INIS)

Arkhipkin, D; Lauret, J; Betts, W; Didenko, L; Van Buren, G

2011-01-01

In recent years, the STAR experiment's database demands have grown in accord not only with simple facility growth, but also with a growing physics program. In addition to the accumulated metadata from a decade of operations, refinements to detector calibrations force user analysis to access database information post data production. Users may access any year's data at any point in time, causing a near random access of the metadata queried, contrary to time-organized production cycles. Moreover, complex online event selection algorithms created a query scarcity ( s parsity ) scenario for offline production further impacting performance. Fundamental changes in our hardware approach were hence necessary to improve query speed. Initial strategic improvements were focused on developing fault-tolerant, load-balanced access to a multi-slave infrastructure. Beyond that, we explored, tested and quantified the benefits of introducing a Tiered storage architecture composed of conventional drives, solid-state disks, and memory-resident databases as well as leveraging the use of smaller database services fitting in memory. The results of our extensive testing in real life usage are presented.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Directory of Open Access Journals (Sweden)

Ghulam Hafeez

2018-03-01

Full Text Available With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV units to reduce electricity cost and peak to average ratio (PAR in demand-side management. For this purpose, we adopted genetic algorithm (GA, binary particle swarm optimization (BPSO, wind-driven optimization (WDO, and our proposed genetic WDO (GWDO algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP and inclined block rate (IBR were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1 load scheduling without renewable energy sources (RESs and energy storage system (ESS, (2 load scheduling with RESs, and (3 load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

A Simple Method for Static Load Balancing of Parallel FDTD Codes

DEFF Research Database (Denmark)

Franek, Ondrej

2016-01-01

A static method for balancing computational loads in parallel implementations of the finite-difference timedomain method is presented. The procedure is fairly straightforward and computationally inexpensive, thus providing an attractive alternative to optimization techniques. The method is descri...

Transport aircraft loading and balancing system: Using a CLIPS expert system for military aircraft load planning

Science.gov (United States)

Richardson, J.; Labbe, M.; Belala, Y.; Leduc, Vincent

1994-01-01

The requirement for improving aircraft utilization and responsiveness in airlift operations has been recognized for quite some time by the Canadian Forces. To date, the utilization of scarce airlift resources has been planned mainly through the employment of manpower-intensive manual methods in combination with the expertise of highly qualified personnel. In this paper, we address the problem of facilitating the load planning process for military aircraft cargo planes through the development of a computer-based system. We introduce TALBAS (Transport Aircraft Loading and BAlancing System), a knowledge-based system designed to assist personnel involved in preparing valid load plans for the C130 Hercules aircraft. The main features of this system which are accessible through a convivial graphical user interface, consists of the automatic generation of valid cargo arrangements given a list of items to be transported, the user-definition of load plans and the automatic validation of such load plans.

Multi-Class load balancing scheme for QoS and energy ...

African Journals Online (AJOL)

Multi-Class load balancing scheme for QoS and energy conservation in cloud computing. ... If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs. Alternatively, you can download the PDF file directly to your computer, from ...

An Image Encryption Algorithm Based on Balanced Pixel and Chaotic Map

Directory of Open Access Journals (Sweden)

Jian Zhang

2014-01-01

Full Text Available Image encryption technology has been applied in many fields and is becoming the main way of protecting the image information security. There are also many ways of image encryption. However, the existing encryption algorithms, in order to obtain a better effect of encryption, always need encrypting several times. There is not an effective method to decide the number of encryption times, generally determined by the human eyes. The paper proposes an image encryption algorithm based on chaos and simultaneously proposes a balanced pixel algorithm to determine the times of image encryption. Many simulation experiments have been done including encryption effect and security analysis. Experimental results show that the proposed method is feasible and effective.

Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

Science.gov (United States)

Ulbrich, N.

2015-01-01

An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

Fatigue Load Sensitivity Based Optimal Active Power Dispatch For Wind Farms

DEFF Research Database (Denmark)

Zhao, Haoran; Wu, Qiuwei; Huang, Shaojun

2017-01-01

This paper proposes an optimal active power dispatch algorithm for wind farms based on Wind Turbine (WT) load sensitivity. The control objectives include tracking power references from the system operator and minimizing fatigue loads experienced by WTs. The sensitivity of WT fatigue loads to power...... sensitivity are derived, which significantly improves the computation efficiency of the local WT controller. The proposed algorithm can be implemented in different active power control schemes. Case studies were conducted with a wind farm under balance control for both low and high wind conditions...

An improved harmony search algorithm for power economic load dispatch

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)], E-mail: leandro.coelho@pucpr.br; Mariani, Viviana Cocco [Pontifical Catholic University of Parana, PUCPR, Department of Mechanical Engineering, PPGEM, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)], E-mail: viviana.mariani@pucpr.br

2009-10-15

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

An improved harmony search algorithm for power economic load dispatch

Energy Technology Data Exchange (ETDEWEB)

Coelho, Leandro dos Santos [Pontifical Catholic Univ. of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, PPGEPS, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil); Mariani, Viviana Cocco [Pontifical Catholic Univ. of Parana, PUCPR, Dept. of Mechanical Engineering, PPGEM, Imaculada Conceicao, 1155, 80215-901 Curitiba, PR (Brazil)

2009-10-15

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature. (author)

An improved harmony search algorithm for power economic load dispatch

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Mariani, Viviana Cocco

2009-01-01

A meta-heuristic algorithm called harmony search (HS), mimicking the improvisation process of music players, has been recently developed. The HS algorithm has been successful in several optimization problems. The HS algorithm does not require derivative information and uses stochastic random search instead of a gradient search. In addition, the HS algorithm is simple in concept, few in parameters, and easy in implementation. This paper presents an improved harmony search (IHS) algorithm based on exponential distribution for solving economic dispatch problems. A 13-unit test system with incremental fuel cost function taking into account the valve-point loading effects is used to illustrate the effectiveness of the proposed IHS method. Numerical results show that the IHS method has good convergence property. Furthermore, the generation costs of the IHS method are lower than those of the classical HS and other optimization algorithms reported in recent literature.

Cellular Genetic Algorithm with Communicating Grids for Assembly Line Balancing Problems

Directory of Open Access Journals (Sweden)

BRUDARU, O.

2010-05-01

Full Text Available This paper presents a new approach with cellular multigrid genetic algorithms for the "I"-shaped and "U"-shaped assembly line balancing problems, including parallel workstations and compatibility constraints. First, a cellular hybrid genetic algorithm that uses a single grid is described. Appropriate operators for mutation, hypermutation, and crossover and two devoration techniques are proposed for creating and maintaining groups based on similarity. This monogrid algorithm is extended for handling many populations placed on different grids. In the multigrid version, the population of each grid is organized in clusters using the positional information of the chromosomes. A similarity preserving communication protocol between the clusters placed on different grids is introduced. The experimental evaluation shows that the multigrid cellular genetic algorithm with communicating grids is better than the hybrid genetic algorithm used for building it, whereas it dominates the monogrid version in all cases. Absolute performance is evaluated using classical benchmarks. The role of certain components of the cellular algorithm is explained and the effect of some parameters is evaluated.

Application of epidemic algorithms for smart grids control

International Nuclear Information System (INIS)

Krkoleva, Aleksandra

2012-01-01

Smart Grids are a new concept for electricity networks development, aiming to provide economically efficient and sustainable power system by integrating effectively the actions and needs of the network users. The thesis addresses the Smart Grids concept, with emphasis on the control strategies developed on the basis of epidemic algorithms, more specifically, gossip algorithms. The thesis is developed around three Smart grid aspects: the changed role of consumers in terms of taking part in providing services within Smart Grids; the possibilities to implement decentralized control strategies based on distributed algorithms; and information exchange and benefits emerging from implementation of information and communication technologies. More specifically, the thesis presents a novel approach for providing ancillary services by implementing gossip algorithms. In a decentralized manner, by exchange of information between the consumers and by making decisions on local level, based on the received information and local parameters, the group achieves its global objective, i. e. providing ancillary services. The thesis presents an overview of the Smart Grids control strategies with emphasises on new strategies developed for the most promising Smart Grids concepts, as Micro grids and Virtual power plants. The thesis also presents the characteristics of epidemic algorithms and possibilities for their implementation in Smart Grids. Based on the research on epidemic algorithms, two applications have been developed. These applications are the main outcome of the research. The first application enables consumers, represented by their commercial aggregators, to participate in load reduction and consequently, to participate in balancing market or reduce the balancing costs of the group. In this context, the gossip algorithms are used for aggregator's message dissemination for load reduction and households and small commercial and industrial consumers to participate in maintaining

An Interval Bound Algorithm of optimizing reactor core loading pattern by using reactivity interval schema

International Nuclear Information System (INIS)

Gong Zhaohu; Wang Kan; Yao Dong

2011-01-01

Highlights: → We present a new Loading Pattern Optimization method - Interval Bound Algorithm (IBA). → IBA directly uses the reactivity of fuel assemblies and burnable poison. → IBA can optimize fuel assembly orientation in a coupled way. → Numerical experiment shows that IBA outperforms genetic algorithm and engineers. → We devise DDWF technique to deal with multiple objectives and constraints. - Abstract: In order to optimize the core loading pattern in Nuclear Power Plants, the paper presents a new optimization method - Interval Bound Algorithm (IBA). Similar to the typical population based algorithms, e.g. genetic algorithm, IBA maintains a population of solutions and evolves them during the optimization process. IBA acquires the solution by statistical learning and sampling the control variable intervals of the population in each iteration. The control variables are the transforms of the reactivity of fuel assemblies or the worth of burnable poisons, which are the crucial heuristic information for loading pattern optimization problems. IBA can deal with the relationship between the dependent variables by defining the control variables. Based on the IBA algorithm, a parallel Loading Pattern Optimization code, named IBALPO, has been developed. To deal with multiple objectives and constraints, the Dynamic Discontinuous Weight Factors (DDWF) for the fitness function have been used in IBALPO. Finally, the code system has been used to solve a realistic reloading problem and a better pattern has been obtained compared with the ones searched by engineers and genetic algorithm, thus the performance of the code is proved.

Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Directory of Open Access Journals (Sweden)

Zhongyi Hu

2013-01-01

Full Text Available Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA based memetic algorithm (FA-MA to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Directory of Open Access Journals (Sweden)

Ming Yang

2018-03-01

Full Text Available In this paper, an on-line parameter identification algorithm to iteratively compute the numerical values of inertia and load torque is proposed. Since inertia and load torque are strongly coupled variables due to the degenerate-rank problem, it is hard to estimate relatively accurate values for them in the cases such as when load torque variation presents or one cannot obtain a relatively accurate priori knowledge of inertia. This paper eliminates this problem and realizes ideal online inertia identification regardless of load condition and initial error. The algorithm in this paper integrates a full-order Kalman Observer and Recursive Least Squares, and introduces adaptive controllers to enhance the robustness. It has a better performance when iteratively computing load torque and moment of inertia. Theoretical sensitivity analysis of the proposed algorithm is conducted. Compared to traditional methods, the validity of the proposed algorithm is proved by simulation and experiment results.

Reimplementing the LBD DNS Load Balancer with concurrency in GO

CERN Multimedia

CERN. Geneva

2016-01-01

Using the current configuration with 430 aliases, today’s implementation of the LBD DNS Load Balancer does one cycle through all aliases in around 240 seconds. We have a scalability limit of 300 seconds - that is the update period of most aliases. This talk will present a PoC showing how the time could be reduced to just 12 seconds.

Optimal Load Control via Frequency Measurement and Neighborhood Area Communication

Energy Technology Data Exchange (ETDEWEB)

Zhao, CH; Topcu, U; Low, SH

2013-11-01

We propose a decentralized optimal load control scheme that provides contingency reserve in the presence of sudden generation drop. The scheme takes advantage of flexibility of frequency responsive loads and neighborhood area communication to solve an optimal load control problem that balances load and generation while minimizing end-use disutility of participating in load control. Local frequency measurements enable individual loads to estimate the total mismatch between load and generation. Neighborhood area communication helps mitigate effects of inconsistencies in the local estimates due to frequency measurement noise. Case studies show that the proposed scheme can balance load with generation and restore the frequency within seconds of time after a generation drop, even when the loads use a highly simplified power system model in their algorithms. We also investigate tradeoffs between the amount of communication and the performance of the proposed scheme through simulation-based experiments.

Multiobjective Economic Load Dispatch in 3-D Space by Genetic Algorithm

Science.gov (United States)

Jain, N. K.; Nangia, Uma; Singh, Iqbal

2017-10-01

This paper presents the application of genetic algorithm to Multiobjective Economic Load Dispatch (MELD) problem considering fuel cost, transmission losses and environmental pollution as objective functions. The MELD problem has been formulated using constraint method. The non-inferior set for IEEE 5, 14 and 30-bus system has been generated by using genetic algorithm and the target point has been obtained by using maximization of minimum relative attainments.

Research reactor loading pattern optimization using estimation of distribution algorithms

International Nuclear Information System (INIS)

Jiang, S.; Ziver, K.; Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H.; Franklin, S. J.; Phillips, H. J.

2006-01-01

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K eff ) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K eff with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Assessment of load of beam-balanced pumping units by electric motor power indicators

Directory of Open Access Journals (Sweden)

Д. И. Шишлянников

2017-10-01

Full Text Available The results of experimental studies on the loading of beam-balanced pumping units (BP of sucker rod- pumping equipment (SRPE are presented. It is noted that the key factor that has the most significant effect causing the SRPE failure is the balance of the beam pumping unit, which determines the amount of specific energy consumption for the rise of reservoir fluid and the level of dynamic loads on the machine units. The urgency of using software-recording systems for estimating the loading of units of oil field pumping installations is substantiated. The principle of operation and design of the «AKD-SK» software recording system is described. The prospects of using this method for controlling the performance parameters and evaluating the technical state of the sicker rod-pumping units is proved on the basis of an analysis of the magnitude and nature of the changes in the loads of drive motors determined by the registration of the instantaneous values of the consumed power. The main provisions of the methodology for analyzing the watt-meters of drive motors of the sucker rod-pumping units are outlined. The nature of the manifestation of the main defects of submersible pumps and beam-balanced pumping units is described. The results of pilot-industrial tests of the beam-balanced pumping units equipped with advanced permanent magnet motors and intelligent control stations are presented. It is proved that the use of permanent magnet motors allows to reduce the specific energy consumption for the rise of reservoir fluid, which increases the efficiency of the SRPE.However, the presence of transient processes and generator operating modes of the permanent magnet motors results in the occurrence of significant dynamic loads, which, due to the rigid fixing of the rotor of magnet motor on the reducer shaft, negatively affect the life of the gearbox bearings. It has been shown that the lack of its own bearings in the tested motors causes a high probability

Enabling high performance computational science through combinatorial algorithms

International Nuclear Information System (INIS)

Boman, Erik G; Bozdag, Doruk; Catalyurek, Umit V; Devine, Karen D; Gebremedhin, Assefaw H; Hovland, Paul D; Pothen, Alex; Strout, Michelle Mills

2007-01-01

The Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) Institute is developing algorithms and software for combinatorial problems that play an enabling role in scientific and engineering computations. Discrete algorithms will be increasingly critical for achieving high performance for irregular problems on petascale architectures. This paper describes recent contributions by researchers at the CSCAPES Institute in the areas of load balancing, parallel graph coloring, performance improvement, and parallel automatic differentiation

Enabling high performance computational science through combinatorial algorithms

Energy Technology Data Exchange (ETDEWEB)

Boman, Erik G [Discrete Algorithms and Math Department, Sandia National Laboratories (United States); Bozdag, Doruk [Biomedical Informatics, and Electrical and Computer Engineering, Ohio State University (United States); Catalyurek, Umit V [Biomedical Informatics, and Electrical and Computer Engineering, Ohio State University (United States); Devine, Karen D [Discrete Algorithms and Math Department, Sandia National Laboratories (United States); Gebremedhin, Assefaw H [Computer Science and Center for Computational Science, Old Dominion University (United States); Hovland, Paul D [Mathematics and Computer Science Division, Argonne National Laboratory (United States); Pothen, Alex [Computer Science and Center for Computational Science, Old Dominion University (United States); Strout, Michelle Mills [Computer Science, Colorado State University (United States)

2007-07-15

The Combinatorial Scientific Computing and Petascale Simulations (CSCAPES) Institute is developing algorithms and software for combinatorial problems that play an enabling role in scientific and engineering computations. Discrete algorithms will be increasingly critical for achieving high performance for irregular problems on petascale architectures. This paper describes recent contributions by researchers at the CSCAPES Institute in the areas of load balancing, parallel graph coloring, performance improvement, and parallel automatic differentiation.

A parallel neural network training algorithm for control of discrete dynamical systems.

Energy Technology Data Exchange (ETDEWEB)

Gordillo, J. L.; Hanebutte, U. R.; Vitela, J. E.

1998-01-20

In this work we present a parallel neural network controller training code, that uses MPI, a portable message passing environment. A comprehensive performance analysis is reported which compares results of a performance model with actual measurements. The analysis is made for three different load assignment schemes: block distribution, strip mining and a sliding average bin packing (best-fit) algorithm. Such analysis is crucial since optimal load balance can not be achieved because the work load information is not available a priori. The speedup results obtained with the above schemes are compared with those corresponding to the bin packing load balance scheme with perfect load prediction based on a priori knowledge of the computing effort. Two multiprocessor platforms: a SGI/Cray Origin 2000 and a IBM SP have been utilized for this study. It is shown that for the best load balance scheme a parallel efficiency of over 50% for the entire computation is achieved by 17 processors of either parallel computers.

Analysis of temperature changes on three-phase synchronous generator using infrared: comparison between balanced and unbalanced load

Science.gov (United States)

Amien, S.; Yoga, W.; Fahmi, F.

2018-02-01

Synchronous generators are a major tool in an electrical energy generating systems, the load supplied by the generator is unbalanced. This paper discusses the effect of synchronous generator temperature on the condition of balanced load and unbalanced load, which will then be compared with the measurement result of both states of the generator. Unbalanced loads can be caused by various asymmetric disturbances in the power system and the failure of load forecasting studies so that the load distribution in each phase is not the same and causing the excessive heat of the generator. The method used in data collection was by using an infrared thermometer and resistance calculation method. The temperature comparison result between the resistive, inductive and capacitive loads in the highest temperature balance occured when the generator is loaded with a resistive load, where T = 31.9 ° C and t = 65 minutes. While in a state of unbalanced load the highest temperature occured when the generator is loaded with a capacitive load, where T = 40.1 ° C and t = 60 minutes. By understanding this behavior, we can maintain the generator for longer operation life.

Advanced and flexible genetic algorithms for BWR fuel loading pattern optimization

International Nuclear Information System (INIS)

Martin-del-Campo, Cecilia; Palomera-Perez, Miguel-Angel; Francois, Juan-Luis

2009-01-01

This work proposes advances in the implementation of a flexible genetic algorithm (GA) for fuel loading pattern optimization for Boiling Water Reactors (BWRs). In order to avoid specific implementations of genetic operators and to obtain a more flexible treatment, a binary representation of the solution was implemented; this representation had to take into account that a little change in the genotype must correspond to a little change in the phenotype. An identifier number is assigned to each assembly by means of a Gray Code of 7 bits and the solution (the loading pattern) is represented by a binary chain of 777 bits of length. Another important contribution is the use of a Fitness Function which includes a Heuristic Function and an Objective Function. The Heuristic Function which is defined to give flexibility on the application of a set of positioning rules based on knowledge, and the Objective Function that contains all the parameters which qualify the neutronic and thermal hydraulic performances of each loading pattern. Experimental results illustrating the effectiveness and flexibility of this optimization algorithm are presented and discussed.

Optimal loading and protection of multi-state systems considering performance sharing mechanism

International Nuclear Information System (INIS)

Xiao, Hui; Shi, Daimin; Ding, Yi; Peng, Rui

2016-01-01

Engineering systems are designed to carry the load. The performance of the system largely depends on how much load it carries. On the other hand, the failure rate of the system is strongly affected by its load. Besides internal failures, such as fatigue and aging process, systems may also fail due to external impacts such as nature disasters and terrorism. In this paper, we integrate the effect of loading and protection of external impacts on multi-state systems with performance sharing mechanism. The objective of this research is to determine how to balance the load and protection on system elements. An availability evaluation algorithm of the proposed system is suggested and the corresponding optimization problem is solved utilizing genetic algorithms. - Highlights: • Performance sharing of multi-state systems is considered. • The effect of load on system elements is analyzed. • Joint optimization model of element loading and protection is formulated. • Genetic Algorithms are adapted to solve the reliability optimization problem.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

Science.gov (United States)

Li, Bai; Gong, Li-gang; Yang, Wen-lun

2014-01-01

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

A differential evolution algorithm for tooth profile optimization with respect to balancing specific sliding coefficients of involute cylindrical spur and helical gears

Directory of Open Access Journals (Sweden)

Hammoudi Abderazek

2015-09-01

Full Text Available Profile shift has an immense effect on the sliding, load capacity, and stability of involute cylindrical gears. Available standards such as ISO/DIS 6336 and BS 436 DIN/3990 currently give the recommendation for the selection of profile shift coefficients. It is, however, very approximate and usually given in the form of implicit graphs or charts. In this article, the optimal selection values of profile shift coefficients for cylindrical involute spur and helical gears are described, using a differential evolution algorithm. The optimization procedure is developed specifically for exact balancing specific sliding coefficients at extremes of contact path and account for gear design constraints. The obtained results are compared with those of standards and research of other authors. They demonstrate the effectiveness and robustness of the applied method. A substantial improvement in balancing specific sliding coefficients is found in this work.

Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks

Directory of Open Access Journals (Sweden)

Tao Chen

2016-01-01

Full Text Available Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs among a large pool of servers greatly affects the performance of data center networks (DCNs. As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB and then proved it to be NP-hard and designed a Longest Processing Time Based Placement algorithm (LPTBP algorithm to solve it. To take advantage of the communication locality, we proposed Locality-Aware Virtual Machine Placement Problem with Traffic-Aware Balancing (LVMPPTB, which is a multiobjective optimization problem of simultaneously minimizing the maximum number of VM partitions of requests and minimizing the maximum bandwidth occupancy on uplinks of Top of Rack (ToR switches. We also proved it to be NP-hard and designed a heuristic algorithm (Least-Load First Based Placement algorithm, LLBP algorithm to solve it. Through extensive simulations, the proposed heuristic algorithm is proven to significantly balance the bandwidth occupancy on uplinks of ToR switches, while keeping the number of VM partitions of each request small enough.

Research reactor loading pattern optimization using estimation of distribution algorithms

Energy Technology Data Exchange (ETDEWEB)

Jiang, S. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Ziver, K. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); AMCG Group, RM Consultants, Abingdon (United Kingdom); Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Franklin, S. J.; Phillips, H. J. [Imperial College, Reactor Centre, Silwood Park, Buckhurst Road, Ascot, Berkshire, SL5 7TE (United Kingdom)

2006-07-01

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K{sub eff}) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K{sub eff} with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

CERN Document Server

Korkhov, Vladimir V; Krzhizhanovskaya, Valeria V

2009-01-01

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.

Genetic algorithm for the optimization of the loading pattern for reactor core fuel management

International Nuclear Information System (INIS)

Zhou Sheng; Hu Yongming; zheng Wenxiang

2000-01-01

The paper discusses the application of a genetic algorithm to the optimization of the loading pattern for in-core fuel management with the NP characteristics. The algorithm develops a matrix model for the fuel assembly loading pattern. The burnable poisons matrix was assigned randomly considering the distributed nature of the poisons. A method based on the traveling salesman problem was used to solve the problem. A integrated code for in-core fuel management was formed by combining this code with a reactor physics code

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA for Load Profiling Applications

Directory of Open Access Journals (Sweden)

Ioannis P. Panapakidis

2018-02-01

Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

A Novel Control Strategy for Autonomous Operation of Isolated Microgrid with Prioritized Loads

Science.gov (United States)

Kumar, R. Hari; Ushakumari, S.

2018-05-01

Maintenance of power balance between generation and demand is one of the most critical requirements for the stable operation of a power system network. To mitigate the power imbalance during the occurrence of any disturbance in the system, fast acting algorithms are inevitable. This paper proposes a novel algorithm for load shedding and network reconfiguration in an isolated microgrid with prioritized loads and multiple islands, which will help to quickly restore the system in the event of a fault. The performance of the proposed algorithm is enhanced using genetic algorithm and its effectiveness is illustrated with simulation results on modified Consortium for Electric Reliability Technology Solutions (CERTS) microgrid.

PWR loading pattern optimization using Harmony Search algorithm

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.

2013-01-01

Highlights: ► Numerical results reveal that the HS method is reliable. ► The great advantage of HS is significant gain in computational cost. ► On the average, the final band width of search fitness values is narrow. ► Our experiments show that the search approaches the optimal value fast. - Abstract: In this paper a core reloading technique using Harmony Search, HS, is presented in the context of finding an optimal configuration of fuel assemblies, FA, in pressurized water reactors. To implement and evaluate the proposed technique a Harmony Search along Nodal Expansion Code for 2-D geometry, HSNEC2D, is developed to obtain nearly optimal arrangement of fuel assemblies in PWR cores. This code consists of two sections including Harmony Search algorithm and Nodal Expansion modules using fourth degree flux expansion which solves two dimensional-multi group diffusion equations with one node per fuel assembly. Two optimization test problems are investigated to demonstrate the HS algorithm capability in converging to near optimal loading pattern in the fuel management field and other subjects. Results, convergence rate and reliability of the method are quite promising and show the HS algorithm performs very well and is comparable to other competitive algorithms such as Genetic Algorithm and Particle Swarm Intelligence. Furthermore, implementation of nodal expansion technique along HS causes considerable reduction of computational time to process and analysis optimization in the core fuel management problems

An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

Directory of Open Access Journals (Sweden)

Bai Li

2014-01-01

Full Text Available Unmanned combat aerial vehicles (UCAVs have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC algorithm improved by a balance-evolution strategy (BES is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Adaptive algorithm for predicting increases in central loads of electrical energy systems

Energy Technology Data Exchange (ETDEWEB)

Arbachyauskene, N A; Pushinaytis, K V

1982-01-01

An adaptive algorithm for predicting increases in central loads of the electrical energy system is suggested for the task of evaluating the condition. The algorithm is based on the Kalman filter. In order to calculate the coefficient of intensification, the a priori assigned noise characteristics with low accuracy are used only in the beginning of the calculation. Further, the coefficient of intensification is calculated from the innovation sequence. This approach makes it possible to correct errors in the assignment of the statistical noise characteristics and to follow their changes. The algorithm is experimentally verified.

Handoff Triggering and Network Selection Algorithms for Load-Balancing Handoff in CDMA-WLAN Integrated Networks

Directory of Open Access Journals (Sweden)

Khalid Qaraqe

2008-10-01

Full Text Available This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection. The handoff trigger is decided based on the received signal strength (RSS. To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service, and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.

Handoff Triggering and Network Selection Algorithms for Load-Balancing Handoff in CDMA-WLAN Integrated Networks

Directory of Open Access Journals (Sweden)

Kim Jang-Sub

2008-01-01

Full Text Available This paper proposes a novel vertical handoff algorithm between WLAN and CDMA networks to enable the integration of these networks. The proposed vertical handoff algorithm assumes a handoff decision process (handoff triggering and network selection. The handoff trigger is decided based on the received signal strength (RSS. To reduce the likelihood of unnecessary false handoffs, the distance criterion is also considered. As a network selection mechanism, based on the wireless channel assignment algorithm, this paper proposes a context-based network selection algorithm and the corresponding communication algorithms between WLAN and CDMA networks. This paper focuses on a handoff triggering criterion which uses both the RSS and distance information, and a network selection method which uses context information such as the dropping probability, blocking probability, GoS (grade of service, and number of handoff attempts. As a decision making criterion, the velocity threshold is determined to optimize the system performance. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations. The optimal velocity threshold is adjusted to assign the available channels to the mobile stations using four handoff strategies. The four handoff strategies are evaluated and compared with each other in terms of GOS. Finally, the proposed scheme is validated by computer simulations.

Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jiang; Guo, Hanqi; Yuan, Xiaoru; Hong, Fan; Peterka, Tom

2018-01-01

Particle tracing is a fundamental technique in flow field data visualization. In this work, we present a novel dynamic load balancing method for parallel particle tracing. Specifically, we employ a constrained k-d tree decomposition approach to dynamically redistribute tasks among processes. Each process is initially assigned a regularly partitioned block along with duplicated ghost layer under the memory limit. During particle tracing, the k-d tree decomposition is dynamically performed by constraining the cutting planes in the overlap range of duplicated data. This ensures that each process is reassigned particles as even as possible, and on the other hand the new assigned particles for a process always locate in its block. Result shows good load balance and high efficiency of our method.

Load-balancing techniques for a parallel electromagnetic particle-in-cell code

Energy Technology Data Exchange (ETDEWEB)

PLIMPTON,STEVEN J.; SEIDEL,DAVID B.; PASIK,MICHAEL F.; COATS,REBECCA S.

2000-01-01

QUICKSILVER is a 3-d electromagnetic particle-in-cell simulation code developed and used at Sandia to model relativistic charged particle transport. It models the time-response of electromagnetic fields and low-density-plasmas in a self-consistent manner: the fields push the plasma particles and the plasma current modifies the fields. Through an LDRD project a new parallel version of QUICKSILVER was created to enable large-scale plasma simulations to be run on massively-parallel distributed-memory supercomputers with thousands of processors, such as the Intel Tflops and DEC CPlant machines at Sandia. The new parallel code implements nearly all the features of the original serial QUICKSILVER and can be run on any platform which supports the message-passing interface (MPI) standard as well as on single-processor workstations. This report describes basic strategies useful for parallelizing and load-balancing particle-in-cell codes, outlines the parallel algorithms used in this implementation, and provides a summary of the modifications made to QUICKSILVER. It also highlights a series of benchmark simulations which have been run with the new code that illustrate its performance and parallel efficiency. These calculations have up to a billion grid cells and particles and were run on thousands of processors. This report also serves as a user manual for people wishing to run parallel QUICKSILVER.

Load-balancing techniques for a parallel electromagnetic particle-in-cell code

International Nuclear Information System (INIS)

Plimpton, Steven J.; Seidel, David B.; Pasik, Michael F.; Coats, Rebecca S.

2000-01-01

QUICKSILVER is a 3-d electromagnetic particle-in-cell simulation code developed and used at Sandia to model relativistic charged particle transport. It models the time-response of electromagnetic fields and low-density-plasmas in a self-consistent manner: the fields push the plasma particles and the plasma current modifies the fields. Through an LDRD project a new parallel version of QUICKSILVER was created to enable large-scale plasma simulations to be run on massively-parallel distributed-memory supercomputers with thousands of processors, such as the Intel Tflops and DEC CPlant machines at Sandia. The new parallel code implements nearly all the features of the original serial QUICKSILVER and can be run on any platform which supports the message-passing interface (MPI) standard as well as on single-processor workstations. This report describes basic strategies useful for parallelizing and load-balancing particle-in-cell codes, outlines the parallel algorithms used in this implementation, and provides a summary of the modifications made to QUICKSILVER. It also highlights a series of benchmark simulations which have been run with the new code that illustrate its performance and parallel efficiency. These calculations have up to a billion grid cells and particles and were run on thousands of processors. This report also serves as a user manual for people wishing to run parallel QUICKSILVER

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

International Nuclear Information System (INIS)

Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

2008-01-01

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

Some computational challenges of developing efficient parallel algorithms for data-dependent computations in thermal-hydraulics supercomputer applications

International Nuclear Information System (INIS)

Woodruff, S.B.

1992-01-01

The Transient Reactor Analysis Code (TRAC), which features a two- fluid treatment of thermal-hydraulics, is designed to model transients in water reactors and related facilities. One of the major computational costs associated with TRAC and similar codes is calculating constitutive coefficients. Although the formulations for these coefficients are local the costs are flow-regime- or data-dependent; i.e., the computations needed for a given spatial node often vary widely as a function of time. Consequently, poor load balancing will degrade efficiency on either vector or data parallel architectures when the data are organized according to spatial location. Unfortunately, a general automatic solution to the load-balancing problem associated with data-dependent computations is not yet available for massively parallel architectures. This document discusses why developers algorithms, such as a neural net representation, that do not exhibit algorithms, such as a neural net representation, that do not exhibit load-balancing problems

MCNP load balancing and fault tolerance with PVM

International Nuclear Information System (INIS)

McKinney, G.W.

1995-01-01

Version 4A of the Monte Carlo neutron, photon, and electron transport code MCNP, developed by LANL (Los Alamos National Laboratory), supports distributed-memory multiprocessing through the software package PVM (Parallel Virtual Machine, version 3.1.4). Using PVM for interprocessor communication, MCNP can simultaneously execute a single problem on a cluster of UNIX-based workstations. This capability provided system efficiencies that exceeded 80% on dedicated workstation clusters, however, on heterogeneous or multiuser systems, the performance was limited by the slowest processor (i.e., equal work was assigned to each processor). The next public release of MCNP will provide multiprocessing enhancements that include load balancing and fault tolerance which are shown to dramatically increase multiuser system efficiency and reliability

Application of self-balanced loading test to socketed pile in weak rock

Science.gov (United States)

Cheng, Ye; Gong, Weiming; Dai, Guoliang; Wu, JingKun

2008-11-01

Method of self-balanced loading test differs from the traditional methods of pile test. The key equipment of the test is a cell. The cell specially designed is used to exert load which is placed in pile body. During the test, displacement values of the top plate and the bottom plate of the cell are recorded according to every level of load. So Q-S curves can be obtained. In terms of test results, the bearing capacity of pile can be judged. Equipments of the test are simply and cost of it is low. Under some special conditions, the method will take a great advantage. In Guangxi Province, tertiary mudstone distributes widely which is typical weak rock. It is usually chosen as the bearing stratum of pile foundation. In order to make full use of its high bearing capacity, pile is generally designed as belled pile. Foundations of two high-rise buildings which are close to each other are made up of belled socketed piles in weak rock. To obtain the bearing capacity of the belled socketed pile in weak rock, loading test in situ should be taken since it is not reasonable that experimental compression strength of the mudstone is used for design. The self-balanced loading test was applied to eight piles of two buildings. To get the best test effect, the assembly of cell should be taken different modes in terms of the depth that pile socketed in rock and the dimension of the enlarged toe. The assembly of cells had been taken three modes, and tests were carried on successfully. By the self-balanced loading test, the large bearing capacities of belled socketed piles were obtained. Several key parameters required in design were achieved from the tests. For the data of tests had been analyzed, the bearing performance of pile tip, pile side and whole pile was revealed. It is further realized that the bearing capacity of belled socketed pile in the mudstone will decrease after the mudstone it socketed in has been immerged. Among kinds of mineral ingredient in the mudstone

Physics Based Model for Cryogenic Chilldown and Loading. Part I: Algorithm

Science.gov (United States)

Luchinsky, Dmitry G.; Smelyanskiy, Vadim N.; Brown, Barbara

2014-01-01

We report the progress in the development of the physics based model for cryogenic chilldown and loading. The chilldown and loading is model as fully separated non-equilibrium two-phase flow of cryogenic fluid thermally coupled to the pipe walls. The solution follow closely nearly-implicit and semi-implicit algorithms developed for autonomous control of thermal-hydraulic systems developed by Idaho National Laboratory. A special attention is paid to the treatment of instabilities. The model is applied to the analysis of chilldown in rapid loading system developed at NASA-Kennedy Space Center. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The numerical predictions are in reasonable agreement with the experimental time traces. The obtained results pave the way to the development of autonomous loading operation on the ground and space.

PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS

KAUST Repository

Prudencio, Ernesto; Cheung, Sai Hung

2012-01-01

In recent years, Bayesian model updating techniques based on measured data have been applied to many engineering and applied science problems. At the same time, parallel computational platforms are becoming increasingly more powerful and are being used more frequently by the engineering and scientific communities. Bayesian techniques usually require the evaluation of multi-dimensional integrals related to the posterior probability density function (PDF) of uncertain model parameters. The fact that such integrals cannot be computed analytically motivates the research of stochastic simulation methods for sampling posterior PDFs. One such algorithm is the adaptive multilevel stochastic simulation algorithm (AMSSA). In this paper we discuss the parallelization of AMSSA, formulating the necessary load balancing step as a binary integer programming problem. We present a variety of results showing the effectiveness of load balancing on the overall performance of AMSSA in a parallel computational environment.

Performance of Estimation of distribution algorithm for initial core loading optimization of AHWR-LEU

International Nuclear Information System (INIS)

Thakur, Amit; Singh, Baltej; Gupta, Anurag; Duggal, Vibhuti; Bhatt, Kislay; Krishnani, P.D.

2016-01-01

Highlights: • EDA has been applied to optimize initial core of AHWR-LEU. • Suitable value of weighing factor ‘α’ and population size in EDA was estimated. • The effect of varying initial distribution function on optimized solution was studied. • For comparison, Genetic algorithm was also applied. - Abstract: Population based evolutionary algorithms now form an integral part of fuel management in nuclear reactors and are frequently being used for fuel loading pattern optimization (LPO) problems. In this paper we have applied Estimation of distribution algorithm (EDA) to optimize initial core loading pattern (LP) of AHWR-LEU. In EDA, new solutions are generated by sampling the probability distribution model estimated from the selected best candidate solutions. The weighing factor ‘α’ decides the fraction of current best solution for updating the probability distribution function after each generation. A wider use of EDA warrants a comprehensive study on parameters like population size, weighing factor ‘α’ and initial probability distribution function. In the present study, we have done an extensive analysis on these parameters (population size, weighing factor ‘α’ and initial probability distribution function) in EDA. It is observed that choosing a very small value of ‘α’ may limit the search of optimized solutions in the near vicinity of initial probability distribution function and better loading patterns which are away from initial distribution function may not be considered with due weightage. It is also observed that increasing the population size improves the optimized loading pattern, however the algorithm still fails if the initial distribution function is not close to the expected optimized solution. We have tried to find out the suitable values for ‘α’ and population size to be considered for AHWR-LEU initial core loading pattern optimization problem. For sake of comparison and completeness, we have also addressed the

ComprehensiveBench: a Benchmark for the Extensive Evaluation of Global Scheduling Algorithms

Science.gov (United States)

Pilla, Laércio L.; Bozzetti, Tiago C.; Castro, Márcio; Navaux, Philippe O. A.; Méhaut, Jean-François

2015-10-01

Parallel applications that present tasks with imbalanced loads or complex communication behavior usually do not exploit the underlying resources of parallel platforms to their full potential. In order to mitigate this issue, global scheduling algorithms are employed. As finding the optimal task distribution is an NP-Hard problem, identifying the most suitable algorithm for a specific scenario and comparing algorithms are not trivial tasks. In this context, this paper presents ComprehensiveBench, a benchmark for global scheduling algorithms that enables the variation of a vast range of parameters that affect performance. ComprehensiveBench can be used to assist in the development and evaluation of new scheduling algorithms, to help choose a specific algorithm for an arbitrary application, to emulate other applications, and to enable statistical tests. We illustrate its use in this paper with an evaluation of Charm++ periodic load balancers that stresses their characteristics.

Security-Oriented and Load-Balancing Wireless Data Routing Game in the Integration of Advanced Metering Infrastructure Network in Smart Grid

Energy Technology Data Exchange (ETDEWEB)

He, Fulin; Cao, Yang; Zhang, Jun Jason; Wei, Jiaolong; Zhang, Yingchen; Muljadi, Eduard; Gao, Wenzhong

2016-11-21

Ensuring flexible and reliable data routing is indispensable for the integration of Advanced Metering Infrastructure (AMI) networks, we propose a secure-oriented and load-balancing wireless data routing scheme. A novel utility function is designed based on security routing scheme. Then, we model the interactive security-oriented routing strategy among meter data concentrators or smart grid meters as a mixed-strategy network formation game. Finally, such problem results in a stable probabilistic routing scheme with proposed distributed learning algorithm. One contributions is that we studied that different types of applications affect the routing selection strategy and the strategy tendency. Another contributions is that the chosen strategy of our mixed routing can adaptively to converge to a new mixed strategy Nash equilibrium (MSNE) during the learning process in the smart grid.

Accelerating Multiple Compound Comparison Using LINGO-Based Load-Balancing Strategies on Multi-GPUs.

Science.gov (United States)

Lin, Chun-Yuan; Wang, Chung-Hung; Hung, Che-Lun; Lin, Yu-Shiang

2015-01-01

Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O(n (2)), where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more and more compounds have been synthesized and extracted now, even more than tens of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (seen as a multiple compound comparison problem, abbreviated to MCC). The intrinsic time complexity of MCC problem is O(k (2) n (2)) with k compounds of maximal length n. In this paper, we propose a GPU-based algorithm for MCC problem, called CUDA-MCC, on single- and multi-GPUs. Four LINGO-based load-balancing strategies are considered in CUDA-MCC in order to accelerate the computation speed among thread blocks on GPUs. CUDA-MCC was implemented by C+OpenMP+CUDA. CUDA-MCC achieved 45 times and 391 times faster than its CPU version on a single NVIDIA Tesla K20m GPU card and a dual-NVIDIA Tesla K20m GPU card, respectively, under the experimental results.

An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems

Directory of Open Access Journals (Sweden)

R Subramanian

2013-12-01

Full Text Available The Economic Load Dispatch (ELD problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA, for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA, Differential Evolution (DE, Particle swarm optimization (PSO, Artificial Bee Colony optimization (ABC, Biogeography-Based Optimization (BBO, Bacterial Foraging optimization (BFO, Firefly Algorithm (FA techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

Science.gov (United States)

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

A 10 bit 200 MS/s pipeline ADC using loading-balanced architecture in 0.18 μm CMOS

Science.gov (United States)

Wang, Linfeng; Meng, Qiao; Zhi, Hao; Li, Fei

2017-07-01

A new loading-balanced architecture for high speed and low power consumption pipeline analog-to-digital converter (ADC) is presented in this paper. The proposed ADC uses SHA-less, op-amp and capacitor-sharing technique, capacitor-scaling scheme to reduce the die area and power consumption. A new capacitor-sharing scheme was proposed to cancel the extra reset phase of the feedback capacitors. The non-standard inter-stage gain increases the feedback factor of the first stage and makes it equal to the second stage, by which, the load capacitor of op-amp shared by the first and second stages is balanced. As for the fourth stage, the capacitor and op-amp no longer scale down. From the system’s point of view, all load capacitors of the shared OTAs are balanced by employing a loading-balanced architecture. The die area and power consumption are optimized maximally. The ADC is implemented in a 0.18 μm 1P6M CMOS technology, and occupies a die area of 1.2 × 1.2 mm{}2. The measurement results show a 55.58 dB signal-to-noise-and-distortion ratio (SNDR) and 62.97 dB spurious-free dynamic range (SFDR) with a 25 MHz input operating at a 200 MS/s sampling rate. The proposed ADC consumes 115 mW at 200 MS/s from a 1.8 V supply.

Monte Carlo methods beyond detailed balance

NARCIS (Netherlands)

Schram, Raoul D.; Barkema, Gerard T.|info:eu-repo/dai/nl/101275080

2015-01-01

Monte Carlo algorithms are nearly always based on the concept of detailed balance and ergodicity. In this paper we focus on algorithms that do not satisfy detailed balance. We introduce a general method for designing non-detailed balance algorithms, starting from a conventional algorithm satisfying

Energy Aware Clustering Algorithms for Wireless Sensor Networks

Science.gov (United States)

Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

2011-09-01

The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

Controlling‏ ‏the Balance of Exploration and ‎Exploitation in ACO Algorithm ‎

Directory of Open Access Journals (Sweden)

Ayad ‎ Mohammed Jabbar

2018-02-01

Full Text Available Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper proposes to modify the main probabilistic method to overcome the drawbacks of the exploration problem and produces global optimal results in high dimensional space. Experiments on six variant of ant colony optimization indicate that the proposed work produces high-quality results in terms of shortest route

Development of a BWR loading pattern design system based on modified genetic algorithms and knowledge

International Nuclear Information System (INIS)

Martin-del-Campo, Cecilia; Francois, Juan Luis; Avendano, Linda; Gonzalez, Mario

2004-01-01

An optimization system based on Genetic Algorithms (GAs), in combination with expert knowledge coded in heuristics rules, was developed for the design of optimized boiling water reactor (BWR) fuel loading patterns. The system was coded in a computer program named Loading Pattern Optimization System based on Genetic Algorithms, in which the optimization code uses GAs to select candidate solutions, and the core simulator code CM-PRESTO to evaluate them. A multi-objective function was built to maximize the cycle energy length while satisfying power and reactivity constraints used as BWR design parameters. Heuristic rules were applied to satisfy standard fuel management recommendations as the Control Cell Core and Low Leakage loading strategies, and octant symmetry. To test the system performance, an optimized cycle was designed and compared against an actual operating cycle of Laguna Verde Nuclear Power Plant, Unit I

Geometric Algorithms for Private-Cache Chip Multiprocessors

DEFF Research Database (Denmark)

Ajwani, Deepak; Sitchinava, Nodari; Zeh, Norbert

2010-01-01

-D convex hulls. These results are obtained by analyzing adaptations of either the PEM merge sort algorithm or PRAM algorithms. For the second group of problems—orthogonal line segment intersection reporting, batched range reporting, and related problems—more effort is required. What distinguishes......We study techniques for obtaining efficient algorithms for geometric problems on private-cache chip multiprocessors. We show how to obtain optimal algorithms for interval stabbing counting, 1-D range counting, weighted 2-D dominance counting, and for computing 3-D maxima, 2-D lower envelopes, and 2...... these problems from the ones in the previous group is the variable output size, which requires I/O-efficient load balancing strategies based on the contribution of the individual input elements to the output size. To obtain nearly optimal algorithms for these problems, we introduce a parallel distribution...

Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

International Nuclear Information System (INIS)

Hong, Wei-Chiang

2011-01-01

Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.

Lifting an unexpectedly heavy object : the effects on low-back loading and balance loss

NARCIS (Netherlands)

van der Burg, J C; van Dieën, J H; Toussaint, H M

OBJECTIVE: This study evaluates the effects of lifting an unexpectedly heavy object on low-back loading and loss of balance. BACKGROUND: It is often suggested that lifting an unexpectedly heavy object may be a major risk factor for low-back pain. This may lead to an increase in muscle activation,

Genetic algorithms and artificial neural networks for loading pattern optimisation of advanced gas-cooled reactors

Energy Technology Data Exchange (ETDEWEB)

Ziver, A.K. E-mail: a.k.ziver@imperial.ac.uk; Pain, C.C; Carter, J.N.; Oliveira, C.R.E. de; Goddard, A.J.H.; Overton, R.S

2004-03-01

A non-generational genetic algorithm (GA) has been developed for fuel management optimisation of Advanced Gas-Cooled Reactors, which are operated by British Energy and produce around 20% of the UK's electricity requirements. An evolutionary search is coded using the genetic operators; namely selection by tournament, two-point crossover, mutation and random assessment of population for multi-cycle loading pattern (LP) optimisation. A detailed description of the chromosomes in the genetic algorithm coded is presented. Artificial Neural Networks (ANNs) have been constructed and trained to accelerate the GA-based search during the optimisation process. The whole package, called GAOPT, is linked to the reactor analysis code PANTHER, which performs fresh fuel loading, burn-up and power shaping calculations for each reactor cycle by imposing station-specific safety and operational constraints. GAOPT has been verified by performing a number of tests, which are applied to the Hinkley Point B and Hartlepool reactors. The test results giving loading pattern (LP) scenarios obtained from single and multi-cycle optimisation calculations applied to realistic reactor states of the Hartlepool and Hinkley Point B reactors are discussed. The results have shown that the GA/ANN algorithms developed can help the fuel engineer to optimise loading patterns in an efficient and more profitable way than currently available for multi-cycle refuelling of AGRs. Research leading to parallel GAs applied to LP optimisation are outlined, which can be adapted to present day LWR fuel management problems.

Accelerating Multiple Compound Comparison Using LINGO-Based Load-Balancing Strategies on Multi-GPUs

Directory of Open Access Journals (Sweden)

Chun-Yuan Lin

2015-01-01

Full Text Available Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O(n2, where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more and more compounds have been synthesized and extracted now, even more than tens of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (seen as a multiple compound comparison problem, abbreviated to MCC. The intrinsic time complexity of MCC problem is O(k2n2 with k compounds of maximal length n. In this paper, we propose a GPU-based algorithm for MCC problem, called CUDA-MCC, on single- and multi-GPUs. Four LINGO-based load-balancing strategies are considered in CUDA-MCC in order to accelerate the computation speed among thread blocks on GPUs. CUDA-MCC was implemented by C+OpenMP+CUDA. CUDA-MCC achieved 45 times and 391 times faster than its CPU version on a single NVIDIA Tesla K20m GPU card and a dual-NVIDIA Tesla K20m GPU card, respectively, under the experimental results.

A Web-Based Tool to Interpolate Nitrogen Loading Using a Genetic Algorithm

Directory of Open Access Journals (Sweden)

Youn Shik Park

2014-09-01

Full Text Available Water quality data may not be collected at a high frequency, nor over the range of streamflow data. For instance, water quality data are often collected monthly, biweekly, or weekly, since collecting and analyzing water quality samples are costly compared to streamflow data. Regression models are often used to interpolate pollutant loads from measurements made intermittently. Web-based Load Interpolation Tool (LOADIN was developed to provide user-friendly interfaces and to allow use of streamflow and water quality data from U.S. Geological Survey (USGS via web access. LOADIN has a regression model assuming that instantaneous load is comprised of the pollutant load based on streamflow and the pollutant load variation within the period. The regression model has eight coefficients determined by a genetic algorithm with measured water quality data. LOADIN was applied to eleven water quality datasets from USGS gage stations located in Illinois, Indiana, Michigan, Minnesota, and Wisconsin states with drainage areas from 44 km2 to 1,847,170 km2. Measured loads were calculated by multiplying nitrogen data by streamflow data associated with measured nitrogen data. The estimated nitrogen loads and measured loads were evaluated using Nash-Sutcliffe Efficiency (NSE and coefficient of determination (R2. NSE ranged from 0.45 to 0.91, and R2 ranged from 0.51 to 0.91 for nitrogen load estimation.

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

Science.gov (United States)

Wong, Raymond K.; Mohammed, Sabah; Fiaidhi, Jinan; Sung, Yunsick

2017-01-01

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method. PMID:28753613

Automatic boiling water reactor loading pattern design using ant colony optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Wang, C.-D. [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China); Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan (China)], E-mail: jdwang@iner.gov.tw; Lin Chaung [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China)

2009-08-15

An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.

a New Graduation Algorithm for Color Balance of Remote Sensing Image

Science.gov (United States)

Zhou, G.; Liu, X.; Yue, T.; Wang, Q.; Sha, H.; Huang, S.; Pan, Q.

2018-05-01

In order to expand the field of view to obtain more data and information when doing research on remote sensing image, workers always need to mosaicking images together. However, the image after mosaic always has the large color differences and produces the gap line. This paper based on the graduation algorithm of tarigonometric function proposed a new algorithm of Two Quarter-rounds Curves (TQC). The paper uses the Gaussian filter to solve the program about the image color noise and the gap line. The paper used one of Greenland compiled data acquired in 1963 from Declassified Intelligence Photography Project (DISP) by ARGON KH-5 satellite, and used the photography of North Gulf, China, by Landsat satellite to experiment. The experimental results show that the proposed method has improved the accuracy of the results in two parts: on the one hand, for the large color differences remote sensing image will become more balanced. On the other hands, the remote sensing image will achieve more smooth transition.

L2-LBMT: A Layered Load Balance Routing Protocol for underwater multimedia data transmission

Science.gov (United States)

Lv, Ze; Tang, Ruichun; Tao, Ye; Sun, Xin; Xu, Xiaowei

2017-12-01

Providing highly efficient underwater transmission of mass multimedia data is challenging due to the particularities of the underwater environment. Although there are many schemes proposed to optimize the underwater acoustic network communication protocols, from physical layer, data link layer, network layer to transport layer, the existing routing protocols for underwater wireless sensor network (UWSN) still cannot well deal with the problems in transmitting multimedia data because of the difficulties involved in high energy consumption, low transmission reliability or high transmission delay. It prevents us from applying underwater multimedia data to real-time monitoring of marine environment in practical application, especially in emergency search, rescue operation and military field. Therefore, the inefficient transmission of marine multimedia data has become a serious problem that needs to be solved urgently. In this paper, A Layered Load Balance Routing Protocol (L2-LBMT) is proposed for underwater multimedia data transmission. In L2-LBMT, we use layered and load-balance Ad Hoc Network to transmit data, and adopt segmented data reliable transfer (SDRT) protocol to improve the data transport reliability. And a 3-node variant of tornado (3-VT) code is also combined with the Ad Hoc Network to transmit little emergency data more quickly. The simulation results show that the proposed protocol can balance energy consumption of each node, effectively prolong the network lifetime and reduce transmission delay of marine multimedia data.

Energy Balance Routing Algorithm Based on Virtual MIMO Scheme for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jianpo Li

2014-01-01

Full Text Available Wireless sensor networks are usually energy limited and therefore an energy-efficient routing algorithm is desired for prolonging the network lifetime. In this paper, we propose a new energy balance routing algorithm which has the following three improvements over the conventional LEACH algorithm. Firstly, we propose a new cluster head selection scheme by taking into consideration the remaining energy and the most recent energy consumption of the nodes and the entire network. In this way, the sensor nodes with smaller remaining energy or larger energy consumption will be much less likely to be chosen as cluster heads. Secondly, according to the ratio of remaining energy to distance, cooperative nodes are selected to form virtual MIMO structures. It mitigates the uneven distribution of clusters and the unbalanced energy consumption of the whole network. Thirdly, we construct a comprehensive energy consumption model, which can reflect more realistically the practical energy consumption. Numerical simulations analyze the influences of cooperative node numbers and cluster head node numbers on the network lifetime. It is shown that the energy consumption of the proposed routing algorithm is lower than the conventional LEACH algorithm and for the simulation example the network lifetime is prolonged about 25%.

Particle Communication and Domain Neighbor Coupling: Scalable Domain Decomposed Algorithms for Monte Carlo Particle Transport

Energy Technology Data Exchange (ETDEWEB)

O' Brien, M. J.; Brantley, P. S.

2015-01-20

In order to run Monte Carlo particle transport calculations on new supercomputers with hundreds of thousands or millions of processors, care must be taken to implement scalable algorithms. This means that the algorithms must continue to perform well as the processor count increases. In this paper, we examine the scalability of:(1) globally resolving the particle locations on the correct processor, (2) deciding that particle streaming communication has finished, and (3) efficiently coupling neighbor domains together with different replication levels. We have run domain decomposed Monte Carlo particle transport on up to 2²¹ = 2,097,152 MPI processes on the IBM BG/Q Sequoia supercomputer and observed scalable results that agree with our theoretical predictions. These calculations were carefully constructed to have the same amount of work on every processor, i.e. the calculation is already load balanced. We also examine load imbalanced calculations where each domain’s replication level is proportional to its particle workload. In this case we show how to efficiently couple together adjacent domains to maintain within workgroup load balance and minimize memory usage.

Modification of the algorithm for steam turbine control under loading drop

International Nuclear Information System (INIS)

Nikitin, Yu.V.; Mirnyj, V.A.; Gritsenko, V.N.; Nesterov, L.V.

1989-01-01

Problem related to powerful steam turbine control in case of emergency loading drop is considered. Two laws of control creating conditions for qualitative operation of control system under conditions considered are compared. The system of turbine control comprises the turbine major actuating mechanisms (electrohydraulic transducer, high-pressure servomotor, cut-off slide valve) actuating mechanisms of pulse discharge channel (low-pressure servomotor cut-off slide valve, low-pressure servomotor) and regulator. The frequency of the turbine rotor rotation is the parameter to be controlled in the mode of loading drop. The algorithms considered are based on linear variant of the optimal control theory. One of them is realized in electrohydraulic system of the K-750-65/3000 turbine control at the Ignalinsk NPP

Impact of different disassembly line balancing algorithms on the performance of dynamic kanban system for disassembly line

Science.gov (United States)

Kizilkaya, Elif A.; Gupta, Surendra M.

2005-11-01

In this paper, we compare the impact of different disassembly line balancing (DLB) algorithms on the performance of our recently introduced Dynamic Kanban System for Disassembly Line (DKSDL) to accommodate the vagaries of uncertainties associated with disassembly and remanufacturing processing. We consider a case study to illustrate the impact of various DLB algorithms on the DKSDL. The approach to the solution, scenario settings, results and the discussions of the results are included.

Road traffic management based on self-load-balancing approach

Directory of Open Access Journals (Sweden)

Adnane Ahmed

2016-01-01

Full Text Available Traffic congestion is one of the most challenging problems for nowadays cities. Several contributions mainly based on V2V (Vehicle-to-Vehicle communication have been published, but most of them have never been applied due to their communication related problems and costs. In this article, a novel cost-effective approach is introduced inspired by social life of insects where direct (V2V communication does not exist anymore. Vehicles are equipped with devices that perform simple tasks, but their interactions with the environment through RSUs (Road Side Units allow the creation of an intelligence which notifies drivers about congested road segments to avoid them. We call this emerging behavior self-load balancing. Description of the fundamentals of this approach and its performance are detailed in this work.

Safe protocols for generating power pulses with heterogeneous populations of thermostatically controlled loads

International Nuclear Information System (INIS)

Sinitsyn, Nikolai A.; Kundu, Soumya; Backhaus, Scott

2013-01-01

Highlights: ► Algorithms to produce useful load response from a heterogeneous group of TCLs. ► Generation of sharp power pulses without initiating any unwanted oscillation. ► Open-loop methods, not requiring any detailed system modeling. ► One-way, utility-to-consumer, communication. ► Potential use in secondary frequency regulation, generation-load balancing, etc. - Abstract: We explore methods to use thermostatically controlled loads (TCLs), such as water heaters and air conditioners, to provide ancillary services by assisting in balancing generation and load. We show that by adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses of demand response without unwanted power oscillations associated with temporary synchronization of the TCL dynamics. By moving a small amount of intelligence to each of the end point TCL devices, we are able to leverage our knowledge of the time dynamics of TCLs to shape the demand response pulses for different power system applications. A significant benefit of our open-loop method is the reduction from two-way to one-way broadcast communication which also eliminates many basic consumer privacy issues. In this work, we focus on developing the algorithms to generate a set of fundamental pulse shapes that can subsequently be used to create demand response with arbitrary profiles. Demand response control methods, such as the one developed here, open the door to fast, nonperturbative control of large aggregations of TCLs

An improved Pattern Search based algorithm to solve the Dynamic Economic Dispatch problem with valve-point effect

International Nuclear Information System (INIS)

Alsumait, J.S.; Qasem, M.; Sykulski, J.K.; Al-Othman, A.K.

2010-01-01

In this paper, an improved algorithm based on Pattern Search method (PS) to solve the Dynamic Economic Dispatch is proposed. The algorithm maintains the essential unit ramp rate constraint, along with all other necessary constraints, not only for the time horizon of operation (24 h), but it preserves these constraints through the transaction period to the next time horizon (next day) in order to avoid the discontinuity of the power system operation. The Dynamic Economic and Emission Dispatch problem (DEED) is also considered. The load balance constraints, operating limits, valve-point loading and network losses are included in the models of both DED and DEED. The numerical results clarify the significance of the improved algorithm and verify its performance.

Optimal Phase Balancing Planning for Loss Reduction in Distribution Systems using a Specialized Genetic Algorithm Planeamiento Optimo de Balance de Fases para Reducción de Perdidas en Sistemas de Distribución usando un Algoritmo Genético Especializado

Directory of Open Access Journals (Sweden)

Mauricio Granada Echeverri

2012-06-01

Full Text Available Unbalanced operation of distribution systems deteriorates power quality andincreases investment and operation costs. Feeder reconfiguration and phaseswapping are the two main approaches for load balancing, being the formermore difficult to execute due to the reduced number of sectionalizing switchesavailable in most distribution systems. On the other hand, phase swappingconstitutes a direct, effective and low cost alternative for load balancing. The main contribution of this paper is the proposal of an optimization model anda solution technique for phase balancing planning in distribution systems. Asregards the optimization model, a mixed integer nonlinear programming formulationis proposed. On the other hand, the proposed solution techniqueconsists on a specialized genetic algorithm. To show the effectiveness of theproposed approach, several tests are carried out with two distribution systemsof 37 and 19 buses, this last one with different load models. Results showedthat in addition to the achievement of the primary objective of loss reduction,phase balancing allows obtaining other technical benefits such as improvementof voltage profile and alleviation of congested lines.La operación desbalanceada de los sistemas de distribución deteriora la calidad de la potencia y aumenta los costos de inversión y operación. La reconfiguración de alimentadores y el intercambio de fases son los dos principales enfoques para balance de fases, siendo el primero más difícil de llevar a cabo debido al número reducido de seccionalizadores disponibles en la mayoría de los sistemas de distribución. Por otro lado, el intercambio de fases constituye una alternativa directa, efectiva y de bajo costo para el balance de fases. La contribución principal de este artículo es la propuesta de un modelo de optimización y una técnica de solución para el planeamiento de balance de fases en sistemas de distribución. En cuanto al modelo de optimización, se

State-of-Charge Estimation and Active Cell Pack Balancing Design of Lithium Battery Power System for Smart Electric Vehicle

Directory of Open Access Journals (Sweden)

Z. C. Gao

2017-01-01

Full Text Available This paper presents an integrated state-of-charge (SOC estimation model and active cell balancing of a 12-cell lithium iron phosphate (LiFePO4 battery power system. The strong tracking cubature extended Kalman filter (STCEKF gave an accurate SOC prediction compared to other Kalman-based filter algorithms. The proposed groupwise balancing of the multiple SOC exhibited a higher balancing speed and lower balancing loss than other cell balancing designs. The experimental results demonstrated the robustness and performance of the battery when subjected to current load profile of an electric vehicle under varying ambient temperature.

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

Directory of Open Access Journals (Sweden)

Bao Wang

2012-11-01

Full Text Available The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA, generalized regression neural network (GRNN and regression model.

Artificial Immune Algorithm for Subtask Industrial Robot Scheduling in Cloud Manufacturing

Science.gov (United States)

Suma, T.; Murugesan, R.

2018-04-01

The current generation of manufacturing industry requires an intelligent scheduling model to achieve an effective utilization of distributed manufacturing resources, which motivated us to work on an Artificial Immune Algorithm for subtask robot scheduling in cloud manufacturing. This scheduling model enables a collaborative work between the industrial robots in different manufacturing centers. This paper discussed two optimizing objectives which includes minimizing the cost and load balance of industrial robots through scheduling. To solve these scheduling problems, we used the algorithm based on Artificial Immune system. The parameters are simulated with MATLAB and the results compared with the existing algorithms. The result shows better performance than existing.

A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

International Nuclear Information System (INIS)

Yu, Feng; Xu, Xiaozhong

2014-01-01

Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

Effects of feed loading on nitrogen balances and fish performance in replicated recirculating aquaculture systems

DEFF Research Database (Denmark)

Pedersen, Lars-Flemming; Suhr, Karin Isabel; Dalsgaard, Anne Johanne Tang

2012-01-01

This study investigated the effects of applying four fixed feed loadings to three replicated recirculating aquaculture systems (RAS) on water quality changes, nitrogenous balances and growth performance of rainbow trout (Oncorhynchus mykiss).Feed loadings ranged from 1.6 to 6.3kgfeed/m3 make-up...... water, with a constant make-up water renewal of 4.7% of total water volume per day in all twelve RAS. Fish densities ranged from 14 to 92kg/m3 during the prolonged trial of 10weeks. Selected water quality parameters were measured during two intensive sampling campaigns, evaluating biofilter...

A fast and robust bulk-loading algorithm for indexing very large digital elevation datasets II. Experimental results

Science.gov (United States)

Rodríguez, Félix R.; Barrena, Manuel

2011-07-01

The spatial indexing of eventually all the available topographic information of Earth is a highly valuable tool for different geoscientific application domains. The Shuttle Radar Topography Mission (SRTM) collected and made available to the public one of the world's largest digital elevation models (DEMs). With the aim of providing on easier and faster access to these data by improving their further analysis and processing, we have indexed the SRTM DEM by means of a spatial index based on the kd-tree data structure, called the Q-tree. This paper is the second in a two-part series that includes a thorough performance analysis to validate the bulk-load algorithm efficiency of the Q-tree. We investigate performance measuring elapsed time in different contexts, analyzing disk space usage, testing response time with typical queries, and validating the final index structure balance. In addition, the paper includes performance comparisons with Oracle 11g that helps to understand the real cost of our proposal. Our tests prove that the proposed algorithm outperforms Oracle 11g using around a 9% of the elapsed time, taking six times less storage with more than 96% of page utilization, and getting faster response times to spatial queries issued on 4.5 million points. In addition to this, the behavior of the spatial index has been successfully tested on both an open GIS (VT Builder) and a visualizer tool derived from the previous one.

Study of regeneration system of 300 MW power unit based on nondeaerating heat balance diagram at reduced load

Science.gov (United States)

Esin, S. B.; Trifonov, N. N.; Sukhorukov, Yu. G.; Yurchenko, A. Yu.; Grigor'eva, E. B.; Snegin, I. P.; Zhivykh, D. A.; Medvedkin, A. V.; Ryabich, V. A.

2015-09-01

More than 30 power units of thermal power stations, based on the nondeaerating heat balance diagram, successfully operate in the former Soviet Union. Most of them are power units with a power of 300 MW, equipped with HTGZ and LMZ turbines. They operate according to a variable electric load curve characterized by deep reductions when undergoing night minimums. Additional extension of the range of power unit adjustment makes it possible to maintain the dispatch load curve and obtain profit for the electric power plant. The objective of this research is to carry out estimated and experimental processing of the operating regimes of the regeneration system of steam-turbine plants within the extended adjustment range and under the conditions when the constraints on the regeneration system and its equipment are removed. Constraints concerning the heat balance diagram that reduce the power unit efficiency when extending the adjustment range have been considered. Test results are presented for the nondeaerating heat balance diagram with the HTGZ turbine. Turbine pump and feed electric pump operation was studied at a power unit load of 120-300 MW. The reliability of feed pump operation is confirmed by a stable vibratory condition and the absence of cavitation noise and vibration at a frequency that characterizes the cavitation condition, as well as by oil temperature maintenance after bearings within normal limits. Cavitation performance of pumps in the studied range of their operation has been determined. Technical solutions are proposed on providing a profitable and stable operation of regeneration systems when extending the range of adjustment of power unit load. A nondeaerating diagram of high-pressure preheater (HPP) condensate discharge to the mixer. A regeneration system has been developed and studied on the operating power unit fitted with a deaeratorless thermal circuit of the system for removing the high-pressure preheater heating steam condensate to the mixer

An Advanced Coupled Genetic Algorithm for Identifying Unknown Moving Loads on Bridge Decks

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Sang-Youl Lee

2014-01-01

Full Text Available This study deals with an inverse method to identify moving loads on bridge decks using the finite element method (FEM and a coupled genetic algorithm (c-GA. We developed the inverse technique using a coupled genetic algorithm that can make global solution searches possible as opposed to classical gradient-based optimization techniques. The technique described in this paper allows us to not only detect the weight of moving vehicles but also find their moving velocities. To demonstrate the feasibility of the method, the algorithm is applied to a bridge deck model with beam elements. In addition, 1D and 3D finite element models are simulated to study the influence of measurement errors and model uncertainty between numerical and real structures. The results demonstrate the excellence of the method from the standpoints of computation efficiency and avoidance of premature convergence.

A versatile computer package for mechanism analysis, part 2: Dynamics and balance

Science.gov (United States)

Davies, T.

The algorithms required for the shaking force components, the shaking moment about the crankshaft axis, and the input torque and bearing load components are discussed using the textile machine as a focus for the discussion. The example is also used to provide illustrations of the output for options on the hodograph of the shaking force vector. This provides estimates of the optimum contrarotating masses and their locations for a generalized primary Lanchester balancer. The suitability of generalized Lanchester balancers particularly for textile machinery, and the overall strategy used during the development of the package are outlined.

Scalable Atomistic Simulation Algorithms for Materials Research

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Aiichiro Nakano

2002-01-01

Full Text Available A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD simulations and quantum-mechanical (QM calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.

Algorithms

Indian Academy of Sciences (India)

to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...

A strategy to load balancing for non-connectivity MapReduce job

Science.gov (United States)

Zhou, Huaping; Liu, Guangzong; Gui, Haixia

2017-09-01

MapReduce has been widely used in large scale and complex datasets as a kind of distributed programming model. Original Hash partitioning function in MapReduce often results the problem of data skew when data distribution is uneven. To solve the imbalance of data partitioning, we proposes a strategy to change the remaining partitioning index when data is skewed. In Map phase, we count the amount of data which will be distributed to each reducer, then Job Tracker monitor the global partitioning information and dynamically modify the original partitioning function according to the data skew model, so the Partitioner can change the index of these partitioning which will cause data skew to the other reducer that has less load in the next partitioning process, and can eventually balance the load of each node. Finally, we experimentally compare our method with existing methods on both synthetic and real datasets, the experimental results show our strategy can solve the problem of data skew with better stability and efficiency than Hash method and Sampling method for non-connectivity MapReduce task.

Investigation of balancing problem for a planar mechanism using genetic algorithm

International Nuclear Information System (INIS)

Erkaya, Selcuk

2013-01-01

In this study, optimal balancing of a planar articulated mechanism is investigated to minimize the shaking force and moment fluctuations. Balancing of a four-bar mechanism is formulated as an optimization problem. On the other hand, an objective function based on the sub-components of shaking force and moment is constituted, and design variables consisting of kinematic and dynamic parameters are defined. Genetic algorithm is used to solve the optimization problem under the appropriate constraints. By using commercial simulation software, optimized values of design variables are also tested to evaluate the effectiveness of the proposed optimization process. This work provides a practical method for reducing the shaking force and moment fluctuations. The results show that both the structure of objective function and particularly the selection of weighting factors have a crucial role to obtain the optimum values of design parameters. By adjusting the value of weighting factor according to the relative sensitivity of the related term, there is a certain decrease at the shaking force and moment fluctuations. Moreover, these arrangements also decrease the initiative of mechanism designer on choosing the values of weighting factors.

Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem

International Nuclear Information System (INIS)

Shayeghi, H.; Mahdavi, M.; Bagheri, A.

2010-01-01

Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e. expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using discrete particle swarm optimization (DPSO) algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. The proposed idea has been tested on the Garvers network and an actual transmission network of the Azerbaijan regional electric company, Iran, and the results are compared with the decimal codification genetic algorithm (DCGA) technique. The results evaluation shows that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is superior to DCGA approach.

Algorithms for parallel flow solvers on message passing architectures

Science.gov (United States)

Vanderwijngaart, Rob F.

1995-01-01

The purpose of this project has been to identify and test suitable technologies for implementation of fluid flow solvers -- possibly coupled with structures and heat equation solvers -- on MIMD parallel computers. In the course of this investigation much attention has been paid to efficient domain decomposition strategies for ADI-type algorithms. Multi-partitioning derives its efficiency from the assignment of several blocks of grid points to each processor in the parallel computer. A coarse-grain parallelism is obtained, and a near-perfect load balance results. In uni-partitioning every processor receives responsibility for exactly one block of grid points instead of several. This necessitates fine-grain pipelined program execution in order to obtain a reasonable load balance. Although fine-grain parallelism is less desirable on many systems, especially high-latency networks of workstations, uni-partition methods are still in wide use in production codes for flow problems. Consequently, it remains important to achieve good efficiency with this technique that has essentially been superseded by multi-partitioning for parallel ADI-type algorithms. Another reason for the concentration on improving the performance of pipeline methods is their applicability in other types of flow solver kernels with stronger implied data dependence. Analytical expressions can be derived for the size of the dynamic load imbalance incurred in traditional pipelines. From these it can be determined what is the optimal first-processor retardation that leads to the shortest total completion time for the pipeline process. Theoretical predictions of pipeline performance with and without optimization match experimental observations on the iPSC/860 very well. Analysis of pipeline performance also highlights the effect of uncareful grid partitioning in flow solvers that employ pipeline algorithms. If grid blocks at boundaries are not at least as large in the wall-normal direction as those

Economic Load Dispatch Unit Pembangkit Termal Mempertimbangkan Penambahan Pembangkit Tenaga Angin Dengan Menggunakan Firefly Algorithm

Directory of Open Access Journals (Sweden)

Ridho Syahrial Ibrahim

2017-03-01

Full Text Available Maraknya isu global warming serta keterbatasan sumber daya alam membuat mulai banyaknya dibangun pembangkit-pembangkit listrik dengan renewable energy, salah satunya adalah pembangkit tenaga angin. Pada jurnal ini, firefly algorithm diterapkan untuk mengoptimasi total biaya pembangkitan 2 buah sistem uji, tanpa dan dengan mempertimbangkan penambahan tenaga angin. Hasil simulasi menunjukkan bahwa dengan penambahan pembangkit tenaga angin ke dalam sistem tenaga listrik, total biaya pembangkitan tidak selalu lebih murah. Selain itu, hasil simulasi juga menunjukkan bahwa firefly algorithm sebagai metode optimasi dapat menyelesaikan permasalahan economic load dispatch (ELD lebih baik dibandingkan metode lain yang sudah dilakukan, yaitu particle swarm optimization (PSO, bat algorithm (BA, biogeography-based optimization (BBO dan plant growth simulation algorithm (PGSA dengan persentase selisih nilai penghematan total biaya berkisar antara 0.32% ($50 hingga 9.27% ($11884.

The Financial Audit of the Enterprise’s «Balance (Financial Status Report»: Organizational-Methodical Aspects and the Algorithmization Process

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Nazarenko Inna М.

2017-04-01

Full Text Available The publication is aimed to provide scientific substantiation for the organizational-methodical aspects and the algorithmization of the financial audit process of the «Balance (financial status report». It has been proven that financial auditing through transformation has expanded the functional role of auditing. The author emphasizes that the financial audit of the «Balance (financial status report» should be directed to confirming the validity and consistency of showings in this form of financial reporting with the requirements of Ukrainian legislation, the internal regulations of the economic entities, and the expert assessment of the financial state. Financial auditing is also designed to diagnose solvency (financial sustainability, liquidity and business activity; to assess the threats of bankruptcy; to determine the worthiness and value of entrepreneur. An algorithm for financial auditing of the «Balance (financial status report» of enterprise has been developed, with organizational, research, and performance phases as constituent parts.

Automatic provisioning, deployment and orchestration for load-balancing THREDDS instances

Science.gov (United States)

Cofino, A. S.; Fernández-Tejería, S.; Kershaw, P.; Cimadevilla, E.; Petri, R.; Pryor, M.; Stephens, A.; Herrera, S.

2017-12-01

THREDDS is a widely used web server to provide to different scientific communities with data access and discovery. Due to THREDDS's lack of horizontal scalability and automatic configuration management and deployment, this service usually deals with service downtimes and time consuming configuration tasks, mainly when an intensive use is done as is usual within the scientific community (e.g. climate). Instead of the typical installation and configuration of a single or multiple independent THREDDS servers, manually configured, this work presents an automatic provisioning, deployment and orchestration cluster of THREDDS servers. This solution it's based on Ansible playbooks, used to control automatically the deployment and configuration setup on a infrastructure and to manage the datasets available in THREDDS instances. The playbooks are based on modules (or roles) of different backends and frontends load-balancing setups and solutions. The frontend load-balancing system enables horizontal scalability by delegating requests to backend workers, consisting in a variable number of instances for the THREDDS server. This implementation allows to configure different infrastructure and deployment scenario setups, as more workers are easily added to the cluster by simply declaring them as Ansible variables and executing the playbooks, and also provides fault-tolerance and better reliability since if any of the workers fail another instance of the cluster can take over it. In order to test the solution proposed, two real scenarios are analyzed in this contribution: The JASMIN Group Workspaces at CEDA and the User Data Gateway (UDG) at the Data Climate Service from the University of Cantabria. On the one hand, the proposed configuration has provided CEDA with a higher level and more scalable Group Workspaces (GWS) service than the previous one based on Unix permissions, improving also the data discovery and data access experience. On the other hand, the UDG has improved its

Kinetic-Monte-Carlo-Based Parallel Evolution Simulation Algorithm of Dust Particles

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2014-01-01

Full Text Available The evolution simulation of dust particles provides an important way to analyze the impact of dust on the environment. KMC-based parallel algorithm is proposed to simulate the evolution of dust particles. In the parallel evolution simulation algorithm of dust particles, data distribution way and communication optimizing strategy are raised to balance the load of every process and reduce the communication expense among processes. The experimental results show that the simulation of diffusion, sediment, and resuspension of dust particles in virtual campus is realized and the simulation time is shortened by parallel algorithm, which makes up for the shortage of serial computing and makes the simulation of large-scale virtual environment possible.

BALANCE

Science.gov (United States)

Carmichael, H.

1953-01-01

A torsional-type analytical balance designed to arrive at its equilibrium point more quickly than previous balances is described. In order to prevent external heat sources creating air currents inside the balance casing that would reiard the attainment of equilibrium conditions, a relatively thick casing shaped as an inverted U is placed over the load support arms and the balance beam. This casing is of a metal of good thernnal conductivity characteristics, such as copper or aluminum, in order that heat applied to one portion of the balance is quickly conducted to all other sensitive areas, thus effectively preventing the fornnation of air currents caused by unequal heating of the balance.

SPORT: An Algorithm for Divisible Load Scheduling with Result Collection on Heterogeneous Systems

Science.gov (United States)

Ghatpande, Abhay; Nakazato, Hidenori; Beaumont, Olivier; Watanabe, Hiroshi

Divisible Load Theory (DLT) is an established mathematical framework to study Divisible Load Scheduling (DLS). However, traditional DLT does not address the scheduling of results back to source (i. e., result collection), nor does it comprehensively deal with system heterogeneity. In this paper, the DLSRCHETS (DLS with Result Collection on HET-erogeneous Systems) problem is addressed. The few papers to date that have dealt with DLSRCHETS, proposed simplistic LIFO (Last In, First Out) and FIFO (First In, First Out) type of schedules as solutions to DLSRCHETS. In this paper, a new polynomial time heuristic algorithm, SPORT (System Parameters based Optimized Result Transfer), is proposed as a solution to the DLSRCHETS problem. With the help of simulations, it is proved that the performance of SPORT is significantly better than existing algorithms. The other major contributions of this paper include, for the first time ever, (a) the derivation of the condition to identify the presence of idle time in a FIFO schedule for two processors, (b) the identification of the limiting condition for the optimality of FIFO and LIFO schedules for two processors, and (c) the introduction of the concept of equivalent processor in DLS for heterogeneous systems with result collection.

An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks

International Nuclear Information System (INIS)

Gherbi, Chirihane; Aliouat, Zibouda; Benmohammed, Mohamed

2016-01-01

Clustering is a well known approach to cope with large nodes density and efficiently conserving energy in Wireless Sensor Networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Hierarchical Energy-Balancing Multipath routing protocol for Wireless Sensor Networks (HEBM) is proposed. The HEBM approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed HEBM protocol increases the profit of energy, and prolongs the network lifetime duration from 32% to 40% compared to DEEAC reference protocol and from 25% to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to FND (Fist node die), and 25

Parallel Computing Strategies for Irregular Algorithms

Science.gov (United States)

Biswas, Rupak; Oliker, Leonid; Shan, Hongzhang; Biegel, Bryan (Technical Monitor)

2002-01-01

Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Moreover, with the proliferation of parallel architectures and programming paradigms, the typical scientist is faced with a plethora of questions that must be answered in order to obtain an acceptable parallel implementation of the solution algorithm. In this paper, we consider three representative irregular applications: unstructured remeshing, sparse matrix computations, and N-body problems, and parallelize them using various popular programming paradigms on a wide spectrum of computer platforms ranging from state-of-the-art supercomputers to PC clusters. We present the underlying problems, the solution algorithms, and the parallel implementation strategies. Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel performance. Overall results demonstrate the complexity of efficiently parallelizing irregular algorithms.

Design Methodology of Strength Verification of Platform During Load Out of the Arkutun Dagi SE-Topside 43.800 MT

Directory of Open Access Journals (Sweden)

Kaup Magdalena

2016-12-01

Full Text Available This paper presents the methodology of strength verification during load out of the heavy cargo, in this case Arkutun Dagi SE-Topside platform. General methodology of making calculation models and load algorithms has been presented. Paper shows results of verification of global shear forces and bending moments using self-developed algorithms to modify centre of gravity, fill tanks and hydrostatically balance a 3D finite element model with commercial hydrostatic code. The NAPA and ANSYS codes were used to calculate hydrostatic pressures and to apply to 3D-FE models and to carry out strength calculation of barge construction.

Design and selection of load control strategies using a multiple objective model and evolutionary algorithms

International Nuclear Information System (INIS)

Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes

2005-01-01

This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)

Analysis of the type II robotic mixed-model assembly line balancing problem

Science.gov (United States)

Çil, Zeynel Abidin; Mete, Süleyman; Ağpak, Kürşad

2017-06-01

In recent years, there has been an increasing trend towards using robots in production systems. Robots are used in different areas such as packaging, transportation, loading/unloading and especially assembly lines. One important step in taking advantage of robots on the assembly line is considering them while balancing the line. On the other hand, market conditions have increased the importance of mixed-model assembly lines. Therefore, in this article, the robotic mixed-model assembly line balancing problem is studied. The aim of this study is to develop a new efficient heuristic algorithm based on beam search in order to minimize the sum of cycle times over all models. In addition, mathematical models of the problem are presented for comparison. The proposed heuristic is tested on benchmark problems and compared with the optimal solutions. The results show that the algorithm is very competitive and is a promising tool for further research.

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Directory of Open Access Journals (Sweden)

Shuyu Dai

2018-01-01

Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector

Parallel implementation and evaluation of motion estimation system algorithms on a distributed memory multiprocessor using knowledge based mappings

Science.gov (United States)

Choudhary, Alok Nidhi; Leung, Mun K.; Huang, Thomas S.; Patel, Janak H.

1989-01-01

Several techniques to perform static and dynamic load balancing techniques for vision systems are presented. These techniques are novel in the sense that they capture the computational requirements of a task by examining the data when it is produced. Furthermore, they can be applied to many vision systems because many algorithms in different systems are either the same, or have similar computational characteristics. These techniques are evaluated by applying them on a parallel implementation of the algorithms in a motion estimation system on a hypercube multiprocessor system. The motion estimation system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from different time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters. It is shown that the performance gains when these data decomposition and load balancing techniques are used are significant and the overhead of using these techniques is minimal.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

Science.gov (United States)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Production Balance of Ship Erection

Institute of Scientific and Technical Information of China (English)

JIANG Ru-hong; TAN Jia-hua; LIU Cun-gen

2008-01-01

A network plan model of ship erection was established based on the network planning technologyand the work-package breakdown system. The load-oriented production control method was introduced to buildup a throughput diagram model thus it is possible to describe the ship erection process numerically. Based onthe digitaiized models some cases of production balance of ship erection were studied and three balance indexeswere put forward, they are the load balance rate, the input manpower balance rate and the maximum gantrycrane operating times. Such an analytic method based on the balance evaluation is the important foundationfor digitization and intelligentization of shipyard production management.

Operational Strategy of CBPs for load balancing of Operators in Advanced Main Control Room

International Nuclear Information System (INIS)

Kim, Seunghwan; Kim, Yochan; Jung, Wondea

2014-01-01

With the using of a computer-based control room in an APR1400 (Advanced Pressurized Reactor-1400), the operators' behaviors in the main control room had changed. However, though the working environment of operators has been changed a great deal, digitalized interfaces can also change the cognitive tasks or activities of operators. First, a shift supervisor (SS) can confirm/check the conduction of the procedures and the execution of actions of board operators (BOs) while confirming directly the operation variables without relying on the BOs. Second, all operators added to their work the use of a new CBP and Soft Controls, increasing their procedural workload. New operational control strategies of CBPs are necessary for load balancing of operator's task load in APR1400. In this paper, we compared the workloads of operators in an APR1400 who work with two different usages of the CBP. They are SS oriented usage and SS-BO collaborative usage. In this research, we evaluated the workloads of operators in an advanced main control room by the COCOA method. Two types of CBP usages were defined and the effects of these usages on the workloads were investigated. The obtained results showed that the workloads between operators in a control room can be balanced according to the CBP usages by assigning control authority to the operators

Operational Strategy of CBPs for load balancing of Operators in Advanced Main Control Room

Energy Technology Data Exchange (ETDEWEB)

Kim, Seunghwan; Kim, Yochan; Jung, Wondea [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2014-05-15

With the using of a computer-based control room in an APR1400 (Advanced Pressurized Reactor-1400), the operators' behaviors in the main control room had changed. However, though the working environment of operators has been changed a great deal, digitalized interfaces can also change the cognitive tasks or activities of operators. First, a shift supervisor (SS) can confirm/check the conduction of the procedures and the execution of actions of board operators (BOs) while confirming directly the operation variables without relying on the BOs. Second, all operators added to their work the use of a new CBP and Soft Controls, increasing their procedural workload. New operational control strategies of CBPs are necessary for load balancing of operator's task load in APR1400. In this paper, we compared the workloads of operators in an APR1400 who work with two different usages of the CBP. They are SS oriented usage and SS-BO collaborative usage. In this research, we evaluated the workloads of operators in an advanced main control room by the COCOA method. Two types of CBP usages were defined and the effects of these usages on the workloads were investigated. The obtained results showed that the workloads between operators in a control room can be balanced according to the CBP usages by assigning control authority to the operators.

Design of a fractional order PID controller using GBMO algorithm for load-frequency control with governor saturation consideration.

Science.gov (United States)

Zamani, Abbasali; Barakati, S Masoud; Yousofi-Darmian, Saeed

2016-09-01

Load-frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load-frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load-frequency control system in confronting with model parameters variations. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

Directory of Open Access Journals (Sweden)

Wei Sun

2015-01-01

Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

Application of the largest Lyapunov exponent and non-linear fractal extrapolation algorithm to short-term load forecasting

International Nuclear Information System (INIS)

Wang Jianzhou; Jia Ruiling; Zhao Weigang; Wu Jie; Dong Yao

2012-01-01

Highlights: ► The maximal predictive step size is determined by the largest Lyapunov exponent. ► A proper forecasting step size is applied to load demand forecasting. ► The improved approach is validated by the actual load demand data. ► Non-linear fractal extrapolation method is compared with three forecasting models. ► Performance of the models is evaluated by three different error measures. - Abstract: Precise short-term load forecasting (STLF) plays a key role in unit commitment, maintenance and economic dispatch problems. Employing a subjective and arbitrary predictive step size is one of the most important factors causing the low forecasting accuracy. To solve this problem, the largest Lyapunov exponent is adopted to estimate the maximal predictive step size so that the step size in the forecasting is no more than this maximal one. In addition, in this paper a seldom used forecasting model, which is based on the non-linear fractal extrapolation (NLFE) algorithm, is considered to develop the accuracy of predictions. The suitability and superiority of the two solutions are illustrated through an application to real load forecasting using New South Wales electricity load data from the Australian National Electricity Market. Meanwhile, three forecasting models: the gray model, the seasonal autoregressive integrated moving average approach and the support vector machine method, which received high approval in STLF, are selected to compare with the NLFE algorithm. Comparison results also show that the NLFE model is outstanding, effective, practical and feasible.

Context Aware Concurrent Execution Framework for Web Browsers

DEFF Research Database (Denmark)

Saeed, Aamir; Erbad, Aiman; Olsen, Rasmus Løvenstein

2016-01-01

of load balancing algorithms. The preliminary results indicated that the performance of game improved with the proposed load-balancing across web workers. The load balancing algorithms were developed on top of DOHA, an open source JavaScript execution layer for multimedia applications. The load between...

Two Dimensional Array Based Overlay Network for Balancing Load of Peer-to-Peer Live Video Streaming

International Nuclear Information System (INIS)

Ibrahimy, Abdullah Faruq Ibn; Rafiqul, Islam Md; Anwar, Farhat; Ibrahimy, Muhammad Ibn

2013-01-01

The live video data is streaming usually in a tree-based overlay network or in a mesh-based overlay network. In case of departure of a peer with additional upload bandwidth, the overlay network becomes very vulnerable to churn. In this paper, a two dimensional array-based overlay network is proposed for streaming the live video stream data. As there is always a peer or a live video streaming server to upload the live video stream data, so the overlay network is very stable and very robust to churn. Peers are placed according to their upload and download bandwidth, which enhances the balance of load and performance. The overlay network utilizes the additional upload bandwidth of peers to minimize chunk delivery delay and to maximize balance of load. The procedure, which is used for distributing the additional upload bandwidth of the peers, distributes the additional upload bandwidth to the heterogeneous strength peers in a fair treat distribution approach and to the homogeneous strength peers in a uniform distribution approach. The proposed overlay network has been simulated by Qualnet from Scalable Network Technologies and results are presented in this paper

Two Dimensional Array Based Overlay Network for Balancing Load of Peer-to-Peer Live Video Streaming

Science.gov (United States)

Faruq Ibn Ibrahimy, Abdullah; Rafiqul, Islam Md; Anwar, Farhat; Ibn Ibrahimy, Muhammad

2013-12-01

The live video data is streaming usually in a tree-based overlay network or in a mesh-based overlay network. In case of departure of a peer with additional upload bandwidth, the overlay network becomes very vulnerable to churn. In this paper, a two dimensional array-based overlay network is proposed for streaming the live video stream data. As there is always a peer or a live video streaming server to upload the live video stream data, so the overlay network is very stable and very robust to churn. Peers are placed according to their upload and download bandwidth, which enhances the balance of load and performance. The overlay network utilizes the additional upload bandwidth of peers to minimize chunk delivery delay and to maximize balance of load. The procedure, which is used for distributing the additional upload bandwidth of the peers, distributes the additional upload bandwidth to the heterogeneous strength peers in a fair treat distribution approach and to the homogeneous strength peers in a uniform distribution approach. The proposed overlay network has been simulated by Qualnet from Scalable Network Technologies and results are presented in this paper.

Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting

Directory of Open Access Journals (Sweden)

Cheng-Wen Lee

2017-11-01

Full Text Available Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models.

Renewable Distributed Generation Models in Three-Phase Load Flow Analysis for Smart Grid

Directory of Open Access Journals (Sweden)

K. M. Nor

2013-11-01

Full Text Available The paper presents renewable distributed generation  (RDG models as three-phase resource in load flow computation and analyzes their effect when they are connected in composite networks. The RDG models that have been considered comprise of photovoltaic (PV and wind turbine generation (WTG. The voltage-controlled node and complex power injection node are used in the models. These improvement models are suitable for smart grid power system analysis. The combination of IEEE transmission and distribution data used to test and analyze the algorithm in solving balanced/unbalanced active systems. The combination of IEEE transmission data and IEEE test feeder are used to test the the algorithm for balanced and unbalanced multi-phase distribution system problem. The simulation results show that by increased number and size of RDG units have improved voltage profile and reduced system losses.

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

OpenAIRE

Thiruvenkadam, T; Karthikeyani, V

2014-01-01

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

Parallel-vector algorithms for particle simulations on shared-memory multiprocessors

International Nuclear Information System (INIS)

Nishiura, Daisuke; Sakaguchi, Hide

2011-01-01

Over the last few decades, the computational demands of massive particle-based simulations for both scientific and industrial purposes have been continuously increasing. Hence, considerable efforts are being made to develop parallel computing techniques on various platforms. In such simulations, particles freely move within a given space, and so on a distributed-memory system, load balancing, i.e., assigning an equal number of particles to each processor, is not guaranteed. However, shared-memory systems achieve better load balancing for particle models, but suffer from the intrinsic drawback of memory access competition, particularly during (1) paring of contact candidates from among neighboring particles and (2) force summation for each particle. Here, novel algorithms are proposed to overcome these two problems. For the first problem, the key is a pre-conditioning process during which particle labels are sorted by a cell label in the domain to which the particles belong. Then, a list of contact candidates is constructed by pairing the sorted particle labels. For the latter problem, a table comprising the list indexes of the contact candidate pairs is created and used to sum the contact forces acting on each particle for all contacts according to Newton's third law. With just these methods, memory access competition is avoided without additional redundant procedures. The parallel efficiency and compatibility of these two algorithms were evaluated in discrete element method (DEM) simulations on four types of shared-memory parallel computers: a multicore multiprocessor computer, scalar supercomputer, vector supercomputer, and graphics processing unit. The computational efficiency of a DEM code was found to be drastically improved with our algorithms on all but the scalar supercomputer. Thus, the developed parallel algorithms are useful on shared-memory parallel computers with sufficient memory bandwidth.

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%.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

Directory of Open Access Journals (Sweden)

Balša Terzić

2014-10-01

Full Text Available In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab’s Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

Validation of a robotic balance system for investigations in the control of human standing balance.

Science.gov (United States)

Luu, Billy L; Huryn, Thomas P; Van der Loos, H F Machiel; Croft, Elizabeth A; Blouin, Jean-Sébastien

2011-08-01

Previous studies have shown that human body sway during standing approximates the mechanics of an inverted pendulum pivoted at the ankle joints. In this study, a robotic balance system incorporating a Stewart platform base was developed to provide a new technique to investigate the neural mechanisms involved in standing balance. The robotic system, programmed with the mechanics of an inverted pendulum, controlled the motion of the body in response to a change in applied ankle torque. The ability of the robotic system to replicate the load properties of standing was validated by comparing the load stiffness generated when subjects balanced their own body to the robot's mechanical load programmed with a low (concentrated-mass model) or high (distributed-mass model) inertia. The results show that static load stiffness was not significantly (p > 0.05) different for standing and the robotic system. Dynamic load stiffness for the robotic system increased with the frequency of sway, as predicted by the mechanics of an inverted pendulum, with the higher inertia being accurately matched to the load properties of the human body. This robotic balance system accurately replicated the physical model of standing and represents a useful tool to simulate the dynamics of a standing person. © 2011 IEEE

Smart power management algorithm in microgrid consisting of photovoltaic, diesel, and battery storage plants considering variations in sunlight, temperature, and load

International Nuclear Information System (INIS)

Koohi-Kamali, Sam; Rahim, N.A.; Mokhlis, H.

2014-01-01

Highlights: • A novel power management algorithm is developed. • An effective power smoothing index is derived. • Application of battery storage in smoothing the power fluctuations is investigated. • An applicable battery sizing and designing algorithm is proposed. • An efficient battery current control algorithm is designed. - Abstract: Integration of utility scaled solar electricity generator into power networks can negatively affect the performance of next generation smartgrid. Rapidly changing output power of this kind is unpredictable and thus one solution is to mitigate it by short-term to mid-term electrical storage systems like battery. The main objective of this paper is to propose a power management system (PMS) which is capable of suppressing these adverse impacts on the main supply. A smart microgrid (MG) including diesel, battery storage, and solar plants has been suggested for this purpose. MG is able to supply its local load based on operator decision and decline the power oscillations caused by solar system together with variable loads. A guideline algorithm is also proposed which helps to precisely design the battery plant. A novel application of time domain signal processing approach to filter oscillating output power of the solar plant is presented as well. In this case, a power smoothing index (PSI) is formulated, which considers both load and generation, and used to dispatch the battery plant. A droop reference estimator to schedule generation is also introduced where diesel plant can share the local load with grid. A current control algorithm is designed as well which adjusts for PSI to ensure battery current magnitude is allowable. MG along with its communication platform and PMS are simulated using PSCAD software. PMS is tested under different scenarios using real load profiles and environmental data in Malaysia to verify the operational abilities of proposed MG. The results indicate that PMS can effectively control the MG

A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Yongquan Dong

2018-04-01

Full Text Available Providing accurate electric load forecasting results plays a crucial role in daily energy management of the power supply system. Due to superior forecasting performance, the hybridizing support vector regression (SVR model with evolutionary algorithms has received attention and deserves to continue being explored widely. The cuckoo search (CS algorithm has the potential to contribute more satisfactory electric load forecasting results. However, the original CS algorithm suffers from its inherent drawbacks, such as parameters that require accurate setting, loss of population diversity, and easy trapping in local optima (i.e., premature convergence. Therefore, proposing some critical improvement mechanisms and employing an improved CS algorithm to determine suitable parameter combinations for an SVR model is essential. This paper proposes the SVR with chaotic cuckoo search (SVRCCS model based on using a tent chaotic mapping function to enrich the cuckoo search space and diversify the population to avoid trapping in local optima. In addition, to deal with the cyclic nature of electric loads, a seasonal mechanism is combined with the SVRCCS model, namely giving a seasonal SVR with chaotic cuckoo search (SSVRCCS model, to produce more accurate forecasting performances. The numerical results, tested by using the datasets from the National Electricity Market (NEM, Queensland, Australia and the New York Independent System Operator (NYISO, NY, USA, show that the proposed SSVRCCS model outperforms other alternative models.

Parallel SN algorithms in shared- and distributed-memory environments

International Nuclear Information System (INIS)

Haghighat, Alireza; Hunter, Melissa A.; Mattis, Ronald E.

1995-01-01

Different 2-D spatial domain partitioning Sn transport theory algorithms have been developed on the basis of the Block-Jacobi iterative scheme. These algorithms have been incorporated into TWOTRAN-II, and tested on a shared-memory CRAY Y-MP C90 and a distributed-memory IBM SP1. For a series of fixed source r-z geometry homogeneous problems, parallel efficiencies in a range of 50-90% are achieved on the C90 with 6 processors, and lower values (20-60%) are obtained on the SP1. It is demonstrated that better performance is attainable if one addresses issues such as convergence rate, load-balancing, and granularity for both architectures, as well as message passing (network bandwidth and latency) for SP1. (author). 17 refs, 4 figs

A Novel Parallel Algorithm for Edit Distance Computation

Directory of Open Access Journals (Sweden)

Muhammad Murtaza Yousaf

2018-01-01

Full Text Available The edit distance between two sequences is the minimum number of weighted transformation-operations that are required to transform one string into the other. The weighted transformation-operations are insert, remove, and substitute. Dynamic programming solution to find edit distance exists but it becomes computationally intensive when the lengths of strings become very large. This work presents a novel parallel algorithm to solve edit distance problem of string matching. The algorithm is based on resolving dependencies in the dynamic programming solution of the problem and it is able to compute each row of edit distance table in parallel. In this way, it becomes possible to compute the complete table in min(m,n iterations for strings of size m and n whereas state-of-the-art parallel algorithm solves the problem in max(m,n iterations. The proposed algorithm also increases the amount of parallelism in each of its iteration. The algorithm is also capable of exploiting spatial locality while its implementation. Additionally, the algorithm works in a load balanced way that further improves its performance. The algorithm is implemented for multicore systems having shared memory. Implementation of the algorithm in OpenMP shows linear speedup and better execution time as compared to state-of-the-art parallel approach. Efficiency of the algorithm is also proven better in comparison to its competitor.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

Directory of Open Access Journals (Sweden)

Bin Xu

2014-10-01

Full Text Available The hydro unit economic load dispatch (ELD is of great importance in energy conservation and emission reduction. Dynamic programming (DP and genetic algorithm (GA are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA's performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

A novel consistent and well-balanced algorithm for simulations of multiphase flows on unstructured grids

Science.gov (United States)

Patel, Jitendra Kumar; Natarajan, Ganesh

2017-12-01

We discuss the development and assessment of a robust numerical algorithm for simulating multiphase flows with complex interfaces and high density ratios on arbitrary polygonal meshes. The algorithm combines the volume-of-fluid method with an incremental projection approach for incompressible multiphase flows in a novel hybrid staggered/non-staggered framework. The key principles that characterise the algorithm are the consistent treatment of discrete mass and momentum transport and the similar discretisation of force terms appearing in the momentum equation. The former is achieved by invoking identical schemes for convective transport of volume fraction and momentum in the respective discrete equations while the latter is realised by representing the gravity and surface tension terms as gradients of suitable scalars which are then discretised in identical fashion resulting in a balanced formulation. The hybrid staggered/non-staggered framework employed herein solves for the scalar normal momentum at the cell faces, while the volume fraction is computed at the cell centroids. This is shown to naturally lead to similar terms for pressure and its correction in the momentum and pressure correction equations respectively, which are again treated discretely in a similar manner. We show that spurious currents that corrupt the solution may arise both from an unbalanced formulation where forces (gravity and surface tension) are discretised in dissimilar manner and from an inconsistent approach where different schemes are used to convect the mass and momentum, with the latter prominent in flows which are convection-dominant with high density ratios. Interestingly, the inconsistent approach is shown to perform as well as the consistent approach even for high density ratio flows in some cases while it exhibits anomalous behaviour for other scenarios, even at low density ratios. Using a plethora of test problems of increasing complexity, we conclusively demonstrate that the

mGrid: A load-balanced distributed computing environment for the remote execution of the user-defined Matlab code

Directory of Open Access Journals (Sweden)

Almeida Jonas S

2006-03-01

Full Text Available Abstract Background Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke. Results mGrid augments the usual process distribution seen in other similar distributed systems by adding facilities for user code distribution. mGrid's client-side interface is an easy-to-use native Matlab toolbox that transparently executes user-defined code on remote machines (i.e. the user is unaware that the code is executing somewhere else. Run-time variables are automatically packed and distributed with the user-defined code and automated load-balancing of remote resources enables smooth concurrent execution. mGrid is an open source environment. Apart from the programming language itself, all other components are also open source, freely available tools: light-weight PHP scripts and the Apache web server. Conclusion Transparent, load-balanced distribution of user-defined Matlab toolboxes and rapid prototyping of many simple parallel applications can now be done with a single easy-to-use Matlab command. Because mGrid utilizes only Matlab, light-weight PHP scripts and the Apache web server, installation and configuration are very simple. Moreover, the web

mGrid: a load-balanced distributed computing environment for the remote execution of the user-defined Matlab code.

Science.gov (United States)

Karpievitch, Yuliya V; Almeida, Jonas S

2006-03-15

Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel) execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke. mGrid augments the usual process distribution seen in other similar distributed systems by adding facilities for user code distribution. mGrid's client-side interface is an easy-to-use native Matlab toolbox that transparently executes user-defined code on remote machines (i.e. the user is unaware that the code is executing somewhere else). Run-time variables are automatically packed and distributed with the user-defined code and automated load-balancing of remote resources enables smooth concurrent execution. mGrid is an open source environment. Apart from the programming language itself, all other components are also open source, freely available tools: light-weight PHP scripts and the Apache web server. Transparent, load-balanced distribution of user-defined Matlab toolboxes and rapid prototyping of many simple parallel applications can now be done with a single easy-to-use Matlab command. Because mGrid utilizes only Matlab, light-weight PHP scripts and the Apache web server, installation and configuration are very simple. Moreover, the web-based infrastructure of mGrid allows for it to be easily extensible over

Nature-inspired optimization algorithms

CERN Document Server

Yang, Xin-She

2014-01-01

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

Model-based Assessment for Balancing Privacy Requirements and Operational Capabilities

Energy Technology Data Exchange (ETDEWEB)

Knirsch, Fabian [Salzburg Univ. (Austria); Engel, Dominik [Salzburg Univ. (Austria); Frincu, Marc [Univ. of Southern California, Los Angeles, CA (United States); Prasanna, Viktor [Univ. of Southern California, Los Angeles, CA (United States)

2015-02-17

The smart grid changes the way energy is produced and distributed. In addition both, energy and information is exchanged bidirectionally among participating parties. Therefore heterogeneous systems have to cooperate effectively in order to achieve a common high-level use case, such as smart metering for billing or demand response for load curtailment. Furthermore, a substantial amount of personal data is often needed for achieving that goal. Capturing and processing personal data in the smart grid increases customer concerns about privacy and in addition, certain statutory and operational requirements regarding privacy aware data processing and storage have to be met. An increase of privacy constraints, however, often limits the operational capabilities of the system. In this paper, we present an approach that automates the process of finding an optimal balance between privacy requirements and operational requirements in a smart grid use case and application scenario. This is achieved by formally describing use cases in an abstract model and by finding an algorithm that determines the optimum balance by forward mapping privacy and operational impacts. For this optimal balancing algorithm both, a numeric approximation and – if feasible – an analytic assessment are presented and investigated. The system is evaluated by applying the tool to a real-world use case from the University of Southern California (USC) microgrid.

Adaptation of a fuzzy controller’s scaling gains using genetic algorithms for balancing an inverted pendulum

Directory of Open Access Journals (Sweden)

Duka Adrian-Vasile

2011-12-01

Full Text Available This paper examines the development of a genetic adaptive fuzzy control system for the Inverted Pendulum. The inverted pendulum is a classical problem in Control Engineering, used for testing different control algorithms. The goal is to balance the inverted pendulum in the upright position by controlling the horizontal force applied to its cart. Because it is unstable and has a complicated nonlinear dynamics, the inverted pendulum is a good testbed for the development of nonconventional advanced control techniques. Fuzzy logic technique has been successfully applied to control this type of system, however most of the time the design of the fuzzy controller is done in an ad-hoc manner, and choosing certain parameters (controller gains, membership functions proves difficult. This paper examines the implementation of an adaptive control method based on genetic algorithms (GA, which can be used on-line to produce the adaptation of the fuzzy controller’s gains in order to achieve the stabilization of the pendulum. The performances of the proposed control algorithms are evaluated and shown by means of digital simulation.

A Seamless Handoff Scheme with Access Point Load Balance for Real-Time Services Support in 802.11 Wireless LANs

Science.gov (United States)

Manodham, Thavisak; Loyola, Luis; Miki, Tetsuya

IEEE 802.11 wirelesses LANs (WLANs) have been rapidly deployed in enterprises, public areas, and households. Voice-over-IP (VoIP) and similar applications are now commonly used in mobile devices over wireless networks. Recent works have improved the quality of service (QoS) offering higher data rates to support various kinds of real-time applications. However, besides the need for higher data rates, seamless handoff and load balancing among APs are key issues that must be addressed in order to continue supporting real-time services across wireless LANs and providing fair services to all users. In this paper, we introduce a novel access point (AP) with two transceivers that improves network efficiency by supporting seamless handoff and traffic load balancing in a wireless network. In our proposed scheme, the novel AP uses the second transceiver to scan and find neighboring STAs in the transmission range and then sends the results to neighboring APs, which compare and analyze whether or not the STA should perform a handoff. The initial results from our simulations show that the novel AP module is more effective than the conventional scheme and a related work in terms of providing a handoff process with low latency and sharing traffic load with neighbor APs.

An IPv6 routing lookup algorithm using weight-balanced tree based on prefix value for virtual router

Science.gov (United States)

Chen, Lingjiang; Zhou, Shuguang; Zhang, Qiaoduo; Li, Fenghua

2016-10-01

Virtual router enables the coexistence of different networks on the same physical facility and has lately attracted a great deal of attention from researchers. As the number of IPv6 addresses is rapidly increasing in virtual routers, designing an efficient IPv6 routing lookup algorithm is of great importance. In this paper, we present an IPv6 lookup algorithm called weight-balanced tree (WBT). WBT merges Forwarding Information Bases (FIBs) of virtual routers into one spanning tree, and compresses the space cost. WBT's average time complexity and the worst case time complexity of lookup and update process are both O(logN) and space complexity is O(cN) where N is the size of routing table and c is a constant. Experiments show that WBT helps reduce more than 80% Static Random Access Memory (SRAM) cost in comparison to those separation schemes. WBT also achieves the least average search depth comparing with other homogeneous algorithms.

Technical note: optimization for improved tube-loading efficiency in the dual-energy computed tomography coupled with balanced filter method.

Science.gov (United States)

Saito, Masatoshi

2010-08-01

This article describes the spectral optimization of dual-energy computed tomography using balanced filters (bf-DECT) to reduce the tube loadings and dose by dedicating to the acquisition of electron density information, which is essential for treatment planning in radiotherapy. For the spectral optimization of bf-DECT, the author calculated the beam-hardening error and air kerma required to achieve a desired noise level in an electron density image of a 50-cm-diameter cylindrical water phantom. The calculation enables the selection of beam parameters such as tube voltage, balanced filter material, and its thickness. The optimal combination of tube voltages was 80 kV/140 kV in conjunction with Tb/Hf and Bi/Mo filter pairs; this combination agrees with that obtained in a previous study [M. Saito, "Spectral optimization for measuring electron density by the dual-energy computed tomography coupled with balanced filter method," Med. Phys. 36, 3631-3642 (2009)], although the thicknesses of the filters that yielded a minimum tube output were slightly different from those obtained in the previous study. The resultant tube loading of a low-energy scan of the present bf-DECT significantly decreased from 57.5 to 4.5 times that of a high-energy scan for conventional DECT. Furthermore, the air kerma of bf-DECT could be reduced to less than that of conventional DECT, while obtaining the same figure of merit for the measurement of electron density and effective atomic number. The tube-loading and dose efficiencies of bf-DECT were considerably improved by sacrificing the quality of the noise level in the images of effective atomic number.

Developing cross entropy genetic algorithm for solving Two-Dimensional Loading Heterogeneous Fleet Vehicle Routing Problem (2L-HFVRP)

Science.gov (United States)

Paramestha, D. L.; Santosa, B.

2018-04-01

Two-dimensional Loading Heterogeneous Fleet Vehicle Routing Problem (2L-HFVRP) is a combination of Heterogeneous Fleet VRP and a packing problem well-known as Two-Dimensional Bin Packing Problem (BPP). 2L-HFVRP is a Heterogeneous Fleet VRP in which these costumer demands are formed by a set of two-dimensional rectangular weighted item. These demands must be served by a heterogeneous fleet of vehicles with a fix and variable cost from the depot. The objective function 2L-HFVRP is to minimize the total transportation cost. All formed routes must be consistent with the capacity and loading process of the vehicle. Sequential and unrestricted scenarios are considered in this paper. We propose a metaheuristic which is a combination of the Genetic Algorithm (GA) and the Cross Entropy (CE) named Cross Entropy Genetic Algorithm (CEGA) to solve the 2L-HFVRP. The mutation concept on GA is used to speed up the algorithm CE to find the optimal solution. The mutation mechanism was based on local improvement (2-opt, 1-1 Exchange, and 1-0 Exchange). The probability transition matrix mechanism on CE is used to avoid getting stuck in the local optimum. The effectiveness of CEGA was tested on benchmark instance based 2L-HFVRP. The result of experiments shows a competitive result compared with the other algorithm.

A new distributed systems scheduling algorithm: a swarm intelligence approach

Science.gov (United States)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Balancing Power Absorption and Fatigue Loads in Irregular Waves for an Oscillating Surge Wave Energy Converter: Preprint

Energy Technology Data Exchange (ETDEWEB)

Tom, Nathan M.; Yu, Yi-Hsiang; Wright, Alan D.; Lawson, Michael

2016-06-01

The aim of this paper is to describe how to control the power-to-load ratio of a novel wave energy converter (WEC) in irregular waves. The novel WEC that is being developed at the National Renewable Energy Laboratory combines an oscillating surge wave energy converter (OSWEC) with control surfaces as part of the structure; however, this work only considers one fixed geometric configuration. This work extends the optimal control problem so as to not solely maximize the time-averaged power, but to also consider the power-take-off (PTO) torque and foundation forces that arise because of WEC motion. The objective function of the controller will include competing terms that force the controller to balance power capture with structural loading. Separate penalty weights were placed on the surge-foundation force and PTO torque magnitude, which allows the controller to be tuned to emphasize either power absorption or load shedding. Results of this study found that, with proper selection of penalty weights, gains in time-averaged power would exceed the gains in structural loading while minimizing the reactive power requirement.

Energy conservation in Newmark based time integration algorithms

DEFF Research Database (Denmark)

Krenk, Steen

2006-01-01

Energy balance equations are established for the Newmark time integration algorithm, and for the derived algorithms with algorithmic damping introduced via averaging, the so-called a-methods. The energy balance equations form a sequence applicable to: Newmark integration of the undamped equations...... of motion, an extended form including structural damping, and finally the generalized form including structural as well as algorithmic damping. In all three cases the expression for energy, appearing in the balance equation, is the mechanical energy plus some additional terms generated by the discretization...

Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint

Energy Technology Data Exchange (ETDEWEB)

Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhou, Xinyang [University of Colorado; Liu, Zhiyuan [University of Colorado; Chen, Lijun [University of Colorado

2017-10-03

This paper considers distribution networks with distributed energy resources and discrete-rate loads, and designs an incentive-based algorithm that allows the network operator and the customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Four major challenges include: (1) the non-convexity from discrete decision variables, (2) the non-convexity due to a Stackelberg game structure, (3) unavailable private information from customers, and (4) different update frequency from two types of devices. In this paper, we first make convex relaxation for discrete variables, then reformulate the non-convex structure into a convex optimization problem together with pricing/reward signal design, and propose a distributed stochastic dual algorithm for solving the reformulated problem while restoring feasible power rates for discrete devices. By doing so, we are able to statistically achieve the solution of the reformulated problem without exposure of any private information from customers. Stability of the proposed schemes is analytically established and numerically corroborated.

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 adaptive clustering algorithm for image matching based on corner feature

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

A Dynamic and Heuristic Phase Balancing Method for LV Feeders

Directory of Open Access Journals (Sweden)

Samad Taghipour Boroujeni

2016-01-01

Full Text Available Due to the single-phase loads and their stochastic behavior, the current in the distribution feeders is not balanced. In addition, the single-phase loads are located in different positions along the LV feeders. So the amount of the unbalanced load and its location affect the feeder losses. An unbalanced load causes the feeder losses and the voltage drop. Because of time-varying behavior of the single-phase loads, phase balancing is a dynamic and combinatorial problem. In this research, a heuristic and dynamic solution for the phase balancing of the LV feeders is proposed. In this method, it is supposed that the loads’ tie could be connected to all phases through a three-phase switch. The aim of the proposed method is to make the feeder conditions as balanced as possible. The amount and the location of single-phase loads are considered in the proposed phase balancing method. Since the proposed method needs no communication interface or no remote controller, it is inexpensive, simple, practical, and robust. Applying this method provides a distributed and dynamic phase balancing control. In addition, the feasibility of reducing the used switches is investigated. The ability of the proposed method in the phase balancing of the LV feeders is approved by carrying out some simulations.

The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

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Jin-peng Liu

2017-07-01

Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

Effects of Holding an External Load on the Standing Balance of Older and Younger Adults With and Without Chronic Low Back Pain.

Science.gov (United States)

Shigaki, Leonardo; Vieira, Edgar Ramos; de Oliveira Gil, André Wilson; Araújo, Cynthia Gobbi Alves; Carmargo, Mariana Zingari; Sturion, Leandro Amaral; de Oliveira, Marcio Roǵerio; da Silva, Rubens A

2017-05-01

The purpose of this study was to assess the effect of holding an external load on the standing balance of younger and older adults with and without chronic low back pain (CLBP). Twenty participants with and 20 without CLBP participated in the study. Each group contained 10 younger (50% men) and 10 older adults (50% men). Participants were instructed to look straight ahead while standing on a force platform during two 120-second trials with and without holding an external load (10% of body mass). The center of pressure area, mean velocity, and mean frequency in the anteroposterior and mediolateral directions were measured. Older adults had worse standing balance than younger adults did (P external load significantly increased postural instability for both age groups and CLBP status, with mean effect size across center of pressure variables of d = 0.82 for older participants without CLBP and d = 2.65 for younger participants without CLBP. These effects for people with CLBP were d = 1.65 for subgroup of older and d = 1.60 for subgroup of younger participants. Holding an external load of 10% of body mass increased postural instability of both younger and older adults with and without CLBP. Copyright © 2017. Published by Elsevier Inc.

Evaluation of Key Dependent S-Box Based Data Security Algorithm using Hamming Distance and Balanced Output

Directory of Open Access Journals (Sweden)

Balajee Maram K.

2016-02-01

Full Text Available Data security is a major issue because of rapid evolution of data communication over unsecured internetwork. Here the proposed system is concerned with the problem of randomly generated S-box. The generation of S-box depends on Pseudo-Random-Number-Generators and shared-secret-key. The process of Pseudo-Random-Number-Generator depends on large prime numbers. All Pseudo-Random-Numbers are scrambled according to shared-secret-key. After scrambling, the S-box is generated. In this research, large prime numbers are the inputs to the Pseudo-Random-Number-Generator. The proposed S-box will reduce the complexity of S-box generation. Based on S-box parameters, it experimentally investigates the quality and robustness of the proposed algorithm which was tested. It yields better results with the S-box parameters like Hamming Distance, Balanced Output and Avalanche Effect and can be embedded to popular cryptography algorithms

Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

2010-01-01

unique features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. Currently, uncertainties associated with wind and load forecasts, as well as uncertainties associated with random generator outages and unexpected disconnection of supply lines, are not taken into account in power grid operation. Thus, operators have little means to weigh the likelihood and magnitude of upcoming events of power imbalance. In this project, funded by the U.S. Department of Energy (DOE), a framework has been developed for incorporating uncertainties associated with wind and load forecast errors, unpredicted ramps, and forced generation disconnections into the energy management system (EMS) as well as generation dispatch and commitment applications. A new approach to evaluate the uncertainty ranges for the required generation performance envelope including balancing capacity, ramping capability, and ramp duration has been proposed. The approach includes three stages: forecast and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence levels. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis, incorporating all sources of uncertainties of both continuous (wind and load forecast errors) and discrete (forced generator outages and start-up failures) nature. A new method called the “flying brick” technique has been developed to evaluate the look-ahead required generation performance envelope for the worst case scenario within a user-specified confidence level. A self-validation algorithm has been developed to validate the accuracy of the confidence intervals.

A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

Directory of Open Access Journals (Sweden)

Jiani Heng

2016-01-01

Full Text Available Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm, and WNN (Wavelet Neural Network, is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network models such as BPNN (Back Propagation Neural Network, GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm, and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

Robust low frequency current ripple elimination algorithm for grid-connected fuel cell systems with power balancing technique

Energy Technology Data Exchange (ETDEWEB)

Kim, Jong-Soo; Choe, Gyu-Yeong; Lee, Byoung-Kuk [School of Information and Communication Engineering, Sungkyunkwan University, 300 Cheoncheon-dong, Jangan-gu, Suwon, Gyeonggi-do 440-746 (Korea, Republic of); Kang, Hyun-Soo [R and D Center, Advanced Drive Technology (ADT) Company, 689-26 Geumjeong-dong, Gunpo-si, Gyeonggi-do 435-862 (Korea, Republic of)

2011-05-15

The low frequency current ripple in grid-connected fuel cell systems is generated from dc-ac inverter operation, which generates 60 Hz fundamental component, and gives harmful effects on fuel cell stack itself, such as making cathode surface responses slower, causing an increase of more than 10% in the fuel consumption, creating oxygen starvation, causing a reduction in the operating lifetime, and incurring a nuisance tripping such as overload situation. With these reasons, low frequency current ripple makes fuel cell system unstable and lifetime of fuel cell stack itself short. This paper presents a fast and robust control algorithm to eliminate low frequency current ripple in grid-connected fuel cell systems. Compared with the conventional methods, in the proposed control algorithm, dc link voltage controller is shifted from dc-dc converter to dc-ac inverter, resulting that dc-ac inverter handles dc link voltage control and output current control simultaneously with help of power balancing technique. The results indicate that the proposed algorithm can not only completely eliminate current ripple but also significantly reduce the overshoot or undershoot during transient states without any extra hardware. The validity of the proposed algorithm is verified by computer simulations and also by experiments with a 1 kW laboratory prototype. (author)

A multi-pattern hash-binary hybrid algorithm for URL matching in the HTTP protocol.

Directory of Open Access Journals (Sweden)

Ping Zeng

Full Text Available In this paper, based on our previous multi-pattern uniform resource locator (URL binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on-all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications.

Diagnosing acute HIV infection: The performance of quantitative HIV-1 RNA testing (viral load) in the 2014 laboratory testing algorithm.

Science.gov (United States)

Wu, Hsiu; Cohen, Stephanie E; Westheimer, Emily; Gay, Cynthia L; Hall, Laura; Rose, Charles; Hightow-Weidman, Lisa B; Gose, Severin; Fu, Jie; Peters, Philip J

2017-08-01

New recommendations for laboratory diagnosis of HIV infection in the United States were published in 2014. The updated testing algorithm includes a qualitative HIV-1 RNA assay to resolve discordant immunoassay results and to identify acute HIV-1 infection (AHI). The qualitative HIV-1 RNA assay is not widely available; therefore, we evaluated the performance of a more widely available quantitative HIV-1 RNA assay, viral load, for diagnosing AHI. We determined that quantitative viral loads consistently distinguished AHI from a false-positive immunoassay result. Among 100 study participants with AHI and a viral load result, the estimated geometric mean viral load was 1,377,793copies/mL. Copyright © 2017 Elsevier B.V. All rights reserved.

Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic

Directory of Open Access Journals (Sweden)

Houda Jouini

2010-01-01

Full Text Available As a perspective to ensure the power system stability and to avoid the vulnerability leading to the blackouts, several preventive and curative means are adopted. In order to avoid the voltage collapse, load shedding schemes represent a suitable action to maintain the power system service quality and to control its vulnerability. In this paper, we try to propose an intelligent load shedding strategy as a new approach based on fuzzy controllers. This strategy was founded on the calculation of generated power sensitivity degree related to those injected at different network buses. During the fault phase, fuzzy controller algorithms generate monitor vectors ensuring a precalculated load shedding ratio in the purpose to reestablish the power balance and conduct the network to a new steady state.

The effect of load imbalances on the performance of Monte Carlo algorithms in LWR analysis

International Nuclear Information System (INIS)

Siegel, A.R.; Smith, K.; Romano, P.K.; Forget, B.; Felker, K.

2013-01-01

A model is developed to predict the impact of particle load imbalances on the performance of domain-decomposed Monte Carlo neutron transport algorithms. Expressions for upper bound performance “penalties” are derived in terms of simple machine characteristics, material characterizations and initial particle distributions. The hope is that these relations can be used to evaluate tradeoffs among different memory decomposition strategies in next generation Monte Carlo codes, and perhaps as a metric for triggering particle redistribution in production codes

Multi-objective optimization algorithms for mixed model assembly line balancing problem with parallel workstations

Directory of Open Access Journals (Sweden)

Masoud Rabbani

2016-12-01

Full Text Available This paper deals with mixed model assembly line (MMAL balancing problem of type-I. In MMALs several products are made on an assembly line while the similarity of these products is so high. As a result, it is possible to assemble several types of products simultaneously without any additional setup times. The problem has some particular features such as parallel workstations and precedence constraints in dynamic periods in which each period also effects on its next period. The research intends to reduce the number of workstations and maximize the workload smoothness between workstations. Dynamic periods are used to determine all variables in different periods to achieve efficient solutions. A non-dominated sorting genetic algorithm (NSGA-II and multi-objective particle swarm optimization (MOPSO are used to solve the problem. The proposed model is validated with GAMS software for small size problem and the performance of the foregoing algorithms is compared with each other based on some comparison metrics. The NSGA-II outperforms MOPSO with respect to some comparison metrics used in this paper, but in other metrics MOPSO is better than NSGA-II. Finally, conclusion and future research is provided.

Coupling Algorithms for Calculating Sensitivities of Population Balances

International Nuclear Information System (INIS)

Man, P. L. W.; Kraft, M.; Norris, J. R.

2008-01-01

We introduce a new class of stochastic algorithms for calculating parametric derivatives of the solution of the space-homogeneous Smoluchowski's coagulation equation. Currently, it is very difficult to produce low variance estimates of these derivatives in reasonable amounts of computational time through the use of stochastic methods. These new algorithms consider a central difference estimator of the parametric derivative which is calculated by evaluating the coagulation equation at two different parameter values simultaneously, and causing variance reduction by maximising the covariance between these. The two different coupling strategies ('Single' and 'Double') have been compared to the case when there is no coupling ('Independent'). Both coupling algorithms converge and the Double coupling is the most 'efficient' algorithm. For the numerical example chosen we obtain a factor of about 100 in efficiency in the best case (small system evolution time and small parameter perturbation).

Hysteresis Control for Shunt Active Power Filter under Unbalanced Three-Phase Load Conditions

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Z. Chelli

2015-01-01

Full Text Available This paper focuses on a four-wire shunt active power filter (APF control scheme proposed to improve the performance of the APF. This filter is used to compensate harmonic distortion in three-phase four-wire systems. Several harmonic suppression techniques have been widely proposed and applied to minimize harmonic effects. The proposed control scheme can compensate harmonics and reactive power of the nonlinear loads simultaneously. This approach is compared to the conventional shunt APF reference compensation strategy. The developed algorithm is validated by simulation tests using MATLAB Simulink. The obtained results have demonstrated the effectiveness of the proposed scheme and confirmed the theoretical developments for balanced and unbalanced nonlinear loads.

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

Science.gov (United States)

Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

2018-04-18

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River

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K. Roshangar

2016-09-01

Full Text Available Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation. Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝, genetic programming (GP and Genetic Algorithm (GA were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE and determination coefficient (DC. RMSE and DC indicate the discrepancy between the observed and computed values. Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067. Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9

Acid Balance, Dietary Acid Load, and Bone Effects—A Controversial Subject

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Lynda Frassetto

2018-04-01

Full Text Available Modern Western diets, with higher contents of animal compared to fruits and vegetable products, have a greater content of acid precursors vs. base precursors, which results in a net acid load to the body. To prevent inexorable accumulation of acid in the body and progressively increasing degrees of metabolic acidosis, the body has multiple systems to buffer and titrate acid, including bone which contains large quantities of alkaline salts of calcium. Both in vitro and in vivo studies in animals and humans suggest that bone base helps neutralize part of the dietary net acid load. This raises the question of whether decades of eating a high acid diet might contribute to the loss of bone mass in osteoporosis. If this idea is true, then additional alkali ingestion in the form of net base-producing foods or alkalinizing salts could potentially prevent this acid-related loss of bone. Presently, data exists that support both the proponents as well as the opponents of this hypothesis. Recent literature reviews have tended to support either one side or the other. Assuming that the data cited by both sides is correct, we suggest a way to reconcile the discordant findings. This overview will first discuss dietary acids and bases and the idea of changes in acid balance with increasing age, then review the evidence for and against the usefulness of alkali therapy as a treatment for osteoporosis, and finally suggest a way of reconciling these two opposing points of view.

Power Load Prediction Based on Fractal Theory

OpenAIRE

Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

2015-01-01

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance

Directory of Open Access Journals (Sweden)

Álvaro Jaramillo-Duque

2018-02-01

Full Text Available In this paper, a model and solution approach for minimizing internal power losses in Transformers Connected in Parallel (TCP with tap-changers is proposed. The model is based on power chargeability balance and seeks to keep the load voltage within an admissible range. For achieving this, tap positions are adjusted in such a way that all TCP are set in similar/same power chargeability. The main contribution of this paper is the inclusion of several construction features (rated voltage, rated power, voltage ratio, short-circuit impedance and tap steps in the minimization of power losses in TCP that are not included in previous works. A Genetic Algorithm (GA is used for solving the proposed model that is a system of nonlinear equations with discrete decision variables. The GA scans different sets for tap positions with the aim of balancing the power supplied by each transformer to the load. For this purpose, a fitness function is used for minimizing two conditions: The first condition consists on the mismatching between power chargeability for each transformer and a desired chargeability; and the second condition is the mismatching between the nominal load voltage and the load voltage obtained by changing the tap positions. The proposed method is generalized for any given number of TCP and was implemented for three TCP, demonstrating that the power losses are minimized and the load voltage remains within an admissible range.

Partial storage optimization and load control strategy of cloud data centers.

Science.gov (United States)

Al Nuaimi, Klaithem; Mohamed, Nader; Al Nuaimi, Mariam; Al-Jaroodi, Jameela

2015-01-01

We present a novel approach to solve the cloud storage issues and provide a fast load balancing algorithm. Our approach is based on partitioning and concurrent dual direction download of the files from multiple cloud nodes. Partitions of the files are saved on the cloud rather than the full files, which provide a good optimization to the cloud storage usage. Only partial replication is used in this algorithm to ensure the reliability and availability of the data. Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud. This algorithm solves the problem of having to fully replicate large data sets, which uses up a lot of precious space on the cloud nodes. Reducing the space needed will help in reducing the cost of providing such space. Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner.

Partial Storage Optimization and Load Control Strategy of Cloud Data Centers

Science.gov (United States)

2015-01-01

We present a novel approach to solve the cloud storage issues and provide a fast load balancing algorithm. Our approach is based on partitioning and concurrent dual direction download of the files from multiple cloud nodes. Partitions of the files are saved on the cloud rather than the full files, which provide a good optimization to the cloud storage usage. Only partial replication is used in this algorithm to ensure the reliability and availability of the data. Our focus is to improve the performance and optimize the storage usage by providing the DaaS on the cloud. This algorithm solves the problem of having to fully replicate large data sets, which uses up a lot of precious space on the cloud nodes. Reducing the space needed will help in reducing the cost of providing such space. Moreover, performance is also increased since multiple cloud servers will collaborate to provide the data to the cloud clients in a faster manner. PMID:25973444

The recommendation of line-balancing improvement on MCM product line 1 using genetics algorithm and moodie young at XYZ Company, Co.

Science.gov (United States)

Sriwana, I. K.; Marie, I. A.; Mangala, D.

2017-12-01

Kencana Gemilang, Co. is one electronics industry engaging in the manufacture sector. This company manufactures and assembles household electronic products, such as rice cooker, fan, iron, blender, etc. The company deals with an issue of underachievement of an established production target on MCM products line 1. This study aimed to calculate line efficiencies, delay times, and initial line smoothness indexes. The research was carried out by means of depicting a precedence diagram and gathering time data of each work element followed by examination and calculation of standard time as well as line balancing using methods of Moodie Young and Generics Algorithm. Based on results of calculation, better line balancing than the existing initial conditions, i.e. improvement in the line efficiency by 18.39%, deterioration in balanced delay by 28.39%, and deterioration of a smoothness index by 23.85% was obtained.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization - doi: 10.4025/actascitechnol.v34i4.12194

Directory of Open Access Journals (Sweden)

Marcelo Botelho da Costa Moraes

2012-10-01

Full Text Available This work aimed to apply genetic algorithms (GA and particle swarm optimization (PSO in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

Load-bearing Characters Analysis of Large Diameter Rock-Socketed Filling Piles Based on Self-Balanced Method

Science.gov (United States)

tongqing, Wu; liang, Li; xinjian, Liu; Xu, nianchun; Tian, Mao

2018-03-01

Self-balanced method is carried out on the large diameter rock-socketed filling piles of high-pile wharf at Inland River, to explore the distribution laws of load-displacement curve, pile internal force, pile tip friction resistance and pile side friction resistance under load force. The results showed that: the tip resistance of S1 and S2 test piles accounted for 53.4% and 53.6% of the pile bearing capacity, respectively, while the total side friction resistance accounted for 46.6% and 46.4% of the pile bearing capacity, respectively; both the pile tip friction resistance and pile side friction resistance can be fully played, and reach to the design requirements. The reasonability of large diameter rock-socketed filling design is verified through test analysis, which can provide basis for the optimization of high-pile wharf structural type, thus reducing the wharf project cost, and also providing reference for the similar large diameter rock-socketed filling piles of high-pile wharf at Inland River.

Kalman-fuzzy algorithm in short term load forecasting

International Nuclear Information System (INIS)

Shah Baki, S.R.; Saibon, H.; Lo, K.L.

1996-01-01

A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique

Numerical study of a stochastic particle algorithm solving a multidimensional population balance model for high shear granulation

International Nuclear Information System (INIS)

Braumann, Andreas; Kraft, Markus; Wagner, Wolfgang

2010-01-01

This paper is concerned with computational aspects of a multidimensional population balance model of a wet granulation process. Wet granulation is a manufacturing method to form composite particles, granules, from small particles and binders. A detailed numerical study of a stochastic particle algorithm for the solution of a five-dimensional population balance model for wet granulation is presented. Each particle consists of two types of solids (containing pores) and of external and internal liquid (located in the pores). Several transformations of particles are considered, including coalescence, compaction and breakage. A convergence study is performed with respect to the parameter that determines the number of numerical particles. Averaged properties of the system are computed. In addition, the ensemble is subdivided into practically relevant size classes and analysed with respect to the amount of mass and the particle porosity in each class. These results illustrate the importance of the multidimensional approach. Finally, the kinetic equation corresponding to the stochastic model is discussed.

Estimation of dynamic rotor loads for the rotor systems research aircraft: Methodology development and validation

Science.gov (United States)

Duval, R. W.; Bahrami, M.

1985-01-01

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission systm from the fuselage. A mathematical model relating applied rotor loads and inertial loads of the rotor/transmission system to the load cell response is required to allow the load cells to be used to estimate rotor loads from flight data. Such a model is derived analytically by applying a force and moment balance to the isolated rotor/transmission system. The model is tested by comparing its estimated values of applied rotor loads with measured values obtained from a ground based shake test. Discrepancies in the comparison are used to isolate sources of unmodeled external loads. Once the structure of the mathematical model has been validated by comparison with experimental data, the parameters must be identified. Since the parameters may vary with flight condition it is desirable to identify the parameters directly from the flight data. A Maximum Likelihood identification algorithm is derived for this purpose and tested using a computer simulation of load cell data. The identification is found to converge within 10 samples. The rapid convergence facilitates tracking of time varying parameters of the load cell model in flight.

A Dynamic Consensus Algorithm to Adjust Virtual Impedance Loops for Discharge Rate Balancing of AC Microgrid Energy Storage Units

DEFF Research Database (Denmark)

Guan, Yajuan; Meng, Lexuan; Li, Chendan

2018-01-01

A dynamic consensus algorithm (DCA)-based coordinated secondary control with an autonomous current-sharing control strategy is proposed in this paper for balancing the discharge rate of energy storage systems (ESSs) in an islanded AC microgrid. The DCA is applied for information sharing between......, the proposed approach can provide higher system reliability, expandability, and flexibility due to its distributed control architecture. The proposed controller can effectively prevent operation failure caused by over-current and unintentional outage of DGs by means of balanced discharge rate control. It can...... also provide fast response and accurate current sharing performance. A generalizable linearized state-space model for n-DG network in the z-domain is also derived and proposed in this paper; the model includes electrical, controller, and communication parts. The system stability and parameter...

Parallel paving: An algorithm for generating distributed, adaptive, all-quadrilateral meshes on parallel computers

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.

Load management through agent based coordination of flexible electricity consumers

DEFF Research Database (Denmark)

Clausen, Anders; Demazeau, Yves; Jørgensen, Bo Nørregaard

2015-01-01

Demand Response (DR) offers a cost-effective and carbonfriendly way of performing load balancing. DR describes a change in the electricity consumption of flexible consumers in response to the supply situation. In DR, flexible consumers may perform their own load balancing through load management...

On load paths and load bearing topology from finite element analysis

International Nuclear Information System (INIS)

Kelly, D; Reidsema, C; Lee, M

2010-01-01

Load paths can be mapped from vector plots of 'pointing stress vectors'. They define a path along which a component of load remains constant as it traverses the solution domain. In this paper the theory for the paths is first defined. Properties of the plots that enable a designer to interpret the structural behavior from the contours are then identified. Because stress is a second order tensor defined on an orthogonal set of axes, the vector plots define separate paths for load transfer in each direction of the set of axes. An algorithm is therefore presented that combines the vectors to define a topology to carry the loads. The algorithm is shown to straighten the paths reducing bending moments and removing stress concentration. Application to a bolted joint, a racing car body and a yacht hull demonstrate the usefulness of the plots.

Watershed model calibration framework developed using an influence coefficient algorithm and a genetic algorithm and analysis of pollutant discharge characteristics and load reduction in a TMDL planning area.

Science.gov (United States)

Cho, Jae Heon; Lee, Jong Ho

2015-11-01

Manual calibration is common in rainfall-runoff model applications. However, rainfall-runoff models include several complicated parameters; thus, significant time and effort are required to manually calibrate the parameters individually and repeatedly. Automatic calibration has relative merit regarding time efficiency and objectivity but shortcomings regarding understanding indigenous processes in the basin. In this study, a watershed model calibration framework was developed using an influence coefficient algorithm and genetic algorithm (WMCIG) to automatically calibrate the distributed models. The optimization problem used to minimize the sum of squares of the normalized residuals of the observed and predicted values was solved using a genetic algorithm (GA). The final model parameters were determined from the iteration with the smallest sum of squares of the normalized residuals of all iterations. The WMCIG was applied to a Gomakwoncheon watershed located in an area that presents a total maximum daily load (TMDL) in Korea. The proportion of urbanized area in this watershed is low, and the diffuse pollution loads of nutrients such as phosphorus are greater than the point-source pollution loads because of the concentration of rainfall that occurs during the summer. The pollution discharges from the watershed were estimated for each land-use type, and the seasonal variations of the pollution loads were analyzed. Consecutive flow measurement gauges have not been installed in this area, and it is difficult to survey the flow and water quality in this area during the frequent heavy rainfall that occurs during the wet season. The Hydrological Simulation Program-Fortran (HSPF) model was used to calculate the runoff flow and water quality in this basin. Using the water quality results, a load duration curve was constructed for the basin, the exceedance frequency of the water quality standard was calculated for each hydrologic condition class, and the percent reduction

A Novel Algorithm of Quantum Random Walk in Server Traffic Control and Task Scheduling

Directory of Open Access Journals (Sweden)

Dong Yumin

2014-01-01

Full Text Available A quantum random walk optimization model and algorithm in network cluster server traffic control and task scheduling is proposed. In order to solve the problem of server load balancing, we research and discuss the distribution theory of energy field in quantum mechanics and apply it to data clustering. We introduce the method of random walk and illuminate what the quantum random walk is. Here, we mainly research the standard model of one-dimensional quantum random walk. For the data clustering problem of high dimensional space, we can decompose one m-dimensional quantum random walk into m one-dimensional quantum random walk. In the end of the paper, we compare the quantum random walk optimization method with GA (genetic algorithm, ACO (ant colony optimization, and SAA (simulated annealing algorithm. In the same time, we prove its validity and rationality by the experiment of analog and simulation.

Modified Cuckoo Search Algorithm for Solving Nonconvex Economic Load Dispatch Problems

Directory of Open Access Journals (Sweden)

Thang Trung Nguyen

2016-01-01

Full Text Available This paper presents the application of modified cuckoo search algorithm (MCSA for solving economic load dispatch (ELD problems. The MCSA method is developed to improve the search ability and solution quality of the conventional CSA method. In the MCSA, the evaluation of eggs has divided the initial eggs into two groups, the top egg group with good quality and the abandoned group with worse quality. Moreover, the value of the updated step size in MCSA is adapted as generating a new solution for the abandoned group and the top group via the Levy flights so that a large zone is searched at the beginning and a local zone is foraged as the maximum number of iterations is nearly reached. The MCSA method has been tested on different systems with different characteristics of thermal units and constraints. The result comparison with other methods in the literature has indicated that the MCSA method can be a powerful method for solving the ELD.

Optimisation of load control

International Nuclear Information System (INIS)

Koponen, P.

1998-01-01

Electricity cannot be stored in large quantities. That is why the electricity supply and consumption are always almost equal in large power supply systems. If this balance were disturbed beyond stability, the system or a part of it would collapse until a new stable equilibrium is reached. The balance between supply and consumption is mainly maintained by controlling the power production, but also the electricity consumption or, in other words, the load is controlled. Controlling the load of the power supply system is important, if easily controllable power production capacity is limited. Temporary shortage of capacity causes high peaks in the energy price in the electricity market. Load control either reduces the electricity consumption during peak consumption and peak price or moves electricity consumption to some other time. The project Optimisation of Load Control is a part of the EDISON research program for distribution automation. The following areas were studied: Optimization of space heating and ventilation, when electricity price is time variable, load control model in power purchase optimization, optimization of direct load control sequences, interaction between load control optimization and power purchase optimization, literature on load control, optimization methods and field tests and response models of direct load control and the effects of the electricity market deregulation on load control. An overview of the main results is given in this chapter

Optimisation of load control

Energy Technology Data Exchange (ETDEWEB)

Koponen, P [VTT Energy, Espoo (Finland)

1998-08-01

Electricity cannot be stored in large quantities. That is why the electricity supply and consumption are always almost equal in large power supply systems. If this balance were disturbed beyond stability, the system or a part of it would collapse until a new stable equilibrium is reached. The balance between supply and consumption is mainly maintained by controlling the power production, but also the electricity consumption or, in other words, the load is controlled. Controlling the load of the power supply system is important, if easily controllable power production capacity is limited. Temporary shortage of capacity causes high peaks in the energy price in the electricity market. Load control either reduces the electricity consumption during peak consumption and peak price or moves electricity consumption to some other time. The project Optimisation of Load Control is a part of the EDISON research program for distribution automation. The following areas were studied: Optimization of space heating and ventilation, when electricity price is time variable, load control model in power purchase optimization, optimization of direct load control sequences, interaction between load control optimization and power purchase optimization, literature on load control, optimization methods and field tests and response models of direct load control and the effects of the electricity market deregulation on load control. An overview of the main results is given in this chapter

Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

Directory of Open Access Journals (Sweden)

Yunsong Liu

Full Text Available Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B converges faster than previously proposed algorithms accelerated proximal algorithm (APG and alternating directional method of multipliers for balanced model (ADMM-B.

Optimization of Boiling Water Reactor Loading Pattern Using Two-Stage Genetic Algorithm

International Nuclear Information System (INIS)

Kobayashi, Yoko; Aiyoshi, Eitaro

2002-01-01

A new two-stage optimization method based on genetic algorithms (GAs) using an if-then heuristic rule was developed to generate optimized boiling water reactor (BWR) loading patterns (LPs). In the first stage, the LP is optimized using an improved GA operator. In the second stage, an exposure-dependent control rod pattern (CRP) is sought using GA with an if-then heuristic rule. The procedure of the improved GA is based on deterministic operators that consist of crossover, mutation, and selection. The handling of the encoding technique and constraint conditions by that GA reflects the peculiar characteristics of the BWR. In addition, strategies such as elitism and self-reproduction are effectively used in order to improve the search speed. The LP evaluations were performed with a three-dimensional diffusion code that coupled neutronic and thermal-hydraulic models. Strong axial heterogeneities and constraints dependent on three dimensions have always necessitated the use of three-dimensional core simulators for BWRs, so that optimization of computational efficiency is required. The proposed algorithm is demonstrated by successfully generating LPs for an actual BWR plant in two phases. One phase is only LP optimization applying the Haling technique. The other phase is an LP optimization that considers the CRP during reactor operation. In test calculations, candidates that shuffled fresh and burned fuel assemblies within a reasonable computation time were obtained

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

Directory of Open Access Journals (Sweden)

Yan Hong Chen

2016-01-01

Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

Direct Load Control by AC Frequency Modulation

DEFF Research Database (Denmark)

Douglass, Philip James; You, Shi

2012-01-01

Fine-grained under frequency load shedding called “demand as a frequency controlled reserve“ (DFCR) has been shown to be a promising method of providingfrequency regulation service from distributed loads [1]. Micro-grids with a large portion of intermittent renewable generation will benefit greatly...... from this technology because their low inertia. The paper proposes a operating procedure for utilizing DFCR loads for energy balancing, expanding DFCR’s well known role as a power balancing resource. The system operator can use DFCR for energy balancing by adjusting the frequency controller...... of generators to schedule off-nominal system frequency values. The feasibility of the proposed system is evaluated on an existing small island power system....

Reusing balanced power flow object components for developing harmonic power flow

Energy Technology Data Exchange (ETDEWEB)

Nadarajah, S. [Peninsular Malaysia Electric Utility Co., Kuala Lumpur (Malaysia). Tenaga Nasional Berhad; Nor, K.M.; Abdel-Akher, M. [Malaysia Univ., Kuala Lumpur (Malaysia). Dept. of Electrical Engineering

2005-07-01

Harmonic power flows are used to examine the effects of nonlinear loads on power systems. In this paper, component technology was re-used for the development of a harmonic power flow. The object-oriented power system model (OO-PSM) was developed separately from a solution algorithm. Nodes, lines, and transformers were modelled as entity objects by classes. Power flow solution algorithms were modelled as control objects and encapsulated inside independent software components within the power system component software architecture (PS-COM). Both the OO-PSM and the PS-COM of the balanced power flow were re-used for developing the proposed harmonic power flow. A no-interaction hypothesis was used to consider both fundamental voltages and nonlinear device data dependence. A direct solution voltage node method was also used. The accuracy of the method was demonstrated using IEEE 14 bus and 30 bus test systems. It was concluded that component technology can be used to develop harmonic power flow programs. 7 refs., 2 tabs., 9 figs.

A Baltic Sea estuary as a phosphorus source and sink after drastic load reduction: seasonal and long-term mass balances for the Stockholm inner archipelago for 1968-2015

Science.gov (United States)

Walve, Jakob; Sandberg, Maria; Larsson, Ulf; Lännergren, Christer

2018-05-01

Internal phosphorus (P) loading from sediments, controlled by hypoxia, is often assumed to hamper the recovery of lakes and coastal areas from eutrophication. In the early 1970s, the external P load to the inner archipelago of Stockholm, Sweden (Baltic Sea), was drastically reduced by improved sewage treatment, but the internal P loading and its controlling factors have been poorly quantified. We use two slightly different four-layer box models to calculate the area's seasonal and annual P balance (input-export) and the internal P exchange with sediments in 1968-2015. For 10-20 years after the main P load reduction, there was a negative P balance, small in comparison to the external load, and probably due to release from legacy sediment P storage. Later, the stabilized, near-neutral P balance indicates no remaining internal loading from legacy P, but P retention is low, despite improved oxygen conditions. Seasonally, sediments are a P sink in spring and a P source in summer and autumn. Most of the deep-water P release from sediments in summer-autumn appears to be derived from the settled spring bloom and is exported to outer areas during winter. Oxygen consumption and P release in the deep water are generally tightly coupled, indicating limited iron control of P release. However, enhanced P release in years of deep-water hypoxia suggests some contribution from redox-sensitive P pools. Increasing deep-water temperatures that stimulate oxygen consumption rates in early summer have counteracted the effect of lowered organic matter sedimentation on oxygen concentrations. Since the P turnover time is short and legacy P small, measures to bind P in Stockholm inner archipelago sediments would primarily accumulate recent P inputs, imported from the Baltic Sea and from Lake Mälaren.

Balanced truncation for linear switched systems

DEFF Research Database (Denmark)

Petreczky, Mihaly; Wisniewski, Rafal; Leth, John-Josef

2013-01-01

In this paper, we present a theoretical analysis of the model reduction algorithm for linear switched systems from Shaker and Wisniewski (2011, 2009) and . This algorithm is a reminiscence of the balanced truncation method for linear parameter varying systems (Wood et al., 1996) [3]. Specifically...

Stochastic solution of population balance equations for reactor networks

International Nuclear Information System (INIS)

Menz, William J.; Akroyd, Jethro; Kraft, Markus

2014-01-01

This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images. Highlights: •An algorithm is presented to solve reactor networks with a population balance model. •A stochastic method is used to solve the population balance equations. •The convergence and efficiency of the reported algorithms are evaluated. •The algorithm is applied to simulate silicon nanoparticle synthesis in a 1D reactor. •Particle structure is reported as a function of reactor length and time

A combination of clinical balance measures and FRAX? to improve identification of high-risk fallers

OpenAIRE

Najafi, David A.; Dahlberg, Leif E.; Hansson, Eva Ekvall

2016-01-01

Background The FRAX? algorithm quantifies a patient?s 10-year probability of a hip or major osteoporotic fracture without taking an individual?s balance into account. Balance measures assess the functional ability of an individual and the FRAX? algorithm is a model that integrates the individual patients clinical risk factors [not balance] and bone mineral density. Thus, clinical balance measures capture aspects that the FRAX? algorithm does not, and vice versa. It is therefore possible that ...

Automatic load sharing in inverter modules

Science.gov (United States)

Nagano, S.

1979-01-01

Active feedback loads transistor equally with little power loss. Circuit is suitable for balancing modular inverters in spacecraft, computer power supplies, solar-electric power generators, and electric vehicles. Current-balancing circuit senses differences between collector current for power transistor and average value of load currents for all power transistors. Principle is effective not only in fixed duty-cycle inverters but also in converters operating at variable duty cycles.

Optimization approaches to mpi and area merging-based parallel buffer algorithm

Directory of Open Access Journals (Sweden)

Junfu Fan

Full Text Available On buffer zone construction, the rasterization-based dilation method inevitably introduces errors, and the double-sided parallel line method involves a series of complex operations. In this paper, we proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface to improve the performances of buffer analyses on processing large datasets. Experimental results reveal that there are three major performance bottlenecks which significantly impact the serial and parallel buffer construction efficiencies, including the area merging strategy, the task load balance method and the MPI inter-process results merging strategy. Corresponding optimization approaches involving tree-like area merging strategy, the vertex number oriented parallel task partition method and the inter-process results merging strategy were suggested to overcome these bottlenecks. Experiments were carried out to examine the performance efficiency of the optimized parallel algorithm. The estimation results suggested that the optimization approaches could provide high performance and processing ability for buffer construction in a cluster parallel environment. Our method could provide insights into the parallelization of spatial analysis algorithm.

Interference of Heavy Aerosol Loading on the VIIRS Aerosol Optical Depth (AOD Retrieval Algorithm

Directory of Open Access Journals (Sweden)

Yang Wang

2017-04-01

Full Text Available Aerosol optical depth (AOD has been widely used in climate research, atmospheric environmental observations, and other applications. However, high AOD retrieval remains challenging over heavily polluted regions, such as the North China Plain (NCP. The Visible Infrared Imaging Radiometer Suite (VIIRS, which was designed as a successor to the Moderate Resolution Imaging Spectroradiometer (MODIS, will undertake the aerosol observations mission in the coming years. Using the VIIRS AOD retrieval algorithm as an example, we analyzed the influence of heavy aerosol loading through the 6SV radiative transfer model (RTM with a focus on three aspects: cloud masking, ephemeral water body tests, and data quality estimation. First, certain pixels were mistakenly screened out as clouds and ephemeral water bodies because of heavy aerosols, resulting in the loss of AOD retrievals. Second, the greenness of the surface could not be accurately identified by the top of atmosphere (TOA index, and the quality of the aggregation data may be artificially high. Thus, the AOD retrieval algorithm did not perform satisfactorily, indicated by the low availability of data coverage (at least 37.97% of all data records were missing according to ground-based observations and overestimation of the data quality (high-quality data increased from 63.42% to 80.97% according to radiative simulations. To resolve these problems, the implementation of a spatial variability cloud mask method and surficial index are suggested in order to improve the algorithm.

Effect of Load Model Using Ranking Identification Technique for Multi Type DG Incorporating Embedded Meta EP-Firefly Algorithm

Directory of Open Access Journals (Sweden)

Abdul Rahim Siti Rafidah

2018-01-01

Full Text Available This paper presents the effect of load model prior to the distributed generation (DG planning in distribution system. In achieving optimal allocation and placement of DG, a ranking identification technique was proposed in order to study the DG planning using pre-developed Embedded Meta Evolutionary Programming–Firefly Algorithm. The aim of this study is to analyze the effect of different type of DG in order to reduce the total losses considering load factor. To realize the effectiveness of the proposed technique, the IEEE 33 bus test systems was utilized as the test specimen. In this study, the proposed techniques were used to determine the DG sizing and the suitable location for DG planning. The results produced are utilized for the optimization process of DG for the benefit of power system operators and planners in the utility. The power system planner can choose the suitable size and location from the result obtained in this study with the appropriate company’s budget. The modeling of voltage dependent loads has been presented and the results show the voltage dependent load models have a significant effect on total losses of a distribution system for different DG type.

AUTHENTICATION ALGORITHM FOR PARTICIPANTS OF INFORMATION INTEROPERABILITY IN PROCESS OF OPERATING SYSTEM REMOTE LOADING ON THIN CLIENT

Directory of Open Access Journals (Sweden)

Y. A. Gatchin

2016-05-01

Full Text Available Subject of Research.This paper presents solution of authentication problem for all components of information interoperabilityin process of operation system network loading on thin client from terminal server. System Definition. In the proposed solution operation system integrity check is made by hardware-software module, including USB-token with protected memory for secure storage of cryptographic keys and loader. The key requirement for the solution is mutual authentication of four participants: terminal server, thin client, token and user. We have created two algorithms for the problem solution. The first of the designed algorithms compares the encrypted one-time password (random number with the reference value stored in the memory of the token and updates this number in case of successful authentication. The second algorithm uses the public and private keys of the token and the server. As a result of cryptographic transformation, participants are authenticated and the secure channel is formed between the token, thin client and terminal server. Main Results. Additional research was carried out to find out if the designed algorithms meet the necessary requirements. Criteria used included applicability in a multi-access terminal system architecture, potential threats evaluation and overall system security. According to analysis results, it is recommended to use the algorithm based on PKI due to its high scalability and usability. High level of data security is proved as a result of asymmetric cryptography application with the guarantee that participants' private keys are never sent in the authentication process. Practical Relevance. The designed PKI-based algorithm allows solving the problem with the use of cryptographic algorithms according to state standard even in its absence on asymmetric cryptography. Thus, it can be applied in the State Information Systems with increased requirements to information security.

Algorithm for Non-proportional Loading in Sequentially Linear Analysis

NARCIS (Netherlands)

Yu, C.; Hoogenboom, P.C.J.; Rots, J.G.; Saouma, V.; Bolander, J.; Landis, E.

2016-01-01

Sequentially linear analysis (SLA) is an alternative to the Newton-Raphson method for analyzing the nonlinear behavior of reinforced concrete and masonry structures. In this paper SLA is extended to load cases that are applied one after the other, for example first dead load and then wind load. It

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

KAUST Repository

Muraraşu, Alin; Weidendorfer, Josef; Bode, Arndt

2012-01-01

load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression

Optimal Control Allocation with Load Sensor Feedback for Active Load Suppression

Science.gov (United States)

Miller, Christopher

2017-01-01

These slide sets describe the OCLA formulation and associated algorithms as a set of new technologies in the first practical application of load limiting flight control utilizing load feedback as a primary control measurement. Slide set one describes Experiment Development and slide set two describes Flight-Test Performance.

Prediction Based Energy Balancing Forwarding in Cellular Networks

Directory of Open Access Journals (Sweden)

Yang Jian-Jun

2017-01-01

Full Text Available In the recent cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. Energy is a very important factor in the forwarding of cellular network since mobile users(cell phones in hot cells often suffer from low throughput due to energy lack problems. In many situations, the energy lack problems take place because the energy loading is not balanced. In this paper, we present a prediction based forwarding algorithm to let a mobile node dynamically select the next relay station with highest potential energy capacity to resume communication. Key to this strategy is that a relay station only maintains three past status, and then it is able to predict the potential energy capacity. Then, the node selects the next hop with potential maximal energy. Moreover, a location based algorithm is developed to let the mobile node figure out the target region in order to avoid flooding. Simulations demonstrate that our approach significantly increase the aggregate throughput and decrease the delay in cellular network environment.

Controllable Load Management Approaches in Smart Grids

Directory of Open Access Journals (Sweden)

Jingshuang Shen

2015-10-01

Full Text Available With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G, and heat storage are reviewed. The “broad controllable loads” management, such as the microgrid, Virtual Power Plant (VPP, and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.

Performance of the Fuel Conditioning Facility electronic in-cell mass balances

International Nuclear Information System (INIS)

Orechwa, Y.; Bucher, R.G.

1996-01-01

An approach to error estimation and measurement control in the analysis of the balance measurements of mass standards on the in-cell electronic mass balances of the Fuel Conditioning Facility is presented. In light of measurement data from one year of operation, the algorithms proposed are evaluated. The need to take into account the effects of facility operations on the estimates of measurement uncertainty is demonstrated. In the case of a newly installed balance, where no historical data exists, an ad hoc procedure of adding a term which takes into account the operational variability is proposed. This procedure allows a sufficiently long operation so as to collect data for the estimate of the contribution of operational effects to the uncertainty estimate. An algorithm for systematically taking into account historical data is developed and demonstrated for two balances over two calibration periods. The algorithm, both asymptotically and in the two samples cases, has the necessary desirable properties for estimating the uncertainty in the measurements of the balances

Dynamic balance abilities of collegiate men for the bench press.

Science.gov (United States)

Piper, Timothy J; Radlo, Steven J; Smith, Thomas J; Woodward, Ryan W

2012-12-01

This study investigated the dynamic balance detection ability of college men for the bench press exercise. Thirty-five college men (mean ± SD: age = 22.4 ± 2.76 years, bench press experience = 8.3 ± 2.79 years, and estimated 1RM = 120.1 ± 21.8 kg) completed 1 repetition of the bench press repetitions for each of 3 bar loading arrangements. In a randomized fashion, subjects performed the bench press with a 20-kg barbell loaded with one of the following: a balanced load, one 20-kg plate on each side; an imbalanced asymmetrical load, one 20-kg plate on one side and a 20-kg plate plus a 1.25-kg plate on the other side; or an imbalanced asymmetrical center of mass, 20-kg plate on one side and sixteen 1.25-kg plates on the other side. Subjects were blindfolded and wore ear protection throughout all testing to decrease the ability to otherwise detect loads. Binomial data analysis indicated that subjects correctly detected the imbalance of the imbalanced asymmetrical center of mass condition (p[correct detection] = 0.89, p < 0.01) but did not correctly detect the balanced condition (p[correct detection] = 0.46, p = 0.74) or the imbalanced asymmetrical condition (p[correct detection] = 0.60, p = 0.31). Although it appears that a substantial shift in the center of mass of plates leads to the detection of barbell imbalance, minor changes of the addition of 1.25 kg (2.5 lb) to the asymmetrical condition did not result in consistent detection. Our data indicate that the establishment of a biofeedback loop capable of determining balance detection was only realized under a high degree of imbalance. Although balance detection was not present in either the even or the slightly uneven loading condition, the inclusion of balance training for upper body may be futile if exercises are unable to establish such a feedback loop and thus eliciting an improvement of balance performance.

Actual Evapotranspiration in the Al-Khazir Gomal Basin (Northern Iraq Using the Surface Energy Balance Algorithm for Land (SEBAL and Water Balance

Directory of Open Access Journals (Sweden)

Hussein Jassas

2015-04-01

Full Text Available Increasing dependence on groundwater requires a detailed determination of the different outputs and inputs of a basin for better water management. Determination of spatial and temporal actual evapotranspiration (ETa, in this regard, is of vital importance as there is significant water loss from drainage basins. This research paper uses the Surface Energy Balance Algorithm for Land (SEBAL, as well as the water balance, to estimate the spatial and temporal ETa in the Al-Khazir Gomal Basin, Northern Iraq. To compensate for the shortage in rainfall, and to irrigate summer crops, farmers in this basin have been depending, to a large extent, on groundwater extracted from the underlying unconfined aquifer, which is considered the major source for both domestic and agricultural uses in this basin. Rainfed farming of wheat and barley is one of the most important activities in the basin in the winter season, while in the summer season, agricultural activity is limited to small rice fields and narrow strips of vegetable cultivation along the Al-Khazir River. The Landsat Thematic Mapper images (TM5 acquired on 21 November 2006, 9 March 2007, 5 May 2007, 21 July 2007, and 23 September 2007 were used, along with a digital elevation model (DEM and ground-based meteorological data, measured within the area of interest. Estimation of seasonal ETa from periods between satellite overpasses was computed using the evaporative fraction (Ʌ. The water balance approach was utilized, using meteorological data and river hydrograph analysis, to estimate the ETa as the only missing input in the predefined water balance equation. The results of the two applied methods were comparable. SEBAL results were compared with the land use land cover (LULC map. The river showed the highest ETa, as evaporation from the free-water surface. Rice fields, irrigated in the summer season, have a high ETa in the images, as these fields are immersed in water during June, July and August

A Secure and Stable Multicast Overlay Network with Load Balancing for Scalable IPTV Services

Directory of Open Access Journals (Sweden)

Tsao-Ta Wei

2012-01-01

Full Text Available The emerging multimedia Internet application IPTV over P2P network preserves significant advantages in scalability. IPTV media content delivered in P2P networks over public Internet still preserves the issues of privacy and intellectual property rights. In this paper, we use SIP protocol to construct a secure application-layer multicast overlay network for IPTV, called SIPTVMON. SIPTVMON can secure all the IPTV media delivery paths against eavesdroppers via elliptic-curve Diffie-Hellman (ECDH key exchange on SIP signaling and AES encryption. Its load-balancing overlay tree is also optimized from peer heterogeneity and churn of peer joining and leaving to minimize both service degradation and latency. The performance results from large-scale simulations and experiments on different optimization criteria demonstrate SIPTVMON's cost effectiveness in quality of privacy protection, stability from user churn, and good perceptual quality of objective PSNR values for scalable IPTV services over Internet.

DISTRIBUTION NETWORK RECONFIGURATION FOR POWER LOSS MINIMIZATION AND VOLTAGE PROFILE ENHANCEMENT USING ANT LION ALGORITHM

Directory of Open Access Journals (Sweden)

Maryam Shokouhi

2017-06-01

Full Text Available Distribution networks are designed as a ring and operated as a radial form. Therefore, the reconfiguration is a simple and cost-effective way to use existing facilities without the need for any new equipment in distribution networks to achieve various objectives such as: power loss reduction, feeder overload reduction, load balancing, voltage profile improvement, reducing the number of switching considering constraints that ultimately result in the power loss reduction. In this paper, a new method based on the Ant Lion algorithm (a modern meta-heuristic algorithm is provided for the reconfiguration of distribution networks. Considering the extension of the distribution networks and complexity of their communications networks, and the various parameters, using smart techniques is inevitable. The proposed approach is tested on the IEEE 33 & 69-bus radial standard distribution networks. The Evaluation of results in MATLAB software shows the effectiveness of the Ant Lion algorithm in the distribution network reconfiguration.

Vibration reduction of composite plates by piezoelectric patches using a modified artificial bee colony algorithm

Directory of Open Access Journals (Sweden)

Hadi Ghashochi-Bargh

Full Text Available In Current paper, power consumption and vertical displacement optimization of composite plates subject to a step load are carried out by piezoelectric patches using the modified multi-objective Elitist-Artificial Bee Colony (E-ABC algorithm. The motivation behind this concept is to well balance the exploration and exploitation capability for attaining better convergence to the optimum. In order to reduce the calculation time, the elitist strategy is also used in Artificial Bee Colony algorithm. The voltages of patches, plate length/width ratios, ply angles, plate thickness/length ratios, number of layers and edge conditions are chosen as design variables. The formulation is based on the classical laminated plate theory (CLPT and Hamilton's principle. The performance of the new ABC approach is compared with the PSO algorithm and shows the good efficiency of the new ABC approach. To check the validity, the transient responses of isotropic and orthotropic plates are compared with those available in the literature and show a good agreement.

An Analytic Equation Partitioning Climate Variation and Human Impacts on River Sediment Load

Science.gov (United States)

Zhang, J.; Gao, G.; Fu, B.

2017-12-01

Spatial or temporal patterns and process-based equations could co-exist in hydrologic model. Yet, existing approaches quantifying the impacts of those variables on river sediment load (RSL) changes are found to be severely limited, and new ways to evaluate the contribution of these variables are thus needed. Actually, the Newtonian modeling is hardly achievable for this process due to the limitation of both observations and knowledge of mechanisms, whereas laws based on the Darwinian approach could provide one component of a developed hydrologic model. Since that streamflow is the carrier of suspended sediment, sediment load changes are documented in changes of streamflow and suspended sediment concentration (SSC) - water discharge relationships. Consequently, an analytic equation for river sediment load changes are proposed to explicitly quantify the relative contributions of climate variation and direct human impacts on river sediment load changes. Initially, the sediment rating curve, which is of great significance in RSL changes analysis, was decomposed as probability distribution of streamflow and the corresponding SSC - water discharge relationships at equally spaced discharge classes. Furthermore, a proposed segmentation algorithm based on the fractal theory was used to decompose RSL changes attributed to these two portions. Additionally, the water balance framework was utilized and the corresponding elastic parameters were calculated. Finally, changes in climate variables (i.e. precipitation and potential evapotranspiration) and direct human impacts on river sediment load could be figured out. By data simulation, the efficiency of the segmentation algorithm was verified. The analytic equation provides a superior Darwinian approach partitioning climate and human impacts on RSL changes, as only data series of precipitation, potential evapotranspiration and SSC - water discharge are demanded.

Vehicle Routing with Three-dimensional Container Loading Constraints—Comparison of Nested and Joint Algorithms

Science.gov (United States)

Koloch, Grzegorz; Kaminski, Bogumil

2010-10-01

In the paper we examine a modification of the classical Vehicle Routing Problem (VRP) in which shapes of transported cargo are accounted for. This problem, known as a three-dimensional VRP with loading constraints (3D-VRP), is appropriate when transported commodities are not perfectly divisible, but they have fixed and heterogeneous dimensions. In the paper restrictions on allowable cargo positionings are also considered. These restrictions are derived from business practice and they extended the baseline 3D-VRP formulation as considered by Koloch and Kaminski (2010). In particular, we investigate how additional restrictions influence relative performance of two proposed optimization algorithms: the nested and the joint one. Performance of both methods is compared on artificial problems and on a big-scale real life case study.

Behind the Meter Grid Services: Intelligent Load Control

Energy Technology Data Exchange (ETDEWEB)

Kim, Woohyun [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Katipamula, Srinivas [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lutes, Robert G. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Underhill, Ronald M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-11-15

This report describes how the intelligent load control (ILC) algorithm can be implemented to achieve peak demand reduction while minimizing impacts on occupant comfort. The algorithm was designed to minimize the additional sensors and minimum configuration requirements to enable a scalable and cost-effective implementation for both large and small-/medium-sized commercial buildings. The ILC algorithm uses an analytic hierarchy process (AHP) to dynamically prioritize the available curtailable loads based on both quantitative (deviation of zone conditions from set point) and qualitative rules (types of zone). Although the ILC algorithm described in this report was highly tailored to work with rooftop units, it can be generalized for application to other building loads such as variable-air-volume (VAV) boxes and lighting systems.

The balanced academic curriculum problem revisited

DEFF Research Database (Denmark)

Chiarandini, Marco; Di Gaspero, Luca; Gualandi, Stefano

2012-01-01

The Balanced Academic Curriculum Problem (BACP) consists in assigning courses to teaching terms satisfying prerequisites and balancing the credit course load within each term. The BACP is part of the CSPLib with three benchmark instances, but its formulation is simpler than the problem solved...... in practice by universities. In this article, we introduce a generalized version of the problem that takes different curricula and professor preferences into account, and we provide a set of real-life problem instances arisen at University of Udine. Since the existing formulation based on a min-max objective...... function does not balance effectively the credit load for the new instances, we also propose alternative objective functions. Whereas all the CSPLib instances are efficiently solved with Integer Linear Programming (ILP) state-of-the-art solvers, our new set of real-life instances turns out to be much more...

Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations

Energy Technology Data Exchange (ETDEWEB)

Makarov, Yuri V.; Huang, Zhenyu; Etingov, Pavel V.; Ma, Jian; Guttromson, Ross T.; Subbarao, Krishnappa; Chakrabarti, Bhujanga B.

2010-09-01

features make this work a significant step forward toward the objective of incorporating of wind, solar, load, and other uncertainties into power system operations. In this report, a new methodology to predict the uncertainty ranges for the required balancing capacity, ramping capability and ramp duration is presented. Uncertainties created by system load forecast errors, wind and solar forecast errors, generation forced outages are taken into account. The uncertainty ranges are evaluated for different confidence levels of having the actual generation requirements within the corresponding limits. The methodology helps to identify system balancing reserve requirement based on a desired system performance levels, identify system “breaking points”, where the generation system becomes unable to follow the generation requirement curve with the user-specified probability level, and determine the time remaining to these potential events. The approach includes three stages: statistical and actual data acquisition, statistical analysis of retrospective information, and prediction of future grid balancing requirements for specified time horizons and confidence intervals. Assessment of the capacity and ramping requirements is performed using a specially developed probabilistic algorithm based on a histogram analysis incorporating all sources of uncertainty and parameters of a continuous (wind forecast and load forecast errors) and discrete (forced generator outages and failures to start up) nature. Preliminary simulations using California Independent System Operator (California ISO) real life data have shown the effectiveness of the proposed approach. A tool developed based on the new methodology described in this report will be integrated with the California ISO systems. Contractual work is currently in place to integrate the tool with the AREVA EMS system.

Heuristic rules embedded genetic algorithm to solve VVER loading pattern optimization problem

International Nuclear Information System (INIS)

Fatih, Alim; Kostandi, Ivanov

2006-01-01

Full text: Loading Pattern (LP) optimization is one of the most important aspects of the operation of nuclear reactors. A genetic algorithm (GA) code GARCO (Genetic Algorithm Reactor Optimization Code) has been developed with embedded heuristic techniques to perform optimization calculations for in-core fuel management tasks. GARCO is a practical tool that includes a unique methodology applicable for all types of Pressurized Water Reactor (PWR) cores having different geometries with an unlimited number of FA types in the inventory. GARCO was developed by modifying the classical representation of the genotype. Both the genotype representation and the basic algorithm have been modified to incorporate the in-core fuel management heuristics rules so as to obtain the best results in a shorter time. GARCO has three modes. Mode 1 optimizes the locations of the fuel assemblies (FAs) in the nuclear reactor core, Mode 2 optimizes the placement of the burnable poisons (BPs) in a selected LP, and Mode 3 optimizes simultaneously both the LP and the BP placement in the core. This study describes the basic algorithm for Mode 1. The GARCO code is applied to the VVER-1000 reactor hexagonal geometry core in this study. The M oby-Dick i s used as reactor physics code to deplete FAs in the core. It was developed to analyze the VVER reactors by SKODA Inc. To use these rules for creating the initial population with GA operators, the worth definition application is developed. Each FA has a worth value for each location. This worth is between 0 and 1. If worth of any FA for a location is larger than 0.5, this FA in this location is a good choice. When creating the initial population of LPs, a subroutine provides a percent of individuals, which have genes with higher than the 0.5 worth. The percentage of the population to be created without using worth definition is defined in the GARCO input. And also age concept has been developed to accelerate the GA calculation process in reaching the

Multimodal Estimation of Distribution Algorithms.

Science.gov (United States)

Yang, Qiang; Chen, Wei-Neng; Li, Yun; Chen, C L Philip; Xu, Xiang-Min; Zhang, Jun

2016-02-15

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.

Load mitigation of unbalanced wind turbines using PI-R individual pitch control

DEFF Research Database (Denmark)

Zhang, Yunqian; Cheng, Ming; Chen, Zhe

2015-01-01

This study discusses the load mitigation of unbalanced wind turbines, including balanced and unbalanced loads. Regarding the wind turbine load reduction, it has been shown that individual pitch control (IPC) is more promising in comparison with collective pitch control. However, wind turbine......) controller and two resonant (R) compensators is presented. The PI-R regulator is implemented in the hub reference frame to reduce both the balanced and unbalanced loads of the turbine. The wind turbine code FAST (fatigue, aerodynamics, structures and turbulence) is used for the wind turbine load modelling....... The simulations are conducted on the NREL upwind 1.5 MW wind turbine model. Elimination of both the balanced and unbalanced loads of the wind turbine has been achieved, so that PI-R IPC is demonstrated as an effective means for load mitigation of unbalanced wind turbines....

Balancing Vibrations at Harmonic Frequencies by Injecting Harmonic Balancing Signals into the Armature of a Linear Motor/Alternator Coupled to a Stirling Machine

Science.gov (United States)

Holliday, Ezekiel S. (Inventor)

2014-01-01

Vibrations at harmonic frequencies are reduced by injecting harmonic balancing signals into the armature of a linear motor/alternator coupled to a Stirling machine. The vibrations are sensed to provide a signal representing the mechanical vibrations. A harmonic balancing signal is generated for selected harmonics of the operating frequency by processing the sensed vibration signal with adaptive filter algorithms of adaptive filters for each harmonic. Reference inputs for each harmonic are applied to the adaptive filter algorithms at the frequency of the selected harmonic. The harmonic balancing signals for all of the harmonics are summed with a principal control signal. The harmonic balancing signals modify the principal electrical drive voltage and drive the motor/alternator with a drive voltage component in opposition to the vibration at each harmonic.

Investigation of Unsteady Pressure-Sensitive Paint (uPSP) and a Dynamic Loads Balance to Predict Launch Vehicle Buffet Environments

Science.gov (United States)

Schuster, David M.; Panda, Jayanta; Ross, James C.; Roozeboom, Nettie H.; Burnside, Nathan J.; Ngo, Christina L.; Kumagai, Hiro; Sellers, Marvin; Powell, Jessica M.; Sekula, Martin K.;

An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading

International Nuclear Information System (INIS)

Shayeghi, H.; Mahdavi, M.; Bagheri, A.

2010-01-01

Static transmission network expansion planning (STNEP) problem acquires a principal role in power system planning and should be evaluated carefully. Up till now, various methods have been presented to solve the STNEP problem. But only in one of them, lines adequacy rate has been considered at the end of planning horizon and the problem has been optimized by discrete particle swarm optimization (DPSO). DPSO is a new population-based intelligence algorithm and exhibits good performance on solution of the large-scale, discrete and non-linear optimization problems like STNEP. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In order to overcome these drawbacks and considering lines adequacy rate, in this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using an improved DPSO algorithm. The proposed improved DPSO is a new conception, collectivity, which is based on similarity between the particle and the current global best particle in the swarm that can prevent the premature convergence of DPSO around the local solution. The proposed method has been tested on the Garver's network and a real transmission network in Iran, and compared with the DPSO based method for solution of the TNEP problem. The results show that the proposed improved DPSO based method by preventing the premature convergence is caused that with almost the same expansion costs, the network adequacy is increased considerably. Also, regarding the convergence curves of both methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than DPSO approach.

Optimized Loading for Particle-in-cell Gyrokinetic Simulations

International Nuclear Information System (INIS)

Lewandowski, J.L.V.

2004-01-01

The problem of particle loading in particle-in-cell gyrokinetic simulations is addressed using a quadratic optimization algorithm. Optimized loading in configuration space dramatically reduces the short wavelength modes in the electrostatic potential that are partly responsible for the non-conservation of total energy; further, the long wavelength modes are resolved with good accuracy. As a result, the conservation of energy for the optimized loading is much better that the conservation of energy for the random loading. The method is valid for any geometry and can be coupled to optimization algorithms in velocity space

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Shawkat K. Guirguis

2017-01-01

Full Text Available Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN. Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the noders residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.

A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile

International Nuclear Information System (INIS)

Yang, Fangfang; Xing, Yinjiao; Wang, Dong; Tsui, Kwok-Leung

2016-01-01

Highlights: • Three different model-based filtering algorithms for SOC estimation are compared. • A combined dynamic loading profile is proposed to evaluate the three algorithms. • Robustness against uncertainty of initial states of SOC estimators are investigated. • Battery capacity degradation is considered in SOC estimation. - Abstract: Accurate state-of-charge (SOC) estimation is critical for the safety and reliability of battery management systems in electric vehicles. Because SOC cannot be directly measured and SOC estimation is affected by many factors, such as ambient temperature, battery aging, and current rate, a robust SOC estimation approach is necessary to be developed so as to deal with time-varying and nonlinear battery systems. In this paper, three popular model-based filtering algorithms, including extended Kalman filter, unscented Kalman filter, and particle filter, are respectively used to estimate SOC and their performances regarding to tracking accuracy, computation time, robustness against uncertainty of initial values of SOC, and battery degradation, are compared. To evaluate the performances of these algorithms, a new combined dynamic loading profile composed of the dynamic stress test, the federal urban driving schedule and the US06 is proposed. The comparison results showed that the unscented Kalman filter is the most robust to different initial values of SOC, while the particle filter owns the fastest convergence ability when an initial guess of SOC is far from a true initial SOC.

A survey of parallel multigrid algorithms

Science.gov (United States)

Chan, Tony F.; Tuminaro, Ray S.

1987-01-01

A typical multigrid algorithm applied to well-behaved linear-elliptic partial-differential equations (PDEs) is described. Criteria for designing and evaluating parallel algorithms are presented. Before evaluating the performance of some parallel multigrid algorithms, consideration is given to some theoretical complexity results for solving PDEs in parallel and for executing the multigrid algorithm. The effect of mapping and load imbalance on the partial efficiency of the algorithm is studied.

An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration

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)

Apparatus and method for optimal phase balancing using dynamic programming with spatial consideration

Science.gov (United States)

Robertazzi, Thomas G.; Skiena, Steven; Wang, Kai

2017-08-08

Provided are an apparatus and method for load-balancing of a three-phase electric power distribution system having a multi-phase feeder, including obtaining topology information of the feeder identifying supply points for customer loads and feeder sections between the supply points, obtaining customer information that includes peak customer load at each of the points between each of the feeder sections, performing a phase balancing analysis, and recommending phase assignment at the customer load supply points.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

Science.gov (United States)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Clinical correlates of between-limb synchronization of standing balance control and falls during inpatient stroke rehabilitation.

Science.gov (United States)

Mansfield, Avril; Mochizuki, George; Inness, Elizabeth L; McIlroy, William E

2012-01-01

Stroke-related sensorimotor impairment potentially contributes to impaired balance. Balance measures that reveal underlying limb-specific control problems, such as a measure of the synchronization of both lower limbs to maintain standing balance, may be uniquely informative about poststroke balance control. This study aimed to determine the relationships between clinical measures of sensorimotor control, functional balance, and fall risk and between-limb synchronization of balance control. The authors conducted a retrospective chart review of 100 individuals with stroke admitted to inpatient rehabilitation. Force plate-based measures were obtained while standing on 2 force plates, including postural sway (root mean square of anteroposterior and mediolateral center of pressure [COP]), stance load asymmetry (percentage of body weight borne on the less-loaded limb), and between-limb synchronization (cross-correlation of the COP recordings under each foot). Clinical measures obtained were motor impairment (Chedoke-McMaster Stroke Assessment), plantar cutaneous sensation, functional balance (Berg Balance Scale), and falls experienced in rehabilitation. Synchronization was significantly related to motor impairment and prospective falls, even when controlling for other force plate-based measures of standing balance control (ie, postural sway and stance load symmetry). Between-limb COP synchronization for standing balance appears to be a uniquely important index of balance control, independent of postural sway and load symmetry during stance.

Fatigue evaluation algorithms: Review

Energy Technology Data Exchange (ETDEWEB)

Passipoularidis, V.A.; Broendsted, P.

2009-11-15

A progressive damage fatigue simulator for variable amplitude loads named FADAS is discussed in this work. FADAS (Fatigue Damage Simulator) performs ply by ply stress analysis using classical lamination theory and implements adequate stiffness discount tactics based on the failure criterion of Puck, to model the degradation caused by failure events in ply level. Residual strength is incorporated as fatigue damage accumulation metric. Once the typical fatigue and static properties of the constitutive ply are determined,the performance of an arbitrary lay-up under uniaxial and/or multiaxial load time series can be simulated. The predictions are validated against fatigue life data both from repeated block tests at a single stress ratio as well as against spectral fatigue using the WISPER, WISPERX and NEW WISPER load sequences on a Glass/Epoxy multidirectional laminate typical of a wind turbine rotor blade construction. Two versions of the algorithm, the one using single-step and the other using incremental application of each load cycle (in case of ply failure) are implemented and compared. Simulation results confirm the ability of the algorithm to take into account load sequence effects. In general, FADAS performs well in predicting life under both spectral and block loading fatigue. (author)

Researching on YH100 Numerical Control Servo Press Hydraulic Control System and Control Algorithm

Directory of Open Access Journals (Sweden)

Kai LI

2014-09-01

Full Text Available In order to study the numerical control (NC servo press hydraulic control system and its control algorithm. The numerical control servo press performance and control principle of hydraulic control system are analyzed. According to the flow equation of the hydraulic control valve, hydraulic cylinder flow continuity equation and the force balance equation of the hydraulic cylinder with load press, the mathematical model of hydraulic control system is established. And the servo press hydraulic system transfer function is deduced. Introducing the suitable immune particle swarm control algorithm for servo press hydraulic system, and the control system block diagram is established. Immune algorithm is used to optimize new control parameters of the system and adopt the new optimization results to optimize the system simulation. The simulation result shows that the hydraulic system’s transition time controlled by the immune particle swarm algorithm is shorter than traditional ones, and the control performance is obviously improved. Finally it can be concluded that immune particle swarm PID control have these characteristics such as quickness, stability and accuracy. Applying this principle into application, the obtained YH100 numerical control servo press hydraulic control system meets the requirement.

AC-DC integrated load flow calculation for variable speed offshore wind farms

DEFF Research Database (Denmark)

Zhao, Menghua; Chen, Zhe; Blaabjerg, Frede

2005-01-01

This paper proposes a sequential AC-DC integrated load flow algorithm for variable speed offshore wind farms. In this algorithm, the variable frequency and the control strategy of variable speed wind turbine systems are considered. In addition, the losses of wind turbine systems and the losses...... of converters are also integrated into the load flow algorithm. As a general algorithm, it can be applied to different types of wind farm configurations, and the load flow is related to the wind speed....

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

KAUST Repository

Muraraşu, Alin

2012-01-01

Multi-core parallelism and accelerators are becoming common features of today’s computer systems, as they allow for computational power without sacrificing energy efficiency. Due to heterogeneity, tuning for each type of compute unit and adequate load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression. Its performance critical part is the interpolation routine used for decompression. Results show that our load balancing scheme allows for an efficient acceleration of interpolation on heterogeneous systems containing multi-core CPUs and GPUs.

Automatic computation and solution of generalized harmonic balance equations

Science.gov (United States)

Peyton Jones, J. C.; Yaser, K. S. A.; Stevenson, J.

2018-02-01

Generalized methods are presented for generating and solving the harmonic balance equations for a broad class of nonlinear differential or difference equations and for a general set of harmonics chosen by the user. In particular, a new algorithm for automatically generating the Jacobian of the balance equations enables efficient solution of these equations using continuation methods. Efficient numeric validation techniques are also presented, and the combined algorithm is applied to the analysis of dc, fundamental, second and third harmonic response of a nonlinear automotive damper.

Algorithms in combinatorial design theory

CERN Document Server

Colbourn, CJ

1985-01-01

The scope of the volume includes all algorithmic and computational aspects of research on combinatorial designs. Algorithmic aspects include generation, isomorphism and analysis techniques - both heuristic methods used in practice, and the computational complexity of these operations. The scope within design theory includes all aspects of block designs, Latin squares and their variants, pairwise balanced designs and projective planes and related geometries.

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

DEFF Research Database (Denmark)

Tan, Kang Miao; Ramachandaramurthy, Vigna K.; Yong, Jia Ying

2017-01-01

-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance...... of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various...

Network characteristics emerging from agent interactions in balanced distributed system.

Science.gov (United States)

Salman, Mahdi Abed; Bertelle, Cyrille; Sanlaville, Eric

2015-01-01

A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between system nodes. It aims at enhancing the resource usage. A load balancing strategy specifies scenarios for the cooperation. Its efficiency depends on quantity, accuracy, and distribution of available information. Nevertheless, the distribution of information on the nodes, together with the initial network structure, may create different logical network structures. In this paper, different load balancing strategies are tested on different network structures using a simulation. The four tested strategies are able to distribute evenly the load so that the system reaches a steady state (the mean response time of the jobs is constant), but it is shown that a given strategy indeed behaves differently according to structural parameters and information spreading. Such a study, devoted to distributed computing systems (DCSs), can be useful to understand and drive the behavior of other complex systems.

An Energy Efficient Stable Election-Based Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Weiwei Yuan

2013-10-01

Full Text Available Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs. However, sensor networks with one fixed sink node often suffer from a hot spots problem since nodes near sinks have more traffic burden to forward during a multi-hop transmission process. The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP, which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.

Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

International Nuclear Information System (INIS)

Dong, Feifei; Liu, Yong; Su, Han; Zou, Rui; Guo, Huaicheng

2015-01-01

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

Energy Technology Data Exchange (ETDEWEB)

Dong, Feifei [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Liu, Yong, E-mail: yongliu@pku.edu.cn [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Institute of Water Sciences, Peking University, Beijing 100871 (China); Su, Han [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Zou, Rui [Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA 22030 (United States); Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming 650034 (China); Guo, Huaicheng [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China)

2015-05-15

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

Multilevel Balancing Domain Decomposition by Constraints Deluxe Algorithms with Adaptive Coarse Spaces for Flow in Porous Media

KAUST Repository

Zampini, Stefano; Tu, Xuemin

2017-01-01

Multilevel balancing domain decomposition by constraints (BDDC) deluxe algorithms are developed for the saddle point problems arising from mixed formulations of Darcy flow in porous media. In addition to the standard no-net-flux constraints on each face, adaptive primal constraints obtained from the solutions of local generalized eigenvalue problems are included to control the condition number. Special deluxe scaling and local generalized eigenvalue problems are designed in order to make sure that these additional primal variables lie in a benign subspace in which the preconditioned operator is positive definite. The current multilevel theory for BDDC methods for porous media flow is complemented with an efficient algorithm for the computation of the so-called malign part of the solution, which is needed to make sure the rest of the solution can be obtained using the conjugate gradient iterates lying in the benign subspace. We also propose a new technique, based on the Sherman--Morrison formula, that lets us preserve the complexity of the subdomain local solvers. Condition number estimates are provided under certain standard assumptions. Extensive numerical experiments confirm the theoretical estimates; additional numerical results prove the effectiveness of the method with higher order elements and high-contrast problems from real-world applications.

Multilevel Balancing Domain Decomposition by Constraints Deluxe Algorithms with Adaptive Coarse Spaces for Flow in Porous Media

KAUST Repository

Zampini, Stefano

2017-08-03

Multilevel balancing domain decomposition by constraints (BDDC) deluxe algorithms are developed for the saddle point problems arising from mixed formulations of Darcy flow in porous media. In addition to the standard no-net-flux constraints on each face, adaptive primal constraints obtained from the solutions of local generalized eigenvalue problems are included to control the condition number. Special deluxe scaling and local generalized eigenvalue problems are designed in order to make sure that these additional primal variables lie in a benign subspace in which the preconditioned operator is positive definite. The current multilevel theory for BDDC methods for porous media flow is complemented with an efficient algorithm for the computation of the so-called malign part of the solution, which is needed to make sure the rest of the solution can be obtained using the conjugate gradient iterates lying in the benign subspace. We also propose a new technique, based on the Sherman--Morrison formula, that lets us preserve the complexity of the subdomain local solvers. Condition number estimates are provided under certain standard assumptions. Extensive numerical experiments confirm the theoretical estimates; additional numerical results prove the effectiveness of the method with higher order elements and high-contrast problems from real-world applications.

Inducer Hydrodynamic Load Measurement Devices

Science.gov (United States)

Skelley, Stephen E.; Zoladz, Thomas F.

2002-01-01

Marshall Space Flight Center (MSFC) has demonstrated two measurement devices for sensing and resolving the hydrodynamic loads on fluid machinery. The first - a derivative of the six component wind tunnel balance - senses the forces and moments on the rotating device through a weakened shaft section instrumented with a series of strain gauges. This "rotating balance" was designed to directly measure the steady and unsteady hydrodynamic loads on an inducer, thereby defining both the amplitude and frequency content associated with operating in various cavitation modes. The second device - a high frequency response pressure transducer surface mounted on a rotating component - was merely an extension of existing technology for application in water. MSFC has recently completed experimental evaluations of both the rotating balance and surface-mount transducers in a water test loop. The measurement bandwidth of the rotating balance was severely limited by the relative flexibility of the device itself, resulting in an unexpectedly low structural bending mode and invalidating the higher frequency response data. Despite these limitations, measurements confirmed that the integrated loads on the four-bladed inducer respond to both cavitation intensity and cavitation phenomena. Likewise, the surface-mount pressure transducers were subjected to a range of temperatures and flow conditions in a non-rotating environment to record bias shifts and transfer functions between the transducers and a reference device. The pressure transducer static performance was within manufacturer's specifications and dynamic response accurately followed that of the reference.

A proposal for off-grid photovoltaic systems with non-controllable loads using fuzzy logic

International Nuclear Information System (INIS)

Yahyaoui, Imene; Sallem, Souhir; Kamoun, M.B.A.; Tadeo, Fernando

2014-01-01

Highlights: • An energy management system is proposed for off-grid PV systems, based on fuzzy logic. • The proposal guarantees the energy balance and battery protection. • The approach is demonstrated using data measured at the target location. - Abstract: A fuzzy-logic based methodology is proposed and evaluated for energy management in off-grid installations with photovoltaic panels as the source of energy and a limited storage capacity in batteries. The decision on the connection or disconnection of components is based on fuzzy rules on the basis of the Photovoltaic Panel Generation measurement, the measured power required by the load, and the estimation of the stored energy in the batteries (this last is obtained from the estimation of the Depth-of-Discharge). The algorithm aims to ensure the system’s autonomy by controlling the switches linking the system components with respect to a multi-objective management criterion developed from the requirements (supply of the load, protection of the battery, etc.). Detailed tests of the proposed system are carried out using data (irradiation, temperature, power consumption, etc.) measured in a household at the target area at several days of the year. The results demonstrate that the proposed approach achieves the objectives of system autonomy, battery protection and power supply stability. Compared with a basic algorithm, the proposed algorithm is not sensitive to sudden changes in atmospheric parameters and avoids overcharging the battery

A balancing act of the brain: activations and deactivations driven by cognitive load.

Science.gov (United States)

Arsalidou, Marie; Pascual-Leone, Juan; Johnson, Janice; Morris, Drew; Taylor, Margot J

2013-05-01

The majority of neuroimaging studies focus on brain activity during performance of cognitive tasks; however, some studies focus on brain areas that activate in the absence of a task. Despite the surge of research comparing these contrasted areas of brain function, their interrelation is not well understood. We systematically manipulated cognitive load in a working memory task to examine concurrently the relation between activity elicited by the task versus activity during control conditions. We presented adults with six levels of task demand, and compared those with three conditions without a task. Using whole-brain analysis, we found positive linear relations between cortical activity and task difficulty in areas including middle frontal gyrus and dorsal cingulate; negative linear relations were found in medial frontal gyrus and posterior cingulate. These findings demonstrated balancing of activation patterns between two mental processes, which were both modulated by task difficulty. Frontal areas followed a graded pattern more closely than other regions. These data also showed that working memory has limited capacity in adults: an upper bound of seven items and a lower bound of four items. Overall, working memory and default-mode processes, when studied concurrently, reveal mutually competing activation patterns.

Calibration Variable Selection and Natural Zero Determination for Semispan and Canard Balances

Science.gov (United States)

Ulbrich, Norbert M.

2013-01-01

Independent calibration variables for the characterization of semispan and canard wind tunnel balances are discussed. It is shown that the variable selection for a semispan balance is determined by the location of the resultant normal and axial forces that act on the balance. These two forces are the first and second calibration variable. The pitching moment becomes the third calibration variable after the normal and axial forces are shifted to the pitch axis of the balance. Two geometric distances, i.e., the rolling and yawing moment arms, are the fourth and fifth calibration variable. They are traditionally substituted by corresponding moments to simplify the use of calibration data during a wind tunnel test. A canard balance is related to a semispan balance. It also only measures loads on one half of a lifting surface. However, the axial force and yawing moment are of no interest to users of a canard balance. Therefore, its calibration variable set is reduced to the normal force, pitching moment, and rolling moment. The combined load diagrams of the rolling and yawing moment for a semispan balance are discussed. They may be used to illustrate connections between the wind tunnel model geometry, the test section size, and the calibration load schedule. Then, methods are reviewed that may be used to obtain the natural zeros of a semispan or canard balance. In addition, characteristics of three semispan balance calibration rigs are discussed. Finally, basic requirements for a full characterization of a semispan balance are reviewed.

Power-aware load balancing of large scale MPI applications

OpenAIRE

Etinski, Maja; Corbalán González, Julita; Labarta Mancho, Jesús José; Valero Cortés, Mateo; Veidenbaum, Alex

2009-01-01

Power consumption is a very important issue for HPC community, both at the level of one application or at the level of whole workload. Load imbalance of a MPI application can be exploited to save CPU energy without penalizing the execution time. An application is load imbalanced when some nodes are assigned more computation than others. The nodes with less computation can be run at lower frequency since otherwise they have to wait for the nodes with more computation blocked in MPI calls. A te...

Balancing the Power-to-Load Ratio for a Novel Variable Geometry Wave Energy Converter with Nonideal Power Take-Off in Regular Waves: Preprint

Energy Technology Data Exchange (ETDEWEB)

Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Yu, Yi-Hsiang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Wright, Alan D [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-09-28

This work attempts to balance power absorption against structural loading for a novel variable geometry wave energy converter. The variable geometry consists of four identical flaps that will be opened in ascending order starting with the flap closest to the seafloor and moving to the free surface. The influence of a pitch motion constraint on power absorption when utilizing a nonideal power take-off (PTO) is examined and found to reduce the losses associated with bidirectional energy flow. The power-to-load ratio is evaluated using pseudo-spectral control to determine the optimum PTO torque based on a multiterm objective function. The pseudo-spectral optimal control problem is extended to include load metrics in the objective function, which may now consist of competing terms. Separate penalty weights are attached to the surge-foundation force and PTO control torque to tune the optimizer performance to emphasize either power absorption or load shedding. PTO efficiency is not included in the objective function, but the penalty weights are utilized to limit the force and torque amplitudes, thereby reducing losses associated with bidirectional energy flow. Results from pseudo-spectral control demonstrate that shedding a portion of the available wave energy can provide greater reductions in structural loads and reactive power.

Solution quality improvement in chiller loading optimization

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

In order to reduce greenhouse gas emission, we can energy-efficiently operate a multiple chiller system using optimization techniques. So far, various optimization techniques have been proposed to the optimal chiller loading problem. Most of those techniques are meta-heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization. However, this study applied a gradient-based method, named generalized reduced gradient, and then obtains better results when compared with other approaches. When two additional approaches (hybridization between meta-heuristic algorithm and gradient-based algorithm; and reformulation of optimization structure by adding a binary variable which denotes chiller's operating status) were introduced, generalized reduced gradient found even better solutions. - Highlights: → Chiller loading problem is optimized by generalized reduced gradient (GRG) method. → Results are compared with meta-heuristic algorithms such as genetic algorithm. → Results are even enhanced by hybridizing meta-heuristic and gradient techniques. → Results are even enhanced by modifying the optimization formulation.

Analysis, calculation and utilization of the k-balance attribute in interdependent networks

Science.gov (United States)

Liu, Zheng; Li, Qing; Wang, Dan; Xu, Mingwei

2018-05-01

Interdependent networks, where two networks depend on each other, are becoming more and more significant in modern systems. From previous work, it can be concluded that interdependent networks are more vulnerable than a single network. The robustness in interdependent networks deserves special attention. In this paper, we propose a metric of robustness from a new perspective-the balance. First, we define the balance-coefficient of the interdependent system. Based on precise analysis and derivation, we prove some significant theories and provide an efficient algorithm to compute the balance-coefficient. Finally, we propose an optimal solution to reduce the balance-coefficient to enhance the robustness of the given system. Comprehensive experiments confirm the efficiency of our algorithms.

Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity

International Nuclear Information System (INIS)

Lee, Kok Foong; Patterson, Robert I.A.; Wagner, Wolfgang; Kraft, Markus

2015-01-01

Graphical abstract: -- Highlights: •Problems concerning multi-compartment population balance equations are studied. •A class of fragmentation weight transfer functions is presented. •Three stochastic weighted algorithms are compared against the direct simulation algorithm. •The numerical errors of the stochastic solutions are assessed as a function of fragmentation rate. •The algorithms are applied to a multi-dimensional granulation model. -- Abstract: This paper introduces stochastic weighted particle algorithms for the solution of multi-compartment population balance equations. In particular, it presents a class of fragmentation weight transfer functions which are constructed such that the number of computational particles stays constant during fragmentation events. The weight transfer functions are constructed based on systems of weighted computational particles and each of it leads to a stochastic particle algorithm for the numerical treatment of population balance equations. Besides fragmentation, the algorithms also consider physical processes such as coagulation and the exchange of mass with the surroundings. The numerical properties of the algorithms are compared to the direct simulation algorithm and an existing method for the fragmentation of weighted particles. It is found that the new algorithms show better numerical performance over the two existing methods especially for systems with significant amount of large particles and high fragmentation rates.

Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity

Energy Technology Data Exchange (ETDEWEB)

Lee, Kok Foong [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom); Patterson, Robert I.A.; Wagner, Wolfgang [Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstraße 39, 10117 Berlin (Germany); Kraft, Markus, E-mail: mk306@cam.ac.uk [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom); School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 (Singapore)

2015-12-15

Graphical abstract: -- Highlights: •Problems concerning multi-compartment population balance equations are studied. •A class of fragmentation weight transfer functions is presented. •Three stochastic weighted algorithms are compared against the direct simulation algorithm. •The numerical errors of the stochastic solutions are assessed as a function of fragmentation rate. •The algorithms are applied to a multi-dimensional granulation model. -- Abstract: This paper introduces stochastic weighted particle algorithms for the solution of multi-compartment population balance equations. In particular, it presents a class of fragmentation weight transfer functions which are constructed such that the number of computational particles stays constant during fragmentation events. The weight transfer functions are constructed based on systems of weighted computational particles and each of it leads to a stochastic particle algorithm for the numerical treatment of population balance equations. Besides fragmentation, the algorithms also consider physical processes such as coagulation and the exchange of mass with the surroundings. The numerical properties of the algorithms are compared to the direct simulation algorithm and an existing method for the fragmentation of weighted particles. It is found that the new algorithms show better numerical performance over the two existing methods especially for systems with significant amount of large particles and high fragmentation rates.

Feed-forward volume rendering algorithm for moderately parallel MIMD machines

Science.gov (United States)

Yagel, Roni

1993-01-01

Algorithms for direct volume rendering on parallel and vector processors are investigated. Volumes are transformed efficiently on parallel processors by dividing the data into slices and beams of voxels. Equal sized sets of slices along one axis are distributed to processors. Parallelism is achieved at two levels. Because each slice can be transformed independently of others, processors transform their assigned slices with no communication, thus providing maximum possible parallelism at the first level. Within each slice, consecutive beams are incrementally transformed using coherency in the transformation computation. Also, coherency across slices can be exploited to further enhance performance. This coherency yields the second level of parallelism through the use of the vector processing or pipelining. Other ongoing efforts include investigations into image reconstruction techniques, load balancing strategies, and improving performance.

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Directory of Open Access Journals (Sweden)

Nadeem Javaid

2017-03-01

Full Text Available In recent years, demand side management (DSM techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA, the binary particle swarm optimization (BPSO algorithm, the bacterial foraging optimization algorithm (BFOA, the wind-driven optimization (WDO algorithm and our proposed hybrid genetic wind-driven (GWD algorithm are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs and off-peak hours (OPHs in a real-time pricing (RTP environment while maximizing user comfort (UC and minimizing both electricity cost and the peak to average ratio (PAR. Moreover, these algorithms are tested in two scenarios: (i scheduling the load of a single home and (ii scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

Residential Load Manageability Factor Analyses by Load Sensitivity Affected by Temperature

Directory of Open Access Journals (Sweden)

N. Eskandari

2016-12-01

Full Text Available Load side management is the basic and significant principle to keeping the balance between generation side and consumption side of electrical power energy. Load side management on typical medium voltage feeder is the power energy consumption control of connected loads with variation of essential parameters that loads do reaction to their variation. Knowing amount of load's reaction to each parameters variation in typical medium voltage feeder during the day, leads to gain Load Manageability Factor (LMF for that specific feeder that helps power utilities to manage their connected loads. Calculating this LMF needs to find out each types of load with unique inherent features behavior to each parameters variation. This paper results and future work results will help us to catch mentioned LMF. In this paper analysis of residential load behavior due to temperature variation with training artificial neural network will be done. Load behavior due to other essential parameters variations like energy pricing variation, major event happening, and power utility announcing to the customers, and etc will study in future works. Collecting all related works results in a unit mathematical equation or an artificial neural network will gain LMF.

Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow

Science.gov (United States)

Gupta, Atma Ram; Kumar, Ashwani

2017-12-01

Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.

Cell Load Balancing Schemes for Uncoordinated Heterogeneous Deployment

DEFF Research Database (Denmark)

Mihaylov, Mihail; Mihovska, Albena D.; Poulkov, Vladimir

2013-01-01

This paper proposes an energy consumption improvement algorithm based on a coefficient determining the reliability of offloading users to a new base station (BS) regarding the additional power needed for them to be compensated during an offload from pico BS (PBS) to macro BS (MBS) in uncoordinate...

Load flow analysis for variable speed offshore wind farms

DEFF Research Database (Denmark)

Chen, Zhe; Zhao, Menghua; Blaabjerg, Frede

2009-01-01

factors such as the different wind farm configurations, the control of wind turbines and the power losses of pulse width modulation converters are considered. The DC/DC converter model is proposed and integrated into load flow algorithm by modifying the Jacobian matrix. Two iterative methods are proposed...... and integrated into the load flow algorithm: one takes into account the control strategy of converters and the other considers the power losses of converters. In addition, different types of variable speed wind turbine systems with different control methods are investigated. Finally, the method is demonstrated......A serial AC-DC integrated load flow algorithm for variable speed offshore wind farms is proposed. It divides the electrical system of a wind farm into several local networks, and different load flow methods are used for these local networks sequentially. This method is fast, more accurate, and many...

Participation of Flexible Loads in Load Frequency Control to Support High Wind Penetration

DEFF Research Database (Denmark)

Uslu, Umur; Zhang, Boyang; Pillai, Jayakrishnan Radhakrishna

2016-01-01

The increasing amount of fluctuating wind power penetration in power systems presents many challenges to its operation and control. The new wind power plants are replacing many of the conventional large power plants that ensure power balancing and ancillary services for stable and reliable...... operation of the grid. Therefore, new solutions for power balancing reserves have to be explored and utilized by the grid utilities. To meet these challenges, large sizable loads like alkaline electrolysers, heat pumps and electric vehicles which are gaining popularity can provide system support to the grid...... through their inherent flexibility and energy storage characteristics. This paper investigates the possibilities and potential of such flexible loads to participate in power system frequency regulation in a wind dominated power system. The results show that these consumption units provide better...

Thermal Storage Power Balancing with Model Predictive Control

DEFF Research Database (Denmark)

Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

2013-01-01

The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...

Improving Large-scale Storage System Performance via Topology-aware and Balanced Data Placement

Energy Technology Data Exchange (ETDEWEB)

Wang, Feiyi [ORNL; Oral, H Sarp [ORNL; Vazhkudai, Sudharshan S [ORNL

2014-01-01

With the advent of big data, the I/O subsystems of large-scale compute clusters are becoming a center of focus, with more applications putting greater demands on end-to-end I/O performance. These subsystems are often complex in design. They comprise of multiple hardware and software layers to cope with the increasing capacity, capability and scalability requirements of data intensive applications. The sharing nature of storage resources and the intrinsic interactions across these layers make it to realize user-level, end-to-end performance gains a great challenge. We propose a topology-aware resource load balancing strategy to improve per-application I/O performance. We demonstrate the effectiveness of our algorithm on an extreme-scale compute cluster, Titan, at the Oak Ridge Leadership Computing Facility (OLCF). Our experiments with both synthetic benchmarks and a real-world application show that, even under congestion, our proposed algorithm can improve large-scale application I/O performance significantly, resulting in both the reduction of application run times and higher resolution simulation runs.

Optimization of line configuration and balancing for flexible machining lines

Science.gov (United States)

Liu, Xuemei; Li, Aiping; Chen, Zurui

2016-05-01

Line configuration and balancing is to select the type of line and allot a given set of operations as well as machines to a sequence of workstations to realize high-efficiency production. Most of the current researches for machining line configuration and balancing problems are related to dedicated transfer lines with dedicated machine workstations. With growing trends towards great product variety and fluctuations in market demand, dedicated transfer lines are being replaced with flexible machining line composed of identical CNC machines. This paper deals with the line configuration and balancing problem for flexible machining lines. The objective is to assign operations to workstations and find the sequence of execution, specify the number of machines in each workstation while minimizing the line cycle time and total number of machines. This problem is subject to precedence, clustering, accessibility and capacity constraints among the features, operations, setups and workstations. The mathematical model and heuristic algorithm based on feature group strategy and polychromatic sets theory are presented to find an optimal solution. The feature group strategy and polychromatic sets theory are used to establish constraint model. A heuristic operations sequencing and assignment algorithm is given. An industrial case study is carried out, and multiple optimal solutions in different line configurations are obtained. The case studying results show that the solutions with shorter cycle time and higher line balancing rate demonstrate the feasibility and effectiveness of the proposed algorithm. This research proposes a heuristic line configuration and balancing algorithm based on feature group strategy and polychromatic sets theory which is able to provide better solutions while achieving an improvement in computing time.

LOADING SIMULATION PROGRAM C

Data.gov (United States)

U.S. Environmental Protection Agency — LSPC is the Loading Simulation Program in C++, a watershed modeling system that includes streamlined Hydrologic Simulation Program Fortran (HSPF) algorithms for...

Load flow analysis using decoupled fuzzy load flow under critical ...

African Journals Online (AJOL)

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3.1 Maximum range selection of input and output variables: ..... Wong K. P., Li A., and Law M.Y., “ Advanced Constrained Genetic Algorithm Load Flow Method”, IEE Proc. ... Dr. Parimal Acharjee passed B.E.E. from North Bengal University ...

Simulation-Based Approach for Studying the Balancing of Local Smart Grids with Electric Vehicle Batteries

Directory of Open Access Journals (Sweden)

Juhani Latvakoski

2015-07-01

Full Text Available Modern society is facing great challenges due to pollution and increased carbon dioxide (CO2 emissions. As part of solving these challenges, the use of renewable energy sources and electric vehicles (EVs is rapidly increasing. However, increased dynamics have triggered problems in balancing energy supply and consumption demand in the power systems. The resulting uncertainty and unpredictability of energy production, consumption, and management of peak loads has caused an increase in costs for energy market actors. Therefore, the means for studying the balancing of local smart grids with EVs is a starting point for this paper. The main contribution is a simulation-based approach which was developed to enable the study of the balancing of local distribution grids with EV batteries in a cost-efficient manner. The simulation-based approach is applied to enable the execution of a distributed system with the simulation of a local distribution grid, including a number of charging stations and EVs. A simulation system has been constructed to support the simulation-based approach. The evaluation has been carried out by executing the scenario related to balancing local distribution grids with EV batteries in a step-by-step manner. The evaluation results indicate that the simulation-based approach is able to facilitate the evaluation of smart grid– and EV-related communication protocols, control algorithms for charging, and functionalities of local distribution grids as part of a complex, critical cyber-physical system. In addition, the simulation system is able to incorporate advanced methods for monitoring, controlling, tracking, and modeling behavior. The simulation model of the local distribution grid can be executed with the smart control of charging and discharging powers of the EVs according to the load situation in the local distribution grid. The resulting simulation system can be applied to the study of balancing local smart grids with EV

Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem

Directory of Open Access Journals (Sweden)

Yang Fei

2016-01-01

Full Text Available Energy efficiency is one of our most economical sources of new energy. When it comes to efficient building design, the computation of the heating load (HL and cooling load (CL is required to determine the specifications of the heating and cooling equipment. The objective of this paper is to model heating load and cooling load buildings using neural networks in order to predict HL load and CL load. Rprop with genetic algorithm was proposed to increase the global convergence capability of Rprop by modifying a corresponding weight. Comparison results show that Rprop with GA can successfully improve the global convergence capability of Rprop and achieve lower MSE than other perceptron training algorithms, such as Back-Propagation or original Rprop. In addition, the trained network has better generalization ability and stabilization performance.