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...
Directory of Open Access Journals (Sweden)
Yu. L. Sayenko
2016-12-01
Full Text Available Purpose. To perform structural and parametric identification of generalized load equivalent circuit of three-phase three-wire load in the network in the space of phase components. Methodology. Underlying structural identification methods are matrix analysis of electrical circuits. Parametric identification is based on the basic laws of electrical engineering. Results. The structure of a generalized load equivalent circuit is composed in three independent nodes. An approximate method for determining its parameters is proposed. The estimation error determination undistorted and distorted parts of the parameters of generalized load equivalent circuit. Originality. Approximate determination of equivalent circuit parameters are based on the results of a single measurement of voltages and phase currents. Practical value. The proposed replacement structure and a method for determining its parameters of the circuit can be used in the problem of the distribution of actual contributions at the point of common coupling.
Bicriteria network design problems
Energy Technology Data Exchange (ETDEWEB)
Marathe, M.V.; Ravi, R.; Sundaram, R.; Ravi, S.S.; Rosenkrantz, D.J.; Hunt, H.B. III
1994-12-31
We study several bicriteria network design problems phrased as follows: given an undirected graph and two minimization objectives with a budget specified on one objective, find a subgraph satisfying certain connectivity requirements that minimizes the second objective subject to the budget on the first. Define an ({alpha}, {beta})-approximation algorithm as a polynomial-time algorithm that produces a solution in which the first objective value is at most {alpha} times the budget, and the second objective value is at most {alpha} times the minimum cost of a network obeying the budget oil the first objective. We, present the first approximation algorithms for bicriteria problems obtained by combining classical minimization objectives such as the total edge cost of the network, the diameter of the network and a weighted generalization of the maximum degree of any node in the network. We first develop some formalism related to bicriteria problems that leads to a clean way to state bicriteria approximation results. Secondly, when the two objectives are similar but only differ based on the cost function under which they are computed we present a general parametric search technique that yields approximation algorithms by reducing the problem to one of minimizing a single objective of the same type. Thirdly, we present an O(log n, log n)-approximation algorithm for finding a diameter-constrained minimum cost spanning tree of an undirected graph on n nodes generalizing the notion of shallow, light trees and light approximate shortest-path trees that have been studied before. Finally, for the class of treewidth-bounded graphs, we provide pseudopolynomial-time algorithms for a number of bicriteria problems using dynamic programming. These pseudopolynomial-time algorithms can be converted to fully polynomial-time approximation schemes using a scaling technique.
Load Balancing of Large Distribution Network Model Calculations
Directory of Open Access Journals (Sweden)
MARTINOVIC, L.
2017-11-01
Full Text Available Performance measurement and evaluation study of calculations based on load flow analysis in power distribution network is presented. The focus is on the choice of load index as it is the basic input for efficient dynamic load balancing. The basic description of problem along with the proposed architecture is given. Different server resources are inspected and analyzed while running calculations, and based on this investigation, recommendations regarding the choice of load index are made. Short description of used static and dynamic load balancing algorithms is given and the proposition of load index choice is supported by tests run on large real-world power distribution network models.
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Networks in social policy problems
Scotti, marco
2012-01-01
Network science is the key to managing social communities, designing the structure of efficient organizations and planning for sustainable development. This book applies network science to contemporary social policy problems. In the first part, tools of diffusion and team design are deployed to challenges in adoption of ideas and the management of creativity. Ideas, unlike information, are generated and adopted in networks of personal ties. Chapters in the second part tackle problems of power and malfeasance in political and business organizations, where mechanisms in accessing and controlling informal networks often outweigh formal processes. The third part uses ideas from biology and physics to understand global economic and financial crises, ecological depletion and challenges to energy security. Ideal for researchers and policy makers involved in social network analysis, business strategy and economic policy, it deals with issues ranging from what makes public advisories effective to how networks influenc...
Fast training of neural networks for load forecasting
Energy Technology Data Exchange (ETDEWEB)
Agosta, J.M.; Nielsen, N.R.; Andeen, G. [SRI International, Menlo Park, CA (United States)
1996-10-01
Predicting load demand (e.g., demand for electric power) in a data-rich environment is basically a regression problem. To be successful, however, any regression technique must take into account the nonlinear nature of the problem. Numerous nonlinear regression methods have become practical, with the availability of more powerful computers. Perhaps the best known of these methods are techniques that have been popularized under the name of neural networks, and the most common of these is the back-propagation neural network (BPNN). This paper explains the advantage of a different nonlinear regression method known as the probabilistic neural network (PNN).
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.
Quantum load balancing in ad hoc networks
Hasanpour, M.; Shariat, S.; Barnaghi, P.; Hoseinitabatabaei, S. A.; Vahid, S.; Tafazolli, R.
2017-06-01
This paper presents a novel approach in targeting load balancing in ad hoc networks utilizing the properties of quantum game theory. This approach benefits from the instantaneous and information-less capability of entangled particles to synchronize the load balancing strategies in ad hoc networks. The quantum load balancing (QLB) algorithm proposed by this work is implemented on top of OLSR as the baseline routing protocol; its performance is analyzed against the baseline OLSR, and considerable gain is reported regarding some of the main QoS metrics such as delay and jitter. Furthermore, it is shown that QLB algorithm supports a solid stability gain in terms of throughput which stands a proof of concept for the load balancing properties of the proposed theory.
CDMA coverage under mobile heterogeneous network load
Saban, D.; van den Berg, Hans Leo; Boucherie, Richardus J.; Endrayanto, A.I.
2002-01-01
We analytically investigate coverage (determined by the uplink) under non-homogeneous and moving traffic load of third generation UMTS mobile networks. In particular, for different call assignment policies, we investigate cell breathing and the movement of the coverage gap occurring between cells
Impact of Electric Vehicle Charging Station Load on Distribution Network
Directory of Open Access Journals (Sweden)
Sanchari Deb
2018-01-01
Full Text Available Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP index. The results obtained indicate the efficacy of the VRP index.
Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach
2016-03-30
Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build
Failure mitigation in software defined networking employing load type prediction
Bouacida, Nader
2017-07-31
The controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.
Suppressing cascades of load in interdependent networks
Brummitt, Charles D; Leicht, E A
2011-01-01
Understanding how interdependence among systems affects cascading behaviors is increasingly important across many fields of science and engineering. Inspired by cascades of load shedding in coupled electric grids and other infrastructure, we study the Bak-Tang-Wiesenfeld sandpile model on modular random graphs and on graphs based on actual, interdependent power grids. Starting from two isolated networks, adding some connectivity between them is beneficial, for it suppresses the largest cascades in each system. Too much interconnectivity, however, becomes detrimental for two reasons. First, interconnections open pathways for neighboring networks to inflict large cascades. Second, as in real infrastructure, new interconnections increase capacity and total possible load, which fuels even larger cascades. Using a multi-type branching process and simulations we show these effects and estimate the optimal level of interconnectivity that balances their tradeoffs. Such equilibria could allow, for example, power grid ...
Josephson junctions loaded by transmission lines: a revisited problem.
Ranfagni, Anedio; Cacciari, Ilaria; Moretti, Paolo
2011-11-01
The problem of evaluating dissipative effects in Josephson junctions loaded by transmission lines is reexamined, for either the symmetric or the asymmetric case, with particular consideration of the time domain in which the interaction between junction and load system occurs.
Daily Nigerian peak load forecasting using artificial neural network ...
African Journals Online (AJOL)
A daily peak load forecasting technique that uses artificial neural network with seasonal indices is presented in this paper. A neural network of relatively smaller size than the main prediction network is used to predict the daily peak load for a period of one year over which the actual daily load data are available using one ...
An Initial Load-Based Green Software Defined Network
Directory of Open Access Journals (Sweden)
Ying Hu
2017-05-01
Full Text Available Software defined network (SDN is a new network architecture in which the control function is decoupled from the data forwarding plane, that is attracting wide attentions from both research and industry sectors. However, SDN still faces the energy waste problem as do traditional networks. At present, research on energy saving in SDN is mainly focused on the static optimization of the network with zero load when new traffic arrives, changing the transmission path of the uncompleted traffic which arrived before the optimization, possibly resulting in route oscillation and other deleterious effects. To avoid this, a dynamical energy saving optimization scheme in which the paths of the uncompleted flows will not be changed when new traffic arrives is designed. To find the optimal solution for energy saving, the problem is modeled as a mixed integer linear programming (MILP problem. As the high complexity of the problem prohibits the optimal solution, an improved heuristic routing algorithm called improved constant weight greedy algorithm (ICWGA is proposed to find a sub-optimal solution. Simulation results show that the energy saving capacity of ICWGA is close to that of the optimal solution, offering desirable improvement in the energy efficiency of the network.
Problem Lokalitas dalam Bisnis Radio Network
Directory of Open Access Journals (Sweden)
- Rahayu
2006-03-01
Full Text Available The emergence of radio network has some consequencies. These relate to the ownership of the network, problems of locality and threads to democracy. Some may view that the networks have strengthened the position of radio. The others believe that the netwrok risks the local power in Indonesia. This article explores the problems of the radio networks in Indonesia, especially when dealing with the local elements.
Controller placement problem in industrial networks
Macián Ribera, Sergi
2016-01-01
SDN is the new trend in networks, for next Mobile and optical networks. Dimensioning, design and optimization of Software Defined Optical Networks. To be done at Technical University Munich (TUM) In this work the Controller Placement Problem (CPP) for SDN architecture is studied when it is applied to industrial networks. En este trabajo se estudia el problema CPP (controller placement problem) para la arquitectura SDN, aplicado a redes industriales. En aquest treball s'estudia el pro...
Networks in Social Policy Problems
Vedres, Balázs; Scotti, Marco
2012-08-01
1. Introduction M. Scotti and B. Vedres; Part I. Information, Collaboration, Innovation: The Creative Power of Networks: 2. Dissemination of health information within social networks C. Dhanjal, S. Blanchemanche, S. Clemençon, A. Rona-Tas and F. Rossi; 3. Scientific teams and networks change the face of knowledge creation S. Wuchty, J. Spiro, B. F. Jones and B. Uzzi; 4. Structural folds: the innovative potential of overlapping groups B. Vedres and D. Stark; 5. Team formation and performance on nanoHub: a network selection challenge in scientific communities D. Margolin, K. Ognyanova, M. Huang, Y. Huang and N. Contractor; Part II. Influence, Capture, Corruption: Networks Perspectives on Policy Institutions: 6. Modes of coordination of collective action: what actors in policy making? M. Diani; 7. Why skewed distributions of pay for executives is the cause of much grief: puzzles and few answers so far B. Kogut and J.-S. Yang; 8. Networks of institutional capture: a case of business in the State apparatus E. Lazega and L. Mounier; 9. The social and institutional structure of corruption: some typical network configurations of corruption transactions in Hungary Z. Szántó, I. J. Tóth and S. Varga; Part III. Crisis, Extinction, World System Change: Network Dynamics on a Large Scale: 10. How creative elements help the recovery of networks after crisis: lessons from biology A. Mihalik, A. S. Kaposi, I. A. Kovács, T. Nánási, R. Palotai, Á. Rák, M. S. Szalay-Beko and P. Csermely; 11. Networks and globalization policies D. R. White; 12. Network science in ecology: the structure of ecological communities and the biodiversity question A. Bodini, S. Allesina and C. Bondavalli; 13. Supply security in the European natural gas pipeline network M. Scotti and B. Vedres; 14. Conclusions and outlook A.-L. Barabási; Index.
Advanced Load Balancing Based on Network Flow Approach in LTE-A Heterogeneous Network
Directory of Open Access Journals (Sweden)
Shucong Jia
2014-01-01
Full Text Available Long-term evolution advanced (LTE-A systems will offer better service to users by applying advanced physical layer transmission techniques and utilizing wider bandwidth. To further improve service quality, low power nodes are overlaid within a macro network, creating what is referred to as a heterogeneous network. However, load imbalance among cells often decreases the network resource utilization ratio and consequently reduces the user experience level. Load balancing (LB is an indispensable function in LTE-A self-organized network (SON to efficiently accommodate the imbalance in traffic. In this paper, we firstly evaluate the negative impact of unbalanced load among cells through Markovian model. Secondly, we formulate LB as an optimization problem which is solved using network flow approach. Furthermore, a novel algorithm named optimal solution-based LB (OSLB is proposed. The proposed OSLB algorithm is shown to be effective in providing up to 20% gain in load distribution index (LDI by a system-level simulation.
Debonding problem of a transversely loaded single lap joint
Energy Technology Data Exchange (ETDEWEB)
Tang, J.H.; Sridhar, I.; Tan, G.E.B. [Nanyang Technological Univ., Singapore (Singapore)
2012-07-01
This paper aims to describe the debonding problem of transversely loaded single lap joint using fracture mechanics approach. This is achieved by first solving the deflection problem of the single lap joint. By evaluating the bending moments at the vicinity of crack tip, non-dimensional energy release rate required for the debonding can be found. The effects of relative position of transverse load and overlap length on the debonding propensity are discussed. As for design consideration, any transverse load moving towards critical side of square edge of the joint should be avoided. Increasing the overlap length improves the joint's strength, particularly when the joint is also axially loaded. (Author)
Integrated network design and scheduling problems :
Energy Technology Data Exchange (ETDEWEB)
Nurre, Sarah G.; Carlson, Jeffrey J.
2014-01-01
We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.
Using Evolutionary Computation to Solve the Economic Load Dispatch Problem
Directory of Open Access Journals (Sweden)
Samir SAYAH
2008-06-01
Full Text Available This paper reports on an evolutionary algorithm based method for solving the economic load dispatch (ELD problem. The objective is to minimize the nonlinear function, which is the total fuel cost of thermal generating units, subject to the usual constraints.The IEEE 30 bus test system was used for testing and validation purposes. The results obtained demonstrate the effectiveness of the proposed method for solving the economic load dispatch problem.
Neural Network Solves "Traveling-Salesman" Problem
Thakoor, Anilkumar P.; Moopenn, Alexander W.
1990-01-01
Experimental electronic neural network solves "traveling-salesman" problem. Plans round trip of minimum distance among N cities, visiting every city once and only once (without backtracking). This problem is paradigm of many problems of global optimization (e.g., routing or allocation of resources) occuring in industry, business, and government. Applied to large number of cities (or resources), circuits of this kind expected to solve problem faster and more cheaply.
Bin-packing problems with load balancing and stability constraints
DEFF Research Database (Denmark)
Trivella, Alessio; Pisinger, David
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....... is the Container Loading Problem (CLP), which addresses the optimization of a spacial arrangement of cargo inside a container or transportation vehicle, with the objective to maximize the value of the cargo loaded or the volume utilization. The CLP focuses on a single container, and has been extended...
neural network based load frequency control for restructuring power
African Journals Online (AJOL)
2012-03-01
Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...
Multiobjective Economic Load Dispatch Problem Solved by New PSO
Directory of Open Access Journals (Sweden)
Nagendra Singh
2015-01-01
Full Text Available Proposed in this paper is a new particle swarm optimization technique for the solution of economic load dispatch as well as environmental emission of the thermal power plant with power balance and generation limit constraints. Economic load dispatch is an online problem to minimize the total generating cost of the thermal power plant and satisfy the equality and inequality constraints. Thermal power plants use fossil fuels for the generation of power; fossil fuel emits many toxic gases which pollute the environment. This paper not only considers the economic load dispatch problem to reduce the total generation cost of the thermal power plant but also deals with environmental emission minimization. In this paper, fuel cost and the environmental emission functions are considered and formulated as a multiobjective economic load dispatch problem. For obtaining the solution of multiobjective economic load dispatch problem a new PSO called moderate random search PSO was used. MRPSO enhances the ability of particles to explore in the search spaces more effectively and increases their convergence rates. The proposed algorithm is tested for the IEEE 30 bus test systems. The results obtained by MRPSO algorithm show that it is effective and efficient.
Generalized network improvement and packing problems
Holzhauser, Michael
2016-01-01
Michael Holzhauser discusses generalizations of well-known network flow and packing problems by additional or modified side constraints. By exploiting the inherent connection between the two problem classes, the author investigates the complexity and approximability of several novel network flow and packing problems and presents combinatorial solution and approximation algorithms. Contents Fractional Packing and Parametric Search Frameworks Budget-Constrained Minimum Cost Flows: The Continuous Case Budget-Constrained Minimum Cost Flows: The Discrete Case Generalized Processing Networks Convex Generalized Flows Target Groups Researchers and students in the fields of mathematics, computer science, and economics Practitioners in operations research and logistics The Author Dr. Michael Holzhauser studied computer science at the University of Kaiserslautern and is now a research fellow in the Optimization Research Group at the Department of Mathematics of the University of Kaiserslautern.
Stochastic Optimization for Network-Constrained Power System Scheduling Problem
Directory of Open Access Journals (Sweden)
D. F. Teshome
2015-01-01
Full Text Available The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP. It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement.
A bankruptcy problem approach to load-shedding in multiagent-based microgrid operation.
Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon
2010-01-01
A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs' output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent's interactions.
Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-07-26
In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.
Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration
Energy Technology Data Exchange (ETDEWEB)
Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-23
In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.
Unstructured P2P Network Load Balance Strategy Based on Multilevel Partitioning of Hypergraph
Feng, Lv; Chunlin, Gao; Kaiyang, Ma
2017-05-01
With rapid development of computer performance and distributed technology, P2P-based resource sharing mode plays important role in Internet. P2P network users continued to increase so the high dynamic characteristics of the system determine that it is difficult to obtain the load of other nodes. Therefore, a dynamic load balance strategy based on hypergraph is proposed in this article. The scheme develops from the idea of hypergraph theory in multilevel partitioning. It adopts optimized multilevel partitioning algorithms to partition P2P network into several small areas, and assigns each area a supernode for the management and load transferring of the nodes in this area. In the case of global scheduling is difficult to be achieved, the priority of a number of small range of load balancing can be ensured first. By the node load balance in each small area the whole network can achieve relative load balance. The experiments indicate that the load distribution of network nodes in our scheme is obviously compacter. It effectively solves the unbalanced problems in P2P network, which also improve the scalability and bandwidth utilization of system.
Multiobjective bacteria foraging algorithm for electrical load dispatch problem
Energy Technology Data Exchange (ETDEWEB)
Panigrahi, B.K., E-mail: bkpanigrahi@ee.iitd.ac.i [Department of Electrical Engineering, IIT, Delhi (India); Pandi, V. Ravikumar [Department of Electrical Engineering, IIT, Delhi (India); Sharma, Renu [Department of Electrical and Electronics Engg, ITER, SOA University, Bhubaneswar, Orissa (India); Das, Swagatam [Department of Electronics and Communication Engineering, Jadavpur University, Kolkata (India); Das, Sanjoy [Department of Electrical and Computer Engineering, Kansas State University (United States)
2011-02-15
In this paper the bacteria foraging meta-heuristic is extended into the domain of multiobjective optimization. In this multiobjective bacteria foraging (MOBF) optimization technique, during chemotaxis a set of intermediate bacteria positions are generated. Next, we use pareto non-dominance criterion to determine final set of bacteria positions, which constitute the superior solutions among current and intermediate solutions. To test the efficacy of our proposed algorithm we have chosen a highly constrained optimization problem namely economic/emission dispatch. Economic dispatch is a constrained optimization problem in power system to distribute the load demand among the committed generators economically. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED). In the proposed work, we have considered the standard IEEE 30-bus six-generator test system on which several other multiobjective evolutionary algorithms are tested. We have also made a comparative study of the proposed algorithm with that of reported in the literature. Results show that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem.
Energy Technology Data Exchange (ETDEWEB)
Gabioud, D.
2008-07-01
This illustrated article takes up the problems related to the variation of the load in electricity networks. How to handle the peak load? Different solutions in the energy demand management are discussed. Method based on the price, method based on the reduction of the load by electric utilities. Information systems are presented which gives the consumer the needed data to participate in the local load management.
An Efficient Simulated Annealing Algorithm for Economic Load Dispatch Problems
Directory of Open Access Journals (Sweden)
Junaidi Junaidi
2013-03-01
Full Text Available This paper presents an efficient simulated annealing (SA algorithm for solving economic load dispatch (ELD problems in electrical power system. The objectives of ELD problems in electric power generation is to programmed the devoted generating unit outputs so as to meet the mandatory load demand at lowest amount operating cost while satisfying all units and system equality and inequality constraints. Global optimization approaches is inspired by annealing process of thermodynamics. The SA algorithm presented here is applied to two case studies, which analyze power systems having three, and six generating units. The results determined by SA algorithm are compared to those found by conventional quadratic programming (QP and genetic algorithm (GA.
Near and long-term load prediction using radial basis function networks
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. [Rollins College, Winter Park, FL (United States)
1995-12-31
A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.
The Generalized Fixed-Charge Network Design Problem
DEFF Research Database (Denmark)
Thomadsen, Tommy; Stidsen, Thomas K.
2007-01-01
In this paper we present the generalized fixed-charge network design (GFCND) problem. The GFCND problem is an instance of the so-called generalized network design problems. In such problems, clusters instead of nodes have to be interconnected by a network. The network interconnecting the clusters...... is a fixed-charge network, and thus the GFCND problem generalizes the fixed-charge network design problem. The GFCND problem is related to the more general problem of designing hierarchical telecommunication networks. A mixed integer programming model is described and a branch-cut-and-price algorithm...... is implemented. Violated constraints and variables with negative reduced costs are found using enumeration. The algorithm is capable of obtaining optimal solutions for problems with up to 30 clusters and up to 300 nodes. This is possible, since the linear programming relaxation bound is very tight...
Neural Network Based Load Frequency Control for Restructuring ...
African Journals Online (AJOL)
Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.
PLATON: Peer-to-Peer load adjusting tree overlay networks
Lymberopoulos, L.; Pittaras, C.; Grammatikou, M.; Papavassiliou, S.; Maglaris, V.
2011-01-01
Peer-to-Peer systems supporting multi attribute and range queries use a number of techniques to partition the multi dimensional data space among participating peers. Load-balancing of data accross peer partitions is necessary in order to avoid the presence of network hotspots which may cause
Analysis of recurrent neural networks for short-term energy load forecasting
Di Persio, Luca; Honchar, Oleksandr
2017-11-01
Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.
Three-Phase Unbalanced Load Flow Tool for Distribution Networks
DEFF Research Database (Denmark)
Demirok, Erhan; Kjær, Søren Bækhøj; Sera, Dezso
2012-01-01
This work develops a three-phase unbalanced load flow tool tailored for radial distribution networks based on Matlab®. The tool can be used to assess steady-state voltage variations, thermal limits of grid components and power losses in radial MV-LV networks with photovoltaic (PV) generators where...... most of the systems are single phase. New ancillary service such as static reactive power support by PV inverters can be also merged together with the load flow solution tool and thus, the impact of the various reactive power control strategies on the steady-state grid operation can be simply...... investigated. Performance of the load flow solution tool in the sense of resulting bus voltage magnitudes is compared and validated with IEEE 13-bus test feeder....
FUZZY ALGORITHM TO CONTROL REACTIVE POWER FLOW IN ELECTRIC NETWORK WITH NONLINEAR LOADS
Directory of Open Access Journals (Sweden)
H. B. Guliyev
2016-01-01
Full Text Available One of the important problems of efficient function of electric networks containing load nodes of nonlinear character of power consumption is reactive power compensation and maintaining voltage quality in a grid. The commonly used methods for compensation of harmonic currents by filtering devices make it possible to solve the problem in a narrow band of variation of current of a nonlinear load. In the reality stochastic character of power consumption of nonlinear load reveals itself in appropriate changes in harmonic components of voltage and their share in total load current. This could considerably change the magnitude and direction of reactive power flow in a grid and impair the existing processes of reactive power control. The scheme and the algorithm of control of capacitor banks in networks with non-linear load that are based on the use of fuzzy logic software are presented in the article. The results of model experiments analysis of the modes of the harmonic of the power flows on behalf of the 14-nodal scheme recommended by IEEE as well as the schemes of a real grid with powerful traction substation are presented. The mentioned results demonstrate that when harmonic components of voltages exceed normative magnitudes, the use of the proposed algorithm eliminates additional loading on the capacitor banks with higher harmonic currents whereas the control procedure acquires quality, the number of commutations is being reduced, the capacitor battery functions longer and the probability of its malfunction decreases.
Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks
Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)
2002-01-01
In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.
Short-line matching networks for circulators and resonant loads
Levy, R.; Helszajn, J.
1983-10-01
An important load circuit encountered in the synthesis of microwave components is a simple parallel RLC network. Replacing the lumped elements by short stubs permits an exact synthesis using relatively short commensurate transmission lines. The synthesis is given for the general case in terms of the maximum and minimum values of passband-reflection coefficients, commensurate electrical length, and bandwidth. Varying either the level of the minima, the basic commensurate electrical length, or both, allows significant variation in the impedance levels of the load circuit to be accommodated. A practical application involving a junction circulator is described.
Unsupervised neural networks for solving Troesch's problem
Muhammad, Asif Zahoor Raja
2014-01-01
In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm (GA) and pattern search (PS) techniques are used as the global search methods and the interior point method (IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM (GA-IPM) and PS hybridized with IPM (PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs.
Applied Knowledge Management to Mitigate Cognitive Load in Network-Enabled Mission Command
2017-11-22
system performance is often referred to as the “brittleness problem of automata” (Hollnagel and Woods 2005). These cautions from the flight management ... perspectives . IEEE Int Sys. 2006;21(4):70–73. Leistner F. Managing organizational knowledge flow: how to make knowledge sharing work. Hoboken (NJ...ARL-TN-0859 ● NOV 2017 US Army Research Laboratory Applied Knowledge Management to Mitigate Cognitive Load in Network-Enabled
MHD generator with improved network coupling electrodes to a load
Rosa, Richard J.
1977-01-01
An MHD generator has a plurality of segmented electrodes extending longitudinally of a duct, whereby progressively increasing high DC voltages are derived from a set of cathode electrodes and progressively increasing low DC voltages are derived from a set of anode electrodes. First and second load terminals are respectively connected to the cathode and anode electrodes by separate coupling networks, each of which includes a number of SCR's and a number of diode rectifiers.
Research on virtual network load balancing based on OpenFlow
Peng, Rong; Ding, Lei
2017-08-01
The Network based on OpenFlow technology separate the control module and data forwarding module. Global deployment of load balancing strategy through network view of control plane is fast and of high efficiency. This paper proposes a Weighted Round-Robin Scheduling algorithm for virtual network and a load balancing plan for server load based on OpenFlow. Load of service nodes and load balancing tasks distribution algorithm will be taken into account.
Deep Neural Network Based Demand Side Short Term Load Forecasting
Directory of Open Access Journals (Sweden)
Seunghyoung Ryu
2016-12-01
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
Directory of Open Access Journals (Sweden)
Steven S. W. Lee
2015-01-01
Full Text Available We propose a multitree based fast failover scheme for Ethernet networks. In our system, only few spanning trees are used to carry working traffic in the normal state. As a failure happens, the nodes adjacent to the failure redirect traffic to the preplanned backup VLAN trees to realize fast failure recovery. In the proposed scheme, a new leaf constraint is enforced on the backup trees. It enables the network being able to provide 100% survivability against any single link and any single node failure. Besides fast failover, we also take load balancing into consideration. We model an Ethernet network as a twolayered graph and propose an Integer Linear Programming (ILP formulation for the problem. We further propose a heuristic algorithm to provide solutions to large networks. The simulation results show that the proposed scheme can achieve high survivability while maintaining load balancing at the same time. In addition, we have implemented the proposed scheme in an FPGA system. The experimental results show that it takes only few μsec to recover a network failure. This is far beyond the 50 msec requirement used in telecommunication networks for network protection.
Solving Hub Network Problem Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Mursyid Hasan Basri
2012-01-01
Full Text Available This paper addresses a network problem that described as follows. There are n ports that interact, and p of those will be designated as hubs. All hubs are fully interconnected. Each spoke will be allocated to only one of available hubs. Direct connection between two spokes is allowed only if they are allocated to the same hub. The latter is a distinct characteristic that differs it from pure hub-and-spoke system. In case of pure hub-and-spoke system, direct connection between two spokes is not allowed. The problem is where to locate hub ports and to which hub a spoke should be allocated so that total transportation cost is minimum. In the first model, there are some additional aspects are taken into consideration in order to achieve a better representation of the problem. The first, weekly service should be accomplished. Secondly, various vessel types should be considered. The last, a concept of inter-hub discount factor is introduced. Regarding the last aspect, it represents cost reduction factor at hub ports due to economies of scale. In practice, it is common that the cost rate for inter-hub movement is less than the cost rate for movement between hub and origin/destination. In this first model, inter-hub discount factor is assumed independent with amount of flows on inter-hub links (denoted as flow-independent discount policy. The results indicated that the patterns of enlargement of container ship size, to some degree, are similar with those in Kurokawa study. However, with regard to hub locations, the results have not represented the real practice. In the proposed model, unsatisfactory result on hub locations is addressed. One aspect that could possibly be improved to find better hub locations is inter-hub discount factor. Then inter-hub discount factor is assumed to depend on amount of inter-hub flows (denoted as flow-dependent discount policy. There are two discount functions examined in this paper. Both functions are characterized by
Inverse kinematics problem in robotics using neural networks
Choi, Benjamin B.; Lawrence, Charles
1992-01-01
In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.
LAMAN: Load Adaptable MAC for Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Realp Marc
2005-01-01
Full Text Available In mobile ad hoc radio networks, mechanisms on how to access the radio channel are extremely important in order to improve network efficiency. In this paper, the load adaptable medium access control for ad hoc networks (LAMAN protocol is described. LAMAN is a novel decentralized multipacket MAC protocol designed following a cross-layer approach. Basically, this protocol is a hybrid CDMA-TDMA-based protocol that aims at throughput maximization in multipacket communication environments by efficiently combining contention and conflict-free protocol components. Such combination of components is used to adapt the nodes' access priority to changes on the traffic load while, at the same time, accounting for the multipacket reception (MPR capability of the receivers. A theoretical analysis of the system is developed presenting closed expressions of network throughput and packet delay. By simulations the validity of our analysis is shown and the performances of a LAMAN-based system and an Aloha-CDMA-based one are compared.
Generalized Load Sharing for Homogeneous Networks of Distributed Environment
Directory of Open Access Journals (Sweden)
A. Satheesh
2008-01-01
Full Text Available We propose a method for job migration policies by considering effective usage of global memory in addition to CPU load sharing in distributed systems. When a node is identified for lacking sufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote nodes with low memory allocations. If the memory space is sufficiently large, the jobs will be scheduled by a CPU-based load sharing policy. Following the principle of sharing both CPU and memory resources, we present several load sharing alternatives. Our objective is to reduce the number of page faults caused by unbalanced memory allocations for jobs among distributed nodes, so that overall performance of a distributed system can be significantly improved. We have conducted trace-driven simulations to compare CPU-based load sharing policies with our policies. We show that our load sharing policies not only improve performance of memory bound jobs, but also maintain the same load sharing quality as the CPU-based policies for CPU-bound jobs. Regarding remote execution and preemptive migration strategies, our experiments indicate that a strategy selection in load sharing is dependent on the amount of memory demand of jobs, remote execution is more effective for memory-bound jobs, and preemptive migration is more effective for CPU-bound jobs. Our CPU-memory-based policy using either high performance or high throughput approach and using the remote execution strategy performs the best for both CPU-bound and memory-bound job in homogeneous networks of distributed environment.
Solving unit commitment and economic load dispatch problems ...
African Journals Online (AJOL)
Economic Load Dispatch (ELD) and Unit Commitment (UC) are very important applications to predict the optimized cost of load in a power system. UC determines working states for existing generating units under some operational constraints and then optimizing the operation cost for all running units w.r.t. load demand ...
Solving network design problems via decomposition, aggregation and approximation
Bärmann, Andreas
2016-01-01
Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time. Contents Decomposition for Multi-Period Network Design Solving Network Design Problems via Ag...
Ad-hoc Network of Smart Sensors for Mechanical Load Measurement
Directory of Open Access Journals (Sweden)
Manuel A. Vieira
2016-07-01
Full Text Available Strain gauges load cells are transducers devices capable of converting changes in applied mechanical load into an electrical analog signal. Those devices have a large spectrum of applications ranging from domestic to industrial or even medical appliances just to name a few. In this work, they are used in the electronic instrumentation of a force platform that will be used to carry out the analysis and characterization of human biomechanical walking. In this platform, four load cells are installed, each one capable of measuring forces along two different axis. A total of eight strain-gauges per load cell are employed. Hence, analog signal transmission, besides requiring a large number of connection wires, is prone to interference and noise. Moreover, with this solution, scalability requires severe changes in the connection topology. In order to circumvent those problems, an alternative in-situ signal conditioning and digital data transmission system was devised. This approach, as far as investigated, presents an innovative solution to signal conditioning and data transmission for load-cells. In particular, the presented solution allows the creation of an ad-hoc network of load cells, using the I²C protocol with a master interface that allows the users to interact and change the parameters of each load cell. This instrumentation structure has been successfully tested and the obtained results are documented in this article.
Enhanced method of fast re-routing with load balancing in software-defined networks
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.
Directory of Open Access Journals (Sweden)
HUSSEIN A. ABDULQADER
2012-08-01
Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.
Load balancing in OCDM optical packet switched networks
Yun, Ling; Qiu, Kun; Jiang, JIhai; Li, Yanqiu
2008-11-01
Optical packet switching (OPS), which transfers the switching function from electrical domain to optical domain and provides the smallest switching granularity, is the most potential candidate of switching form in the future optical networks. Optical code division multiplexing (OCDM) is the mostly practical all-optical processing technology at the state of the art. The experiments of optical packet switching with optical code (OC) label have demonstrated the switching capability and advantages. But the timing of erasing and inserting label, which is similar with the bit-serial label processing, is the stringent requirement of this scheme. OCDM optical packet switching, which encodes the payload directly and removes the label when the payload is recovered at the decoder, has no stringent timing requirement. Multiple access interference (MAI) is the main factor degrading the performance of OCDM optical packet-switched networks. In this paper, the effects of MAI are studied at the end of optical label path where the packets experience multiple hops. For eliminating the end-to-end BER, the optical label paths need to be established in an optimum way and the load are required to be balanced. One load-balancing algorithm based on the end-to-end BER of OCDM path is proposed to improve the network performance.
PRIVACY PROTECTION PROBLEMS IN SOCIAL NETWORKS
OKUR, M. Cudi
2011-01-01
Protecting privacy has become a major concern for most social network users because of increased difficulties of controlling the online data. This article presents an assessment of the common privacy related risks of social networking sites. Open and hidden privacy risks of active and passive online profiles are examined and increasing share of social networking in these phenomena is discussed. Inadequacy of available legal and institutional protection is demonstrated and the effectiveness of...
Short-term load forecasting using neural network for future smart grid application
Zennamo, Joseph Anthony, III
Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.
Short-term load and wind power forecasting using neural network-based prediction intervals.
Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas
2014-02-01
Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.
Information Dissemination Through Networking In Nigeria: Problems ...
African Journals Online (AJOL)
Information dissemination is an important element in teaching and research around the world. Dissemination of information through electronic networking has transformed the conduct of research and teaching in institutions and organizations. Electronic networks are offering researchers a wide range of opportunities in ...
Secure Wireless Sensor Networks: Problems and Solutions
Directory of Open Access Journals (Sweden)
Fei Hu
2003-08-01
Full Text Available As sensor networks edge closer towards wide-spread deployment, security issues become a central concern. So far, the main research focus has been on making sensor networks feasible and useful, and less emphasis was placed on security. This paper analyzes security challenges in wireless sensor networks and summarizes key issues that should be solved for achieving the ad hoc security. It gives an overview of the current state of solutions on such key issues as secure routing, prevention of denial-of-service and key management service. We also present some secure methods to achieve security in wireless sensor networks. Finally we present our integrated approach to securing sensor networks.
Electric load forecasting for northern Vietnam, using an artificial neural network
Energy Technology Data Exchange (ETDEWEB)
Bhattacharyya, S.C. [Asian Institute of Technology, Pathum Thani (Thailand); Thanh, L.T. [Power Company No. 1 (Viet Nam)
2003-06-01
This paper employs a feed-forward neural network with a back-propagation algorithm for the short-term electric load forecasting of daily peak (valley) loads and hourly loads in the northern areas of Vietnam. A large set of data on peak loads, valley loads, hourly loads and temperatures was used to train and calibrate the artificial neural network (ANN). The calibrated network was used for load forecasting. The mean percentage errors for the peak load, valley load, one-hour-ahead hourly load and 24-hour-ahead hourly load were -1.47%, -3.29%, -2.64% and -4.39%, respectively. These results compare well with similar studies. (author)
Network Monitoring as a Streaming Analytics Problem
Gupta, Arpit
2016-11-02
Programmable switches make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. Unfortunately, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. Others have network monitoring in the context of streaming; yet, previous work has not closed the loop in a way that allows network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple and efficiently partitioning each query between the switches and the stream processor through iterative refinement. Sonata extracts only the traffic that pertains to each query, ensuring that the stream processor can scale traffic rates of several terabits per second. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world\\'s largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude. Copyright 2016 ACM.
Directory of Open Access Journals (Sweden)
Beibei Wang
2017-01-01
Full Text Available There are many uncertain factors in the modern distribution network, including the access of renewable energy sources and the heavy load level. The existence of these factors has brought challenges to the stability of the power distribution network, as well as increasing the risk of exceeding transmission capacity of distribution lines. The appearance of flexible load control technology provides a new idea to solve the above problems. Air conditioners (ACs account for a great proportion of all loads. In this paper, the model of dispatching AC loads in the regional power grid is constructed, and the direct load control (DLC method is adopted to reduce the load of ACs. An improved tabu search technique is proposed to solve the problem of network dispatch in distribution systems in order to reduce the resistive line losses and to eliminate the transmission congestion in lines under normal operating conditions. The optimal node solution is obtained to find the best location and reduction capacity of ACs for load control. To demonstrate the validity and effectiveness of the proposed method, a test system is studied. The numerical results are also given in this article, which reveal that the proposed method is promising.
Pricing and Capacity Planning Problems in Energy Transmission Networks
DEFF Research Database (Denmark)
Villumsen, Jonas Christoffer
and transmission pricing problems in energy transmission networks. Although the modelling framework applies to energy networks in general, most of the applications discussed concern the transmission of electricity. A number of the problems presented involves transmission switching, which allows the operator...... of an electricity transmission network to switch lines in and out in an operational context in order to optimise the network flow. We show that transmission switching in systems with large-scale wind power may alleviate network congestions and reduce curtailment of wind power leading to higher utilisation...... of installed wind power capacity. We present formulations of — and efficient solution methods for— the transmission line capacity expansion problem and the unit commitment problem with transmission switching. We also show that transmission switching may radically change the optimal line capacity expansion...
Decomposition method for zonal resource allocation problems in telecommunication networks
Konnov, I. V.; Kashuba, A. Yu
2016-11-01
We consider problems of optimal resource allocation in telecommunication networks. We first give an optimization formulation for the case where the network manager aims to distribute some homogeneous resource (bandwidth) among users of one region with quadratic charge and fee functions and present simple and efficient solution methods. Next, we consider a more general problem for a provider of a wireless communication network divided into zones (clusters) with common capacity constraints. We obtain a convex quadratic optimization problem involving capacity and balance constraints. By using the dual Lagrangian method with respect to the capacity constraint, we suggest to reduce the initial problem to a single-dimensional optimization problem, but calculation of the cost function value leads to independent solution of zonal problems, which coincide with the above single region problem. Some results of computational experiments confirm the applicability of the new methods.
A Survey of Coverage Problems in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Junbin LIANG
2014-01-01
Full Text Available Coverage problem is an important issue in wireless sensor networks, which has a great impact on the performance of wireless sensor networks. Given a sensor network, the coverage problem is to determine how well the sensing field is monitored or tracked by sensors. In this paper, we classify the coverage problem into three categories: area coverage, target coverage, and barrier coverage, give detailed description of different algorithms belong to these three categories. Moreover, we specify the advantages and disadvantages of the existing classic algorithms, which can give a useful direction in this area.
Lin, John Jr-Hung; Lin, Sunny S. J.
2014-01-01
The present study investigated (a) whether the perceived cognitive load was different when geometry problems with various levels of configuration comprehension were solved and (b) whether eye movements in comprehending geometry problems showed sources of cognitive loads. In the first investigation, three characteristics of geometry configurations…
Problem solving for wireless sensor networks
Garcia-Hernando, Ana-Belen; Lopez-Navarro, Juan-Manuel; Prayati, Aggeliki; Redondo-Lopez, Luis
2008-01-01
Wireless Sensor Networks (WSN) is an area of huge research interest, attracting substantial attention from industry and academia for its enormous potential and its inherent challenges. This reader-friendly text delivers a comprehensive review of the developments related to the important technological issues in WSN.
Computable majorants of the limit load in Hencky's plasticity problems
Czech Academy of Sciences Publication Activity Database
Repin, S.; Sysala, Stanislav; Haslinger, Jaroslav
2018-01-01
Roč. 75, č. 1 (2018), s. 199-217 ISSN 0898-1221 R&D Projects: GA MŠk LQ1602 Institutional support: RVO:68145535 Keywords : computable bounds * divergence free fields * Hencky's plasticity * limit load * penalization Subject RIV: BA - General Mathematics Impact factor: 1.531, year: 2016 http://www.sciencedirect.com/science/article/pii/S0898122117305552
Dynamic queuing transmission model for dynamic network loading
DEFF Research Database (Denmark)
Raovic, Nevena; Nielsen, Otto Anker; Prato, Carlo Giacomo
2017-01-01
This paper presents a new macroscopic multi-class dynamic network loading model called Dynamic Queuing Transmission Model (DQTM). The model utilizes ‘good’ properties of the Dynamic Queuing Model (DQM) and the Link Transmission Model (LTM) by offering a DQM consistent with the kinematic wave theory...... and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is compared...... for two vehicle classes. Moreover, the results show that the travel time will be underestimated without considering the shock wave property...
Robust p-median problem in changing networks
Directory of Open Access Journals (Sweden)
Štefan PEŠKO
2015-09-01
Full Text Available The robust p-median problem in changing networks is a version of known discrete p-median problem in network with uncertain edge lengths where uncertainty is characterised by given interval. The uncertainty in edge lengths may appear in travel time along the edges in any network location problem. Several possible future scenarios with respect to the lengths of edges are presented. The planner will want a strategy of positioning p medians that will be working “as well as possible" over the future scenarios. We present MILP formulation of the problem and the solution method based on exchange MILP heuristic. The cluster of each median is presented by rooted tree with the median as root. The performance of the proposed heuristic is compared to the optimal solution found via Gurobi solver for MILP models through some illustrative instances of Slovak road network in Žilina.
Network capacity with probit-based stochastic user equilibrium problem.
Lu, Lili; Wang, Jian; Zheng, Pengjun; Wang, Wei
2017-01-01
Among different stochastic user equilibrium (SUE) traffic assignment models, the Logit-based stochastic user equilibrium (SUE) is extensively investigated by researchers. It is constantly formulated as the low-level problem to describe the drivers' route choice behavior in bi-level problems such as network design, toll optimization et al. The Probit-based SUE model receives far less attention compared with Logit-based model albeit the assignment result is more consistent with drivers' behavior. It is well-known that due to the identical and irrelevant alternative (IIA) assumption, the Logit-based SUE model is incapable to deal with route overlapping problem and cannot account for perception variance with respect to trips. This paper aims to explore the network capacity with Probit-based traffic assignment model and investigate the differences of it is with Logit-based SUE traffic assignment models. The network capacity is formulated as a bi-level programming where the up-level program is to maximize the network capacity through optimizing input parameters (O-D multiplies and signal splits) while the low-level program is the Logit-based or Probit-based SUE problem formulated to model the drivers' route choice. A heuristic algorithm based on sensitivity analysis of SUE problem is detailed presented to solve the proposed bi-level program. Three numerical example networks are used to discuss the differences of network capacity between Logit-based SUE constraint and Probit-based SUE constraint. This study finds that while the network capacity show different results between Probit-based SUE and Logit-based SUE constraints, the variation pattern of network capacity with respect to increased level of travelers' information for general network under the two type of SUE problems is the same, and with certain level of travelers' information, both of them can achieve the same maximum network capacity.
An Electromagnetic Interference Problem via the Mains Distribution Networks
Directory of Open Access Journals (Sweden)
BUZDUGAN, M. I.
2007-11-01
Full Text Available The paper presents an electromagnetic interference problem, due to the proximity of two radio broadcasting stations which injected especially common mode conducted emissions over the maximal limits specified by the national regulations in the public low voltage mains network. These emissions determined the malfunction of the gas heating centrals Themaclassic Saunier Duval installed in the area. The problem was solved by the retro fitting of an extra EMI filter for the mains network, as presented in the paper.
Cellular neural networks for the stereo matching problem
Energy Technology Data Exchange (ETDEWEB)
Taraglio, S. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dipt. Innovazione; Zanela, A. [Rome Univ. `La Sapienza` (Italy). Dipt. di Fisica
1997-03-01
The applicability of the Cellular Neural Network (CNN) paradigm to the problem of recovering information on the tridimensional structure of the environment is investigated. The approach proposed is the stereo matching of video images. The starting point of this work is the Zhou-Chellappa neural network implementation for the same problem. The CNN based system we present here yields the same results as the previous approach, but without the many existing drawbacks.
Problems in the Deployment of Learning Networks In Small Organizations
Shankle, Dean E.; Shankle, Jeremy P.
2006-01-01
Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:
Solving constraint satisfaction problems with networks of spiking neurons
Directory of Open Access Journals (Sweden)
Zeno eJonke
2016-03-01
Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.
Analysis of feeder bus network design and scheduling problems.
Almasi, Mohammad Hadi; Mirzapour Mounes, Sina; Koting, Suhana; Karim, Mohamed Rehan
2014-01-01
A growing concern for public transit is its inability to shift passenger's mode from private to public transport. In order to overcome this problem, a more developed feeder bus network and matched schedules will play important roles. The present paper aims to review some of the studies performed on Feeder Bus Network Design and Scheduling Problem (FNDSP) based on three distinctive parts of the FNDSP setup, namely, problem description, problem characteristics, and solution approaches. The problems consist of different subproblems including data preparation, feeder bus network design, route generation, and feeder bus scheduling. Subsequently, descriptive analysis and classification of previous works are presented to highlight the main characteristics and solution methods. Finally, some of the issues and trends for future research are identified. This paper is targeted at dealing with the FNDSP to exhibit strategic and tactical goals and also contributes to the unification of the field which might be a useful complement to the few existing reviews.
Analysing Stagecoach Network Problem Using Dynamic ...
African Journals Online (AJOL)
In this paper we present a recursive dynamic programming algorithm for solving the stagecoach problem. The algorithm is computationally more efficient than the first method as it obtains its minimum total cost using the suboptimal policies of the different stages without computing the cost of all the routes. By the dynamic ...
Hub location problems in transportation networks
DEFF Research Database (Denmark)
Gelareh, Shahin; Nickel, Stefan
2011-01-01
. While the existing state-of-the-art MIP solvers fail to solve even small size instances of problem, our accelerated and efficient primal (Benders) decomposition solves larger ones. In addition, a very efficient greedy heuristic, proven to be capable of obtaining high quality solutions, is proposed. We...
An Efficient Algorithm for Congestion Control in Highly Loaded DiffServ/MPLS Networks
Directory of Open Access Journals (Sweden)
Srecko Krile
2009-06-01
Full Text Available The optimal QoS path provisioning of coexisted and aggregated traffic in networks is still demanding problem. All traffic flows in a domain are distributed among LSPs (Label Switching Path related to N service classes, but the congestion problem of concurrent flows can appear. As we know the IGP (Interior Getaway Protocol uses simple on-line routing algorithms (e.g. OSPFS, IS-IS based on shortest path methodology. In QoS end-to-end provisioning where some links may be reserved for certain traffic classes (for particular set of users it becomes insufficient technique. On other hand, constraint based explicit routing (CR based on IGP metric ensures traffic engineering (TE capabilities. The algorithm proposed in this paper may find a longer but lightly loaded path, better than the heavily loaded shortest path. LSP can be pre-computed much earlier, possibly during SLA (Service Level Agreement negotiation process. As we need firm correlation with bandwidth management and traffic engineering (TE the initial (pro-active routing can be pre-computed in the context of all priority traffic flows (former contracted SLAs traversing the network simultaneously. It could be a very good solution for congestion avoidance and for better load-balancing purpose where links are running close to capacity. Also, such technique could be useful in inter-domain end-to-end provisioning, where bandwidth reservation has to be negotiated with neighbor ASes (Autonomous System. To be acceptable for real applications such complicated routing algorithm can be significantly improved. Algorithm was tested on the network of M core routers on the path (between edge routers and results are given for N=3 service classes. Further improvements through heuristic approach are made and results are discussed.
Directory of Open Access Journals (Sweden)
Y. A. Gatchin
2015-11-01
Full Text Available The paper deals with the problem of protection for the process of operating system loading from the server to the diskless workstation through a network and the analysis of the existing ways of integrity monitoring for information transferred under network protocols. Within the scope of research, solution is proposed making it possible to perform integrity monitoring of the operating system loaded image before control is transferred to it. For security protection of loading, key information elements are marked which integrity needs to be guaranteed. The developed solution, as an information security product, should meet the requirements of information security and at the same time be compatible to other hardware and software tools used for protection of the automated systems. The proposed solution implements the algorithm of integrity monitoring for an operating system designed with the use of public key infrastructure. Analysis of hardware configuration for the projected solution from the point of view of its usability and administration ease is provided, and possibilities of intruder’s attacks to the protected information are estimated, as well.
Load forecasting method considering temperature effect for distribution network
Directory of Open Access Journals (Sweden)
Meng Xiao Fang
2016-01-01
Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.
Probability problems in seismic risk analysis and load combinations for nuclear power plants
Energy Technology Data Exchange (ETDEWEB)
George, L.L.
1983-01-01
This workshop describes some probability problems in power plant reliability and maintenance analysis. The problems are seismic risk analysis, loss of load probability, load combinations, and load sharing. The seismic risk problem is to compute power plant reliability given an earthquake and the resulting risk. Component survival occurs if its peak random response to the earthquake does not exceed its strength. Power plant survival is a complicated Boolean function of component failures and survivals. The responses and strengths of components are dependent random processes, and the peak responses are maxima of random processes. The resulting risk is the expected cost of power plant failure.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy
2012-01-01
We present a matheuristic, an integer programming based heuristic, for the Liner Shipping Network Design Problem. The heuristic applies a greedy construction heuristic based on an interpretation of the liner shipping network design problem as a multiple quadratic knapsack problem. The construction...... heuristic is combined with an improvement heuristic with a neighborhood defined by the solution space of a mixed integer program. The mixed integer program optimizes the removal and insertion of several port calls on a liner shipping service. The objective function is based on evaluation functions...
Performance analysis of dynamic load balancing algorithm for multiprocessor interconnection network
Directory of Open Access Journals (Sweden)
M.U. Bokhari
2016-09-01
Full Text Available Multiprocessor interconnection network have become powerful parallel computing system for real-time applications. Nowadays the many researchers posses studies on the dynamic load balancing in multiprocessor system. Load balancing is the method of dividing the total load among the processors of the distributed system to progress task's response time as well as resource utilization whereas ignoring a condition where few processors are overloaded or underloaded or moderately loaded. However, in dynamic load balancing algorithm presumes no priori information about behaviour of tasks or the global state of the system. There are numerous issues while designing an efficient dynamic load balancing algorithm that involves utilization of system, amount of information transferred among processors, selection of tasks for migration, load evaluation, comparison of load levels and many more. This paper enlightens the performance analysis on dynamic load balancing strategy (DLBS algorithm, used for hypercube network in multiprocessor system.
Topics on data transmission problem in software definition network
Gao, Wei; Liang, Li; Xu, Tianwei; Gan, Jianhou
2017-08-01
In normal computer networks, the data transmission between two sites go through the shortest path between two corresponding vertices. However, in the setting of software definition network (SDN), it should monitor the network traffic flow in each site and channel timely, and the data transmission path between two sites in SDN should consider the congestion in current networks. Hence, the difference of available data transmission theory between normal computer network and software definition network is that we should consider the prohibit graph structures in SDN, and these forbidden subgraphs represent the sites and channels in which data can't be passed by the serious congestion. Inspired by theoretical analysis of an available data transmission in SDN, we consider some computational problems from the perspective of the graph theory. Several results determined in the paper imply the sufficient conditions of data transmission in SDN in the various graph settings.
Solving unit commitment and economic load dispatch problems ...
African Journals Online (AJOL)
This paper introduces Genetic Algorithm (GA) or Dynamic Programming (DP) to solve UC and then Shuffled BAT (BAT) technique as an evolutionary based approach is presented to solve the constrained ELD problem of thermal plants depending on the results obtained from UC solution. The IEEE 30 bus system is used to ...
The Network Completion Problem: Inferring Missing Nodes and Edges in Networks
Energy Technology Data Exchange (ETDEWEB)
Kim, M; Leskovec, J
2011-11-14
Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.
PROBLEMS ARE CAUSED TO CHANGE TYPICAL TEMPORARY LOADS AC FOR BRIDGES CONSTRUCTIONS
Directory of Open Access Journals (Sweden)
P. M. Salamakhin
2010-03-01
Full Text Available The article is devoted to the analysis of new temporal normative loads of high-way bridges, the determination of dynamic coefficient and the problems of the using these loads in calculation of endurance and determination of remaining resource of elements of bridge structures.
A Memetic Approach for Improving Minimum Cost of Economic Load Dispatch Problems
Directory of Open Access Journals (Sweden)
Jinho Kim
2014-01-01
Full Text Available Economic load dispatch problem is a popular optimization problem in electrical power system field, which has been so far tackled by various mathematical and metaheuristic approaches including Lagrangian relaxation, branch and bound method, genetic algorithm, tabu search, particle swarm optimization, harmony search, and Taguchi method. On top of these techniques, this study proposes a novel memetic algorithm scheme combining metaheuristic algorithm and gradient-based technique to find better solutions for an economic load dispatch problem with valve-point loading. Because metaheuristic algorithms have the strength in global search and gradient-based techniques have the strength in local search, the combination approach obtains better results than those of any single approach. A bench-mark example of 40 generating-unit economic load dispatch problem demonstrates that the memetic approach can further improve the existing best solutions from the literature.
National Research Council Canada - National Science Library
Kumar, Vijay M; Murthy, ANN; Chandrashekara, K
2012-01-01
.... The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made...
Neural network for solving convex quadratic bilevel programming problems.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie
2014-03-01
In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
Social networking sites: an adjunctive treatment modality for psychological problems.
Menon, Indu S; Sharma, Manoj Kumar; Chandra, Prabha S; Thennarasu, K
2014-07-01
Social networking is seen as a way to enhance social support and feeling of well-being. The present work explores the potentials of social networking sites as an adjunctive treatment modality for initiating treatment contact as well as for managing psychological problems. Interview schedule, Facebook intensity questionnaire were administered on 28 subjects with a combination of 18 males and 10 females. They were taken from the in-patient and out-patient psychiatry setting of the hospital. Facebook was the most popular sites and used to seek emotional support on the basis of the frequent updates of emotional content that users put in their profile; reconciliations, escape from the problems or to manage the loneliness; getting information about illness and its treatment and interaction with experts and also manifested as problematic use. It has implications for developing social networking based adjunctive treatment modality for psychological problems.
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.
Directory of Open Access Journals (Sweden)
Kiyotaka Oe
2015-01-01
Full Text Available Wireless Mesh Networks (WMNs can provide wide range Wireless Local Area Networks (WLANs area by connecting Access Points (APs of WLANs with each other using radio communications. A routing protocol is very important to keep communication quality over radio multihop communications because radio waves are impacted much by surrounding environment. When we use multiuser shared applications like a video conference and an IP phone, it is predicted that large amount of traffic flows on network. Therefore, we should consider network loads to use these applications. In this paper, we propose a multicast routing protocol for WMNs which considers network loads and hop count. Furthermore, we evaluate performance by simulation. In the simulation results, we show that the proposed protocol has better performance than a conventional protocol (MAODV at high loaded scenario.
Sub-problem Optimization With Regression and Neural Network Approximators
Guptill, James D.; Hopkins, Dale A.; Patnaik, Surya N.
2003-01-01
Design optimization of large systems can be attempted through a sub-problem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of sub-problems. Solution to the large problem is attempted iteratively through repeated solutions to the modest sub-problems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of sub-problems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as sub-problems. A sub-problem, if required, can be further broken down to accommodate sub-disciplines. The sub-problem strategy is being implemented into the NASA design optimization test bed, referred to as "CometBoards." Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the sub-problem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.
Client-Centered Problem-Solving Networks in Complex Organizations.
Tucker, Charles; Hanna, Michael
Employees in different kinds of organizations were surveyed for their perceptions of their companies' client and operational problem-solving networks. The individuals came from a manufacturing firm, a community college, a telephone company, a farmers' cooperative, and a hospital. Interviews were conducted with those people reporting numerous…
Countervailing Social Network Influences on Problem Behaviors among Homeless Youth
Rice, Eric; Stein, Judith A.; Milburn, Norweeta
2008-01-01
The impact of countervailing social network influences (i.e., pro-social, anti-social or HIV risk peers) on problem behaviors (i.e., HIV drug risk, HIV sex risk or anti-social behaviors) among 696 homeless youth was assessed using structural equation modeling. Results revealed that older youth were less likely to report having pro-social peers and…
Evolving neural networks for strategic decision-making problems.
Kohl, Nate; Miikkulainen, Risto
2009-04-01
Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems-such as those involving strategic decision-making-have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed and, based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.
Failure load prediction of single lap adhesive joints using artificial neural networks
Directory of Open Access Journals (Sweden)
Erdi Tosun
2016-06-01
Full Text Available The objective of this paper was to predict the failure load in single lap adhesive joints subjected to tensile loading by using artificial neural networks. Experimental data obtained from the literature cover the single lap adhesive joints with various geometric models under the tensile loading. The data are arranged in a format such that two input parameters cover the length and width of bond area in single lap adhesive joints and the corresponding output is the ultimate failure load. An artificial neural network model was developed to estimate relationship between failure loads by using geometric dimensions of bond area as input data. A three-layer feedforward artificial neural network that utilized Levenberg–Marquardt learning algorithm model was used in order to train network. It was observed that artificial neural network model can estimate failure load of single lap adhesive joints with acceptable error. Mean absolute percentage error and Nash–Sutcliffe coefficient of efficiency values of both training and testing data were 3.523 and 3.524 and 0.997 and 0.992, respectively. The results showed that the artificial neural network is an efficient alternative method to predict the failure load of single lap adhesive joints. Also estimated results are in very good agreement with the experimental data.
Subgradient-based neural networks for nonsmooth nonconvex optimization problems.
Bian, Wei; Xue, Xiaoping
2009-06-01
This paper presents a subgradient-based neural network to solve a nonsmooth nonconvex optimization problem with a nonsmooth nonconvex objective function, a class of affine equality constraints, and a class of nonsmooth convex inequality constraints. The proposed neural network is modeled with a differential inclusion. Under a suitable assumption on the constraint set and a proper assumption on the objective function, it is proved that for a sufficiently large penalty parameter, there exists a unique global solution to the neural network and the trajectory of the network can reach the feasible region in finite time and stay there thereafter. It is proved that the trajectory of the neural network converges to the set which consists of the equilibrium points of the neural network, and coincides with the set which consists of the critical points of the objective function in the feasible region. A condition is given to ensure the convergence to the equilibrium point set in finite time. Moreover, under suitable assumptions, the coincidence between the solution to the differential inclusion and the "slow solution" of it is also proved. Furthermore, three typical examples are given to present the effectiveness of the theoretic results obtained in this paper and the good performance of the proposed neural network.
A PSO based Artificial Neural Network approach for short term unit commitment problem
Directory of Open Access Journals (Sweden)
AFTAB AHMAD
2010-10-01
Full Text Available Unit commitment (UC is a non-linear, large scale, complex, mixed-integer combinatorial constrained optimization problem. This paper proposes, a new hybrid approach for generating unit commitment schedules using swarm intelligence learning rule based neural network. The training data has been generated using dynamic programming for machines without valve point effects and using genetic algorithm for machines with valve point effects. A set of load patterns as inputs and the corresponding unit generation schedules as outputs are used to train the network. The neural network fine tunes the best results to the desired targets. The proposed approach has been validated for three thermal machines with valve point effects and without valve point effects. The results are compared with the approaches available in the literature. The PSO-ANN trained model gives better results which show the promise of the proposed methodology.
Impact of the traffic load on performance of an alternative LTE railway communication network
DEFF Research Database (Denmark)
Sniady, Aleksander; Soler, José
2013-01-01
obstructing railway operations at big train stations and junctions. Hence, other technologies, such as Long Term Evolution (LTE), need to be considered as an alternative to GSM-R. The goal of this paper is to demonstrate the capacity increase that railways can expect, from the introduction of LTE as internal...... communication infrastructure supporting railway signaling. This work is based on OPNET realistic network simulations, which show the relation between the traffic load (the number of trains transmitting and receiving data in an LTE cell) and the delay performance of the European Train Control System (ETCS......) signaling, which is one of the essential railway communication services. Results of the simulations demonstrate that LTE can solve the urgent capacity problem faced by railways currently deploying GSM-R....
neural network based load frequency control for restructuring power
African Journals Online (AJOL)
2012-03-01
Mar 1, 2012 ... power system is chosen and load frequency con- trol of this system is made by a ANN controller and a conventional PI controller. Basically, power system consists of a governor, a turbine, and a generator with feedback of reg- ulation constant. System also includes step load change input to the generator.
Genetic algorithms for nuclear reactor fuel load and reload optimization problems
Directory of Open Access Journals (Sweden)
A.V. Sobolev
2017-09-01
The efficiency of use of the developed model of the genetic algorithm is demonstrated by the test example of a BN type reactor. The results of the test run demonstrated that the use of the proposed approach allows searching for optimal reactor load mapping for each separate core reshuffling operation. The main objective of the performed study was to demonstrate the applicability and efficiency of the new up-to-date approach to solving the problem of fuel loading into a nuclear reactor.
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, Mark; Schniter, Philip; Rangan, Sundeep
2017-08-01
Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a "learned AMP" network that significantly improves upon Gregor and LeCun's "learned ISTA." Second, inspired by the recently proposed "vector AMP" (VAMP) algorithm, we propose a "learned VAMP" network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.
Robustness of scale-free networks to cascading failures induced by fluctuating loads.
Mizutaka, Shogo; Yakubo, Kousuke
2015-07-01
Taking into account the fact that overload failures in real-world functional networks are usually caused by extreme values of temporally fluctuating loads that exceed the allowable range, we study the robustness of scale-free networks against cascading overload failures induced by fluctuating loads. In our model, loads are described by random walkers moving on a network and a node fails when the number of walkers on the node is beyond the node capacity. Our results obtained by using the generating function method show that scale-free networks are more robust against cascading overload failures than Erdős-Rényi random graphs with homogeneous degree distributions. This conclusion is contrary to that predicted by previous works, which neglect the effect of fluctuations of loads.
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2010-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010, 4 November). Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective. Presentation at ICO-Toogdag, Amstelveen, The Netherlands: VU Amsterdam.
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2010-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010). Effect of using peer tutoring to support knowledge sharing in Learning Networks: A cognitive load perspective. ICO-Toogdag. November, 4, 2010, Amstelveen, The Netherlands: VU Amsterdam.
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2010-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2010, 15 April). Mechanisms of peer tutoring on optimizing cognitive load during knowledge sharing in learning networks. Presentation at NELLL Colloqium, Heerlen, The Netherlands: Open University of the Netherlands.
Designing optimal peer support to alleviate learner cognitive load in Learning Networks
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2012-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012, 21 July). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. Presentation at IADIS International Conference Web-Based Communities and Social Media 2012, Lisbon, Portugal.
A Formal Model and Verification Problems for Software Defined Networks
Directory of Open Access Journals (Sweden)
V. A. Zakharov
2013-01-01
Full Text Available Software-defined networking (SDN is an approach to building computer networks that separate and abstract data planes and control planes of these systems. In a SDN a centralized controller manages a distributed set of switches. A set of open commands for packet forwarding and flow-table updating was defined in the form of a protocol known as OpenFlow. In this paper we describe an abstract formal model of SDN, introduce a tentative language for specification of SDN forwarding policies, and set up formally model-checking problems for SDN.
Deep Convolutional Neural Network for Inverse Problems in Imaging.
Jin, Kyong Hwan; McCann, Michael T; Froustey, Emmanuel; Unser, Michael
2017-06-15
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on the GPU.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Jin, Kyong Hwan; McCann, Michael T.; Froustey, Emmanuel; Unser, Michael
2017-09-01
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU.
Islam, Mujahidul
A sustainable energy delivery infrastructure implies the safe and reliable accommodation of large scale penetration of renewable sources in the power grid. In this dissertation it is assumed there will be no significant change in the power transmission and distribution structure currently in place; except in the operating strategy and regulatory policy. That is to say, with the same old structure, the path towards unveiling a high penetration of switching power converters in the power system will be challenging. Some of the dimensions of this challenge are power quality degradation, frequent false trips due to power system imbalance, and losses due to a large neutral current. The ultimate result is the reduced life of many power distribution components - transformers, switches and sophisticated loads. Numerous ancillary services are being developed and offered by the utility operators to mitigate these problems. These services will likely raise the system's operational cost, not only from the utility operators' end, but also reflected on the Independent System Operators and by the Regional Transmission Operators (RTO) due to an unforeseen backlash of frequent variation in the load-side generation or distributed generation. The North American transmission grid is an interconnected system similar to a large electrical circuit. This circuit was not planned but designed over 100 years. The natural laws of physics govern the power flow among loads and generators except where control mechanisms are installed. The control mechanism has not matured enough to withstand the high penetration of variable generators at uncontrolled distribution ends. Unlike a radial distribution system, mesh or loop networks can alleviate complex channels for real and reactive power flow. Significant variation in real power injection and absorption on the distribution side can emerge as a bias signal on the routing reactive power in some physical links or channels that are not distinguishable
Impact evaluation of conducted UWB transients on loads in power-line networks
Li, Bing; Månsson, Daniel
2017-09-01
Nowadays, faced with the ever-increasing dependence on diverse electronic devices and systems, the proliferation of potential electromagnetic interference (EMI) becomes a critical threat for reliable operation. A typical issue is the electronics working reliably in power-line networks when exposed to electromagnetic environment. In this paper, we consider a conducted ultra-wideband (UWB) disturbance, as an example of intentional electromagnetic interference (IEMI) source, and perform the impact evaluation at the loads in a network. With the aid of fast Fourier transform (FFT), the UWB transient is characterized in the frequency domain. Based on a modified Baum-Liu-Tesche (BLT) method, the EMI received at the loads, with complex impedance, is computed. Through inverse FFT (IFFT), we obtain time-domain responses of the loads. To evaluate the impact on loads, we employ five common, but important quantifiers, i.e., time-domain peak, total signal energy, peak signal power, peak time rate of change and peak time integral of the pulse. Moreover, to perform a comprehensive analysis, we also investigate the effects of the attributes (capacitive, resistive, or inductive) of other loads connected to the network, the rise time and pulse width of the UWB transient, and the lengths of power lines. It is seen that, for the loads distributed in a network, the impact evaluation of IEMI should be based on the characteristics of the IEMI source, and the network features, such as load impedances, layout, and characteristics of cables.
Directory of Open Access Journals (Sweden)
H. Hadj Abdallah
2005-09-01
Full Text Available This work presents a method for solving the problem of load flow in electric power systems including a wind power station with asynchronous generators. For this type of power station, the generated active power is only known and consequently the absorbed reactive power must be determined. So we have used the circular diagram at each iteration and by considering this node as a consuming node in the load flow program. Since the wind speed is not constant, the generated power is neither constant. To predict the state of the network in real time, we have used the artificial neural networks after a stage of training using a rich base of data.
A simulated annealing approach for redesigning a warehouse network problem
Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia
2017-09-01
Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.
Problems of Using Femtocells in Public Cellular Networks
Directory of Open Access Journals (Sweden)
Karolis Žvinys
2013-05-01
Full Text Available This paper analyses the use of femtocells connected into a single macro network infrastructure. Different problems and possible solutions were discussed. The paper is focused on two separate benefits, which HNB could bring an operator and user. Femtocells are especially appealing due to the freedom of installation, increased macro network capacity, femto zone rates and etc. They provide users with better service quality, including voice service and higher throughput; while operators can reduce their network deployment expenditures. On the other hand, unplanned deployment, mobility issues and different types of user groups can cause a headache both for operators and customers. The analysis demonstrated that the majority of features of femtocells from the operator’s point of view were positive. Looking from the user’s point of view, most of shortcomings are difficult to remove. Positive and negative features both for operators and clients are presented in the HNB model.Article in Lithuanian
artificial neural network (ann) approach to electrical load
African Journals Online (AJOL)
2004-08-18
Aug 18, 2004 ... UNIVERSITY POWER HOUSE. A.A.AKINTOLA", G.A. ADEROUNMU and O.E. ... The model was tested using two of the seven feeders of the Obafemi. Awolowo University electric network. The results of .... The architecture of a neural network is the specific arrangement and connections of the neurons that.
Energy savings in mobile broadband network based on load predictions
DEFF Research Database (Denmark)
Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels Bundgaard
2012-01-01
in wireless networks. To save energy in MBNs, one of the options is to turn off parts of the network equipment in areas where traffic falls below a specific predefined threshold. This paper looks at a methodology for identifying periods of the day when cells or sites carrying low traffic are candidates...... for being totally or partly switched off, given that the decrease in service quality can be controlled gracefully when the sites are switched off. Based on traffic data from an operational network, potential average energy savings of approximately 30% with some few low traffic cells/sites reaching up to 99......Abstract—The deployment of new network equipment is resulting in increasing energy consumption in mobile broadband networks (MBNs). This contributes to higher CO2 emissions. Over the last 10 years MBNs have grown considerably, and are still growing to meet the evolution in traffic volume carried...
Applying DNA computation to intractable problems in social network analysis.
Chen, Rick C S; Yang, Stephen J H
2010-09-01
From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Kinetic Transition Networks for the Thomson Problem and Smale's Seventh Problem
Mehta, Dhagash; Chen, Jianxu; Chen, Danny Z.; Kusumaatmaja, Halim; Wales, David J.
2016-07-01
The Thomson problem, arrangement of identical charges on the surface of a sphere, has found many applications in physics, chemistry and biology. Here, we show that the energy landscape of the Thomson problem for N particles with N =132 , 135, 138, 141, 144, 147, and 150 is single funneled, characteristic of a structure-seeking organization where the global minimum is easily accessible. Algorithmically, constructing starting points close to the global minimum of such a potential with spherical constraints is one of Smale's 18 unsolved problems in mathematics for the 21st century because it is important in the solution of univariate and bivariate random polynomial equations. By analyzing the kinetic transition networks, we show that a randomly chosen minimum is, in fact, always "close" to the global minimum in terms of the number of transition states that separate them, a characteristic of small world networks.
The load-balanced multi-dimensional bin-packing problem
DEFF Research Database (Denmark)
Trivella, Alessio; Pisinger, David
2016-01-01
The bin-packing problem is one of the most investigated and applicable combinatorial optimization problems. In this paper we consider its multi-dimensional version with the practical extension of load balancing, i.e. to find the packing requiring the minimum number of bins while ensuring...... that the average center of mass of the loaded bins falls as close as possible to an ideal point, for instance, the center of the bin. We formally describe the problem using mixed-integer linear programming models, from the simple case where we want to optimally balance a set of items already assigned to a single...... bin, to the general balanced bin-packing problem. Given the difficulty for standard solvers to deal even with small size instances, a multi-level local search heuristic is presented. The algorithm takes advantage of the Fekete-Schepers representation of feasible packings in terms of particular classes...
Neural network for regression problems with reduced training sets.
Bataineh, Mohammad; Marler, Timothy
2017-11-01
Although they are powerful and successful in many applications, artificial neural networks (ANNs) typically do not perform well with complex problems that have a limited number of training cases. Often, collecting additional training data may not be feasible or may be costly. Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data. This new design involves a multi-stage training process that couples an orthogonal least squares (OLS) technique with gradient-based optimization. New termination criteria are also introduced to improve accuracy. In addition, the algorithms are designed to require minimal heuristic parameters, thus improving ease of use and consistency in performance. The proposed approach is tested with experimental and practical regression problems, and the results are compared with those from typical network models. The results show that the new design demonstrates improved accuracy with reduced dependence on the amount of training data. As demonstrated, this new ANN provides a platform for approximating potentially slow but high-fidelity computational models, and thus fostering inter-model connectivity and multi-scale modeling. Copyright © 2017 Elsevier Ltd. All rights reserved.
Practical Solutions for Harmonics Problems Produced in the Distribution Networks
Directory of Open Access Journals (Sweden)
A. F. Zobaa
2006-03-01
Full Text Available Harmonic distortion on the power system is a modern concern due to the technological advances in silicon technology as it presents an increased non-linear loading of the power system. The effects of harmonics are well known: customers could experience major production losses due to the loss of supply as an example, on the other hand, harmonic load currents cause the utility to supply a higher real energy input then the actual real power needed to maintain a plant’s production at a certain level. The utility carries the extra transmission losses due to the harmonic currents. Different solutions will be reviewed as concepts for solving certain types of problems related to power quality. Both theoretical and a case study are presented.
Load control strategies in 2G mobile network for W-CDMA radio ...
African Journals Online (AJOL)
Network planning requires a faithful analysis of each individual cell's capacity. In this paper, we examine load control equations as a resource allocation tool to analyse cell capacity for the uplink and downlink of Wideband Code Division Multiple Access (W-CDMA) networks. In the uplink, the noise rise is a parameter of ...
Address Translation Problems in IMS Based Next Generation Networks
Directory of Open Access Journals (Sweden)
Balazs Godor
2006-01-01
Full Text Available The development of packed based multimedia networks reached a turning point when the ITU-T and the ETSIhave incorporated the IMS to the NGN. With the fast development of mobile communication more and more services andcontent are available. In contrast with fix network telephony both the services and the devices are personalized in the “mobileworld”. Services, known from the Internet - like e-mail, chat, browsing, presence, etc. – are already available via mobiledevices as well. The IMS originally wanted to exploit both the benefits of mobile networks and the fancy services of theInternet. But today it is already more than that. IMS is the core of the next generation telecommunication networks and abasis for fix-mobile convergent services. The fact however that IMS was originally a “mobile” standard, where IPv6 was notoddity generated some problems for the fix networks, where IPv4 is used. In this article I give an overview of these problemsand mention some solutions as well.
Complex network problems in physics, computer science and biology
Cojocaru, Radu Ionut
There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe
BOUNDARY VALUE PROBLEM FOR A LOADED EQUATION ELLIPTIC-HYPERBOLIC TYPE IN A DOUBLY CONNECTED DOMAIN
Directory of Open Access Journals (Sweden)
O.Kh. Abdullaev
2014-06-01
Full Text Available We study the existence and uniqueness of the solution of one boundary value problem for the loaded elliptic-hyperbolic equation of the second order with two lines of change of type in double-connected domain. Similar results have been received by D.M.Kuryhazov, when investigated domain is one-connected.
Gammon - A load balancing strategy for local computer systems with multiaccess networks
Baumgartner, Katherine M.; Wah, Benjamin W.
1989-01-01
Consideration is given to an efficient load-balancing strategy, Gammon (global allocation from maximum to minimum in constant time), for distributed computing systems connected by multiaccess local area networks. The broadcast capability of these networks is utilized to implement an identification procedure at the applications level for the maximally and the minimally loaded processors. The search technique has an average overhead which is independent of the number of participating stations. An implementation of Gammon on a network of Sun workstations is described. Its performance is found to be better than that of other known methods.
Towards overcoming the Monte Carlo sign problem with tensor networks
Energy Technology Data Exchange (ETDEWEB)
Banuls, Mari Carmen; Cirac, J. Ignacio; Kuehn, Stefan [Max-Planck-Institut fuer Quantenoptik (MPQ), Garching (Germany); Cichy, Krzysztof [Frankfurt Univ. (Germany). Inst. fuer Theoretische Physik; Adam Mickiewicz Univ., Poznan (Poland). Faculty of Physics; Jansen, Karl [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Saito, Hana [AISIN AW Co., Ltd., Aichi (Japan)
2016-11-15
The study of lattice gauge theories with Monte Carlo simulations is hindered by the infamous sign problem that appears under certain circumstances, in particular at non-zero chemical potential. So far, there is no universal method to overcome this problem. However, recent years brought a new class of non-perturbative Hamiltonian techniques named tensor networks, where the sign problem is absent. In previous work, we have demonstrated that this approach, in particular matrix product states in 1+1 dimensions, can be used to perform precise calculations in a lattice gauge theory, the massless and massive Schwinger model. We have computed the mass spectrum of this theory, its thermal properties and real-time dynamics. In this work, we review these results and we extend our calculations to the case of two flavours and non-zero chemical potential. We are able to reliably reproduce known analytical results for this model, thus demonstrating that tensor networks can tackle the sign problem of a lattice gauge theory at finite density.
Load Balanced Mapping of Distributed Objects to Minimize Network Communication
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
Directory of Open Access Journals (Sweden)
S. Kim
2013-08-01
Full Text Available The IEEE 802.11e EDCA (Enhanced Distributed Channel Access is able to provide QoS (Quality of Service by adjusting the transmission opportunities (TXOPs, which control the period to access the medium. The EDCA has a fairness problem among competing stations, which support multimedia applications with different delay bounds. In this paper, we propose a simple and effective scheme for alleviating the fairness problem. The proposed scheme dynamically allocates the TXOP value based on the delay bounds of the data packets in a queue and the traffic load of network. Performance of the proposed scheme is investigated by simulation. Our results show that compared to conventional scheme, the proposed scheme significantly improves network performance, and achieves a high degree of fairness among stations with different multimedia applications.
The Dorsal Attention Network Reflects Both Encoding Load and Top-down Control during Working Memory.
Majerus, Steve; Péters, Frédéric; Bouffier, Marion; Cowan, Nelson; Phillips, Christophe
2018-02-01
The dorsal attention network is consistently involved in verbal and visual working memory (WM) tasks and has been associated with task-related, top-down control of attention. At the same time, WM capacity has been shown to depend on the amount of information that can be encoded in the focus of attention independently of top-down strategic control. We examined the role of the dorsal attention network in encoding load and top-down memory control during WM by manipulating encoding load and memory control requirements during a short-term probe recognition task for sequences of auditory (digits, letters) or visual (lines, unfamiliar faces) stimuli. Encoding load was manipulated by presenting sequences with small or large sets of memoranda while maintaining the amount of sensory stimuli constant. Top-down control was manipulated by instructing participants to passively maintain all stimuli or to selectively maintain stimuli from a predefined category. By using ROI and searchlight multivariate analysis strategies, we observed that the dorsal attention network encoded information for both load and control conditions in verbal and visuospatial modalities. Decoding of load conditions was in addition observed in modality-specific sensory cortices. These results highlight the complexity of the role of the dorsal attention network in WM by showing that this network supports both quantitative and qualitative aspects of attention during WM encoding, and this is in a partially modality-specific manner.
A load-balance path selection algorithm in automatically swiched optical network (ASON)
Gao, Fei; Lu, Yueming; Ji, Yuefeng
2007-11-01
In this paper, a novel load-balance algorithm is proposed to provide an approach to optimized path selection in automatically swiched optical network (ASON). By using this algorithm, improved survivability and low congestion can be achieved. The static nature of current routing algorithms, such as OSPF or IS-IS, has made the situation worse since the traffic is concentrated on the "least-cost" paths which causes the congestion for some links while leaving other links lightly loaded. So, the key is to select suitable paths to balance the network load to optimize network resource utilization and traffic performance. We present a method to provide the capability to control traffic engineering so that the carriers can define their own strategies for optimizations and apply them to path selection for dynamic load balancing. With considering load distribution and topology information, capacity utilization factor is introduced into Dijkstra (shortest path selection) for path selection to achieve balancing traffic over network. Routing simulations have been done over mesh networks to compare the two different algorithms. With the simulation results, a conclusion can be made on the performance of different algorithms.
Dynamic link load balancing based integrated routing algorithm in IP-over-WDM networks
Zhang, Zhizhong; Zhang, Yunlin; Zeng, Qingji; Wang, Jianxin; Ye, Tong; Zhou, Yuli
2004-04-01
Integrated routing is a routing approach to support the peer interconnection model in IP over WDM networks. To have a better network link load distribution and network usage in IP over WDM networks, in which network nodes may have the ability to handle traffic in fine granularities, it is important to take into account the combined routing at the IP and WDM layers. Based upon this, this paper develops an algorithm for integrated dynamic routing of bandwidth guaranteed paths in IP over WDM networks. For newly dynamic arriving requests, as the developed algorithm takes into account the combined topology and resource usage information at the IP and WDM layers, and the routing procedure makes full use of the statistic information of the users" bandwidth requirement and considers carefully both the routing cost and the corresponding length of the routing path, thus a better link load balancing and network usage can be achieved. Simulation results show that the developed scheme performs well in terms of performance metrics such as the number of rejected demands and the network link load balancing.
Bulk Electric Load Cost Calculation Methods: Iraqi Network Comparative Study
Directory of Open Access Journals (Sweden)
Qais M. Alias
2016-09-01
Full Text Available It is vital in any industry to regain the spent capitals plus running costs and a margin of profits for the industry to flourish. The electricity industry is an everyday life touching industry which follows the same finance-economic strategy. Cost allocation is a major issue in all sectors of the electric industry, viz, generation, transmission and distribution. Generation and distribution service costing’s well documented in the literature, while the transmission share is still of need for research. In this work, the cost of supplying a bulk electric load connected to the EHV system is calculated. A sample basic lump-average method is used to provide a rough costing guide. Also, two transmission pricing methods are employed, namely, the postage-stamp and the load-flow based MW-distance methods to calculate transmission share in the total cost of each individual bulk load. The three costing methods results are then analyzed and compared for the 400kV Iraqi power grid considered for a case study.
Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Antonio J. Sanchez-Esguevillas
2013-03-01
Full Text Available Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc., which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN that performs Short-Term Load Forecasting (STLF. In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.
A recurrent neural network for solving bilevel linear programming problem.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian
2014-04-01
In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.
Directory of Open Access Journals (Sweden)
Maen Z. Kreishan
2016-11-01
Full Text Available In this paper a realistic medium voltage (MV network with four different distributed generation technologies (diesel, gas, hydro and wind along with their excitation and governor control systems is modelled and simulated. Moreover, an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies was investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding. The load sharing islanding method has been used for controlling the distributed generation units during grid-connected and islanding operation. The simulation results were validated through various case studies and have shown that properly planned islanding transition could provide support to critical loads at the event of utility outages. However, a reliable protection scheme would be required to mitigate the adverse effect of unplanned islanding as all unplanned sub-cases returned severe negative results.
Mental Health, School Problems, and Social Networks: Modeling Urban Adolescent Substance Use
Mason, Michael J.
2010-01-01
This study tested a mediation model of the relationship with school problems, social network quality, and substance use with a primary care sample of 301 urban adolescents. It was theorized that social network quality (level of risk or protection in network) would mediate the effects of school problems, accounting for internalizing problems and…
Artificial neural networks in high voltage transmission line problems
Ekonomou, L.; Kontargyri, V. T.; Kourtesi, St.; Maris, T. I.; Stathopulos, I. A.
2007-07-01
According to the literature high voltage transmission line problems are faced using conventional analytical methods, which include in most cases empirical and/or approximating equations. Artificial intelligence and more specifically artificial neural networks (ANN) are addressed in this work, in order to give accurate solutions to high voltage transmission line problems using in the calculations only actual field data. Two different case studies are studied, i.e., the estimation of critical flashover voltage on polluted insulators and the estimation of lightning performance of high voltage transmission lines. ANN models are developed and are tested on operating high voltage transmission lines and polluted insulators, producing very satisfactory results. These two ANN models can be used in electrical engineers' studies aiming at the more effective protection of high voltage equipment.
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.
Non-Markovian State-Dependent Networks in Critical Loading
2015-02-04
detail in Section 5.) State-dependent features are present in congestion control protocols in communication networks, such as TCP (see Refs...information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. Colorado State University - Ft...Puhalskii FIGURE 1 The tandem queue. the workload and investigate the effect of controlling the arrival rate. We will adopt a similar model. We
Impact evaluation of conducted UWB transients on loads in power-line networks
Directory of Open Access Journals (Sweden)
B. Li
2017-09-01
Full Text Available Nowadays, faced with the ever-increasing dependence on diverse electronic devices and systems, the proliferation of potential electromagnetic interference (EMI becomes a critical threat for reliable operation. A typical issue is the electronics working reliably in power-line networks when exposed to electromagnetic environment. In this paper, we consider a conducted ultra-wideband (UWB disturbance, as an example of intentional electromagnetic interference (IEMI source, and perform the impact evaluation at the loads in a network. With the aid of fast Fourier transform (FFT, the UWB transient is characterized in the frequency domain. Based on a modified Baum–Liu–Tesche (BLT method, the EMI received at the loads, with complex impedance, is computed. Through inverse FFT (IFFT, we obtain time-domain responses of the loads. To evaluate the impact on loads, we employ five common, but important quantifiers, i.e., time-domain peak, total signal energy, peak signal power, peak time rate of change and peak time integral of the pulse. Moreover, to perform a comprehensive analysis, we also investigate the effects of the attributes (capacitive, resistive, or inductive of other loads connected to the network, the rise time and pulse width of the UWB transient, and the lengths of power lines. It is seen that, for the loads distributed in a network, the impact evaluation of IEMI should be based on the characteristics of the IEMI source, and the network features, such as load impedances, layout, and characteristics of cables.
Convolutional Neural Networks for Inverse Problems in Imaging: A Review
McCann, Michael T.; Jin, Kyong Hwan; Unser, Michael
2017-11-01
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, super-resolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions: Where does the training data come from? What is the architecture of the CNN? and How is the learning problem formulated and solved? We also bring together a few key theoretical papers that offer perspective on why CNNs are appropriate for inverse problems and point to some next steps in the field.
High-precision solution to the moving load problem using an improved spectral element method
Wen, Shu-Rui; Wu, Zhi-Jing; Lu, Nian-Li
2017-06-01
In this paper, the spectral element method (SEM) is improved to solve the moving load problem. In this method, a structure with uniform geometry and material properties is considered as a spectral element, which means that the element number and the degree of freedom can be reduced significantly. Based on the variational method and the Laplace transform theory, the spectral stiffness matrix and the equivalent nodal force of the beam-column element are established. The static Green function is employed to deduce the improved function. The proposed method is applied to two typical engineering practices—the one-span bridge and the horizontal jib of the tower crane. The results have revealed the following. First, the new method can yield extremely high-precision results of the dynamic deflection, the bending moment and the shear force in the moving load problem. In most cases, the relative errors are smaller than 1%. Second, by comparing with the finite element method, one can obtain the highly accurate results using the improved SEM with smaller element numbers. Moreover, the method can be widely used for statically determinate as well as statically indeterminate structures. Third, the dynamic deflection of the twin-lift jib decreases with the increase in the moving load speed, whereas the curvature of the deflection increases. Finally, the dynamic deflection, the bending moment and the shear force of the jib will all increase as the magnitude of the moving load increases.
Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Directory of Open Access Journals (Sweden)
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
Route Selection Problem Based on Hopfield Neural Network
Directory of Open Access Journals (Sweden)
N. Kojic
2013-12-01
Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.
Pollaris, H.; Braekers, K.; Caris, A.; Janssens, G.K.; S Limbourg
2014-01-01
A mixed integer linear programming model for a two-dimensional capacitated vehicle routing problem (2L-CVRP) with sequence based loading and axle weight restrictions is provided. To the authors’ knowledge, it is the first time that axle weight restrictions are incorporated in a VRP. Axle weight limits impose a great challenge for transportation companies. Trucks with overloaded axles represent a significant threat for traffic safety and may cause serious damage to the road surface. Transpor...
Capacitated vehicle routing problem with sequence-based pallet loading and axle weight constraints
Pollaris, Hanne; Braekers, Kris; Caris, An; Janssens, Gerrit, K.; Limbourg, Sabine
2016-01-01
In this paper, we introduce and study the capacitated vehicle routing problem with sequence-based pallet loading and axle weight constraints. To the best of our knowledge, it is the first time that axle weight restrictions are incorporated in a vehicle routing model. The aim of this paper is to demonstrate that incorporating axle weight restrictions in a vehicle routing model is possible and necessary for a feasible route planning. Axle weight limits impose a great challenge for transportatio...
A Framework Design for Load-balanced Green Access Networks supporting GSM Femtocell
Directory of Open Access Journals (Sweden)
Ray-Guang Cheng
2015-02-01
Full Text Available Reducing the energy consumption and carbon footprint emissions to improve the global climate change has become the global concern. However, CO2 generated from the current mobile devices and infrastructure has increased. Many researchers intended to develop the communication systems with low energy-consumption technologies, called the green communication. This paper proposes a framework of the load balanced green access network supporting the GSM femtocell service. By using the USRP software-defined radio device, we can build a GSM femtocell base station by software configuration. Besides, the proposed network can also extend the coverage of base stations by integrating with radio over fiber technology. With the load balancer, the proposed green access network can accomplish low power consumption, high energy efficiency, and easy to maintain. The experimental results showed that it can effectively save 24% energy consumption for the overall network and meet the quality-of-service of user when the proposed framework is applied.
Matlab for Forecasting of Electric Power Load Based on BP Neural Network
Wang, Xi-Ping; Shi, Ming-Xi
Modeling and predicting electricity consumption play a vital role both in developed and developing countries for policy makers and related organizations. Improve load forecasting technology level is not only beneficial to plan power management and make reasonable construction plan, but also good for saving energy and reducing power cost, and then, it can improve the economic benefits and social benefit for power system. BP neural network is one of the most widely used neural networks and it has many advantages in the power load forecasting. Matlab has become the best technology application software which has been internationally recognized, the software has many characteristics, such as data visualization function and neural network toolbox, for these, it is the essential software when we do some research on neural network.
Rules Placement Problem in OpenFlow Networks: a Survey
Nguyen, Xuan Nam; Saucez, Damien; Barakat, Chadi; Turletti, Thierry
2016-01-01
International audience; Software-Defined Networking (SDN) abstracts low- level network functionalities to simplify network management and reduce costs. The OpenFlow protocol implements the SDN concept by abstracting network communications as flows to be processed by network elements. In OpenFlow, the high-level policies are translated into network primitives called rules that are distributed over the network. While the abstraction offered by OpenFlow allows to potentially implement any policy...
Wada, Daichi; Sugimoto, Yohei
2017-04-01
Aerodynamic loads on aircraft wings are one of the key parameters to be monitored for reliable and effective aircraft operations and management. Flight data of the aerodynamic loads would be used onboard to control the aircraft and accumulated data would be used for the condition-based maintenance and the feedback for the fatigue and critical load modeling. The effective sensing techniques such as fiber optic distributed sensing have been developed and demonstrated promising capability of monitoring structural responses, i.e., strains on the surface of the aircraft wings. By using the developed techniques, load identification methods for structural health monitoring are expected to be established. The typical inverse analysis for load identification using strains calculates the loads in a discrete form of concentrated forces, however, the distributed form of the loads is essential for the accurate and reliable estimation of the critical stress at structural parts. In this study, we demonstrate an inverse analysis to identify the distributed loads from measured strain information. The introduced inverse analysis technique calculates aerodynamic loads not in a discrete but in a distributed manner based on a finite element model. In order to verify the technique through numerical simulations, we apply static aerodynamic loads on a flat panel model, and conduct the inverse identification of the load distributions. We take two approaches to build the inverse system between loads and strains. The first one uses structural models and the second one uses neural networks. We compare the performance of the two approaches, and discuss the effect of the amount of the strain sensing information.
CAC DPLB MCN: A Distributed Load Balancing Scheme in Multimedia Mobile Cellular Networks
Directory of Open Access Journals (Sweden)
Sharma Abhijit
2016-11-01
Full Text Available The problem of non-uniform traffic demand in different cells of a cellular network may lead to a gross imbalance in the system performance. Thus, the users in hot cells may suffer from low throughput. In this paper, an effective and simple load balancing scheme CAC_DPLB_MCN is proposed that can effectively reduce the overall call blocking. This model considers dealing with multi-media traffic as well as time-varying geographical traffic distribution. The proposed scheme uses the concept of cell-tiering thereby creating fractional frequency reuse environment. A message exchange based distributed scheme instead of centralized one is used which help the proposed scheme be implemented in a multiple hot cell environment also. Furthermore, concept of dynamic pricing is used to serve the best interest of the users as well as for the service providers. The performance of the proposed scheme is compared with two other existing schemes in terms of call blocking probability and bandwidth utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell with highest bandwidth utilization. Use of dynamic pricing also makes the scheme useful to increase revenue of the service providers in contrast with compared schemes.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825
Directory of Open Access Journals (Sweden)
Eleonora Bottani
2017-06-01
Full Text Available This paper proposes a modified discrete firefly algorithm (DFA applied to the machine loading problem of the flexible manufacturing systems (FMSs starting from the mathematical formulation adopted by Swarnkar & Tiwari (2004. The aim of the problem is to identify the optimal jobs sequence that simultaneously maximizes the throughput and minimizes the system unbalance according to given technological constraints (e.g. available tool slots and machining time. The results of the algorithm proposed have been compared with the existing and most recent swarm-based approaches available in the open literature using as benchmark the set of ten problems proposed by Mukhopadhyay et al. (1992. The algorithm shows results that are comparable and sometimes even better than most of the other approaches considering both the quality of the results provided and the computational times obtained.
Finding Multiple Optimal Solutions to Optimal Load Distribution Problem in Hydropower Plant
Directory of Open Access Journals (Sweden)
Xinhao Jiang
2012-05-01
Full Text Available Optimal load distribution (OLD among generator units of a hydropower plant is a vital task for hydropower generation scheduling and management. Traditional optimization methods for solving this problem focus on finding a single optimal solution. However, many practical constraints on hydropower plant operation are very difficult, if not impossible, to be modeled, and the optimal solution found by those models might be of limited practical uses. This motivates us to find multiple optimal solutions to the OLD problem, which can provide more flexible choices for decision-making. Based on a special dynamic programming model, we use a modified shortest path algorithm to produce multiple solutions to the problem. It is shown that multiple optimal solutions exist for the case study of China’s Geheyan hydropower plant, and they are valuable for assessing the stability of generator units, showing the potential of reducing occurrence times of units across vibration areas.
Probability problems in seismic risk analysis and load combinations for nuclear power plants
Energy Technology Data Exchange (ETDEWEB)
George, L.L.
1983-01-01
This paper describes seismic risk, load combination, and probabilistic risk problems in power plant reliability, and it suggests applications of extreme value theory. Seismic risk analysis computes the probability of power plant failure in an earthquake and the resulting risk. Components fail if their peak responses to an earthquake exceed their strengths. Dependent stochastic processes represent responses, and peak responses are maxima. A Boolean function of component failures and survivals represents plant failure. Load combinations analysis computes the cdf of the peak of the superposition of stochastic processes that represent earthquake and operating loads. It also computes the probability of pipe fracture due to crack growth, a Markov process, caused by loads. Pipe fracture is an absorbing state. Probabilistic risk analysis computes the cdf's of probabilities which represent uncertainty. These Cdf's are induced by randomizing parameters of cdf's and by randomizing properties of stochastic processes such as initial crack size distributions, marginal cdf's, and failure criteria.
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...
Designing optimal peer support to alleviate learner cognitive load in Learning Networks
Hsiao, Amy; Brouns, Francis; Sloep, Peter
2012-01-01
Hsiao, Y. P., Brouns, F., & Sloep, P. B. (2012). Designing optimal peer support to alleviate learner cognitive load in Learning Networks. In P. Kommers, & N. Bessis (Eds.), Proceedings of IADIS International Conference Web-Based Communities and Social Media 2012 (pp. 73-80). July, 19-21, 2012,
Neural network for quality control of submunitions produced by injection loading
Energy Technology Data Exchange (ETDEWEB)
Smith, R.E.; Parkinson, W.J.; Hinde, R.F. Jr.; Wantuck, P.J. [Los Alamos National Lab., NM (United States). Engineering Sciences and Applications Div.; Newman, K.E. [Naval Surface Warfare Center, Yorktown, VA (United States)
1998-12-01
Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing.
Development of mathematical models for forecasting hydraulic loads of water and wastewater networks
Energy Technology Data Exchange (ETDEWEB)
Studzinki, Jan [Polish Academy of Sciences, Warsaw (Poland). Systems Research Institute; Bartkiewicz, Lidia [Technical Univ. Kielce (Poland); Stachura, Marcin [Warsaw University of Technology (Poland)
2013-07-01
In municipal waterworks the operation of water and wastewater networks decides about the functioning of the sewage treatment plant that is the last element of the whole water and sewage system. The both networks are connected each other and the work of the water net affects the operation of the wastewater one. The parameters which are important for right leading of all waterworks objects are their hydraulic loads that have to be not exceeded. Too large loads can cause accidents in the wastewater net or the treatment plant and an early knowledge of them is of importance for undertaking some counteractions. In the paper different algorithms to model hydraulic loads of municipal water and wastewater nets are described and compared regarding their computation velocity and accuracy. Some exemplary computations have been done with some real data received from a Polish water company. (orig.)
Directory of Open Access Journals (Sweden)
Mohammad Dreidy
2017-01-01
Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.
A Hub Location Problem with Fully Interconnected Backbone and Access Networks
DEFF Research Database (Denmark)
Thomadsen, Tommy; Larsen, Jesper
2007-01-01
This paper considers the design of two-layered fully interconnected networks. A two-layered network consists of clusters of nodes, each defining an access network and a backbone network. We consider the integrated problem of determining the access networks and the backbone network simultaneously...... problems. We obtain superior bounds using the column generation approach than with the linear programming relaxation. The column generation method is therefore developed into an exact approach using the Branch-and-Price framework. With this approach we are able to solve problems consisting of up to 25...
Enhancement of a model for Large-scale Airline Network Planning Problems
Kölker, K.; Lopes dos Santos, B.F.; Lütjens, K.
2016-01-01
The main focus of this study is to solve the network planning problem based on passenger decision criteria including the preferred departure time and travel time for a real-sized airline network. For this purpose, a model of the integrated network planning problem is formulated including scheduling
Sensitivity analysis of linear programming problem through a recurrent neural network
Das, Raja
2017-11-01
In this paper we study the recurrent neural network for solving linear programming problems. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate the sensitivity analysis of linear programming problem through the neural network. A detailed example is also presented to demonstrate the performance of the recurrent neural network.
A Model to Simulate Multimodality in a Mesoscopic Dynamic Network Loading Framework
Directory of Open Access Journals (Sweden)
Massimo Di Gangi
2017-01-01
Full Text Available A dynamic network loading (DNL model using a mesoscopic approach is proposed to simulate a multimodal transport network considering en-route change of the transport modes. The classic mesoscopic approach, where packets of users belonging to the same mode move following a path, is modified to take into account multiple modes interacting with each other, simultaneously and on the same multimodal network. In particular, to simulate modal change, functional aspects of multimodal arcs have been developed; those arcs are properly located on the network where modal change occurs and users are packed (or unpacked in a new modal resource that moves up to destination or to another multimodal arc. A test on a simple network reproducing a real situation is performed in order to show model peculiarities; some indicators, used to describe performances of the considered transport system, are shown.
DEFF Research Database (Denmark)
Fetene, Gebeyehu Manie
such as electricity, transport (con- gestion), water and telecommunication. Linear and non-linear peak load pricing alternatives have been suggested to curb this problem, particularly when demand is cyclical (Mohsenian-Rad and Leon-Garcia, 2010; Tan and Varaiya, 1993; Chao et al., 1986; Finsinger; Roberts, 1979......). Peak load pricing (PLP) is an attempt to shift demand, or consumption of the good, to accommodate supply. While peak load problem and PLP are well documented in the literature, this paper, to the authors’ knowledge, is the first to analyze the EV users time of charg- ing decision problem under PLP...
Empirical comparison of heuristic load distribution in point-to-point multicomputer networks
Grunwald, Dirk C.; Nazief, Bobby A. A.; Reed, Daniel A.
1990-01-01
The study compared several load placement algorithms using instrumented programs and synthetic program models. Salient characteristics of these program traces (total computation time, total number of messages sent, and average message time) span two orders of magnitude. Load distribution algorithms determine the initial placement for processes, a precursor to the more general problem of load redistribution. It is found that desirable workload distribution strategies will place new processes globally, rather than locally, to spread processes rapidly, but that local information should be used to refine global placement.
Impact of the load curve on losses In the power supply network of the company
Directory of Open Access Journals (Sweden)
Я. Э. Шклярский
2016-12-01
Full Text Available In the recent years, the researchers and experts in the field of energetics often mention in their publications a need to reduce power transmission losses. Among different ways to accomplish this goal the method of the company load leveling stands out due to its simplicity, accessibility and efficiency. The paper proposes a new assessment factor for additional power losses in distribution network. It is known that dispersion of the load curve correlates with the amount of power losses, which is why the proposed factor is put in a position of dependency on the shape of the load curve of the company. It is demonstrated that the proposed factor can help to identify without any strain a need in technical measures for levelling the load curve of the company and to assess efficiency thereof.
Directory of Open Access Journals (Sweden)
Cheng-Ming Lee
2016-11-01
Full Text Available A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF in this article. The proposed model integrates radial basis function neural network (RBFNN, support vector regression (SVR, and adaptive annealing learning algorithm (AALA. In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN. In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC. Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various RBFNNs.
A New Approach to Blending and Loading Problem of Molten Aluminum
Directory of Open Access Journals (Sweden)
Li Jianhua
2014-12-01
Full Text Available The problems of blending electrolyzer and multi-constraint optimization of electrolytic aluminum scheduling in the electrolytic aluminum production process were addressed. Based on a mathematical model analysis, a novel hybrid optimization algorithm is proposed for optimization of blending together the molten aluminum in different electrolytic cells. An affinity degree function was designed to represent the path of aluminum scheduling. The mutation operators were designed to implement the transformation of electrolyzer combination and change the route of loading. A typical optimization example from an aluminum plant in northwest China is given in this paper, the results of which demonstrate the effectiveness of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Osman, M.S. [High Institute of Technology, 10th Ramadan City (Egypt); Abo-Sinna, M.A.; Mousa, A.A. [Faculty of Engineering, Shebin El-Kom, Menoufia University (Egypt)
2009-11-15
In this paper, a novel multiobjective genetic algorithm approach for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a non-linear constrained multiobjective optimization problem with both equality and inequality constraints. A new optimization algorithm which is based on concept of co-evolution and repair algorithm for handling non-linear constraints is presented. The algorithm maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of {epsilon}-dominance. The use of {epsilon}-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto-set approximation by choosing an appropriate {epsilon} value. The proposed approach is carried out on the standard IEEE 30-bus 6-genrator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions of the multiobjective EELD problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem. (author)
Directory of Open Access Journals (Sweden)
E. Khalilzadeh Vahidi
2016-01-01
Full Text Available The effects of different parameters on steel plate shear wall (SPSW are investigated. The studied parameters are thickness of plate, location of the opening, thickness of diagonal stiffeners, and thickness of circular stiffener. Load-carrying capacity of the SPSW is studied under static load using nonlinear geometrical and material analysis in ABAQUS and the obtained simulation results are verified. An artificial neural network (ANN is proposed to model the effects of these parameters. According to the results the circular stiffener has more effect compared with the diagonal stiffeners. However, the thickness of the plate has the most significant effect on the SPSW behavior. The results show that the best place for the opening location is the center of SPSW. Multilayer perceptron (MLP neural network was used to predict the maximum load in SPSW with opening. The predicted maximum load values using the proposed MLP model were compared with the simulated validated data. The obtained results show that the proposed ANN model has achieved good agreement with the validated simulated data, with correlation coefficient of more than 0.9975. Therefore, the proposed model is useful, reliable, fast, and cheap tools to predict the maximum load in SPSW.
Modeling of pulsed heat load in a cryogenic SHe loop using Artificial Neural Networks
Savoldi Richard, L.; Bonifetto, R.; Carli, S.; Grand Blanc, M.; Zanino, R.
2013-10-01
The pulsed heat load to the cryoplant is an important issue in the design and operation of tokamaks adopting superconducting (SC) magnets for the magnetic confinement, as the International Thermonuclear Experimental Reactor (ITER). The smoothing of the heat load during plasma operation is being addressed by experiments, e.g. in the HELIOS facility at CEA Grenoble, and simulations. The assessment of the operation of the cryoplant mainly requires the knowledge of the evolution of the heat load to the liquid helium (LHe) baths that are used as interfaces/buffers between the magnets cooling loops and the cryoplant itself. In this paper, an innovative approach based on Artificial Neural Networks (ANNs) is presented, leading to a simplified but fast model of the transient heat load from the magnets to the LHe baths. An ANN model is developed for the HELIOS loop and the resulting network is trained using detailed transient simulations performed with the 4C code, which was previously extensively validated against experimental data from HELIOS. The predictive capability of the (simplified) ANN model is then demonstrated by considering another, independent dataset, not used during the ANN training, and comparing the evolution of the heat load to the LHe bath computed by the ANNs with that obtained from the (detailed) 4C model.
Open Problems in Network-aware Data Management in Exa-scale Computing and Terabit Networking Era
Energy Technology Data Exchange (ETDEWEB)
Balman, Mehmet; Byna, Surendra
2011-12-06
Accessing and managing large amounts of data is a great challenge in collaborative computing environments where resources and users are geographically distributed. Recent advances in network technology led to next-generation high-performance networks, allowing high-bandwidth connectivity. Efficient use of the network infrastructure is necessary in order to address the increasing data and compute requirements of large-scale applications. We discuss several open problems, evaluate emerging trends, and articulate our perspectives in network-aware data management.
Iizaka, Tatsuya; Matsui, Tetsuro; Fukuyama, Yoshikazu
This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.
Directory of Open Access Journals (Sweden)
Bozor Islomov
2015-08-01
Full Text Available We prove the unique solvability of a boundary-value problems for a third-order loaded integro-differential equation with variable coefficients, by reducing the equation to a Volterra integral equation.
Load distribution using multipath-routing in wired packet networks: A comparative study
Directory of Open Access Journals (Sweden)
N. Krishna Chaitanya
2016-09-01
Full Text Available This paper aimed towards the analysis of various multipath routing techniques with and without load balancing as a comparative study. Most of the routing techniques targeted to find best path from source to destination. The basic routing techniques are based on single path; there is only one path from sending end to receiving end. There after multipath routing techniques has been proposed to send the data in multiple paths and eliminates the drawbacks in single path routing. This study gives better idea about multipath routing techniques along with load balance for avoiding congestion in wired packet networks.
An Asymptotic Approach for the Elastodynamic Problem of a Plate under Impact Loading
Directory of Open Access Journals (Sweden)
Penelope Michalopoulou
2010-01-01
Full Text Available An approach is presented for analyzing the transient elastodynamic problem of a plate under an impact loading. The plate is considered to be in the form of a long strip under plane strain conditions. The loading is taken as a concentrated line force applied normal to the plate surface. It is assumed that this line force is suddenly applied and maintained thereafter (i.e., it is a Heaviside step function of time. Inertia effects are taken into consideration and the problem is treated exactly within the framework of elastodynamic theory. The approach is based on multiple Laplace transforms and on certain asymptotic arguments. In particular, the one-sided Laplace transform is applied to suppress time dependence and the two-sided Laplace transform to suppress the dependence upon a spatial variable (along the extent of the infinite strip. Exact inversions are then followed by invoking the asymptotic Tauber theorem and the Cagniard-deHoop technique. Various extensions of this basic analysis are also discussed.
Game Theoretic Solutions to Cyber Attack and Network Defense Problems
National Research Council Canada - National Science Library
Shen, Dan; Chen, Genshe; Cruz, Jr., , Jose B; Blasch, Erik; Kruger, Martin
2007-01-01
.... The protection and defense against cyber attacks to computer network is becoming inadequate as the hacker knowledge sophisticates and as the network and each computer system become more complex...
Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang
2011-05-01
A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.
DEFF Research Database (Denmark)
Karsten, Christian Vad; Pisinger, David; Røpke, Stefan
2015-01-01
The multi-commodity network flow problem is an important sub-problem in several heuristics and exact methods for designing route networks for container ships. The sub-problem decides how cargoes should be transported through the network provided by shipping routes. This paper studies the multi......-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time...... is essential to offer customers a competitive product. © 2015 Elsevier Ltd. All rights reserved....
Agentless robust load sharing strategy for utilising hetero-geneous resources over wide area network
Directory of Open Access Journals (Sweden)
Natthakrit Sanguandikul
2011-06-01
Full Text Available Resource monitoring and performance prediction services have always been regarded as important keys to improving the performance of load sharing strategy. However, the traditional methodologies usually require specific performance information, which can only be collected by installing proprietary agents on all participating resources. This requirement of implementing a single unified monitoring service may not be feasible because of the differences in the underlying systems and organisation policies. To address this problem, we define a new load sharing strategy which bases the load decision on a simple performance estimation that can be measured easily at the coordinator node. Our proposed strategy relies on a stage-based dynamic task allocation to handle the imprecision of our performance estimation and to correct load distribution on-the-fly. The simulation results showed that the performance of our strategy is comparable or better than traditional strategies, especially when the performance information from the monitoring service is not accurate.
Weinstock, Jeremiah; Burton, Steve; Rash, Carla J; Moran, Sheila; Biller, Warren; Krudelbach, Norman; Phoenix, Natalie; Morasco, Benjamin J
2011-06-01
Gambling help-lines are an essential access point, or frontline resource, for treatment seeking. This study investigated treatment engagement after calling a gambling help-line. From 2000-2007 over 2,900 unique callers were offered an in-person assessment appointment. Logistic regression analyses assessed predictors of (a) accepting the referral to the in-person assessment appointment and (b) attending the in-person assessment appointment. Over 76% of callers accepted the referral and 55% of all callers attended the in-person assessment appointment. This treatment engagement rate is higher than typically found for other help-lines. Demographic factors and clinical factors such as gender, severity of gambling problems, amount of gambling debt, and coercion by legal and social networks predicted engagement in treatment. Programmatic factors such as offering an appointment within 72 hr also aided treatment engagement. Results suggest gambling help-lines can be a convenient and confidential way for many individuals with gambling problems to access gambling-specific treatment. Alternative services such as telephone counseling may be beneficial for those who do not engage in treatment. (PsycINFO Database Record (c) 2011 APA, all rights reserved).
Traffic Load on Interconnection Lines of Generalized Double Ring Network Structures
DEFF Research Database (Denmark)
Pedersen, Jens Myrup; Riaz, Muhammad Tahir; Madsen, Ole Brun
2005-01-01
Generalized Double Ring (N2R) network structures possess a number of good properties, but being not planar they are hard to physically embed in communication networks. However, if some of the lines, the interconnection lines, are implemented by wireless technologies, the remaining structure...... consists of two planar rings, which are easily embedded by fiber or other wired solutions. It is shown that for large N2R structures, the interconnection lines carry notably lower loads than the other lines if shortest-path routing is used, and the effects of two other routing schemes are explored, leading...
Traffic Load on Interconnection Lines of Generalized Double Ring Network Structures
DEFF Research Database (Denmark)
Pedersen, Jens Myrup; Riaz, Muhammad Tahir; Madsen, Ole Brun
2004-01-01
Generalized Double Ring (N2R) network structures possess a number of good properties, but being not planar they are hard to physically embed in communication networks. However, if some of the lines, the interconnection lines, are implemented by wireless technologies, the remaining structure...... consists of two planar rings, which are easily embedded by fiber or other wired solutions. It is shown that for large N2R structures, the interconnection lines carry notably lower loads than the other lines if shortest-path routing is used, and the effects of two other routing schemes are explored, leading...
Visualization of protein interaction networks: problems and solutions.
Agapito, Giuseppe; Guzzi, Pietro Hiram; Cannataro, Mario
2013-01-01
Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external
ANALYSIS OF A DATABASE REPLICATION ALGORITHM UNDER LOAD SHARING IN NETWORKS
Directory of Open Access Journals (Sweden)
SANJAY KUMAR YADAV
2016-02-01
Full Text Available Recently, (PDDRA a Pre-fetching based dynamic data replication algorithm has been published. In our previous work, modifications to the algorithm have been suggested to minimize the delay in data replication. In this paper, a simulation framework is presented and results are obtained to estimate the throughput and average delay. The overall network is divided into two parts as local and global networks. The data requests are generated only at the local nodes. However, the service can be obtained form both local and global servers. In our previous work it has been found that the throughput and average delay heavily depends on buffer capacity of sever node and if server load is below 80% then, nearly 100% throughput is possible with very small average delay. In this paper, we have shown that shown the delay can be further minimized by sharing the load among servers, still throughput remains nearly 100 percent.
Directory of Open Access Journals (Sweden)
Wang Chao
2016-03-01
Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.
Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.
2014-01-01
Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective
New Measurement Base De-embedded CPU Load Model for Power Delivery Network Design
Okano, Motochika; Watanabe, Koji; Naitoh, Masamichi; Omura, Ichiro
2015-01-01
CPU load model including on-chip wiring and package interconnection has been required for printed circuit board (PCB) design of digital products according to the improvement in the speed of CPU operation in recent years. Especially, accurate power delivery network (PDN) information inside CPU is indispensable for PCB design according to requirement of low-impedance and the broadband (from DC to GHz) from the inside of CPU to DC-DC converter. While the detailed impedance information inside CPU...
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.
A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems.
Xia, Youshen; Wang, Jun
2016-02-01
In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural network has a very small model size owing to its bi-projection structure. Furthermore, an application to data fusion shows that the proposed neural network is very effective. Numerical results demonstrate that the proposed neural network is much faster than the existing PNNs.
Directory of Open Access Journals (Sweden)
Jiao-Hong Yi
2016-01-01
Full Text Available Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2 are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.
DEFF Research Database (Denmark)
Hansen, Jesper
2003-01-01
The three-dimensional bin packing problem is concerned with packing a given set of rectangular items into rectangular bins. We are interested in solving real-life problems where rotations of items are allowed and the packings must be packable and stable. Load bearing of items is taken into account...
Modified Cuckoo Search Algorithm for Solving Nonconvex Economic Load Dispatch Problems
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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.
N.H. Luong (Ngoc Hoang); J.A. La Poutré (Han); P.A.N. Bosman (Peter)
2015-01-01
htmlabstractThis paper tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. We compare two
N.H. Luong (Ngoc Hoang); J.A. La Poutré (Han); P.A.N. Bosman (Peter)
2017-01-01
textabstractThis article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introd uced to satisfy the future power demands. Because of many
Neural networks art: solving problems with multiple solutions and new teaching algorithm.
Dmitrienko, V D; Zakovorotnyi, A Yu; Leonov, S Yu; Khavina, I P
2014-01-01
A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.
A Secure and Stable Multicast Overlay Network with Load Balancing for Scalable IPTV Services
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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.
Hu, Xiaolin; Wang, Jun
2006-11-01
In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalities and related pseudoconvex optimization problems. Under various pseudomonotonicity conditions and other conditions, the projection neural network is proved to be stable in the sense of Lyapunov and globally convergent, globally asymptotically stable, and globally exponentially stable. Since monotonicity is a special case of pseudomononicity, the projection neural network can be applied to solve a broader class of constrained optimization problems related to variational inequalities. Moreover, a new concept, called componentwise pseudomononicity, different from pseudomononicity in general, is introduced. Under this new concept, two stability results of the projection neural network for solving variational inequalities are also obtained. Finally, numerical examples show the effectiveness and performance of the projection neural network.
Zhang, Li
With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman
Insights and issues with simulating terrestrial DOC loading of Arctic river networks
Kicklighter, David W.; Hayes, Daniel J.; McClelland, James W.; Peterson, Bruce J.; McGuire, A. David; Melillo, Jerry M.
2013-01-01
Terrestrial carbon dynamics inﬂuence the contribution of dissolved organic carbon (DOC) to river networks in addition to hydrology. In this study, we use a biogeochemical process model to simulate the lateral transfer of DOC from land to the Arctic Ocean via riverine transport. We estimate that, over the 20th century, the pan-Arctic watershed has contributed, on average, 32 Tg C/yr of DOC to river networks emptying into the Arctic Ocean with most of the DOC coming from the extensive area of boreal deciduous needle-leaved forests and forested wetlands in Eurasian watersheds. We also estimate that the rate of terrestrial DOC loading has been increasing by 0.037 Tg C/yr2 over the 20th century primarily as a result of climate-induced increases in water yield. These increases have been offset by decreases in terrestrial DOC loading caused by wildﬁres. Other environmental factors (CO2 fertilization, ozone pollution, atmospheric nitrogen deposition, timber harvest, agriculture) are estimated to have relatively small effects on terrestrial DOC loading to Arctic rivers. The effects of the various environmental factors on terrestrial carbon dynamics have both offset and enhanced concurrent effects on hydrology to inﬂuence terrestrial DOC loading and may be changing the relative importance of terrestrial carbon dynamics on this carbon ﬂux. Improvements in simulating terrestrial DOC loading to pan-Arctic rivers in the future will require better information on the production and consumption of DOC within the soil proﬁle, the transfer of DOC from land to headwater streams, the spatial distribution of precipitation and its temporal trends, carbon dynamics of larch-dominated ecosystems in eastern Siberia, and the role of industrial organic efﬂuents on carbon budgets of rivers in western Russia.
Directory of Open Access Journals (Sweden)
Ranbir Singh
2016-04-01
Full Text Available Flexible manufacturing system (FMS promises a wide range of manufacturing benefits in terms of flexibility and productivity. These benefits are targeted by efficient production planning. Part type selection, machine grouping, deciding production ratio, resource allocation and machine loading are five identified production planning problems. Machine loading is the most identified complex problem solved with aid of computers. System up gradation and newer technology adoption are the primary needs of efficient FMS generating new scopes of research in the field. The literature review is carried and the critical analysis is being executed in the present work. This paper presents the outcomes of the mathematical modelling techniques for loading of machines in FMS’s. It was also analysed that the mathematical modelling is necessary for accurate and reliable analysis for practical applications. However, excessive computations need to be avoided and heuristics have to be used for real-world problems. This paper presents the heuristics-mathematical modelling of loading problem with machine processing time as primary input. The aim of the present work is to solve a real-world machine loading problem with an objective of balancing the workload of the FMS with decreased computational time. A Matlab code is developed for the solution and the results are found most accurate and reliable as presented in the paper.
Lee, Casey J.; Murphy, Jennifer C.; Crawford, Charles G.; Deacon, Jeffrey R.
2017-10-24
The U.S. Geological Survey publishes information on concentrations and loads of water-quality constituents at 111 sites across the United States as part of the U.S. Geological Survey National Water Quality Network (NWQN). This report details historical and updated methods for computing water-quality loads at NWQN sites. The primary updates to historical load estimation methods include (1) an adaptation to methods for computing loads to the Gulf of Mexico; (2) the inclusion of loads computed using the Weighted Regressions on Time, Discharge, and Season (WRTDS) method; and (3) the inclusion of loads computed using continuous water-quality data. Loads computed using WRTDS and continuous water-quality data are provided along with those computed using historical methods. Various aspects of method updates are evaluated in this report to help users of water-quality loading data determine which estimation methods best suit their particular application.
Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network
Directory of Open Access Journals (Sweden)
Dongxiao Niu
2015-01-01
Full Text Available In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.
Kim, Jeong-Nam
2018-01-01
This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.
Least loaded and route fragmentation aware RSA strategies for elastic optical networks
Batham, Deepak; Yadav, Dharmendra Singh; Prakash, Shashi
2017-12-01
Elastic optical networks (EONs) provide flexibility to assign wide range of spectral resources to the connection requests. In this manuscript, we address two issues related to spectrum assignment in EONs: the non uniform spectrum assignment along different links of the route and the spectrum fragmentation in the network. To address these issues, two routing and spectrum assignment (RSA) strategies have been proposed: Least Loaded RSA (LLRSA) and Route Fragmentation Aware RSA (RFARSA). The LLRSA allocates spectrum homogeneously along different links in the network, where as RFARSA accords priority to the routes which are less fragmented. To highlight the salient features of the two strategies, two new metrics, route fragmentation index (RFI) and standard deviation (SD) are introduced. RFI is defined as the ratio of non-contiguous FSs to the total available free FSs on the route, and SD relates to the measure of non-uniformity in the allocation of resources on the links in the network. A simulation program has been developed to evaluate the performance of the proposed (LLRSA and RFARSA) strategies, and the existing strategies of shortest path RSA (SPRSA) and spectrum compactness based defragmentation (SCD) strategies, on the metric of RFI, bandwidth blocking probability (BBP), network capacity utilized, and SD. The variation in the metrics on the basis of number of requests and the bandwidth (number of FSs) requested has been studied. It has been conclusively established that the proposed strategies (LLRSA and RFARSA) outperform the existing strategies in terms of all the metrics.
Directory of Open Access Journals (Sweden)
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
Time series prediction in the case of nonlinear loads by using ADALINE and NAR neural networks
Ghiormez, L.; Panoiu, M.; Panoiu, C.; Tirian, O.
2018-01-01
This paper presents a study regarding the time series prediction in the case of an electric arc furnace. The considered furnace is a three phase load and it is used to melt scrap in order to obtain liquid steel. The furnace is powered by a three-phase electrical supply and therefore has three graphite electrodes. The furnace is a nonlinear load that can influence the equipment connected to the same electrical power supply network. The nonlinearity is given by the electric arc that appears at the furnace between the graphite electrode and the scrap. Because of the disturbances caused by the electric arc furnace during the elaboration process of steel it is very useful to predict the current of the electric arc and the voltage from the measuring point in the secondary side of the furnace transformer. In order to make the predictions were used ADALINE and NAR neural networks. To train the networks and to make the predictions were used data acquired from the real technological plant.
Kuliwaba, J. S.; Truong, L.; Codrington, J. D.; Fazzalari, N. L.
2010-06-01
The human skeleton has the ability to modify its material composition and structure to accommodate loads through adaptive modelling and remodelling. The osteocyte cell network is now considered to be central to the regulation of skeletal homeostasis; however, very little is known of the integrity of the osteocyte cell network in osteoporotic fragility fracture. This study was designed to characterise osteocyte morphology, the extent of osteocyte cell apoptosis and expression of sclerostin protein (a negative regulator of bone formation) in trabecular bone from the intertrochanteric region of the proximal femur, for postmenopausal women with fragility hip fracture compared to age-matched women who had not sustained fragility fracture. Osteocyte morphology (osteocyte, empty lacunar, and total lacunar densities) and the degree of osteocyte apoptosis (percent caspase-3 positive osteocyte lacunae) were similar between the fracture patients and non-fracture women. The fragility hip fracture patients had a lower proportion of sclerostin-positive osteocyte lacunae in comparison to sclerostin-negative osteocyte lacunae, in contrast to similar percent sclerostin-positive/sclerostin-negative lacunae for non-fracture women. The unexpected finding of decreased sclerostin expression in trabecular bone osteocytes from fracture cases may be indicative of elevated bone turnover and under-mineralisation, characteristic of postmenopausal osteoporosis. Further, altered osteocytic expression of sclerostin may be involved in the mechano-responsiveness of bone. Optimal function of the osteocyte cell network is likely to be a critical determinant of bone strength, acting via mechanical load adaptation, and thus contributing to osteoporotic fracture risk.
Second-order design problem in the Ancona geodetic network
Energy Technology Data Exchange (ETDEWEB)
Baldi, P.; Ferrari, G. (Bologna Univ. (Italy). Ist. di Geofisica); Postpischl, D.; Unguendoli, M. (Bologna Univ. (Italy). Ist. di Topografia, Geodesia e Geofisica Mineraria)
In this note an examination is made of the control network installed in the Ancona area in 1975 for seismotectonic studies. From an analysis of the network there arises the possibility of achieving a considerable improvement in the results by considering a plan of work derived from the a prior analysis of the covariance matrix and improving the atmospheric data fo the correction of electronic distance measurements, by the use of meteorological balloons.
Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan
2017-01-01
Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.
Strodl, Johannes; Doerner, Karl F.; Tricoire, Fabien; Hartl, Richard F.
In this paper we study the impact of different index structures used within hybrid solution approaches for vehicle routing problems with hard feasibility checks. We examine the case of the vehicle routing problem with two-dimensional loading constraints, which combines the loading of freight into the vehicles and the routing of the vehicles to satisfy the demands of the customers. The problem is solved by a variable neighborhood search for the routing part, in which we embed an exact procedure for the loading subproblem. The contribution of the paper is threefold: i) Four different index mechanisms for managing the subproblems are implemented and tested. It is shown that simple index structures tend to lead to better solutions than more powerful albeit complex ones, when using the same runtime limits. ii) The problem of balancing the CPU budget between exploration of different solutions and exact solution of the loading subproblem is investigated; experiments show that solving exactly hard subproblems can lead to better solution quality over the whole solution process. iii) New best results are presented on existing benchmark instances.
Solving the RWA Problem in WDM Optical Networks Using the BCO Meta-Heuristic
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V. S. Aćimović-Raspopović
2010-06-01
Full Text Available This paper researches the routing and wavelength assignment (RWA problem in wavelength routed optical WDM networks with the wavelength conversion capability at different network nodes. We studied the static case in which all connection requests are known in advance, thus a routing decision can be made based on the complete knowledge of the traffic to be served by the network. The Bee Colony Optimization (BCO metaheuristic is applied to solve the RWA problem. We carried out a comprehensive simulation study of the performance of the proposed BCORWA algorithm for different network topologies and traffic scenarios. The obtained simulation results are analyzed and compared.
Automated system for load flow prediction in power substations using artificial neural networks
Directory of Open Access Journals (Sweden)
Arlys Michel Lastre Aleaga
2015-09-01
Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.
A low overhead load balancing router for network-on-chip
Xiaofeng, Zhou; Lu, Liu; Zhangming, Zhu; Duan, Zhou
2016-11-01
The design of a router in a network-on-chip (NoC) system has an important impact on some performance criteria. In this paper, we propose a low overhead load balancing router (LOLBR) for 2D mesh NoC to enhance routing performance criteria with low hardware overhead. The proposed LOLBR employs a balance toggle identifier to control the initial routing direction of X or Y for flit injection. The simplified demultiplexers and multiplexers are used to handle output ports allocation and contention, which provide a guarantee of deadlock avoidance. Simulation results show that the proposed LOLBR yields an improvement of routing performance over the reported routing schemes in average packet latency by 26.5%. The layout area and power consumption of the network compared with the reported routing schemes are 15.3% and 11.6% less respectively. Project supported by the National Natural Science Foundation of China (Nos. 61474087, 61322405, 61376039).
Directory of Open Access Journals (Sweden)
Omar Payán-Serrano
2017-05-01
Full Text Available The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF structures subjected to dynamics wind load using Artificial Neural Networks (ANNs through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.
Bilevel programming problems theory, algorithms and applications to energy networks
Dempe, Stephan; Pérez-Valdés, Gerardo A; Kalashnykova, Nataliya; Kalashnikova, Nataliya
2015-01-01
This book describes recent theoretical findings relevant to bilevel programming in general, and in mixed-integer bilevel programming in particular. It describes recent applications in energy problems, such as the stochastic bilevel optimization approaches used in the natural gas industry. New algorithms for solving linear and mixed-integer bilevel programming problems are presented and explained.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy; Pisinger, David
the available fleet of container vessels. The cargo transports make extensive use of transshipments between routes and the number of transshipments of the cargo flow is decisive for network profitability. Computational results are reported for the benchmark suite LINER-LIB 2012 following the industry standard...... of weekly departures on every schedule. The heuristic shows overall good performance and is able to find high quality solutions within competitive execution times. The matheuristic can also be applied as a decision support tool to improve an existing network by optimizing on a designated subset...
Relative importance of multiple factors on terrestrial loading of DOC to Arctic river networks
Energy Technology Data Exchange (ETDEWEB)
Kicklighter, David W. [Ecosystem Center, The; Hayes, Daniel J [ORNL; Mcclelland, James W [University of Texas; Peterson, Bruce [Marine Biological Laboratory; Mcguire, David [University of Alaska; Melillo, Jerry [Marine Biological Laboratory
2014-01-01
Terrestrial carbon dynamics influence the contribution of dissolved organic carbon (DOC) to river networks in addition to controlling carbon fluxes between the land surface and the atmosphere. In this study, we use a biogeochemical process model to simulate the lateral transfer of DOC from land to the Arctic Ocean via riverine transport. We estimate that the pan-arctic watershed has contributed, on average, 32 Tg C/yr of DOC to the Arctic Ocean over the 20th century with most coming from the extensive area of boreal deciduous needle-leaved forests and forested wetlands in Eurasian watersheds. We also estimate that the rate of terrestrial DOC loading has been increasing by 0.037 Tg C/yr2 over the 20th century primarily as a result of increases in air temperatures and precipitation. These increases have been partially compensated by decreases in terrestrial DOC loading caused by wildfires. Other environmental factors (CO2 fertilization, ozone pollution, atmospheric nitrogen deposition, timber harvest, agriculture) are estimated to have relatively small effects on terrestrial DOC loading to arctic rivers. The effects of the various environmental factors on terrestrial carbon dynamics have both compensated and enhanced concurrent effects on hydrology to influence terrestrial DOC loading. Future increases in riverine DOC concentrations and export may occur from warming-induced increases in terrestrial DOC production associated with enhanced microbial metabolism and the exposure of additional organic matter from permafrost degradation along with decreases in water yield associated with warming-induced increases in evapotranspiration. Improvements in simulating terrestrial DOC loading to pan-arctic rivers in the future will require better information on the spatial distribution of precipitation and its temporal trends, carbon dynamics of larch-dominated ecosystems in eastern Siberia, and the role of industrial organic effluents on carbon budgets of rivers in western
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.
Energy Technology Data Exchange (ETDEWEB)
Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark
2006-01-25
End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our
Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui
2017-10-01
Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.
Design and Optimisation Problems in Wireless Sensor Networks
Indian Academy of Sciences (India)
Premkumar Karumbu,1.05 ECE,,+91-9448227167
2010-11-14
Nov 14, 2010 ... Recent motivation: event detection in wireless sensor networks. The classical model does not account for many practical aspects. Sleep-wake scheduling of sensors (cost for making observations). Optimal detection with sleep-wake scheduling: [Infocom 2008]. Transient change: The event occurs and then ...
Wavelength and fiber assignment problems on avionic networks
DEFF Research Database (Denmark)
Zhang, Jiang; An, Yi; Berger, Michael Stübert
2011-01-01
system isolation requirements on the networks with shorter span traffics will not greatly increase the wavelength consumption and it will grow faster after the isolation constrains being larger up to certain scale. Regarding the traffics with longer span, the system isolation constrains slowly cause...
Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.
Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J
2016-11-01
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.
A recurrent neural network for solving a class of generalized convex optimization problems.
Hosseini, Alireza; Wang, Jun; Hosseini, S Mohammad
2013-08-01
In this paper, we propose a penalty-based recurrent neural network for solving a class of constrained optimization problems with generalized convex objective functions. The model has a simple structure described by using a differential inclusion. It is also applicable for any nonsmooth optimization problem with affine equality and convex inequality constraints, provided that the objective function is regular and pseudoconvex on feasible region of the problem. It is proven herein that the state vector of the proposed neural network globally converges to and stays thereafter in the feasible region in finite time, and converges to the optimal solution set of the problem. Copyright © 2013 Elsevier Ltd. All rights reserved.
Solving the Bi-Objective Maximum-Flow Network-Interdiction Problem
National Research Council Canada - National Science Library
Royset, Johannes O; Wood, R. K
2006-01-01
...." In this problem, an "interdictor" seeks to interdict (destroy) a set of arcs in a capacitated network that are Pareto-optimal with respect to two objectives, minimizing total interdiction cost and minimizing maximum flow...
National Research Council Canada - National Science Library
Sutopo, Wahyudi; Erliza, Ayu; Heryansyah, Arien
2016-01-01
.... The network design problem is used to implement supply chain collaboration. In the buying and selling process, sharing information between buyer and supplier are important to obtain a transaction decision...
Directory of Open Access Journals (Sweden)
Obidjon Kh. Abdullaev
2016-06-01
Full Text Available In this work, we study the existence and uniqueness of solutions to non-local boundary value problems with integral gluing condition. Mixed type equations (parabolic-hyperbolic involving the Caputo fractional derivative have loaded parts in Riemann-Liouville integrals. Thus we use the method of integral energy to prove uniqueness, and the method of integral equations to prove existence.
Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network
Energy Technology Data Exchange (ETDEWEB)
Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; Henze, Gregor P.; Sarkar, Soumik
2018-02-01
Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.
Energy Technology Data Exchange (ETDEWEB)
Souza, Bruno B.; Neto, Oriane M. [Universidade Federal de Minas Gerais - Department of Electrical Engineering (Brazil); Carrano, Eduardo G. [Centro Federal de Educacao Tecnologica de Minas Gerais - Department of Computer Engineering (Brazil); Takahashi, Ricardo H.C. [Universidade Federal de Minas Gerais - Department of Mathematics (Brazil)
2011-02-15
A recent paper, has proposed a methodology for taking into account uncertainties in the load evolution within the design of electric distribution networks. That paper has presented an immunological algorithm that is used for finding a set of solutions which are sub-optimal under the viewpoint of the ''mean scenario'' load conditions, and which are submitted to a sensitivity analysis for the load uncertainty. This paper presents a further development of the algorithm presented in, employing now a memetic algorithm (an algorithm endowed with local search operators) instead of the original immunological algorithm. The new algorithm is shown to present a better behavior, achieving a better set of candidate solutions, which dominate the solution set of the former algorithm. The solution set of the proposed algorithm is also stable, in the senses that: (i) the same set of solutions is found systematically; and (ii) the merit function values associated to those solutions vary smoothly from one solution to another one. It can be concluded that the design procedure proposed in should be performed preferentially with the algorithm proposed here. (author)
Optimal locations for a class of nonlinear, single-facility location problems on a network.
Shier, D R; Dearing, P M
1983-01-01
This paper investigates a class of single-facility location problems on an arbitrary network. Necessary and sufficient conditions are obtained for characterizing locally optimal locations with respect to a certain nonlinear objective function. This approach produces a number of new results for locating a facility on an arbitrary network, and in addition it unifies several known results for the special case of tree networks. It also suggests algorithmic procedures for obtaining such optimal locations.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
Directory of Open Access Journals (Sweden)
T. A. Bozhanova
2009-09-01
Full Text Available We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also proved.
On the existence of efficient solutions to vector optimization problem of traffic flow on network
T. A. Bozhanova
2009-01-01
We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also...
A Note on the Art of Network Design Problems | Osagiede | Journal ...
African Journals Online (AJOL)
In this study, we describe some Network Design Problems (NDPs) as well as the network flowbased improvement algorithm for neighbourhood search defined by cycles. The main part of the study is structured around the formulation of the expected duration of stay in the educational system as a NDP. The fundamental ...
Brands, Ties; van Berkum, Eric C.
2014-01-01
The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train
The Problem of Finding the Maximal Multiple Flow in the Divisible Network and its Special Cases
Directory of Open Access Journals (Sweden)
A. V. Smirnov
2015-01-01
Full Text Available In the article the problem of ﬁnding the maximal multiple ﬂow in the network of any natural multiplicity k is studied. There are arcs of three types: ordinary arcs, multiple arcs and multi-arcs. Each multiple and multi-arc is a union of k linked arcs, which are adjusted with each other. The network constructing rules are described. The deﬁnitions of a divisible network and some associated subjects are stated. The important property of the divisible network is that every divisible network can be partitioned into k parts, which are adjusted on the linked arcs of each multiple and multi-arc. Each part is the ordinary transportation network. The main results of the article are the following subclasses of the problem of ﬁnding the maximal multiple ﬂow in the divisible network. 1. The divisible networks with the multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in k −1 network parts. In this case the problem can be solved in a polynomial time. 2. The divisible networks with the weak multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in s network parts (1 ≤ s < k − 1 and other parts have at least two such vertices. In that case the multiplicity of the multiple ﬂow problem can be decreased to k − s. 3. The divisible network of the parallel structure. Assume that the divisible network component, which consists of all multiple arcs, can be partitioned into subcomponents, each of them containing exactly one vertex-beginning of a multi-arc. Suppose that intersection of each pair of subcomponents is the only vertex-network source x0. If k = 2, the maximal ﬂow problem can be solved in a polynomial time. If k ≥ 3, the problem is NP-complete. The algorithms for each polynomial subclass are suggested. Also, the multiplicity decreasing algorithm for the divisible network with weak multi-arc constraints is formulated.
Adolescent problem behavior in school : the role of peer networks
Geven, S.A.J.
2016-01-01
Adolescence is a notable period during which a considerable share of students tends to engage in problem behavior in school. Students for example skip class, fail to do their best in school, or have serious arguments with their teachers. A student’s decision to engage in such behavior is not usually
Enabling Cognitive Load-Aware AR with Rateless Coding on a Wearable Network
Directory of Open Access Journals (Sweden)
R. Razavi
2008-01-01
Full Text Available Augmented reality (AR on a head-mounted display is conveniently supported by a wearable wireless network. If, in addition, the AR display is moderated to take account of the cognitive load of the wearer, then additional biosensors form part of the network. In this paper, the impact of these additional traffic sources is assessed. Rateless coding is proposed to not only protect the fragile encoded video stream from wireless noise and interference but also to reduce coding overhead. The paper proposes a block-based form of rateless channel coding in which the unit of coding is a block within a packet. The contribution of this paper is that it minimizes energy consumption by reducing the overhead from forward error correction (FEC, while error correction properties are conserved. Compared to simple packet-based rateless coding, with this form of block-based coding, data loss is reduced and energy efficiency is improved. Cross-layer organization of piggy-backed response blocks must take place in response to feedback, as detailed in the paper. Compared also to variants of its default FEC scheme, results from a Bluetooth (IEEE 802.15.1 wireless network show a consistent improvement in energy consumption, packet arrival latency, and video quality at the AR display.
Load-aware modeling for uplink cellular networks in a multi-channel environment
AlAmmouri, Ahmad
2014-09-01
We exploit tools from stochastic geometry to develop a tractable analytical approach for modeling uplink cellular networks. The developed model is load aware and accounts for per-user power control as well as the limited transmit power constraint for the users\\' equipment (UEs). The proposed analytical paradigm is based on a simple per-user power control scheme in which each user inverts his path-loss such that the signal is received at his serving base station (BS) with a certain power threshold ρ Due to the limited transmit power of the UEs, users that cannot invert their path-loss to their serving BSs are allowed to transmit with their maximum transmit power. We show that the proposed power control scheme not only provides a balanced cell center and cell edge user performance, it also facilitates the analysis when compared to the state-of-the-art approaches in the literature. To this end, we discuss how to manipulate the design variable ρ in response to the network parameters to optimize one or more of the performance metrics such as the outage probability, the network capacity, and the energy efficiency.
A Network Centrality Method for the Rating Problem
2015-01-01
We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity. PMID:25830502
Neural network for solving Nash equilibrium problem in application of multiuser power control.
He, Xing; Yu, Junzhi; Huang, Tingwen; Li, Chuandong; Li, Chaojie
2014-09-01
In this paper, based on an equivalent mixed linear complementarity problem, we propose a neural network to solve multiuser power control optimization problems (MPCOP), which is modeled as the noncooperative Nash game in modern digital subscriber line (DSL). If the channel crosstalk coefficients matrix is positive semidefinite, it is shown that the proposed neural network is stable in the sense of Lyapunov and global convergence to a Nash equilibrium, and the Nash equilibrium is unique if the channel crosstalk coefficients matrix is positive definite. Finally, simulation results on two numerical examples show the effectiveness and performance of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
Innovation, imitation, and problem-solving in a networked group.
Wisdom, Thomas N; Goldstone, Robert L
2011-04-01
We implemented a problem-solving task in which groups of participants simultaneously played a simple innovation game in a complex problem space, with score feedback provided after each of a number of rounds. Each participant in a group was allowed to view and imitate the guesses of others during the game. The results showed the use of social learning strategies previously studied in other species, and demonstrated benefits of social learning and nonlinear effects of group size on strategy and performance. Rather than simply encouraging conformity, groups provided information to each individual about the distribution of useful innovations in the problem space. Imitation facilitated innovation rather than displacing it, because the former allowed good solutions to be propagated and preserved for further cumulative innovations in the group. Participants generally improved their solutions through the use of fairly conservative strategies, such as changing only a small portion of one's solution at a time, and tending to imitate solutions similar to one's own. Changes in these strategies over time had the effect of making solutions increasingly entrenched, both at individual and group levels. These results showed evidence of nonlinear dynamics in the decentralization of innovation, the emergence of group phenomena from complex interactions of individual efforts, stigmergy in the use of social information, and dynamic tradeoffs between exploration and exploitation of solutions. These results also support the idea that innovation and creativity can be recognized at the group level even when group members are generally cautious and imitative.
A STATISTICAL CORRELATION TECHNIQUE AND A NEURAL-NETWORK FOR THE MOTION CORRESPONDENCE PROBLEM
VANDEEMTER, JH; MASTEBROEK, HAK
A statistical correlation technique (SCT) and two variants of a neural network are presented to solve the motion correspondence problem. Solutions of the motion correspondence problem aim to maintain the identities of individuated elements as they move. In a preprocessing stage, two snapshots of a
Dynamic Network Design Problem under Demand Uncertainty: An Adjustable Robust Optimization Approach
Directory of Open Access Journals (Sweden)
Hua Sun
2014-01-01
Full Text Available This paper develops an adjustable robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, a cell transmission model based network design problem of linear programming type is considered to describe dynamic traffic flows, and a polyhedral uncertainty set is used to characterize the demand uncertainty. The major contribution of this paper is to formulate such an adjustable robust network design problem as a tractable linear programming model and justify the model which is less conservative by comparing its solution performance with the robust solution from the usual robust model. The numerical results using one network from the literature demonstrate the modeling advantage of the adjustable robust optimization and provided strategic managerial insights for enacting capacity expansion policies under demand uncertainty.
Assessment of network inference methods: how to cope with an underdetermined problem.
Directory of Open Access Journals (Sweden)
Caroline Siegenthaler
Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.
A two-layer recurrent neural network for nonsmooth convex optimization problems.
Qin, Sitian; Xue, Xiaoping
2015-06-01
In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.
A service flow model for the liner shipping network design problem
DEFF Research Database (Denmark)
Plum, Christian Edinger Munk; Pisinger, David; Sigurd, Mikkel M.
2014-01-01
Global liner shipping is a competitive industry, requiring liner carriers to carefully deploy their vessels efficiently to construct a cost competitive network. This paper presents a novel compact formulation of the liner shipping network design problem (LSNDP) based on service flows...... of the network and a penalty for not flowed cargo. The model can be used to design liner shipping networks to utilize a container carrier’s assets efficiently and to investigate possible scenarios of changed market conditions. The model is solved as a Mixed Integer Program. Results are presented for the two...
A network centrality method for the rating problem
Li, Yongli; Wu, Chong
2014-01-01
We propose a new method for aggregating the information of multiple reviewers rating multiple products. Our approach is based on the network relations induced between products by the rating activity of the reviewers. We show that our method is algorithmically implementable even for large numbers of both products and consumers, as is the case for many online sites. Moreover, comparing it with the simple average, which is mostly used in practice, and with other methods previously proposed in the literature, it performs very well under various dimension, proving itself to be an optimal trade--off between computational efficiency, accordance with the reviewers original orderings, and robustness with respect to the inclusion of systematically biased reports.
DEFF Research Database (Denmark)
Cetin, Bilge Kartal; Prasad, Neeli R.; Prasad, Ramjee
2011-01-01
protocols, and the energy model for transmission. In this paper, we tackle the routing challenge for maximum lifetime of the sensor network. We introduce a novel linear programming approach to the maximum lifetime routing problem. To the best of our knowledge, this is the first mathematical programming...... of the maximum lifetime routing problem that considers the operation modes of the node. Solution of the linear programming gives the upper analytical bound for the network lifetime. In order to illustrate teh application of the optimization model, we solved teh problem for different parameter settings...
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Pisinger, David
2012-01-01
programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers......The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...
Barbarosou, Maria P; Maratos, Nicholas G
2008-10-01
In this paper, a recurrent neural network for both convex and nonconvex equality-constrained optimization problems is proposed, which makes use of a cost gradient projection onto the tangent space of the constraints. The proposed neural network constructs a generically nonfeasible trajectory, satisfying the constraints only as t --> infinity. Local convergence results are given that do not assume convexity of the optimization problem to be solved. Global convergence results are established for convex optimization problems. An exponential convergence rate is shown to hold both for the convex case and the nonconvex case. Numerical results indicate that the proposed method is efficient and accurate.
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Kallehauge, Brian; Pisinger, David
The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...... programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers...
Electronic neural network for solving traveling salesman and similar global optimization problems
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
The minimum cost multicommodity flow problem in dynamic networks and an algorithm for its solving
Directory of Open Access Journals (Sweden)
Maria A. Fonoberova
2005-05-01
Full Text Available The dynamic version of the minimum cost multicommodity flow problem that generalizes the static minimum cost multicommodity flow problem is formulated and studied. This dynamic problem is considered on directed networks with a set of commodities, time-varying capacities, fixed transit times on arcs, and a given time horizon. We assume that cost functions, defined on edges, are nonlinear and depend on time and flow and the demand function also depends on time. The corresponding algorithm, based on reducing the dynamic problem to a static problem on a time-expanded network, to solve the minimum cost dynamic multicommodity flow problem is proposed and some details concerning its complexity are discussed. Mathematics Subject Classification 2000: 90B10, 90C35, 90C27.
On the Integrated Job Scheduling and Constrained Network Routing Problem
DEFF Research Database (Denmark)
Gamst, Mette
This paper examines the NP-hard problem of scheduling a number of jobs on a finite set of machines such that the overall profit of executed jobs is maximized. Each job demands a number of resources, which must be sent to the executing machine via constrained paths. Furthermore, two resource demand...... transmissions cannot use the same edge in the same time period. An exact solution approach based on Dantzig-Wolfe decomposition is proposed along with several heuristics. The methods are computationally evaluated on test instances arising from telecommunications with up to 500 jobs and 500 machines. Results...
Pricing and Capacity Planning Problems in Energy Transmission Networks
Villumsen, Jonas Christoffer; Clausen, Jens; Pisinger, David
2011-01-01
Effektiv brug af energi er et stadig vigtigere emne. Miljø-og klima problemer samt bekymring for forsyningssikkerhed har gjort vedvarende energikilder til et reelt alternativ til traditionelle energikilder. Men den fluktuerende karakter af for eksempel vind- og solenergi nødvendiggør en radikal ændring i den m°ade vi planlægger og driver energisystemer. Et andet paradigmeskift, som begyndte i 1990’erne for el-systemer er markedsderegulering, hvilket har ført til en række forskellige markedsst...
Optimality problem of network topology in stocks market analysis
Djauhari, Maman Abdurachman; Gan, Siew Lee
2015-02-01
Since its introduction fifteen years ago, minimal spanning tree has become an indispensible tool in econophysics. It is to filter the important economic information contained in a complex system of financial markets' commodities. Here we show that, in general, that tool is not optimal in terms of topological properties. Consequently, the economic interpretation of the filtered information might be misleading. To overcome that non-optimality problem, a set of criteria and a selection procedure of an optimal minimal spanning tree will be developed. By using New York Stock Exchange data, the advantages of the proposed method will be illustrated in terms of the power-law of degree distribution.
Directory of Open Access Journals (Sweden)
Chih-Chiang Lin
2010-01-01
Full Text Available The broadcast scheduling problem (BSP in packet radio networks is a well-known NP-complete combinatorial optimization problem. The broadcast scheduling avoids packet collisions by allowing only one node transmission in each collision domain of a time division multiple access (TDMA network. It also improves the transmission utilization by assigning one transmission time slot to one or more nodes; thus, each node transmits at least once in each time frame. An optimum transmission schedule could minimize the length of a time frame while minimizing the number of idle nodes. In this paper, we propose a new iterated local search (ILS algorithm that consists of two special perturbation and local search operators to solve the BSPs. Computational experiments are applied to benchmark data sets and randomly generated problem instances. The experimental results show that our ILS approach is effective in solving the problems with only a few runtimes, even for very large networks with 2,500 nodes.
Directory of Open Access Journals (Sweden)
Mohammed Kh. AL-Nussairi
2017-10-01
Full Text Available This paper provides a comprehensive review of the major concepts associated with the μgrid, such as constant power load (CPL, incremental negative resistance or impedance (INR/I and its dynamic behaviours on the μgrid, and power system distribution (PSD. In general, a μgrid is defined as a cluster of different types of electrical loads and renewable energy sources (distributed generations under a unified controller within a certain local area. It is considered a perfect solution to integrate renewable energy sources with loads as well as with a traditional grid. In addition, it can operate with a conventional grid, for example, by energy sourcing or a controllable load, or it can operate alone as an islanding mode to feed required electric energy to a grid. Hence, one of the important issues regarding the μgrid is the constant power load that results from the tightly designed control when it is applied to power electronic converters. The effect of CPL is incremental negative resistance that impacts the power quality of a power system and makes it at negative damping. Also, in this paper, a comprehensive study on major control and compensation techniques for μgrid has been included to face the instability effects of constant power loads. Finally, the merits and limitations of the compensation techniques are discussed.
2016-06-01
in NIE CPs primarily involve ICTs . Levy and Murnane (2012) argue that the increasing use of ...require higher levels of mental ability as well as higher levels of education , training, and experience for effective use . In short, ICT insertions such...key aspects of cognitive load in mission command and network/S6 operations. ARL/HRED personnel also used this SME after the fact to assist in
DEFF Research Database (Denmark)
Simonsen, L; Bülow, J; Madsen, Jan Lysgård
1993-01-01
the glucose load and had not returned to baseline level at the end of the experiment. Whole-body respiratory quotient (RQ) was, on average, 0.80 (SD 0.05) in the baseline condition and increased to a maximum of 0.91 (0.03) and then decreased to baseline level at the end of the experiment. The local forearm.......17) to 0.63 (0.17) 30 min after the glucose load (P experiments emphasize several methodological problems in the measurement of local forearm RQ. The whole-body RQ......The effects of an oral glucose load of 75 g on the local forearm and whole-body energy thermogenesis were measured in normal subjects during the 4 h after the glucose intake. Simultaneous assessment of substrate metabolism in the forearm was performed. Energy expenditure (EE) increased after...
Energy Technology Data Exchange (ETDEWEB)
Duran, Ana Cecilia
1990-03-01
This thesis aims to find a better way to solve large scale nonlinear sparse system problems giving special emphasis to load flow in electric power networks. The suggested algorithms are presented 63 refs., 28 figs., 16 tabs.
Agarwal, Mukul
2010-01-01
Consider a scenerio where various users want to communicate with each other over an unknown network to within a fidelity criterion. Thus, there are various users. Each user has a source that it wants to send to another user to within some distortion level. We abstract this problem as that of universal communication of random sources over networks to within a distortion criterion. We compute ta universally reliably achievable region for a set of networks where networks in the set are defined in terms of end to end distortion that they achieve for transmission of independent signals between various nodes assuming that there is common randomness between sender and corresponding receiver. Using this, we provide results for when communication of independent signals to within particular fidelity criteria is possible in terms of when reliable communication is possible. Using this, we show that when the sources at the various nodes are independent of each other, it is sufficient to consider separation architectures: ...
The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey
Directory of Open Access Journals (Sweden)
Luiz Affonso Guedes
2010-09-01
Full Text Available Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest. When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario. The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions. Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks.
The coverage problem in video-based wireless sensor networks: a survey.
Costa, Daniel G; Guedes, Luiz Affonso
2010-01-01
Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest. When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario. The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions. Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks.
Energy Dependent Divisible Load Theory for Wireless Sensor Network Workload Allocation
Directory of Open Access Journals (Sweden)
Haiyan Shi
2012-01-01
Full Text Available The wireless sensor network (WSN, consisting of a large number of microsensors with wireless communication abilities, has become an indispensable tool for use in monitoring and surveillance applications. Despite its advantages in deployment flexibility and fault tolerance, the WSN is vulnerable to failures due to the depletion of limited onboard battery energy. A major portion of energy consumption is caused by the transmission of sensed results to the master processor. The amount of energy used, in fact, is related to both the duration of sensing and data transmission. Hence, in order to extend the operation lifespan of the WSN, a proper allocation of sensing workload among the sensors is necessary. An assignment scheme is here formulated on the basis of the divisible load theory, namely, the energy dependent divisible load theory (EDDLT for sensing workload allocations. In particular, the amount of residual energies onboard sensors are considered while deciding the workload assigned to each sensor. Sensors with smaller amount of residual energy are assigned lighter workloads, thus, allowing for a reduced energy consumption and the sensor lifespan is extended. Simulation studies are conducted and results have illustrated the effectiveness of the proposed workload allocation method.
Tuneable drug-loading capability of chitosan hydrogels with varied network architectures
Tronci, Giuseppe; Russell, Stephen J; Wood, David J; Akashi, Mitsuru
2013-01-01
Advanced bioactive systems with defined macroscopic properties and spatio-temporal sequestration of extracellular biomacromolecules are highly desirable for next generation therapeutics. Here, chitosan hydrogels were prepared with neutral or negatively-charged crosslinkers in order to promote selective electrostatic complexation with charged drugs. Chitosan (CT) was functionalised with varied dicarboxylic acids, such as tartaric acid (TA), poly(ethylene glycol) bis(carboxymethyl) ether (PEG), 1.4-Phenylenediacetic acid (4Ph) and 5-Sulfoisophthalic acid monosodium salt (PhS), whereby PhS was hypothesised to act as a simple mimetic of heparin. ATR FT-IR showed the presence of C=O amide I, N-H amide II and C=O ester bands, providing evidence of covalent network formation. The crosslinker content was reversely quantified by 1H-NMR on partially-degraded network oligomers, so that 18 mol% PhS was exemplarily determined. Swellability, compressability, material morphology, and drug-loading capability were successfull...
A morphological investigation of conductive networks in polymers loaded with carbon nanotubes
Lubineau, Gilles
2017-01-13
Loading polymers with conductive nanoparticles, such as carbon nanotubes, is a popular approach toward improving their electrical properties. Resultant materials are typically described by the weight or volume fractions of their nanoparticles. Because these conductive particles are only capable of charge transfer over a very short range, most do not interact with the percolated paths nor do they participate to the electrical transfer. Understanding how these particles are arranged is necessary to increase their efficiency. It is of special interest to understand how these particles participate in creating percolated clusters, either in a specific or in all directions, and non-percolated clusters. For this, we present a computational modeling strategy based on a full morphological analysis of a network to systematically analyse conductive networks and show how particles are arranged. This study provides useful information for designing these types of materials and examples suitable for characterizing important features, such as representative volume element, the role of nanotube tortuosity and the role of tunneling cutoff distance.
Astrocytes restrict discharge duration and neuronal sodium loads during recurrent network activity.
Karus, Claudia; Mondragão, Miguel A; Ziemens, Daniel; Rose, Christine R
2015-06-01
Influx of sodium ions into active neurons is a highly energy-expensive process which must be strictly limited. Astrocytes could play an important role herein because they take up glutamate and potassium from the extracellular space, thereby dampening neuronal excitation. Here, we performed sodium imaging in mouse hippocampal slices combined with field potential and whole-cell patch-clamp recordings and measurement of extracellular potassium ([K(+)]o). Network activity was induced by Mg(2+)-free, bicuculline-containing saline, during which neurons showed recurring epileptiform bursting, accompanied by transient increases in [K(+)]o and astrocyte depolarizations. During bursts, neurons displayed sodium increases by up to 22 mM. Astrocyte sodium concentration increased by up to 8.5 mM, which could be followed by an undershoot below baseline. Network sodium oscillations were dependent on action potentials and activation of ionotropic glutamate receptors. Inhibition of glutamate uptake caused acceleration, followed by cessation of electrical activity, irreversible sodium increases, and swelling of neurons. The gliotoxin NaFAc (sodium-fluoroacetate) resulted in elevation of astrocyte sodium concentration and reduced glial uptake of glutamate and potassium uptake through Na(+) /K(+)-ATPase. Moreover, NaFAc extended epileptiform bursts, caused elevation of neuronal sodium, and dramatically prolonged accompanying sodium signals, most likely because of the decreased clearance of glutamate and potassium by astrocytes. Our experiments establish that recurrent neuronal bursting evokes sodium transients in neurons and astrocytes and confirm the essential role of glutamate transporters for network activity. They suggest that astrocytes restrict discharge duration and show that an intact astrocyte metabolism is critical for the neurons' capacity to recover from sodium loads during synchronized activity. © 2015 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Shuhei Kimura
Full Text Available The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.
Harmony search optimization algorithm for a novel transportation problem in a consolidation network
Davod Hosseini, Seyed; Akbarpour Shirazi, Mohsen; Taghi Fatemi Ghomi, Seyed Mohammad
2014-11-01
This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.
Fault-tolerance of a neural network solving the traveling salesman problem
Protzel, P.; Palumbo, D.; Arras, M.
1989-01-01
This study presents the results of a fault-injection experiment that stimulates a neural network solving the Traveling Salesman Problem (TSP). The network is based on a modified version of Hopfield's and Tank's original method. We define a performance characteristic for the TSP that allows an overall assessment of the solution quality for different city-distributions and problem sizes. Five different 10-, 20-, and 30- city cases are sued for the injection of up to 13 simultaneous stuck-at-0 and stuck-at-1 faults. The results of more than 4000 simulation-runs show the extreme fault-tolerance of the network, especially with respect to stuck-at-0 faults. One possible explanation for the overall surprising result is the redundancy of the problem representation.
Directory of Open Access Journals (Sweden)
Caixian Sun
2014-01-01
Full Text Available This paper is devoted to the average consensus problems in directed networks of agents with unknown control direction. In this paper, by using Nussbaum function techniques and Laplacian matrix, novel average consensus protocols are designed for multiagent systems with unknown control direction in the cases of directed networks with fixed and switching topology. In the case of switching topology, the disagreement vector is utilized. Finally, simulation is provided to demonstrate the effectiveness of our results.
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.
a fuzzy logic approach to non-linearity problem of load frequency
African Journals Online (AJOL)
user
2016-07-03
Jul 3, 2016 ... Keywords: fuzzy logic control, Area control error (ACE), power system control, load frequency control, Artificial intelligence. 1. INTRODUCTION. Power system is an interconnection of generating ... quality of the electric power system requires both the frequency and voltage to remain at standard values.
In risky environments, emotional children have more behavioral problems but lower allostatic load.
Dich, Nadya; Doan, Stacey N; Evans, Gary W
2017-05-01
Developmental models of temperament by environment interactions predict that children's negative emotionality exacerbates the detrimental effects of risky environments, increasing the risk for pathology. However, negative emotions may have an adaptive function. Accordingly, the present study explores an alternative hypothesis that in the context of high adversity, negative emotionality may be a manifestation of an adaptive coping style and thus be protective against the harmful effects of a stressful environment. Prospective combined effects of negative emotionality and cumulative risk (confluence of multiple risk factors related to poverty) on children's internalizing and externalizing symptoms and allostatic load, an index of cumulative physiological dysregulation, were assessed in 239 children (46% female, baseline age = 9). Negative emotionality and cumulative risk were assessed at baseline. Internalizing and externalizing behaviors were measured at 4- and 8-year follow-ups. Allostatic load was measured at baseline and both follow-ups using neuroendocrine, cardiovascular, and metabolic parameters. Linear mixed-effect models were used to analyze the prospective associations between negative emotionality, cumulative risk, and the outcomes-allostatic load and internalizing and externalizing behaviors. The combination of high cumulative risk exposure and high negative emotionality was associated with highest levels of internalizing and externalizing behaviors. However, consistent with the alternative hypothesis, negative emotionality reduced the effects of high cumulative risk on allostatic load. In the context of risky environments, negative emotionality may offer some physical health benefits. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Constituent loads in small streams: the process and problems of estimating sediment flux
R. B. Thomas
1989-01-01
Constituent loads in small streams are often estimated poorly. This is especially true for discharge-related constituents like sediment, since their flux is highly variable and mainly occurs during infrequent high-flow events. One reason for low-quality estimates is that most prevailing data collection methods ignore sampling probabilities and only partly account for...
DOE Network 2025: Network Research Problems and Challenges for DOE Scientists. Workshop Report
Energy Technology Data Exchange (ETDEWEB)
None, None
2016-02-01
The growing investments in large science instruments and supercomputers by the US Department of Energy (DOE) hold enormous promise for accelerating the scientific discovery process. They facilitate unprecedented collaborations of geographically dispersed teams of scientists that use these resources. These collaborations critically depend on the production, sharing, moving, and management of, as well as interactive access to, large, complex data sets at sites dispersed across the country and around the globe. In particular, they call for significant enhancements in network capacities to sustain large data volumes and, equally important, the capabilities to collaboratively access the data across computing, storage, and instrument facilities by science users and automated scripts and systems. Improvements in network backbone capacities of several orders of magnitude are essential to meet these challenges, in particular, to support exascale initiatives. Yet, raw network speed represents only a part of the solution. Indeed, the speed must be matched by network and transport layer protocols and higher layer tools that scale in ways that aggregate, compose, and integrate the disparate subsystems into a complete science ecosystem. Just as important, agile monitoring and management services need to be developed to operate the network at peak performance levels. Finally, these solutions must be made an integral part of the production facilities by using sound approaches to develop, deploy, diagnose, operate, and maintain them over the science infrastructure.
The problem of colliding networks and its relation to cell fusion and cancer.
Koulakov, Alexei A; Lazebnik, Yuri
2012-11-07
Cell fusion, a process that merges two or more cells into one, is required for normal development and has been explored as a tool for stem cell therapy. It has also been proposed that cell fusion causes cancer and contributes to its progression. These functions rely on a poorly understood ability of cell fusion to create new cell types. We suggest that this ability can be understood by considering cells as attractor networks whose basic property is to adopt a set of distinct, stable, self-maintaining states called attractors. According to this view, fusion of two cell types is a collision of two networks that have adopted distinct attractors. To learn how these networks reach a consensus, we model cell fusion computationally. To do so, we simulate patterns of gene activities using a formalism developed to simulate patterns of memory in neural networks. We find that the hybrid networks can assume attractors that are unrelated to parental attractors, implying that cell fusion can create new cell types by nearly instantaneously moving cells between attractors. We also show that hybrid networks are prone to assume spurious attractors, which are emergent and sporadic network states. This finding means that cell fusion can produce abnormal cell types, including cancerous types, by placing cells into normally inaccessible spurious states. Finally, we suggest that the problem of colliding networks has general significance in many processes represented by attractor networks, including biological, social, and political phenomena. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Problem-Solving Methods for the Prospective Development of Urban Power Distribution Network
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2014-01-01
Full Text Available This article succeeds the former A. P. K nko’ and A. I. Kuzmina’ ubl t on titled "A mathematical model of urban distribution electro-network considering its future development" (electronic scientific and technical magazine "Science and education" No. 5, 2014.The article offers a model of urban power distribution network as a set of transformer and distribution substations and cable lines. All elements of the network and new consumers are determined owing to vectors of parameters consistent with them.A problem of the urban power distribution network design, taking into account a prospective development of the city, is presented as a problem of discrete programming. It is in deciding on the optimal option to connect new consumers to the power supply network, on the number and sites to build new substations, and on the option to include them in the power supply network.Two methods, namely a reduction method for a set the nested tasks of global minimization and a decomposition method are offered to solve the problem.In reduction method the problem of prospective development of power supply network breaks into three subtasks of smaller dimension: a subtask to define the number and sites of new transformer and distribution substations, a subtask to define the option to connect new consumers to the power supply network, and a subtask to include new substations in the power supply network. The vector of the varied parameters is broken into three subvectors consistent with the subtasks. Each subtask is solved using an area of admissible vector values of the varied parameters at the fixed components of the subvectors obtained when solving the higher subtasks.In decomposition method the task is presented as a set of three, similar to reduction method, reductions of subtasks and a problem of coordination. The problem of coordination specifies a sequence of the subtasks solution, defines the moment of calculation termination. Coordination is realized by
Nasertdinova, A. D.; Bochkarev, V. V.
2017-11-01
Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.
A Matheuristic for the Liner Shipping Network Design Problem with Transit Time Restrictions
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy; Karsten, Christian Vad
2015-01-01
We present a mathematical model for the liner shipping network design problem with transit time restrictions on the cargo flow. We extend an existing matheuristic for the liner shipping network design problem to consider transit time restrictions. The matheuristic is an improvement heuristic, where...... an integer program is solved iteratively as a move operator in a large-scale neighborhood search. To assess the effects of insertions/removals of port calls, flow and revenue changes are estimated for relevant commodities along with an estimation of the change in the vessel cost. Computational results...
Multi-objective optimization model and evolutional solution of network node matching problem
Yao, Xiangjuan; Gong, Dunwei; Wang, Peipei; Chen, Lina
2017-10-01
In reality, many systems can be abstracted as a network. The research results show that there are close relations between different networks. How to find out the corresponding relationship between the nodes of different networks, i.e. the node matching problem, is a topic worthy of further study. The existing network node matching methods often use a single criteria to measure the matching precision of two networks, therefore may obtain inaccurate results. In fact, the matching accuracy of two networks can be measured using different structural information, so as to improve the reliability and accuracy of the matching method. In view of this, this paper establishes a multi-objective optimization model of network node matching problem in which the matching accuracy is measured by multiple criteria. When using evolutionary algorithm to solve the model, the multiple objectives are unified into a fitness function. The experimental results show that this method can obtain better matching accuracy than single-objective method and the random method while using less running time.
Link-prediction to tackle the boundary specification problem in social network surveys
De Wilde, Philippe; Buarque de Lima-Neto, Fernando
2017-01-01
Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes. PMID:28426826
Neural Network-based Load Forecasting and Error Implication for Short-term Horizon
Khuntia, S.R.; Rueda Torres, José L.; van der Meijden, M.A.M.M.
2016-01-01
Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often
A hybrid model for multi-objective capacitated facility location network design problem
Directory of Open Access Journals (Sweden)
Mohammad saeed JabalAmeli
2011-01-01
Full Text Available One of the primary concerns on many traditional capacitated facility location/network problems is to consider transportation and setup facilities in one single objective function. This simple assumption may lead to misleading solutions since the cost of transportation is normally considered for a short period time and, obviously, the higher cost of setting up the facilities may reduce the importance of the transportation cost/network. In this paper, we introduce capacitated facility location/network design problem (CFLNDP with two separate objective functions in forms of multi-objective with limited capacity. The proposed model is solved using a new hybrid algorithm where there are two stages. In the first stage, locations of facilities and design of fundamental network are determined and in the second stage demands are allocated to the facilities. The resulted multi-objective problem is solved using Lexicography method for a well-known example from the literature with 21 node instances. We study the behaviour of the resulted problem under different scenarios in order to gain insight into the behaviour of the model in response to changes in key problem parameters.
Management of the ESA Tracking Network- Load Forecasts and Mission Interfaces
Dreihahn, H.; di Giulio, M.
2012-08-01
The European Space Agency is operating a network of tracking ground stations distributed all over the world called ESTRACK. These tracking stations provide the space to ground communications for ESA and external space missions and is operated from ESOC in Darmstadt. In the recent years ESOC has successfully developed and deployed the ESTRACK Management System (EMS). The EMS, or more specifically it’s planning and scheduling components are in charge of planning the times, when a certain tracking station is allocated to a space mission. Initially the EMS has been designed as an automated planning system with minimum user interaction. However, looking back number of lessons have been learned:• While automated planning is very valuable, the need for interaction and negotiation of planning results with users, i.e. space missions, has been underestimated.• Interoperability with external space mission and external network providers is already required at the stage of resource allocation planning. This has been addressed by [1].• The originally envisaged planning horizon of 2-3 weeks into the future was too short. Now, depending on the mission characteristic, ground station allocation planning is done routinely for a planning horizon of 1+ year.This paper will focus on the first and the last point of the lessons learned. We will present and discuss an approach for an interface of the ESTRACK Management System with space missions, which extends the currently implement machine to machine interface with a (web based) man to machine interface. The second aspect addressed in this paper is the so called ESTRACK Load Analysis. The capability of the EMS to automatically plan station allocation for long periods into the future enables ESTRACK load forecasts and conflict analysis at an early stage. These forecasts are essential to plan the evolution of ESTRACK and to facilitate the support of future missions. Furthermore it helps to identify resource bottlenecks which
Newton, Allen T; Morgan, Victoria L; Rogers, Baxter P; Gore, John C
2011-10-01
Interregional correlations between blood oxygen level dependent (BOLD) magnetic resonance imaging (fMRI) signals in the resting state have been interpreted as measures of connectivity across the brain. Here we investigate whether such connectivity in the working memory and default mode networks is modulated by changes in cognitive load. Functional connectivity was measured in a steady-state verbal identity N-back task for three different conditions (N = 1, 2, and 3) as well as in the resting state. We found that as cognitive load increases, the functional connectivity within both the working memory the default mode network increases. To test whether functional connectivity between the working memory and the default mode networks changed, we constructed maps of functional connectivity to the working memory network as a whole and found that increasingly negative correlations emerged in a dorsal region of the posterior cingulate cortex. These results provide further evidence that low frequency fluctuations in BOLD signals reflect variations in neural activity and suggests interaction between the default mode network and other cognitive networks. Copyright © 2010 Wiley-Liss, Inc.
METHOD OF COMPENSATING LOADS FOR SHALLOW SHELLS. VIBRATION AND STABILITY PROBLEMS
Directory of Open Access Journals (Sweden)
Tran Duc Chinh
2015-12-01
Full Text Available Based on the integral representation of the displacements functions through Green's functions, the author proposed a method to solve the system of differential equations of the given problem. The equations were solved approximately by reducing to algebraic equations by finite difference techniques in Samarsky scheme. Some examples are given for calculation of eigenvalues of shallow shell vibration problem, which are compared with results received by Onyashvili using Galerkin method.
Directory of Open Access Journals (Sweden)
Masoud Rabbani
2017-02-01
Full Text Available Nowadays, fiber-optic due to having greater bandwidth and being more efficient compared with other similar technologies, are counted as one the most important tools for data transfer. In this article, an integrated mathematical model for a three-level fiber-optic distribution network with consideration of simultaneous backbone and local access networks is presented in which the backbone network is a ring and the access networks has a star-star topology. The aim of the model is to determine the location of the central offices and splitters, how connections are made between central offices, and allocation of each demand node to a splitter or central office in a way that the wiring cost of fiber optical and concentrator installation are minimized. Moreover, each user’s desired bandwidth should be provided efficiently. Then, the proposed model is validated by GAMS software in small-sized problems, afterwards the model is solved by two meta-heuristic methods including differential evolution (DE and genetic algorithm (GA in large-scaled problems and the results of two algorithms are compared with respect to computational time and objective function obtained value. Finally, a sensitivity analysis is provided. Keyword: Fiber-optic, telecommunication network, hub-location, passive splitter, three-level network.
Transient Analysis of Lumped Circuit Networks Loaded Thin Wires By DGTD Method
Li, Ping
2016-03-31
With the purpose of avoiding very fine mesh cells in the proximity of a thin wire, the modified telegrapher’s equations (MTEs) are employed to describe the thin wire voltage and current distributions, which consequently results in reduced number of unknowns and augmented Courant-Friedrichs-Lewy (CFL) number. As hyperbolic systems, both the MTEs and the Maxwell’s equations are solved by the discontinuous Galerkin time-domain (DGTD) method. In realistic situations, the thin wires could be either driven or loaded by circuit networks. The thin wire-circuit interface performs as a boundary condition for the thin wire solver, where the thin wire voltage and current used for the incoming flux evaluation involved in the DGTD analyzed MTEs are not available. To obtain this voltage and current, an auxiliary current flowing through the thin wire-circuit interface is introduced at each interface. Corresponding auxiliary equations derived from the invariable property of characteristic variable for hyperbolic systems are developed and solved together with the circuit equations established by the modified nodal analysis (MNA) modality. Furthermore, in order to characterize the field and thin wire interactions, a weighted electric field and a volume current density are added into the MTEs and Maxwell-Ampere’s law equation, respectively. To validate the proposed algorithm, three representative examples are presented.
Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments
Eagle, Michael; Hicks, Drew; Barnes, Tiffany
2015-01-01
Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…
A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.
Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming
2015-01-01
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.
Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach
Directory of Open Access Journals (Sweden)
Khalid Haddouch
2016-09-01
Full Text Available A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs. In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances.
Asynchronous teams for solving the loading and routing auto-carrier problem
Parolin, Erick Skorupa
2016-01-01
Orientador: Prof. Dr. Cláudio Nogueira de Meneses Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2016. Beyond a complex real world system composed by a set of sophisticated machines and qualied human resources distributed around manufacturing environment, the Auto In- dustry needs a little more to allow their products to reach the nal costumers. Loading vehicles like cars, trucks and vans into auto-carriers and designin...
Directory of Open Access Journals (Sweden)
Botond Molnár
Full Text Available There has been a long history of using neural networks for combinatorial optimization and constraint satisfaction problems. Symmetric Hopfield networks and similar approaches use steepest descent dynamics, and they always converge to the closest local minimum of the energy landscape. For finding global minima additional parameter-sensitive techniques are used, such as classical simulated annealing or the so-called chaotic simulated annealing, which induces chaotic dynamics by addition of extra terms to the energy landscape. Here we show that asymmetric continuous-time neural networks can solve constraint satisfaction problems without getting trapped in non-solution attractors. We concentrate on a model solving Boolean satisfiability (k-SAT, which is a quintessential NP-complete problem. There is a one-to-one correspondence between the stable fixed points of the neural network and the k-SAT solutions and we present numerical evidence that limit cycles may also be avoided by appropriately choosing the parameters of the model. This optimal parameter region is fairly independent of the size and hardness of instances, this way parameters can be chosen independently of the properties of problems and no tuning is required during the dynamical process. The model is similar to cellular neural networks already used in CNN computers. On an analog device solving a SAT problem would take a single operation: the connection weights are determined by the k-SAT instance and starting from any initial condition the system searches until finding a solution. In this new approach transient chaotic behavior appears as a natural consequence of optimization hardness and not as an externally induced effect.
Energy Technology Data Exchange (ETDEWEB)
Cogo, Joao Roberto [Escola Federal de Engenharia de Itajuba, MG (Brazil)
1994-12-31
The non linear electrical loads can give rise to a number of disturbances in electrical power networks. Among them, the high consumption of relative power is to be noted and so is the several harmonic components which may be injected in the industry system and very often in the utility system. So, by using appropriate technical considerations, as well as measurements in typical special electrical loads, such negative effects are analyzed and ways of minimizing them are suggested. (author) 3 refs., 11 figs., 6 tabs.
PROACTIVE APPROACH TO THE INCIDENT AND PROBLEM MANAGEMENT IN COMMUNICATION NETWORKS
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Vjeran Strahonja
2007-06-01
Full Text Available Proactive approach to communication network maintenance has the capability of enhancing the integrity and reliability of communication networks, as well as of reducing maintenance costs and overall number of incidents. This paper presents approaches to problem and incident prevention with the help of root-cause analysis, aligning that with the goal to foresee software performance. Implementation of proactive approach requires recognition of enterprise's current level of maintenance better insights into available approaches and tools, as well as their comparison, interoperability, integration and further development. The approach we are proposing and elaborating in this paper lies on the construction of a metamodel of the problem management of information technology, particularly the proactive problem management. The metamodel is derived from the original ITIL specification and presented in an object-oriented fashion by using structure (class diagrams conform to UML notation. Based on current research, appropriate metrics based on the concept of Key Performance Indicators is suggested.
Kumar, Anil; Mukhopadhyay, Santwana
2017-08-01
The present work is concerned with the investigation of thermoelastic interactions inside a spherical shell with temperature-dependent material parameters. We employ the heat conduction model with a single delay term. The problem is studied by considering three different kinds of time-dependent temperature and stress distributions applied at the inner and outer surfaces of the shell. The problem is formulated by considering that the thermal properties vary as linear function of temperature that yield nonlinear governing equations. The problem is solved by applying Kirchhoff transformation along with integral transform technique. The numerical results of the field variables are shown in the different graphs to study the influence of temperature-dependent thermal parameters in various cases. It has been shown that the temperature-dependent effect is more prominent in case of stress distribution as compared to other fields and also the effect is significant in case of thermal shock applied at the two boundary surfaces of the spherical shell.
Method of Geometric Connected Disk Cover Problem for UAV realy network deployment
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Chuang Liu
2017-01-01
Full Text Available Aiming at the problem of the effective connectivity of a large number of mobile combat units in the future aeronautic swarm operation, this paper proposes an idea of using UAV(Unmanned Aerial Vehicle to build, and studies the deployment of the network. User coverage and network connectivity are important for a relay network planning which are studied separately in traditional ways. In order to effectively combine these two factors while the network’s survivability is taken into account. Firstly, the concept of node aggregation degree is proposed. Secondly, a performance evaluation parameter for UAV relay network is proposed based on node aggregation degree, then analyzes the lack of deterministic deployment and presents one a PSO (VFA-PSO deployment algorithm based on virtual force. Finally, compared with the existing algorithms, the validity and stability of the algorithm are verified. The experimental results show that the VFA-PSO algorithm can effectively improve the network coverage and the survivability of the network under the premise of ensuring the network connectivity, and has better deployment effect.
In Risky Environments, Emotional Children Have More Behavioral Problems but Lower Allostatic Load
DEFF Research Database (Denmark)
Dich, Nadya; Doan, Stacey N.; Evans, Gary W.
2017-01-01
Objective: Developmental models of temperament by environment interactions predict that children’s negative emotionality exacerbates the detrimental effects of risky environments, increasing the risk for pathology. However, negative emotions may have an adaptive function. Accordingly, the present...... study explores an alternative hypothesis that in the context of high adversity, negative emotionality may be a manifestation of an adaptive coping style and thus be protective against the harmful effects of a stressful environment. Method: Prospective combined effects of negative emotionality...... and cumulative risk (confluence of multiple risk factors related to poverty) on children’s internalizing and externalizing symptoms and allostatic load, an index of cumulative physiological dysregulation, were assessed in 239 children (46% female, baseline age = 9). Negative emotionality and cumulative risk were...
A Very Large Area Network (VLAN) knowledge-base applied to space communication problems
Zander, Carol S.
1988-01-01
This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.
Tight bounds on the size of neural networks for classification problems
Energy Technology Data Exchange (ETDEWEB)
Beiu, V. [Los Alamos National Lab., NM (United States); Pauw, T. de [Universite Catholique de Louvain, Louvain-la-Neuve (Belgium). Dept. de Mathematique
1997-06-01
This paper relies on the entropy of a data-set (i.e., number-of-bits) to prove tight bounds on the size of neural networks solving a classification problem. First, based on a sequence of geometrical steps, the authors constructively compute an upper bound of O(mn) on the number-of-bits for a given data-set - here m is the number of examples and n is the number of dimensions (i.e., R{sup n}). This result is used further in a nonconstructive way to bound the size of neural networks which correctly classify that data-set.
A note on the consensus finding problem in communication networks with switching topologies
Haskovec, Jan
2014-05-07
In this note, we discuss the problem of consensus finding in communication networks of agents with dynamically switching topologies. In particular, we consider the case of directed networks with unbalanced matrices of communication rates. We formulate sufficient conditions for consensus finding in terms of strong connectivity of the underlying directed graphs and prove that, given these conditions, consensus is found asymptotically. Moreover, we show that this consensus is an emergent property of the system, being encoded in its dynamics and not just an invariant of its initial configuration. © 2014 © 2014 Taylor & Francis.
What a Load of Rubbish: Japan’s Problem with Increasing Disposable Container and Packaging Waste
原田 卓哉; Harrison, Brian
2017-01-01
Huge amounts of plastic are used in everyday life. However, much of the plastic used for disposable containers and packaging is used once and then discarded. This can then cause a serious environmental problem. After describing the environmental effects and possible ways of treatment, this paper examines the situation in Japan, particularly with respect to how reuse and waste reduction can be achieved in Japan by adopting better extended producer responsibility schemes, introducing bans or fe...
Fortuin, Janna; van Geel, Mitch; Vedder, Paul
2015-04-01
Adolescents who like each other may become more similar to each other with regard to internalizing and externalizing problems, though it is not yet clear which social mechanisms explain these similarities. In this longitudinal study, we analyzed four mechanisms that may explain similarity in adolescent peer networks with regard to externalizing and internalizing problems: selection, socialization, avoidance and withdrawal. At three moments during one school-year, we asked 542 adolescents (8th grade, M-age = 13.3 years, 51 % female) to report who they liked in their classroom, and their own internalizing and externalizing problems. Adolescents tend to prefer peers who have similar externalizing problem scores, but no significant selection effect was found for internalizing problems. Adolescents who share the same group of friends socialize each other and then become more similar with respect to externalizing problems, but not with respect to internalizing problems. We found no significant effects for avoidance or withdrawal. Adolescents may choose to belong to a peer group that is similar to them in terms of externalizing problem behaviors, and through peer group socialization (e.g., enticing, modelling, mimicking, and peer pressure) become more similar to that group over time.
Lin, Yi-Kuei; Yeh, Cheng-Ta
2013-03-01
Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating Non-dominated Sorting Genetic Algorithm II and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.
Directory of Open Access Journals (Sweden)
Fuller Jeffrey
2012-06-01
Full Text Available Abstract Background While participatory social network analysis can help health service partnerships to solve problems, little is known about its acceptability in cross-cultural settings. We conducted two case studies of chronic illness service partnerships in 2007 and 2008 to determine whether participatory research incorporating social network analysis is acceptable for problem-solving in Australian Aboriginal health service delivery. Methods Local research groups comprising 13–19 partnership staff, policy officers and community members were established at each of two sites to guide the research and to reflect and act on the findings. Network and work practice surveys were conducted with 42 staff, and the results were fed back to the research groups. At the end of the project, 19 informants at the two sites were interviewed, and the researchers conducted critical reflection. The effectiveness and acceptability of the participatory social network method were determined quantitatively and qualitatively. Results Participants in both local research groups considered that the network survey had accurately described the links between workers related to the exchange of clinical and cultural information, team care relationships, involvement in service management and planning and involvement in policy development. This revealed the function of the teams and the roles of workers in each partnership. Aboriginal workers had a high number of direct links in the exchange of cultural information, illustrating their role as the cultural resource, whereas they had fewer direct links with other network members on clinical information exchange and team care. The problem of their current and future roles was discussed inside and outside the local research groups. According to the interview informants the participatory network analysis had opened the way for problem-solving by “putting issues on the table”. While there were confronting and ethically
Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem
Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf
2017-08-01
Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.
Directory of Open Access Journals (Sweden)
N.R.Yusupbekov
2014-07-01
Full Text Available This paper provides a new approach for solving a problem of modeling and structural syntheses of information networks of automated control systems by applying fuzzy sets theory, fuzzy logic and genetic algorithms. The procedure of formalizing structural syntheses of multi-level dispersed information networks of automated control systems is proposed. Also, the paper proposes a conceptual model of evolutionary syntheses based on genetic algorithms, which do not require additional information about the characteristics and features of target function. Modified genetic operators of crossover, mutation and algorithms of evolutionary syntheses of information networks systems are developed. Finally, the results of computational experiments on researching the influence of probability of the use of crossover and mutation operators, method of choosing parental pairs, and the size of initial population on the speed and precision of final results are provided.
Abdulghafoor, O. B.; Shaat, M. M. R.; Ismail, M.; Nordin, R.; Yuwono, T.; Alwahedy, O. N. A.
2017-05-01
In this paper, the problem of resource allocation in OFDM-based downlink cognitive radio (CR) networks has been proposed. The purpose of this research is to decrease the computational complexity of the resource allocation algorithm for downlink CR network while concerning the interference constraint of primary network. The objective has been secured by adopting pricing scheme to develop power allocation algorithm with the following concerns: (i) reducing the complexity of the proposed algorithm and (ii) providing firm power control to the interference introduced to primary users (PUs). The performance of the proposed algorithm is tested for OFDM- CRNs. The simulation results show that the performance of the proposed algorithm approached the performance of the optimal algorithm at a lower computational complexity, i.e., O(NlogN), which makes the proposed algorithm suitable for more practical applications.
The free boundary problem describing information diffusion in online social networks
Lei, Chengxia; Lin, Zhigui; Wang, Haiyan
In this paper we consider a free boundary problem for a reaction-diffusion logistic equation with a time-dependent growth rate. Such a problem arises in the modeling of information diffusion in online social networks, with the free boundary representing the spreading front of news among users. We present several sharp thresholds for information diffusion that either lasts forever or suspends in finite time. In the former case, we give the asymptotic spreading speed which is determined by a corresponding elliptic equation.
The Technical Problems of Anti-theft Diagnostics in a Traction Network
Mikulski, Jerzy; Młynczak, Jakub
2012-02-01
The paper presents an analysis of traction lines theft in the Katowice division of the Railroad Development Company (Zakład Linii Kolejowych - ZLK) as well as the principles for the anti-theft protection system, currently in development. The problem of theft is a very important issue concerning the safety of rail transportation. It is also a significant economic problem, as the cost of recreating a stolen network is very high. Moreover, the Administrator of the infrastructure bears the cost of compensation for any delays in train schedules.
PROBLEM OF INTERNET COMMUNICATION OF UPPER SECONDARY SCHOOL PUPILS IN ELECTRONIC SOCIAL NETWORKS
Directory of Open Access Journals (Sweden)
Olga E. Konevshchynska
2017-09-01
Full Text Available The article deals with the problem of Internet communication of upper secondary school pupils during interpersonal communication in electronic social networks. The actuality is proved, the system analysis of the psychological and pedagogical, educational-methodical literature of the researched problem is carried out. External written Internet speaking is considered as an important factor in the communication culture of the individual. It has been determined that high level of Internet communication of both students and teachers in the process of interpersonal communication in electronic social networks are important aspects of raising the level of media culture, media information competence of the individual. Also, these ones are necessary requirements of the information society to fulfill project-oriented, educational-cognitive and effective innovative activity in educational practice.
The Bilevel Design Problem for Communication Networks on Trains: Model, Algorithm, and Verification
Directory of Open Access Journals (Sweden)
Yin Tian
2014-01-01
Full Text Available This paper proposes a novel method to solve the problem of train communication network design. Firstly, we put forward a general description of such problem. Then, taking advantage of the bilevel programming theory, we created the cost-reliability-delay model (CRD model that consisted of two parts: the physical topology part aimed at obtaining the networks with the maximum reliability under constrained cost, while the logical topology part focused on the communication paths yielding minimum delay based on the physical topology delivered from upper level. We also suggested a method to solve the CRD model, which combined the genetic algorithm and the Floyd-Warshall algorithm. Finally, we used a practical example to verify the accuracy and the effectiveness of the CRD model and further applied the novel method on a train with six carriages.
A Bayesian network approach to the database search problem in criminal proceedings
2012-01-01
Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions
The analysis of split graphs in social networks based on the K-Cardinality assignment problem
Belik, Ivan
2014-01-01
In terms of social networks, split graphs correspond to the variety of interpersonal and intergroup relations. In this paper we analyse the interaction between the cliques (socially strong and trusty groups) and the independent sets (fragmented and non-connected groups of people) as the basic components of any split graph. Based on the Semi-Lagrangean relaxation for the k-cardinality assignment problem, we show the way of minimizing the socially risky interactions between the cliques and the ...
The analysis of split graphs in social networks based on the K-Cardinality assignment problem
Belik, Ivan
2014-01-01
In terms of social networks, split graphs correspond to the variety of interpersonal and intergroup relations. In this paper we analyse the interaction between the cliques (socially strong and trusty groups) and the independent sets (fragmented and non-connected groups of people) as the basic components of any split graph. Based on the Semi-Lagrangean relaxation for the kcardinality assignment problem, we show the way of minimizing the socially risky interactions between the cl...
Li, Ming; Miao, Chunyan; Leung, Cyril
2015-12-04
Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.
Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian
2016-10-27
To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.
DEFF Research Database (Denmark)
Zecchino, Antonio; Hu, Junjie; Coppo, Massimiliano
2016-01-01
this problem, distribution transformers with on-load tapping capability are under development. This paper presents model and experimental validation of a 35 kVA three-phase power distribution transformer with independent on-load tap changer control capability on each phase. With the purpose of investigating...... to reproduce the main feature of an unbalanced grid. The experimental activities are recreated in by carrying out dynamics simulation studies, aiming at validating the implemented models of both the transformer as well as the other grid components. Phase-neutral voltages’ deviations are limited, proving...
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.
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.
An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks
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Vala Ali Rohani
2014-01-01
Full Text Available Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN, the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.
Kurniawan, N.A.; Vos, B.E.; Biebricher, A.S.; Wuite, G.J.L.; Peterman, E.J.G.; Koenderink, G.H.
2016-01-01
Tissues and cells sustain recurring mechanical loads that span a wide range of loading amplitudes and timescales as a consequence of exposure to blood flow, muscle activity, and external impact. Both tissues and cells derive their mechanical strength from fibrous protein scaffolds, which typically
Barnett, Nancy P; Ott, Miles Q; Clark, Melissa A
2014-12-01
Peer associations are influential for substance use among college students, but relatively few investigations have been conducted on the social network characteristics that are associated with problematic alcohol use in college. This study investigated the association between network characteristics of prestige, expansiveness, and reciprocity and alcohol use variables in a college residence hall network. Undergraduate students in 1 residence hall (N = 129; 51.9% female; 48.1% non-Hispanic White; 84.5% first-year) reported on their alcohol use and alcohol-related problems in the past semester and nominated up to 10 residence hall peers who were important to them. Network autocorrelation modeling evaluated the association between 5 network variables reflecting prestige (indegree and betweenness centrality), expansiveness (outdegree), and relationship agreement (indegree reciprocity and outdegree reciprocity) and 3 indicators of alcohol use (drinks per week, number of heavy drinking days, number of alcohol problems). Moderation by gender of the associations between network characteristics and alcohol variables was also investigated. Models controlled for demographics and network autocorrelation. A higher outdegree and higher betweenness centrality within the residence hall network were significantly related to the number of heavy drinking days and number of alcohol problems, respectively. Higher indegree and higher betweenness centrality were associated with more alcohol problems for women when alcohol use was controlled. Having higher prestige and indicating oneself as having more friends in a college residential network may convey alcohol-related risks, with some risks higher for women.
Cell Load Balancing in Heterogeneous Scenarios
DEFF Research Database (Denmark)
Eduardo, Simao; Rodrigues, Antonio; Mihovska, Albena D.
2013-01-01
Cell load imbalances in wireless networks degrade performance. Macro and small cell collocated deployments (heterogeneous scenario) constitutes a new problem for load balancing. This paper proposes a novel admission control algorithm for an optimal solution to the assignment of traffic flows...
Energy Technology Data Exchange (ETDEWEB)
Cristin V, Miguel A; Ortega S, Cesar A [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)
2005-07-01
The charge of lead-acid batteries (LAB), as in any other type of batteries, consists of replacing the energy consumed during the discharge. Nevertheless, as no physical or chemical process is good enough to totality recharge a battery, it is necessary to supply to it more than the 100% of the energy demanded during its discharge. A critical factor to make a suitable load control of the batteries is to determine its own state of load. That is to say, to have an efficient load control, it is necessary to count on means that allow to accurately determining the residual capacity of the battery to deliver load. This one is the one of the aspects of greater interest in the research centers around world. For this reason, in this work it was pretended to develop a calculation algorithm of the state of load of batteries based on a fuzzy-neural network that could calculate the state of load without using the battery current as an input. This is because one of the main problems for the designers of battery load controllers is the correct supervision of the current that circulates around the system in all the rank of operation of the same one because the sensors do not have a linear behavior. [Spanish] La recarga de baterias plomo-acido (BPA), como cualquier otro tipo de baterias, consiste en reponer la energia consumida durante la descarga. Sin embargo, como ningun proceso fisico o quimico es lo bastante eficiente para recargar a totalidad una bateria, es necesario suministrarle mas del 100% de la energia demandada durante su descarga. Un factor critico para realizar un adecuado control de carga de las baterias, es determinar su propio estado de carga. Es decir, para tener un control de carga eficiente, es necesario contar con un medio que permita determinar con precision la capacidad remanente de la bateria para entregar carga. Este es uno de los aspectos de mayor interes en los centros de investigacion alrededor el mundo. Por tal razon, en este trabajo se propuso
A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm
Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu
Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.
Directory of Open Access Journals (Sweden)
Beathe Haatveit
2016-01-01
Conclusion: These results support a general load-dependent DMN dysfunction in schizophrenia spectrum disorder across two demanding executive tasks that is not merely an epiphenomenon of cognitive dysfunction.
Urbanoski, Karen; van Mierlo, Trevor; Cunningham, John
2016-08-22
This study contributes to emerging literature on online health networks by modeling communication patterns between members of a moderated online support group for problem drinking. Using social network analysis, we described members' patterns of joint participation in threads, parsing out the role of site moderators, and explored differences in member characteristics by network position. Posts made to the online support group of Alcohol Help Centre during 2013 were structured as a two-mode network of members (n = 205) connected via threads (n = 506). Metrics included degree centrality, clique membership, and tie strength. The network consisted of one component and no cliques of members, although most made few posts and a small number communicated only with the site's moderators. Highly active members were older and tended to have started posting prior to 2013. The distribution of members across threads varied from threads containing posts by one member to others that connected multiple members. Moderators accounted for sizable proportions of the connectivity between both members and threads. After 5 years of operation, the AHC online support group appears to be fairly cohesive and stable, in the sense that there were no isolated subnetworks comprised of specific types of members or devoted to specific topics. Participation and connectedness at the member-level was varied, however, and tended to be low on average. The moderators were among the most central in the network, although there were also members who emerged as central and dedicated contributors to the online discussions across topics. Study findings highlight a number of areas for consideration by online support group developers and managers.
A time-delay neural network for solving time-dependent shortest path problem.
Huang, Wei; Yan, Chunwang; Wang, Jinsong; Wang, Wei
2017-06-01
This paper concerns the time-dependent shortest path problem, which is difficult to come up with global optimal solution by means of classical shortest path approaches such as Dijkstra, and pulse-coupled neural network (PCNN). In this study, we propose a time-delay neural network (TDNN) framework that comes with the globally optimal solution when solving the time-dependent shortest path problem. The underlying idea of TDNN comes from the following mechanism: the shortest path depends on the earliest auto-wave (from start node) that arrives at the destination node. In the design of TDNN, each node on a network is considered as a neuron, which comes in the form of two units: time-window unit and auto-wave unit. Time-window unit is used to generate auto-wave in each time window, while auto-wave unit is exploited here to update the state of auto-wave. Whether or not an auto-wave leaves a node (neuron) depends on the state of auto-wave. The evaluation of the performance of the proposed approach was carried out based on online public Cordeau instances and New York Road instances. The proposed TDNN was also compared with the quality of classical approaches such as Dijkstra and PCNN. Copyright © 2017 Elsevier Ltd. All rights reserved.
Fairness problems at the media access level for high-speed networks
Maly, Kurt J.; Zhang, L.; Game, David
1990-01-01
Most lower speed (approx. 10 Mbps) local area networks use adaptive or random access protocols like Ethernet. Others at higher speed use demand assignment like token or slotted rings. These include Cambridge ring and electronic token ring systems. Fairness issues in representatives of such protocols are discussed. In particular, Fiber Distributed Data Interface (FDDI) was selected as a demand access protocol using tokens, Carrier Sensed Multiple Access/Ring Network (CSMA/RN) a random access protocol, and Distributed Queue Dual Bus (DQDB) a demand access protocol using reservations. Fairness at the media access level was the focus, i.e., attaining access or being excessively delayed when a message is queued to be sent as a function of network location. Within that framework, the essential fairness of FDDI was observed along with severe fairness problems in DQDB and some problems for CSMA/RN. Several modifications were investigated and their ameliorative effect is shown. Finally, a unified presentation which allows comparisons of the three protocols' fairness when normalized to their capacity is given.
Modeling and forecasting residential loads as probabilistic currents for LV network design
Energy Technology Data Exchange (ETDEWEB)
Herman, R.; Gaunt, C.T. [Cape Town Univ., Rondebosch (South Africa)
2007-07-01
This paper presented different approaches to the design of low voltage (LV) electrical distribution systems found in North America and Europe. Systems based on the European approach have long LV feeders that require careful consideration at the design stage. The common basic principle of the 2 different systems is the uncertainty associated with customer loads due to their stochastic behaviour. The most important criterion in conductor sizing is the estimation of the design loads. This comparative study of various deterministic design procedures was conducted in response to concerns regarding the validity and accuracy of design calculations. The study involved extensive residential load modeling and probabilistic design methods. It described how the Beta probability density function (PDF) is used in South Africa to describe the statistical properties of residential loads. The Beta parameters may be readily applied in voltage design calculations. The study demonstrated how these load parameters may be derived in other developing countries from a typical customer group after-diversity-maximum demand (ADMD) survey. The Herman Beta algorithm can be used to calculate feeder voltage drop, given the relationship between ADMD and the Beta load parameters .8 refs., 5 figs.
Directory of Open Access Journals (Sweden)
Madleňák Radovan
2016-09-01
Full Text Available The article deals with the optimizing the postal transportation network with two different optimizing methods. The research adopted in this article uses allocation models within graph theory to obtain results for addressed optimization problem. The article presents and compares two types of these models: p-median and uncapacitated fixed charge facility location model. The aim of p-median model is to find the location of P facilities in network, serving all demands in a way ensuring the average transport cost to be minimal. Fixed charge location model approach the issue of facility location based on minimizing the overall costs of implementation of selected variants. The latter this two models are subsequently applied on the postal network to determine the optimal location of postal facilities. These two models are adopted in the condition of large country with area above 300 000 km2. The Italy was chosen as a typical country that fits this condition. The underlying infrastructure of Italy is represented by simplified model of a postal network, abstracted by a graph G = (V, E, c, w.
Mikami, Amori Yee; Szwedo, David E.; Allen, Joseph P.; Evans, Meredyth A.; Hare, Amanda L.
2010-01-01
This study examined online communication on social networking web pages in a longitudinal sample of 92 youths (39 male, 53 female). Participants' social and behavioral adjustment was assessed when they were ages 13–14 years and again at ages 20–22 years. At ages 20–22 years, participants' social networking website use and indicators of friendship quality on their web pages were coded by observers. Results suggested that youths who had been better adjusted at ages 13–14 years were more likely to be using social networking web pages at ages 20–22 years, after statistically controlling for age, gender, ethnicity, and parental income. Overall, youths' patterns of peer relationships, friendship quality, and behavioral adjustment at ages 13–14 years and at ages 20–22 years predicted similar qualities of interaction and problem behavior on their social networking websites at ages 20–22 years. Findings are consistent with developmental theory asserting that youths display cross-situational continuity in their social behaviors and suggest that the conceptualization of continuity may be extended into the online domain. PMID:20053005
Diniş, C. M.; Cunţan, C. D.; Rob, R. O. S.; Popa, G. N.
2018-01-01
The paper presents the analysis of a power factor with capacitors banks, without series coils, used for improving power factor for a three-phase and single-phase inductive loads. In the experimental measurements, to improve the power factor, the Roederstein ESTAmat RPR power factor controller can command up to twelve capacitors banks, while experimenting using only six capacitors banks. Six delta capacitors banks with approximately equal reactive powers were used for experimentation. The experimental measurements were carried out with a three-phase power quality analyser which worked in three cases: a case without a controller with all capacitors banks permanently parallel connected with network, and two other cases with power factor controller (one with setting power factor at 0.92 and the other one at 1). When performing experiments with the power factor controller, a current transformer was used to measure the current on one phase (at a more charged or less loaded phase).
Directory of Open Access Journals (Sweden)
R. Rajakumar
2017-01-01
Full Text Available Seyedali Mirjalili et al. (2014 introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO. This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO and modified bat algorithm (MBA. The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.
Directory of Open Access Journals (Sweden)
Wahyudi Sutopo
2016-12-01
Full Text Available In recent years, the rising competitive environment with shorter product life cycles and high customization forces industries to increase their flexibility, speed up their response, and enhance concurrent engineering designs. To integrate these prospects, supply chain collaboration becomes a pertinent strategy for industries to strengthen their competitiveness. The network design problem is used to implement supply chain collaboration. In the buying and selling process, sharing information between buyer and supplier are important to obtain a transaction decision. The optimimum supply chain profit can be identified by mathematical model of network design problem. The Mathematical Model takes into consideration the uncertainity in negotiation of supply chain, transportation problems, and locationallocation of products from supplier to buyer in the planning based on the time value of money. The results show that the model can be used to optimize the supply chain profit. The supplier gets a profit because income were received in the initial contract, while the buyer profit comes from lower pay.
GNU Oflox: an academic software for the minimal cost network flow problem
Directory of Open Access Journals (Sweden)
Andrés M. Sajo-Castelli
2013-07-01
Full Text Available We present an open-source software package written for GNU Octave. The software is an implementation of the Simplex algorithm for the minimal cost network flow problem oriented towards the academic environment. The implementation supports the use of Big-M and Phase I/Phase II methods and it can also start from a given feasible solution. Flexibility of the package's output configuration provides many attractive possibilities. The outputs are plain editable \\LaTeX\\ files that can be modified and orchestrated to fit most academic needs. It can be used in examination materials, homework assignments or even form part of a project. The format used to describe the network is the DIMACS min file format to which a simple extension was added in order to support the description of feasible trees in the file.
INTERCONNECTING NETWORKS WITH DIFFERENT LEVELS OF SECURITY – A PRESENT NATO PROBLEM
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LIVIU TATOMIR
2016-07-01
Full Text Available A situation often met in the Romanian Armed Forces in recent years is the need for interconnecting two networks (domains with different levels of classification. Considering that the Romanian armed troops are involved in numerous missions with NATO partners, solutions, already implemented across the organization, are considered to be applied in domestic systems, also. This paper presents the solutions adopted by NATO in order to solve the problem of cross -domains interconnections. We present the maturity level reached by these solutions and the possibility of implementing these solutions in the Romanian Armed Forces, with or without specific adaptation to our own rules and regulations. The goal is to use a NATO already proved solution to our national classified networks.
Application of Set Covering Location Problem for Organizing the Public Postal Network
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Dragana Šarac
2016-08-01
Full Text Available Most countries of the European Union ensure certain obligations (criteria which universal service providers must meet to ensure the realization of the universal service. These criteria vary from country to country, giving their own choice of an optimal model for the density of the postal network. Such postal network of the operator providing universal postal service must be organized so that post offices are accessible at the optimal distance from the user. This paper presents two different approaches. The first one is based on the population criteria determined in the previous study. The second one is new, a general method created to determine the minimum number of postal unit applications of Set Covering Location Problem. The authors apply both methods on real data collected from the Serbian municipalities and finally, compare the obtained results.
An Integrated Approach for Reliable Facility Location/Network Design Problem with Link Disruption
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Davood Shishebori
2015-05-01
Full Text Available Proposing a robust designed facility location is one of the most effective ways to hedge against unexpected disruptions and failures in a transportation network system. This paper considers the combined facility location/network design problem with regard to transportation link disruptions and develops a mixed integer linear programming formulation to model it. With respect to the probability of link disruptions, the objective function of the model minimizes the total costs, including location costs, link construction costs and also the expected transportation costs. An efficient hybrid algorithm based on LP relaxation and variable neighbourhood search metaheuristic is developed in order to solve the mathematical model. Numerical results demonstrate that the proposed hybrid algorithm has suitable efficiency in terms of duration of solution time and determining excellent solution quality.
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Renuga Verayiah
2016-12-01
Full Text Available Existing power systems are significantly susceptible to voltage instability problem since such systems are stressed with the huge power transfers across the grids. Various power tracing techniques have been developed but are limited to the application of transmission service pricing in a deregulated environment. This paper presents a novel approach which adopts the power tracing theory for voltage stability improvement via the development of reactive power tracing capable index, named as LQP_LT. The index is tested in IEEE 14 Test Bus System in various contingency states and comparison were made using the results obtained from the industrial graded software PSS/E in evaluating the critical transmission lines in severe contingencies. The LQP_LT index is found to be effective in determining the weak load buses in a transmission system which ultimately responsible to cause stressed lines and overall voltage instability in a system.
Gujarathi, Ashish M.; Purohit, S.; Srikanth, B.
2015-06-01
Detailed working principle of jumping gene adaptation of differential evolution (DE-JGa) is presented. The performance of the DE-JGa algorithm is compared with the performance of differential evolution (DE) and modified DE (MDE) by applying these algorithms on industrial problems. In this study Reactor network design (RND) problem is solved using DE, MDE, and DE-JGa algorithms: These industrial processes are highly nonlinear and complex with reference to optimal operating conditions with many equality and inequality constraints. Extensive computational comparisons have been made for all the chemical engineering problems considered. The results obtained in the present study show that DE-JGa algorithm outperforms the other algorithms (DE and MDE). Several comparisons are made among the algorithms with regard to the number of function evaluations (NFE)/CPU- time required to find the global optimum. The standard deviation and the variance values obtained using DE-JGa, DE and MDE algorithms also show that the DE-JGa algorithm gives consistent set of results for the majority of the test problems and the industrial real world problems.
Melvin Ballera; Ismail Ateya Lukandu; Abdalla Radwan
2013-01-01
This paper examines the use of social network media at three aspects in African and Libyan perspective. Firstly, to use social network media as an open network learning environment that provide service for interaction necessary for learners to support socialization and collaboration during problem solving. Secondly, to use social media as a tool to support blended learning in e-learning system and encourage non-native English students to express their ideas and fill the gap of communication p...
Hsu, Ching-Kun; Hwang, Gwo-Jen; Chuang, Chien-Wen; Chang, Chih-Kai
2012-01-01
Owing to the popularity of computers and computer networks, fostering the web-based problem-solving ability of students has become an important educational objective in recent years. This study attempted to compare the effects of using selected and open network resources on students' intentions with regard to their information system usage by…
Load Balancing Metric with Diversity for Energy Efficient Routing in Wireless Sensor Networks
DEFF Research Database (Denmark)
Moad, Sofiane; Hansen, Morten Tranberg; Jurdak, Raja
2011-01-01
The expected number of transmission (ETX) represents a routing metric that considers the highly variable link qualities for a specific radio in Wireless Sensor Networks (WSNs). To adapt to these differences, radio diversity is a recently explored solution for WSNs. In this paper, we propose...... an energy balancing metric which explores the diversity in link qualities present at different radios. The goal is to effectively use the energy of the network and therefore extend the network lifetime. The proposed metric takes into account the transmission and reception costs for a specific radio in order...... to choose an energy efficient radio. In addition, the metric uses the remaining energy of nodes in order to regulate the traffic so that critical nodes are avoided. We show by simulations that our metric can improve the network lifetime up to 20%....
Achieving full connectivity of sites in the multiperiod reserve network design problem
Jafari, Nahid; Nuse, Bryan L.; Moore, Clinton; Dilkina, Bistra; Hepinstall-Cymerman, Jeffrey
2017-01-01
The conservation reserve design problem is a challenge to solve because of the spatial and temporal nature of the problem, uncertainties in the decision process, and the possibility of alternative conservation actions for any given land parcel. Conservation agencies tasked with reserve design may benefit from a dynamic decision system that provides tactical guidance for short-term decision opportunities while maintaining focus on a long-term objective of assembling the best set of protected areas possible. To plan cost-effective conservation over time under time-varying action costs and budget, we propose a multi-period mixed integer programming model for the budget-constrained selection of fully connected sites. The objective is to maximize a summed conservation value over all network parcels at the end of the planning horizon. The originality of this work is in achieving full spatial connectivity of the selected sites during the schedule of conservation actions.
A Branch-and-Price Approach to the Feeder Network Design Problem
DEFF Research Database (Denmark)
Santini, Alberto; Plum, Christian Edinger Munk; Røpke, Stefan
2017-01-01
In this paper we consider the problem of designing a container liner shipping feeder network. The designer has to choose which port to serve during many rotations that start and end at a central hub. Many operational characteristics are considered, such as variable leg-by-leg speeds and cargo tra....... These insights were obtained by means of an analysis where scenarios are generated varying internal and external conditions, such as fuel costs and port demands....... transit times. Realistic instances are generated from the LinerLib benchmark suite. The problem is solved with a branch-and-price algorithm, which can solve most instances to optimality within one hour. The results also provide insights on the cost structure and desirable features of optimal routes...
Fernández Caballero, Juan Carlos; Martínez, Francisco José; Hervás, César; Gutiérrez, Pedro Antonio
2010-05-01
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.
Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems
Fonseca Guerra, Gabriel A.; Furber, Steve B.
2017-01-01
Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.
Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems
Directory of Open Access Journals (Sweden)
Gabriel A. Fonseca Guerra
2017-12-01
Full Text Available Constraint satisfaction problems (CSP are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems. The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs, and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart.
Problem of Channel Utilization and Merging Flows in Buffered Optical Burst Switching Networks
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Milos Kozak
2013-01-01
Full Text Available In the paper authors verify two problems of methods of operational research in optical burst switching. The first problem is at edge node, related to the medium access delay. The second problem is at an intermediate node related to buffering delay. A correction coefficient K of transmission speed is obtained from the first analysis. It is used in to provide a full-featured link of nominal data rate. Simulations of the second problem reveal interesting results. It is not viable to prepare routing and wavelength assignment based on end-to-end delay, i.e. link's length or number of hops, as commonly used in other frameworks (OCS, Ethernet, IP, etc. nowadays. Other parameters such as buffering probability must be taken into consideration as well. Based on the buffering probability an estimation of the number of optical/electrical converters can be made. This paper concentrates important traffic constraints of buffered optical burst switching. It allows authors to prepare optimization algorithms for regenerators placement in CAROBS networks using methods of operational research.
Directory of Open Access Journals (Sweden)
Mi Gan
2014-01-01
Full Text Available The multiproduct two-layer supply chain is very common in various industries. In this paper, we introduce a possible modeling and algorithms to solve a multiproduct two-layer supply chain network design problem. The decisions involved are the DCs location and capacity design decision and the initial distribution planning decision. First we describe the problem and give a mixed integer programming (MIP model; such problem is NP-hard and it is not easy to reduce the complexity. Inspired by it, we develop a transformation mechanism of relaxing the fixed cost and adding some virtual nodes and arcs to the original network. Thus, a network flow problem (NFP corresponding to the original problem has been formulated. Given that we could solve the NFP as a minimal cost flow problem. The solution procedures and network simplex algorithm (INS are discussed. To verify the effectiveness and efficiency of the model and algorithms, the performance measure experimental has been conducted. The experiments and result showed that comparing with MIP model solved by genetic algorithm (GA and Benders, decomposition algorithm (BD the NFP model and INS are also effective and even more efficient for both small-scale and large-scale problems.
AN EVOLUTIONARY ALGORITHM FOR CHANNEL ASSIGNMENT PROBLEM IN WIRELESS MOBILE NETWORKS
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Yee Shin Chia
2012-12-01
Full Text Available The channel assignment problem in wireless mobile network is the assignment of appropriate frequency spectrum to incoming calls while maintaining a satisfactory level of electromagnetic compatibility (EMC constraints. An effective channel assignment strategy is important due to the limited capacity of frequency spectrum in wireless mobile network. Most of the existing channel assignment strategies are based on deterministic methods. In this paper, an adaptive genetic algorithm (GA based channel assignment strategy is introduced for resource management and to reduce the effect of EMC interferences. The most significant advantage of the proposed optimization method is its capability to handle both the reassignment of channels for existing calls as well as the allocation of channel to a new incoming call in an adaptive process to maximize the utility of the limited resources. It is capable to adapt the population size to the number of eligible channels for a particular cell upon new call arrivals to achieve reasonable convergence speed. The MATLAB simulation on a 49-cells network model for both uniform and nonuniform call traffic distributions showed that the proposed channel optimization method can always achieve a lower average new incoming call blocking probability compared to the deterministic based channel assignment strategy.
Zhang, Yin; Wei, Zhiyuan; Zhang, Yinping; Wang, Xin
2017-12-01
Urban heating in northern China accounts for 40% of total building energy usage. In central heating systems, heat is often transferred from heat source to users by the heat network where several heat exchangers are installed at heat source, substations and terminals respectively. For given overall heating capacity and heat source temperature, increasing the terminal fluid temperature is an effective way to improve the thermal performance of such cascade heat exchange network for energy saving. In this paper, the mathematical optimization model of the cascade heat exchange network with three-stage heat exchangers in series is established. Aim at maximizing the cold fluid temperature for given hot fluid temperature and overall heating capacity, the optimal heat exchange area distribution and the medium fluids' flow rates are determined through inverse problem and variation method. The preliminary results show that the heat exchange areas should be distributed equally for each heat exchanger. It also indicates that in order to improve the thermal performance of the whole system, more heat exchange areas should be allocated to the heat exchanger where flow rate difference between two fluids is relatively small. This work is important for guiding the optimization design of practical cascade heating systems.
Sampasa-Kanyinga, H; Hamilton, H A
2015-11-01
Previous research has suggested an association between the use of social networking sites (SNSs) and mental health problems such as psychological distress, suicidal ideation and attempts in adolescents. However, little is known about the factors that might mediate these relationships. The present study examined the link between the use of social networking sites and psychological distress, suicidal ideation and suicide attempts, and tested the mediating role of cyberbullying victimization on these associations in adolescents. The sample consisted of a group of 11-to-20-year-old individuals (n=5126, 48% females; mean±SD age: 15.2±1.9 years) who completed the mental health portion of the Ontario Student Drug Use and Health Survey (OSDUHS) in 2013. Multiple logistic regression analyses were used to test the mediation models. After adjustment for age, sex, ethnicity, subjective socioeconomic status (SES), and parental education, use of SNSs was associated with psychological distress (adjusted odds ratio, 95% confidence interval=2.03, 1.22-3.37), suicidal ideation (3.44, 1.54-7.66) and attempts (5.10, 1.45-17.88). Cyberbullying victimization was found to fully mediate the relationships between the use of SNSs with psychological distress and attempts; whereas, it partially mediated the link between the use of SNSs and suicidal ideation. Findings provide supporting evidence that addressing cyberbullying victimization and the use of SNSs among adolescents may help reduce the risk of mental health problems. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
An MPCC Formulation and Its Smooth Solution Algorithm for Continuous Network Design Problem
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Guangmin Wang
2017-12-01
Full Text Available Continuous network design problem (CNDP is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC by describing UE as a non-linear complementarity problem (NCP. To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.
Directory of Open Access Journals (Sweden)
Yun Wang
2017-02-01
Full Text Available To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS is introduced to construct a CPS in Power Systems (Power CPS. The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is presented to reduce model complexity and improve calculation speed. The effectiveness of all the models and methods are demonstrated in the case study.
Directory of Open Access Journals (Sweden)
Руслан Володимирович Власенко
2016-07-01
Full Text Available Electricity quality improving is extremely relevant nowadays. With such industrial loads as induction motors, induction furnaces, welding machines, controlled or uncontrolled rectifiers, frequency converters and others reactive power, harmonics and unbalance are generated in power grid. Reactive power, higher harmonic currents and asymmetry loads influence the functioning of electric devices and electrical mains. An effective technical solution is the use of new compensating devices, that is active power filters. The emergence of consumers with a unit capacity of four wire networks requires a new approach to building system control active power filter. When designing the active power filter control system the current flowing in the neutral wire must be taken into account. To assess the power balance in the four wire active power filter, scientists have proposed to apply pqr theory of power based on the Clarke transformation. There are different topologies of three-phase four wire active power filters. A visual simulation of Matlab / Simulink model with an active power filter based on pqr theory of power has been created. A method of pulse width modulation with four control channels was used as pulses forming systems with transistor keys. Operating conditions of three-phase four wire active power filter with asymmetry, non-sinosoidal voltage source and asymmetric load have been studied. The correction taking into account the means improving the active power filter has been offered as pqr theory of power does not take into account non-sinosoidal voltage
Gaaloul, Fakhreddine
2013-05-01
This paper proposes adequate methods to improve the interference mitigation capability of a recently investigated switched-based interference reduction scheme for single downlink channel assignment in over-loaded small-cell networks. The model assumes that the available orthogonal channels for small cells are distributed among access points in close vicinity, where each access point knows its allocated channels a priori. Each cell has a single antenna, employs the open access strategy, and can reuse its allocated channels simultaneously, while scheduling concurrent service requests. Moreover, the access points can not coordinate their transmissions, and can receive limited feedback from active users. The paper presents low-complexity schemes to identify a suitable channel to serve the scheduled user by maintaining the interference power level within a tolerable range. They attempt to either complement the switched-based scheme by minimum interference channel selection or adopt different interference thresholds on available channels, while reducing the channel examination load. The optimal thresholds for interference mitigation at the desired receive station are quantified for various performance criteria. The performance and processing load of the proposed schemes are obtained analytically, and then compared to those of the single-threshold scheme via numerical and simulation results. © 2002-2012 IEEE.
Liang, Zhen; Li, Bin; Huang, Mo; Zheng, Yanqi; Ye, Hui; Xu, Ken; Deng, Fangming
2017-04-19
In this work, a low cost Bluetooth Low Energy (BLE) transceiver for wireless sensor network (WSN) applications, with a receiver (RX)-matching network-reusing power amplifier (PA) load inductor, is presented. In order to decrease the die area, only two inductors were used in this work. Besides the one used in the voltage control oscillator (VCO), the PA load inductor was reused as the RX impedance matching component in the front-end. Proper controls have been applied to achieve high transmitter (TX) input impedance when the transceiver is in the receiving mode, and vice versa. This allows the TRX-switch/matching network integration without significant performance degradation. The RX adopted a low-IF structure and integrated a single-ended low noise amplifier (LNA), a current bleeding mixer, a 4th complex filter and a delta-sigma continuous time (CT) analog-to-digital converter (ADC). The TX employed a two-point PLL-based architecture with a non-linear PA. The RX achieved a sensitivity of -93 dBm and consumes 9.7 mW, while the TX achieved a 2.97% error vector magnitude (EVM) with 9.4 mW at 0 dBm output power. This design was fabricated in a 0.11 μm complementary metal oxide semiconductor (CMOS) technology and the front-end circuit only occupies 0.24 mm². The measurement results verify the effectiveness and applicability of the proposed BLE transceiver for WSN applications.
Directory of Open Access Journals (Sweden)
Tao Jia
2014-01-01
Full Text Available We investigate an integrated inventory routing problem (IRP in which one supplier with limited production capacity distributes a single item to a set of retailers using homogeneous vehicles. In the objective function we consider a loading cost which is often neglected in previous research. Considering the deterioration in the products, we set a soft time window during the transportation stage and a hard time window during the sales stage, and to prevent jams and waiting cost, the time interval of two successive vehicles returning to the supplier’s facilities is required not to be overly short. Combining all of these factors, a two-echelon supply chain mixed integer programming model under discrete time is proposed, and a two-phase algorithm is developed. The first phase uses tabu search to obtain the retailers’ ordering matrix. The second phase is to generate production scheduling and distribution routing, adopting a saving algorithm and a neighbourhood search, respectively. Computational experiments are conducted to illustrate the effectiveness of the proposed model and algorithm.
Application of high-resolution domestic electricity load profiles in network modelling
DEFF Research Database (Denmark)
Marszal, Anna Joanna; Mendaza, Iker Diaz de Cerio; Heiselberg, Per Kvols
2016-01-01
-mentioned model, as they are closely related to the thermal properties of a building. Therefore, two type of single family houses equipped with heat pump are simulated in EnergyPlus with 1-minute time step. The PV generation profile is obtained from a model developed in Matlab environment. In the second part...... the particularities of electricity demand and on-site generation, e.g. the short-term spikes due use of high electricity consumption appliances such like electric kettle, and get a full picture of network performance, a high-resolution input data are needed. This paper compares the business-as-usual network modeling...... with modeling when 1-minute domestic electricity demand and generation profiles are used as inputs. The analysis is done with a case study of low-voltage network located in Northern Denmark. The analysis includes two parts. The first part focuses on modeling the domestic demands and on-site generation in 1...
Hattam, Laura
2016-01-01
In the near future various types of low-carbon technologies (LCTs) are expected to be widely employed throughout the United Kingdom. However, the effect that these technologies will have at a household level on the existing low voltage (LV) network is still an area of extensive research. We propose an agent based model that estimates the growth of LCTs within local neighbourhoods, where social influence is imposed. Real-life data from a LV network is used that comprises of many socially diverse neighbourhoods. Both electric vehicle uptake and the combined scenario of electric vehicle and photovoltaic adoption are investigated with this data. A probabilistic approach is outlined, which determines lower and upper bounds for the model response at every neighbourhood. This technique is used to assess the implications of modifying model assumptions and introducing new model features. Moreover, we discuss how the calculation of these bounds can inform future network planning decisions.
A novel constructive-optimizer neural network for the traveling salesman problem.
Saadatmand-Tarzjan, Mahdi; Khademi, Morteza; Akbarzadeh-T, Mohammad-R; Moghaddam, Hamid Abrishami
2007-08-01
In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.
Directory of Open Access Journals (Sweden)
Hai-Ling Bi
2016-01-01
Full Text Available Hubs disruptions are taken into account in design of a resilient power projection network. The problem is tackled from a multiple criteria decision-making (MCDM perspective. Not only the network cost in normal state is considered, but also the cost in the worst-case situation is taken into account. A biobjective and trilevel integer programming model is proposed using game theory. Moreover, we develop a metaheuristic based on tabu search and shortest path algorithm for the resolution of the complex model. Computational example indicates that making tradeoffs between the performances of the network in different situations is helpful for designing a resilient network.
Integrating lv network models and load-flow calculations into smart grid planning
Hoogsteen, Gerwin; Molderink, Albert; Bakker, Vincent; Smit, Gerardus Johannes Maria
2013-01-01
Increasing energy prices and the greenhouse effect demand a more efficient supply of energy. More residents start to install their own energy generation sources such as photovoltaic cells. The introduction of distributed generation in the low-voltage network can have effects that were unexpected
Cerebellum-inspired neural network solution of the inverse kinematics problem.
Asadi-Eydivand, Mitra; Ebadzadeh, Mohammad Mehdi; Solati-Hashjin, Mehran; Darlot, Christian; Abu Osman, Noor Azuan
2015-12-01
The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.
Solving ill-posed inverse problems using iterative deep neural networks
Adler, Jonas; Öktem, Ozan
2017-12-01
We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).
A framework to approach problems of forensic anthropology using complex networks
Caridi, Inés; Dorso, Claudio O.; Gallo, Pablo; Somigliana, Carlos
2011-05-01
We have developed a method to analyze and interpret emerging structures in a set of data which lacks some information. It has been conceived to be applied to the problem of getting information about people who disappeared in the Argentine state of Tucumán from 1974 to 1981. Even if the military dictatorship formally started in Argentina had begun in 1976 and lasted until 1983, the disappearance and assassination of people began some months earlier. During this period several circuits of Illegal Detention Centres (IDC) were set up in different locations all over the country. In these secret centres, disappeared people were illegally kept without any sort of constitutional guarantees, and later assassinated. Even today, the final destination of most of the disappeared people’s remains is still unknown. The fundamental hypothesis in this work is that a group of people with the same political affiliation whose disappearances were closely related in time and space shared the same place of captivity (the same IDC or circuit of IDCs). This hypothesis makes sense when applied to the systematic method of repression and disappearances which was actually launched in Tucumán, Argentina (2007) [11]. In this work, the missing individuals are identified as nodes on a network and connections are established among them based on the individuals’ attributes while they were alive, by using rules to link them. In order to determine which rules are the most effective in defining the network, we use other kind of knowledge available in this problem: previous results from the anthropological point of view (based on other sources of information, both oral and written, historical and anthropological data, etc.); and information about the place (one or more IDCs) where some people were kept during their captivity. For these best rules, a prediction about these people’s possible destination is assigned (one or more IDCs where they could have been kept), and the success of the
Distribution load estimation (DLE)
Energy Technology Data Exchange (ETDEWEB)
Seppaelae, A.; Lehtonen, M. [VTT Energy, Espoo (Finland)
1998-08-01
The load research has produced customer class load models to convert the customers` annual energy consumption to hourly load values. The reliability of load models applied from a nation-wide sample is limited in any specific network because many local circumstances are different from utility to utility and time to time. Therefore there is a need to find improvements to the load models or, in general, improvements to the load estimates. In Distribution Load Estimation (DLE) the measurements from the network are utilized to improve the customer class load models. The results of DLE will be new load models that better correspond to the loading of the distribution network but are still close to the original load models obtained by load research. The principal data flow of DLE is presented
A numerical modeling of nonlinear load behavior using artificial neural networks
Panoiu, Manuela; Ghiormez, Loredana; Panoiu, Caius; Iordan, Anca
2013-10-01
In this paper it is performed a numerical study of the voltage-current characteristic of an electric arc. To predict voltages and currents values, a multi-layer perceptron Artificial Neural Networks was used under the Matlab 2012 environment. The study is based on actual recorded data obtained from a 100 tones AC Electric Arc Furnace. Results obtained by simulation are compared with the measured one.
Export dynamics as an optimal growth problem in the network of global economy
Caraglio, Michele; Stella, Attilio L
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the cali...
On Application of Least-delay Variation Problem in Ethernet Networks Using SDN Concept
Directory of Open Access Journals (Sweden)
Tomas Hegr
2016-01-01
Full Text Available The goal of this paper is to present an application idea of SDN in Smart Grids, particularly, in the area of L2 multicast as defined by IEC 61850-9-2. Authors propose an Integer Linear Formulation (ILP dealing with a Least-Delay-Variation multicast forwarding problem that has a potential to utilize Ethernet networks in a new way. The proposed ILP formulation is numerically evaluated on random graph topologies and results are compared to a shortest path tree approach that is traditionally a product of Spanning Tree Protocols. Results confirm the correctness of the ILP formulation and illustrate dependency of a solution quality on the selected graph models, especially, in a case of scale-free topologies.
Energy Technology Data Exchange (ETDEWEB)
Carlson, K.E.; Ransom, V.H.; Roth, P.A.
1987-03-01
The ATHENA (Advanced Thermal Hydraulic Energy Network Analyzer) code has been developed to perform transient simulation of the thermal hydraulic systems that may be found in fusion reactors, space reactors, and other advanced systems. As an assessment of current capability the code was applied to a number of physical problems, both conceptual and actual experiments. Results indicate that the numerical solution to the basic conservation equations is technically sound, and that generally good agreement can be obtained when modeling relevant hydrodynamic experiments. The assessment also demonstrates basic fusion system modeling capability and verifies compatibility of the code with both CDC and CRAY mainframes. Areas where improvements could be made include constitutive modeling, which describes the interfacial exchange term. 13 refs., 84 figs.
Jayawickreme, Nuwan; Mootoo, Candace; Fountain, Christine; Rasmussen, Andrew; Jayawickreme, Eranda; Bertuccio, Rebecca F
2017-10-01
A growing body of literature indicates that the mental distress experienced by survivors of war is a function of both experienced trauma and stressful life events. However, the majority of these studies are limited in that they 1) employ models of psychological distress that emphasize underlying latent constructs and do not allow researchers to examine the unique associations between particular symptoms and various stressors; and 2) use one or more measures that were not developed for that particular context and thus may exclude key traumas, stressful life events and symptoms of psychopathology. The current study addresses both these limitations by 1) using a novel conceptual model, network analysis, which assumes that symptoms covary with each other not because they stem from a latent construct, but rather because they represent meaningful relationships between the symptoms; and 2) employing a locally developed measure of experienced trauma, stressful life problems and symptoms of psychopathology. Over the course of 2009-2011, 337 survivors of the Sri Lankan civil war were administered the Penn-RESIST-Peradeniya War Problems Questionnaire (PRPWPQ). Network analysis revealed that symptoms of psychopathology, problems pertaining to lack of basic needs, and social problems were central to the network relative to experienced trauma and other types of problems. After controlling for shared associations, social problems in particular were the most central, significantly more so than traumatic events and family problems. Several particular traumatic events, stressful life events and symptoms of psychopathology that were central to the network were also identified. Discussion emphasizes the utility of such network models to researchers and practitioners determining how to spend limited resources in the most impactful way possible. Copyright © 2017 Elsevier Ltd. All rights reserved.
Tabu search methods for multicommodity capcitated fixed charge network design problem
Energy Technology Data Exchange (ETDEWEB)
Crainic, T.; Farvolden, J.; Gendreau, M.; Soriano, P.
1994-12-31
We address the fixed charge capacitated multicommodity network flow problem with linear costs and no additional side constraints, and present two solution approaches based on the tabu search metaheuristic. In the first case, the search is conducted by exploring the space of the design (integer) variables, while neighbors are evaluated and moves are selected via a capacitated multicommodity minimum cost network flow subproblem. The second approach integrates simplex pivoting rules into a tabu search framework. Here, the search explores the space of flow path variables when all design arcs are open, by using column generation to obtain new variables, and pivoting to determine and evaluate the neighbors of any given solution. Adapting this idea within our tabu search framework represents an interesting challenge, since several of the standard assumptions upon which column generation schemes are based are no longer verified. In particular, the monotonic decrease of the objective function value is no longer ensured, and both variable and fixed costs characterize arcs. On the other hand, the precise definition and generation of the tabu search neighborhoods in a column generation context poses an additional challenge, linked particularly to the description and identification of path variables. We describe the various components, implementation challenges and behaviour of each of the two algorithms, and compare their computational and solution quality performance. Comparisons with known bounding procedures will be presented as well.
Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko
2013-01-01
The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.
Osgood, D Wayne; Feinberg, Mark E; Ragan, Daniel T
2015-08-01
Seeking to reduce problematic peer influence is a prominent theme of programs to prevent adolescent problem behavior. To support the refinement of this aspect of prevention programming, we examined peer influence and selection processes for three problem behaviors (delinquency, alcohol use, and smoking). We assessed not only the overall strengths of these peer processes, but also their consistency versus variability across settings. We used dynamic stochastic actor-based models to analyze five waves of friendship network data across sixth through ninth grades for a large sample of U.S. adolescents. Our sample included two successive grade cohorts of youth in 26 school districts participating in the PROSPER study, yielding 51 longitudinal social networks based on respondents' friendship nominations. For all three self-reported antisocial behaviors, we found evidence of both peer influence and selection processes tied to antisocial behavior. There was little reliable variance in these processes across the networks, suggesting that the statistical imprecision of the peer influence and selection estimates in previous studies likely accounts for inconsistencies in results. Adolescent friendship networks play a strong role in shaping problem behavior, but problem behaviors also inform friendship choices. In addition to preferring friends with similar levels of problem behavior, adolescents tend to choose friends who engage in problem behaviors, thus creating broader diffusion.
Radaydeh, Redha Mahmoud
2013-06-01
This paper proposes a reduced-complexity downlink multi-channel assignment scheme when feedback links are capacity-limited. The system model treats the case when multiple access points are allocated to serve scheduled users in over-loaded (i.e. dense) pico/femtocell networks. It assumes that the deployed access points can be shared simultaneously and employ isotropic antenna arrays of arbitrary sizes. Moreover, they transmit their data on a common physical channel and can not coordinate their transmissions. On the other hand, each scheduled user can be served by single transmit channel from each active access point at a time, and it lacks coordination with concurrent active users. The scheme operates according to the occupancy of available transmit channels, wherein extensively occupied access points are avoided adaptively, while reducing the load of processing. The operation is linked to a target performance via controlling the observed aggregate interference from the projected set of serving points. Through the analysis, results for the scheduled user outage performance, and the average number of active access points are presented. Numerical and simulations studies clarify the gains of the proposed scheme for different operating conditions. © 2013 IEEE.
Directory of Open Access Journals (Sweden)
Jaime Lloret
2013-08-01
Full Text Available Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.
Directory of Open Access Journals (Sweden)
Yun-Hao Li
2015-12-01
Full Text Available This paper presents a flexible transmission network expansion planning (TNEP approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.
Walker, Sandra; Kennedy, Anne; Vassilev, Ivaylo; Rogers, Anne
2017-10-10
Social network processes impact on the genesis and management of mental health problems. There is currently less understanding of the way people negotiate networked relationships in times of crisis compared to how they manage at other times. This paper explores the patterns and nature of personal network involvement at times of crises and how these may differ from day-to-day networks of recovery and maintenance. Semi-structured interviews with 25 participants with a diagnosis of long-term mental health (MH) problems drawn from recovery settings in the south of England. Interviews centred on personal network mapping of members and resources providing support. The mapping interviews explored the work of network members and changes in times of crisis. Interviews were recorded, transcribed and analysed using a framework analysis. Three key themes were identified: the fluidity of network relationality between crisis and recovery; isolation as a means of crises management; leaning towards peer support. Personal network input retreated at times of crisis often as result of "ejection" from the network by participants who used self-isolation as a personal management strategy in an attempt to deal with crises. Peer support is considered useful during a crisis, whilst the role of services was viewed with some ambiguity. Social networks membership, and type and depth of involvement, is subject to change between times of crisis and everyday support. This has implications for managing mental health in terms of engaging with network support differently in times of crises versus recovery and everyday living. © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.
Traveling salesman problems with PageRank Distance on complex networks reveal community structure
Jiang, Zhongzhou; Liu, Jing; Wang, Shuai
2016-12-01
In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.
Directory of Open Access Journals (Sweden)
M.A. Murray–Lasso.
2010-01-01
Full Text Available The connection matrix of oriented graphs and a generalization introduced by Gondran and Minoux to solve a great variety of path problems, including various optimization problems (maximize or minimize lengths, minimum capacity, probability, etc., ennumeration of paths, path counting, and connection. To achieve this the matrix components are treated as elements of an algebraic structure called semiring or diod (an extension of a monoid. The possibilities of using MATLAB for handling the matrices are explored and listings of educational programs (not for production runs are provided. The purpose is to rescue a topic which has not become very popular due, in the authors opinion, to the fact that the originators Gondran and Minoux (Ref. 3 have treated the topic in a very abstract manner, oriented to mathematicians and difficult to grasp by engineers. In this article the topics are treated informally and illustrative examples are given (something that Ref. 3 does not provide in great detail as well as listings in the MATLAB language. The topic is ammenable to extensions and it is possible to design educational computerized projects for learning important network topics with very wide applications.
Duan, Peibo; Zhang, Changsheng; Mao, Guoqiang; Zhang, Bin
2017-09-22
User association has emerged as a distributed resource allocation problem in the heterogeneous networks (HetNets). Although an approximate solution is obtainable using the approaches like combinatorial optimization and game theory-based schemes, these techniques can be easily trapped in local optima. Furthermore, the lack of exploring the relation between the quality of the solution and the parameters in the HetNet [e.g., the number of users and base stations (BSs)], at what levels, impairs the practicability of deploying these approaches in a real world environment. To address these issues, this paper investigates how to model the problem as a distributed constraint optimization problem (DCOP) from the point of the view of the multiagent system. More specifically, we develop two models named each connection as variable (ECAV) and each BS and user as variable (EBUAV). Hereinafter, we propose a DCOP solver which not only sets up the model in a distributed way but also enables us to efficiently obtain the solution by means of a complete DCOP algorithm based on distributed message-passing. Naturally, both theoretical analysis and simulation show that different qualitative solutions can be obtained in terms of an introduced parameter η which has a close relation with the parameters in the HetNet. It is also apparent that there is 6% improvement on the throughput by the DCOP solver comparing with other counterparts when η=3. Particularly, it demonstrates up to 18% increase in the ability to make BSs service more users when the number of users is above 200 while the available resource blocks (RBs) are limited. In addition, it appears that the distribution of RBs allocated to users by BSs is better with the variation of the volume of RBs at the macro BS.
Possel, B.; Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.
2016-01-01
Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types
Directory of Open Access Journals (Sweden)
Ahmet Kuzu
2014-01-01
Full Text Available This paper proposes two novel master-slave configurations that provide improvements in both control and communication aspects of teleoperation systems to achieve an overall improved performance in position control. The proposed novel master-slave configurations integrate modular control and communication approaches, consisting of a delay regulator to address problems related to variable network delay common to such systems, and a model tracking control that runs on the slave side for the compensation of uncertainties and model mismatch on the slave side. One of the configurations uses a sliding mode observer and the other one uses a modified Smith predictor scheme on the master side to ensure position transparency between the master and slave, while reference tracking of the slave is ensured by a proportional-differentiator type controller in both configurations. Experiments conducted for the networked position control of a single-link arm under system uncertainties and randomly varying network delays demonstrate significant performance improvements with both configurations over the past literature.
Zainudin, Suhaila; Arif, Shereena M.
2017-01-01
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767
Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M
2017-01-01
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Directory of Open Access Journals (Sweden)
Faridah Hani Mohamed Salleh
2017-01-01
Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Budinich, M
1996-02-15
Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.
Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems
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Luis Hernández
2014-03-01
Full Text Available The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
Directory of Open Access Journals (Sweden)
Chen Ying Jie
2016-01-01
Full Text Available In this paper, with the principle of least action with variables to solve the problems of forced vibration of the Rectangular plate with three clamped and the other free with concentrated load, and the stable solution can be worked out. We can compare the results with the literate; it also can be proved to be true. So the results by calculating not only it have important academic value, but also it can be directly referred in the actual work.
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Koffi Enakoutsa
2014-01-01
Full Text Available In this work we propose to replace the GLPD hypo-elasticity law by a more rigorous generalized Hooke's law based on classical material symmetry characterization assumptions. This law introduces in addition to the two well-known Lame's moduli, five constitutive constants. An analytical solution is derived for the problem of a spherical shell subjected to axisymmetric loading conditions to illustrate the potential of the proposed generalized Hooke's law.
Enakoutsa, Koffi
2014-01-01
In this work we propose to replace the GLPD hypo-elasticity law by a more rigorous generalized Hooke's law based on classical material symmetry characterization assumptions. This law introduces in addition to the two well-known Lame's moduli, five constitutive constants. An analytical solution is derived for the problem of a spherical shell subjected to axisymmetric loading conditions to illustrate the potential of the proposed generalized Hooke's law.
Nathoo, Arif N; Goldhoff, Patricia; Quattrochi, James J
2005-08-01
This study sought to assess the introduction of a web-based innovation in medical education that complements traditional problem-based learning curricula. Utilizing the case method as its fundamental educational approach, the Interactive Case-based Online Network (ICON) allows students to interact with each other, faculty and a virtual patient in difficult neurological cases. Given the paucity of available metrics to benchmark online systems, we complement user perceptions with data on system utilization. We describe a case study of distinct, small group tutorials over 2 years as part of the Human Nervous System and Behavior (HNSB) course at the Harvard Medical School. Participating students and faculty were interviewed following completion of the course and their utilization of the system was recorded and examined. Students each spent 3.2+/-1.3 h (mean+/-SD) through 8.6+/-2.8 accessions per week using ICON outside of required tutorial time. Faculty each spent 4.8+/-3.4 h through 16.6+/-8.9 accessions per week on ICON. Students identified real-time engagement, stronger relationships with faculty, increased accountability to the tutorial group and self-selected pace as the most beneficial characteristics of the ICON-based tutorial in comparison to traditional problem based learning (PBL) tutorials. Faculty identified enhanced collaboration with students and more realistic student experiences as the most beneficial characteristics. Both students and faculty reported that limitations of ICON included increased time investment for faculty and increased reliance on good faculty mentorship. This is the first study of the ICON learning system in undergraduate medical education, a platform designed to facilitate collaboration outside of the classroom. Data on user perceptions and system utilization suggest that both faculty and students chose to adopt this online learning system as a means for collaboration. The study also outlines future avenues for research in assessing
Shorikov, A. F.; Butsenko, E. V.
2017-10-01
This paper discusses the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. On the basis of network modeling proposed a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. Network economic and mathematical modeling allows you to determine the optimal time and calendar schedule for the implementation of the investment project and serves as an instrument to increase the economic potential and competitiveness of the enterprise. On a meaningful practical example, the processes of forming network models are shown, including the definition of the sequence of actions of a particular investment projecting process, the network-based work schedules are constructed. The calculation of the parameters of network models is carried out. Optimal (critical) paths have been formed and the optimal time for implementing the chosen technologies of the investment project has been calculated. It also shows the selection of the optimal technology from a set of possible technologies for project implementation, taking into account the time and cost of the work. The proposed model and method for solving the problem of managing investment projects can serve as a basis for the development, creation and application of appropriate computer information systems to support the adoption of managerial decisions by business people.
A path based model for a green liner shipping network design problem
DEFF Research Database (Denmark)
Jepsen, Mads Kehlet; Brouer, Berit Dangaard; Plum, Christian Edinger Munk
2011-01-01
Liner shipping networks are the backbone of international trade providing low transportation cost, which is a major driver of globalization. These networks are under constant pressure to deliver capacity, cost effectiveness and environmentally conscious transport solutions. This article proposes...
The problems of calculating the load-bearing structures made of light steel thin-walled profiles
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Roy Vera
2016-01-01
Full Text Available The article presents the results of a study of bearing capacity of thin-walled cold-formed steel beam of the guide profile. Such profiles have a small thickness and complex cross-sectional shape. Bending deformation develops in the cross-sectional plane under the influence of loads in beam. In addition, deformation of constrained torsion and warping arise. These deformations influence the stress distribution at the points of the cross-section of the beam and thereby determine its load-bearing capacity.
Cloud Model-Based Artificial Immune Network for Complex Optimization Problem
Wang, Mingan; Li, Jianming; Guo, Dongliang
2017-01-01
This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm. PMID:28630620
PRIMARY IMMUNODEFICIENCY: STATUS OF A PROBLEM TODAY. RUSSIAN NETWORK OF JMF-CENTERS
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E. A. Latysheva
2013-01-01
Full Text Available The problems of primary immunodeficiency in Russia and the ways of solving of them are discussed in the article. Primary immunodeficiency is a group of rare diseases, so awareness of this pathology in the medical community and among patients is very low. This leads to late diagnosis and inadequate treatment of patients with such conditions. The result of the late beginning of treatment is early development of disability, and the high mortality rate of patients, as well as the high costs of the treatment of complications of primary immunodeficiency and sick-leave certificates for the government. Today in time and adequate therapy allows patients not only to reach adulthood without signs of disability, and to lead an active way of life, but to have healthy children. Given the high cost of therapy in many countries, the issue of providing patients with life-saving drugs remains unresolved. The global practice is to involve social organizations and funds. One of the foundations supporting educational programs, development of laboratories and research in the field of primary immunodeficiency is the Foundation of the Jeffrey Modell. A network of centres for primary immunodeficiency supported by the Jeffrey Modell Foundation (JMF-centers has started its functioning over the territory of the Russian Federation since 2011 in order to improve diagnostics and treatment of patients with primary immunodeficiency. A brief description of activity of these centers is presented in the article.
Inverse and direct problems of optics: usage of artificial neural networks
Abrukov, Victor S.; Pavlov, Roman I.; Malinin, Gennadiy I.
2004-08-01
We describe an application of artificial neural networks (ANN) for solving of inverse and direct problems of optics. Using the ANN we calculate local and integral characteristics of object by means of incomplete set of data that characterize optical images. Possibilities of usage the only one value of a function of signal intensity distributionn in a plane of a registration for full determination of distribution of local characteristics in an object are shown. It is very important for optical fiber sensors, smart sensors and MEMS. Examples of ANN usage for a case of object with a cylindrical symmetry in a field of interferometry are presented. Results obtained show that determination of object local and integral characteristics can be perform very much simpler than by means of standard procedures and numerical approaches for signal processing, reduction and analysis. The ANN can allow also to solve number of tasks that could not be solved by means of usual approaches. In prospects, this method can be used for creation of automated systems for diagnostics, testing and control in various fields of scientific and applied research as well as in industry.
Cloud Model-Based Artificial Immune Network for Complex Optimization Problem
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Mingan Wang
2017-01-01
Full Text Available This paper proposes an artificial immune network based on cloud model (AINet-CM for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR filter design and proportional-integral-differential (PID controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.
Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.
Al-Saggaf, Yeslam; Islam, Md Zahidul
2015-08-01
This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.
Cloud Model-Based Artificial Immune Network for Complex Optimization Problem.
Wang, Mingan; Feng, Shuo; Li, Jianming; Li, Zhonghua; Xue, Yu; Guo, Dongliang
2017-01-01
This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications-finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning-are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.
Mushkin, I.; Solomon, S.
2017-10-01
We study the inverse contagion problem (ICP). As opposed to the direct contagion problem, in which the network structure is known and the question is when each node will be contaminated, in the inverse problem the links of the network are unknown but a sequence of contagion histories (the times when each node was contaminated) is observed. We consider two versions of the ICP: The strong problem (SICP), which is the reconstruction of the network and has been studied before, and the weak problem (WICP), which requires "only" the prediction (at each time step) of the nodes that will be contaminated at the next time step (this is often the real life situation in which a contagion is observed and predictions are made in real time). Moreover, our focus is on analyzing the increasing accuracy of the solution, as a function of the number of contagion histories already observed. For simplicity, we discuss the simplest (deterministic and synchronous) contagion dynamics and the simplest solution algorithm, which we have applied to different network types. The main result of this paper is that the complex problem of the convergence of the ICP for a network can be reduced to an individual property of pairs of nodes: the "false link difficulty". By definition, given a pair of unlinked nodes i and j, the difficulty of the false link (i,j) is the probability that in a random contagion history, the nodes i and j are not contaminated at the same time step (or at consecutive time steps). In other words, the "false link difficulty" of a non-existing network link is the probability that the observations during a random contagion history would not rule out that link. This probability is relatively straightforward to calculate, and in most instances relies only on the relative positions of the two nodes (i,j) and not on the entire network structure. We have observed the distribution of false link difficulty for various network types, estimated it theoretically and confronted it
Hippert, Henrique S; Taylor, James W
2010-04-01
Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.
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Suprayogi Suprayogi
2016-12-01
Full Text Available This paper considers a location problem in a supply chain network. The problem addressed in this paper is motivated by an initiative to develop an efficient supply chain network for supporting the agricultural activities. The supply chain network consists of regions, warehouses, distribution centers, plants, and markets. The products include a set of inbound products and a set of outbound products. In this paper, definitions of the inbound and outbound products are seen from the region’s point of view. The inbound product is the product demanded by regions and produced by plants which flows on a sequence of the following entities: plants, distribution centers, warehouses, and regions. The outbound product is the product demanded by markets and produced by regions and it flows on a sequence of the following entities: regions, warehouses, and markets. The problem deals with determining locations of the warehouses and the distribution centers to be opened and shipment quantities associated with all links on the network that minimizes the total cost. The problem can be considered as a strategic supply chain network problem. A solution approach based on genetic algorithm (GA is proposed. The proposed GA is examined using hypothetical instances and its results are compared to the solution obtained by solving the mixed integer linear programming (MILP model. The comparison shows that there is a small gap (0.23%, on average between the proposed GA and MILP model in terms of the total cost. The proposed GA consistently provides solutions with least total cost. In terms of total cost, based on the experiment, it is demonstrated that coefficients of variation are closed to 0.
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V. A. Sednin
2009-01-01
Full Text Available The paper presents a problem statement, a developed mathematical model and proposed algorithm for solving optimization of capital investments in modernization (introduction of automatic controlsystems of thermal processes of large systems of centralized heat supply which are based on application of network model.The formulated problem refers to the problems of combinatory (discrete optimization. Methods of «branches and boundaries» or dynamic programming are applied nowadays for solving problems of this type. These methods are not considered as universal ones because they greatly depend on description of solution feasible area. As a result of it it is not possible to develop a universal software for solving any assignments which can be formulated as problems of combinatory optimization.The presented network model of the investigated problem does not have above-mentioned disadvantages and an algorithm is proposed for solving this problem which admits a simple programming realization.
Nourifar, Raheleh; Mahdavi, Iraj; Mahdavi-Amiri, Nezam; Paydar, Mohammad Mahdi
2017-09-01
Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.
Energy Technology Data Exchange (ETDEWEB)
Harvey, D.
2000-10-01
In developing the standards for the fibre optics Gigabit Ethernet network, the IEEE Committee 802.3z discovered certain problems involved with the 62.5 microns multi-mode fibres when employed at a wave-length of 850 nanometers. When looking for an explanation, it was discovered that equipment which conform to the Gigabit Ethernet standards utilize a laser for generating the light required for conveying information over the fibre optics network. The design of the 62.5 micron fibre contains certain impurities about the core of the fibre which reduces the light transmission. The result is no light , no information transmission through the core, and considerably reduced signal transmission towards the periphery of the fibre. Corning Corporation and other manufacturers of fibre optics came to the rescue by developing fibres specifically for the Gigabit Ethernet network. Corning, for example, manufactures four different varieties of optical fibres which are either 50 microns or 62.5 microns thick and operate optimally at a wavelength of 850 nanometers but at different distances ranging from 300 m to 600 m. The advantage of the different varieties of optical fibres is a matter of cost; since not all equipment requires the highest transmission distance to function properly, the ability to choose optical fibres that are optimal at a given level reduces the cost of the network. 1 tab., 1 fig.
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Ali Ghorbani
2017-01-01
Full Text Available Coupled Piled Raft Foundations (CPRFs are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.
Wan, Jun; Zhu, Chang; Hu, Jiong; Zhang, Tian C.; Richter-Egger, Dana; Feng, Xiaonan; Zhou, Aijiao; Tao, Tao
2017-11-01
Phosphorus (P) recovery from the aquatic environment by sorption depends mainly on effective sorbents. In this study, a novel zirconium-loaded magnetic chitosan/poly(vinyl alcohol) interpenetrating network (IPN) hydrogel was synthesized, characterized with different methods and then tested for P sorption. The effects of sorbent dosage, pH, co-existing anions and natural organic matter (NOM) were investigated. Isotherm results showed monolayer sorption was dominant. The max sorption capacity reached at pH = 5. Thermodynamically, the sorption process was spontaneous and exothermic. The pseudo-first-order kinetic model and intra-particle diffusion model fitted experimental data well. Besides, the hydrogels exhibited selectivity towards P sorption, and its maximum sorption capacity was favorable compared with other sorbents. Results of desorption and regeneration illustrate that the sorption capacity of hydrogels stayed relatively high and stable. The sorption mechanism was inner-sphere complex and ligand exchange. This study provides a promising sorbent for P recovery from the aqueous environment.
Energy Technology Data Exchange (ETDEWEB)
Ji, Haoran; Wang, Chengshan; Li, Peng; Zhao, Jinli; Song, Guanyu; Ding, Fei; Wu, Jianzhong
2017-09-01
The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposed in this paper. By regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. To tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.
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Elena B. Koreneva
2017-06-01
Full Text Available Unsymmetric flexure of an infinite ice slab with circular opening is under examination. The men-tioned construction is considered as an infinite plate of constant thickness resting on an elastic subgrade which properties are described by Winkler’s model. The plate’s thickness is variable in the area ajoining to the opening. Method of compensating loads is used. Basic and compensating solutions are received. The obtained solutions are produced in closed form in terms of Bessel functions.
RPL LOAD BALANCING IN INTERNET OF THINGS
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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.
The application of deep confidence network in the problem of image recognition
Directory of Open Access Journals (Sweden)
Chumachenko О.І.
2016-12-01
Full Text Available In order to study the concept of deep learning, in particular the substitution of multilayer perceptron on the corresponding network of deep confidence, computer simulations of the learning process to test voters was carried out. Multi-layer perceptron has been replaced by a network of deep confidence, consisting of successive limited Boltzmann machines. After training of a network of deep confidence algorithm of layer-wise training it was found that the use of networks of deep confidence greatly improves the accuracy of multilayer perceptron training by method of reverse distribution errors.
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Julien Maheut
2013-07-01
Full Text Available Purpose: The purpose of this paper is to present an algorithm that solves the supply network configuration and operations scheduling problem in a mass customization company that faces alternative operations for one specific tool machine order in a multiplant context. Design/methodology/approach: To achieve this objective, the supply chain network configuration and operations scheduling problem is presented. A model based on stroke graphs allows the design of an algorithm that enumerates all the feasible solutions. The algorithm considers the arrival of a new customized order proposal which has to be inserted into a scheduled program. A selection function is then used to choose the solutions to be simulated in a specific simulation tool implemented in a Decision Support System. Findings and Originality/value: The algorithm itself proves efficient to find all feasible solutions when alternative operations must be considered. The stroke structure is successfully used to schedule operations when considering more than one manufacturing and supply option in each step. Research limitations/implications: This paper includes only the algorithm structure for a one-by-one, sequenced introduction of new products into the list of units to be manufactured. Therefore, the lotsizing process is done on a lot-per-lot basis. Moreover, the validation analysis is done through a case study and no generalization can be done without risk. Practical implications: The result of this research would help stakeholders to determine all the feasible and practical solutions for their problem. It would also allow to assessing the total costs and delivery times of each solution. Moreover, the Decision Support System proves useful to assess alternative solutions. Originality/value: This research offers a simple algorithm that helps solve the supply network configuration problem and, simultaneously, the scheduling problem by considering alternative operations. The proposed system
Distribution load estimation - DLE
Energy Technology Data Exchange (ETDEWEB)
Seppaelae, A. [VTT Energy, Espoo (Finland)
1996-12-31
The load research project has produced statistical information in the form of load models to convert the figures of annual energy consumption to hourly load values. The reliability of load models is limited to a certain network because many local circumstances are different from utility to utility and time to time. Therefore there is a need to make improvements in the load models. Distribution load estimation (DLE) is the method developed here to improve load estimates from the load models. The method is also quite cheap to apply as it utilises information that is already available in SCADA systems
PANDIAN, Sevugarathinam MUTHU VIJAYA; THANUSHKODI, Keppanagowder
2014-01-01
Economic dispatch (ED) is one of the most important optimization problems in a power system. The objective of ED is sharing the power demand among the online generators while keeping the minimum cost of generation as a constraint. The aim of this paper is to operate an electric power system as economically as possible within its security limits. This paper proposes the following 2 new particle swarm optimization (PSO) algorithms to solve a nonconvex economic dispatch problem: an efficien...
Nowakowski, Piotr
2017-02-01
Waste electrical and electronic equipment (WEEE), also known as e-waste, is one of the most important waste streams with high recycling potential. Materials used in these products are valuable, but some of them are hazardous. The urban mining approach attempts to recycle as many materials as possible, so efficiency in collection is vital. There are two main methods used to collect WEEE: stationary and mobile, each with different variants. The responsibility of WEEE organizations and waste collection companies is to assure all resources required for these activities - bins, containers, collection vehicles and staff - are available, taking into account cost minimization. Therefore, it is necessary to correctly determine the capacity of containers and number of collection vehicles for an area where WEEE need to be collected. There are two main problems encountered in collection, storage and transportation of WEEE: container loading problems and vehicle routing problems. In this study, an adaptation of these two models for packing and collecting WEEE is proposed, along with a practical implementation plan designed to be useful for collection companies' guidelines for container loading and route optimization. The solutions are presented in the case studies of real-world conditions for WEEE collection companies in Poland. Copyright © 2016 Elsevier Ltd. All rights reserved.
The anatomy of urban social networks and its implications in the searchability problem
Herrera-Yagüe, C.; Schneider, C. M.; Couronné, T.; Smoreda, Z.; Benito, R. M.; Zufiria, P. J.; González, M. C.
2015-01-01
The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure. PMID:26035529
Research on Intellectual Property Right Problems of Peer-to-Peer Networks.
Dong, Ying; Li, Mingshu; Chen, Meizhang; Zheng, Shengli
2002-01-01
Discusses digital intellectual property rights relating to peer-to-peer networks, using Napster as an example. Suggests anti-piracy solutions to prevent litigation and considers how libraries can develop potential service models using peer-to-peer networks, including the development of personal libraries on the Internet, interlibrary loan,…
Directory of Open Access Journals (Sweden)
Enrique Castillo
2015-01-01
Full Text Available A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.
Energy Technology Data Exchange (ETDEWEB)
Ozkan, Semra; Dincer, Salih [Department of Chemical Engineering, Chemical-Metallurgical Faculty, Yildiz Technical University, Davutpasa Kampusu, No 127, 34210 Esenler, Istanbul (Turkey)
2001-12-01
In this work, the methods used in pinch design were applied to a heat exchanger network with the aid of an improved problem algorithm table. This table enables one to compose composite and grand composite curves in a simplified way. A user friendly computer code entitled DarboTEK, compiled by using Visual Basic 3.0, was developed for the design of integrated heat exchanger networks and estimation of related capital costs. Based on the data obtained from the TUPRAS petroleum refinery at Izmit, a retrofit design of heat exchanger networks was accomplished using DarboTEK. An investment of 3,576,627 dollars is needed which will be paid back in 1.69 years simply by energy conservation due to heat integration. (Author)
Memin, A.; Watson, C. S.; Tregoning, P.
2013-12-01
We investigate the influence of high-frequency non-tidal ocean loading on the displacement induced at a global set of geodetic stations and on estimating the geocenter motion from a geodetic network. Ground displacements of each geodetic site induced by atmospheric and ocean loading are computed by convolving surface mass or pressure variations with Green functions for the vertical and horizontal displacement. The displacements resulting from atmospheric loading are computed using the surface pressure variations provided by the European Center for Medium-range Weather Forecasts model (1.5° space and 3h time sampling). The ocean response is taken into account assuming an inverted barometer and a non-inverted barometer response of the ocean to changes in the atmosphere. The first one is derived from the atmospheric model. The latter is computed using the sea height variations from the global barotropic ocean model named Toulouse Unstructured Grid Ocean model (0.25° grid and 3h time sampling). To examine the spatial and temporal effects of the high-frequency non-tidal atmospheric and ocean deformations spanning the network, made of 157 stations, from 2002 to 2011, we remove a seasonal component from the loading and geodetic time series. We find that high-frequency non-tidal ocean loading induces a larger long term variability (mean increase of 25% and up to 80%) in the vertical displacement than the non-tidal atmospheric loading at 131 stations. A similar conclusion holds for the induced sub-daily scatter at 127 stations (mean increase of 37% and up to 90%). Using the dynamic ocean's response, when correcting the geodetic time series for non-tidal ocean loading, reduces the weighted variance of the geodetic time series at 118 sites, the largest reductions (> 11%) are obtained along the Baltic sea. We compute the deformation in a center of mass and center of figure reference frame and estimate the time series of the translation of the geocenter. Comparing the
Data-Intensive Cloud Service Provision for Research Institutes: the Network Connectivity Problem
Cass, Tony; CERN. Geneva. IT Department
2016-01-01
Much effort (and money) has been invested in recent years to ensure that academic and research sites are well interconnected with high-capacity networks that, in most cases, span national and continental boundaries. However, these dedicated research and education networks, whether national (NRENs) or trans-continental (RENs), frequently have Acceptable Use Policies (AUPs) that restrict their use by commercial entities, notably Cloud Service Providers (CSPs). After a brief summary of the issues involved, we describe three approaches to removing the network connectivity barrier that threatens to limit the ability of academic and research institutions to profit effectively from services offered by CSPs.
Energy Technology Data Exchange (ETDEWEB)
Sauget, M
2007-12-15
This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)
GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting
Lintao Yang; Honggeng Yang; Hongyan Yang; Haitao Liu
2018-01-01
With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel...
Menshykov, O. V.; Menshykova, M. V.; Wendland, W. L.
2005-11-01
The problem of contact interaction of the opposite faces of a linear crack under a normally incident harmonic tension-compression wave is numerically solved by the Galerkin method with piecewise-linear continuous elements. The dependence of the stress intensity factor (opening mode) on the wave number is investigated
Directory of Open Access Journals (Sweden)
Ijaz Ahmed
2014-01-01
Full Text Available The paper presents the metaheuristic approaches based on pattern search and simulated annealing hybridized with sequential quadratic programming, a powerful nonlinear adaptive technique for estimation of the finest combination of generated power in a given system at lowest operating cost while sustaining the operating condition of system efficiently. The fuel cost is minimized by satisfying the nonlinear operating conditions of thermal units mainly based on generation capacity constraints, generator ramp limit, power balance constraints, and valve point loading effect and by keeping in view the prohibited operating zones, respectively. About the optimization, a comparative study is made for pattern search (PS and simulated annealing (SA, as a viable global search technique and sequential quadratic programming, an efficient local optimizer and their hybrid versions. The applicability, stability, and reliability of the designed approaches are validated through comprehensive statistical analysis based on Monte Carlo simulations.
Libraries and Networks in Transition: Problems and Prospects for the 1980's.
De Gennaro, Richard
1981-01-01
Discusses the possible effects of library automation and networking on standards of catalog accuracy, catalog maintenance, circulation control, and other processes, and describes the mission of the Research Libraries Group (RLG), a consortium of 25 research libraries. (FM)
Isak Shabani; Betim Cico; Agni Dika
2012-01-01
In this paper, we have presented an algorithm for data synchronization based on Web Services (WS), which allows software applications to work well on both configurations Online and "Offline", in the absence of the network. For this purpose is in use Electronic Student Management System (ESMS) at University of Prishtina (UP) with the appropriate module. Since the use of ESMS, because of a uncertain supply of electricity, disconnecting the network and for other reasons which are not under the c...
Short-term load forecasting by a neuro-fuzzy based approach
Energy Technology Data Exchange (ETDEWEB)
Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering
2002-02-01
An approach based on an artificial neural network (ANN) combined with a fuzzy system is proposed for short-term load forecasting. This approach was developed in order to reach the desired short-term load forecasting in an efficient manner. Over the past few years, ANNs have attained the ability to manage a great deal of system complexity and are now being proposed as powerful computational tools. In order to select the appropriate load as the input for the desired forecasting, the Pearson analysis method is first applied to choose two historical record load patterns that are similar to the forecasted load pattern. These two load patterns and the required weather parameters are then fuzzified and input into a neural network for training or testing the network. The back-propagation (BP) neural network is applied to determine the preliminary forecasted load. In addition, the rule base for the fuzzy inference machine contains important linguistic membership function terms with knowledge in the form of fuzzy IF-THEN rules. This produces the load correction inference from the historical information and past forecasted load errors to obtain an inferred load error. Adding the inferred load error to the preliminary forecasted load, we can obtain the finial forecasted load. The effectiveness of the proposed approach to the short-term load-forecasting problem is demonstrated using practical data from the Taiwan Power Company (TPC). (Author)
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.
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.
Oshri, Assaf; Himelboim, Itai; Kwon, Josephine A; Sutton, Tara E; Mackillop, James
2015-11-01
The aim of the present study was to examine the links between severities of child abuse (physical vs. sexual), and alcohol use versus problems via social media (Facebook) peer connection structures. A total of 318 undergraduate female students at a public university in the United States reported severity of child abuse experiences and current alcohol use and problems. Social network data were obtained directly from the individuals' Facebook network. Severity of childhood physical abuse was positively linked to alcohol use and problems via eigenvector centrality, whereas severity of childhood sexual abuse was negatively linked to alcohol use and problems via clustering coefficient. Childhood physical and sexual abuse were linked positively and negatively, respectively, to online social network patterns associated with alcohol use and problems. The study suggests the potential utility of these online network patterns as risk indices and ultimately using social media as a platform for targeted preventive interventions.
Dorado-Moreno, Manuel; Pérez-Ortiz, María; Gutiérrez, Pedro A; Ciria, Rubén; Briceño, Javier; Hervás-Martínez, César
2017-03-01
Create an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of organ survival at different thresholds for each donor-recipient pair considered. Currently, this decision is mainly based on the Model for End-stage Liver Disease, which depends only on the severity of the recipient and obviates donor-recipient compatibility. We therefore propose to use information concerning the donor, the recipient and the surgery, with the objective of allocating the organ correctly. The database consists of information concerning transplants conducted in 7 different Spanish hospitals and the King's College Hospital (United Kingdom). The state of the patients is followed up for 12 months. We propose to treat the problem as an ordinal classification one, where we predict the organ survival at different thresholds: less than 15 days, between 15 and 90 days, between 90 and 365 days and more than 365 days. This discretization is intended to produce finer-grain survival information (compared with the common binary approach). However, it results in a highly imbalanced dataset in which more than 85% of cases belong to the last class. To solve this, we combine two approaches, a cost-sensitive evolutionary ordinal artificial neural network (ANN) (in which we propose to incorporate dynamic weights to make more emphasis on the worst classified classes) and an ordinal over-sampling technique (which adds virtual patterns to the minority classes and thus alleviates the imbalanced nature of the dataset). The results obtained by our proposal are promising and satisfactory, considering the overall accuracy, the ordering of the classes and the sensitivity of minority classes. In this sense, both the dynamic costs and the over-sampling technique improve the base results of the considered ANN-based method. Comparing our model with
Civaner, Murat
2008-01-01
The promotional activities of pharmaceutical companies are becoming an increasingly hot topic among healthcare workers and the general public. There are many studies in the literature claiming that drug promotion may lead to ethical problems, irrational use of medication, and increased costs, as well as negative effects on the patient-physician relationship and the medical profession. When considering that healthcare workers generally acquire their knowledge from the pharmaceutical industry, the problems mentioned, which are indeed of paramount importance, and the need for effective and sustainable interventions are clearly revealed. Many kinds of interventions have been recommended by various authorities and studies in order to prevent the kinds of problems mentioned above, including training healthcare workers, publishing professional codes to serve as guidelines about which professional values should be protected and how to cope with different situations in relationship to the pharmaceutical industry, or applying the business ethics codes of the pharmaceutical companies. Studies that assessed the effectiveness of different interventions, however, revealed that educating healthcare workers about marketing methods and state regulations are the only effective interventions. In this article, after defining the problem, a proposed national network for drug information is to decrease the negative effects of drug promotion and to promote the rational choice of medicines is described. According to the World Health Organization, rational use of medicine is the most effective, safe, applicable/suitable, and, lastly, the most cost effective option. A national network that will gather drug information by compiling evidence-based knowledge and taking rational use of medicine measures into account should be established. It should transmit information to all healthcare workers in a fast, equal, up to date, easily accessible, and free way. The network should also support
DEFF Research Database (Denmark)
Dodds, Chris M; Henson, Richard N; Suckling, John
2013-01-01
. In contrast to previous studies, we found no evidence for an effect of BDNF genotype or met load during episodic memory encoding. Met allele carriers showed increased activation during successful retrieval in right hippocampus but this was contrast-specific and unaffected by met allele load. These results...
DEFF Research Database (Denmark)
Quaglia, Alberto; Sarup, Bent; Sin, Gürkan
2013-01-01
structure for efficient formulation of enterprise-wide optimization problems is presented. Through the integration of the described data structure in our synthesis and design framework, the problem formulation workflow is automated in a software tool, reducing time and resources needed to formulate large...
DEFF Research Database (Denmark)
Gamst, M.
2014-01-01
arrived at the machine. Furthermore, two resource demand transmissions cannot use the same edge in the same time period. The problem has application in grid computing, where a number of geographically distributed machines work together for solving large problems. The machines are connected through...
Problems associated with the use of social networks--a pilot study.
Szczegielniak, Anna; Pałka, Karol; Krysta, Krzysztof
2013-09-01
The definition of addiction is that it is an acquired, strong need to perform a specific activity or continued use of mood alerting substances. Increasing discussion about the development of Internet addiction, which like other addictions, have their roots in depression, impaired assessment esteem and social anxiety shows that it affects all users of the global network, regardless of gender or age. The aim of the study was to assess the impact of social networking on the ongoing behavior of respondents- the first step of a study on the possibility of dependence on social networks. The study was based on an authors questionnaire placed on popular polish websites on February 2013. Questions related to the types and frequency of specific activities undertaken by the private profiles of users. The study involved 221 respondents, 193 questionnaires were filled in completely and correctly, without missing any questions. 83.24% admitted to using social networking sites, 16.76% indicated that they never had their own profile. An overwhelming number of respondents are a member of Facebook (79.17%), specialized portals related to their profession or work were used by only 13.89%, Our-class (6.25%) and Twitter was a primary portal for one person only. Nobody marked a participation in dating services. There is a big difference between the addiction to the Internet and addictions existing within the Internet; the same pattern applies to social networking. There is a need to recognize the "social networking" for a particular activity, irrespective of Facebook, Twitter and Nasza-Klasa, which are commercial products.
Bellingeri, Michele; Agliari, Elena; Cassi, Davide
2015-10-01
The best strategy to immunize a complex network is usually evaluated in terms of the percolation threshold, i.e. the number of vaccine doses which make the largest connected cluster (LCC) vanish. The strategy inducing the minimum percolation threshold represents the optimal way to immunize the network. Here we show that the efficacy of the immunization strategies can change during the immunization process. This means that, if the number of doses is limited, the best strategy is not necessarily the one leading to the smallest percolation threshold. This outcome should warn about the adoption of global measures in order to evaluate the best immunization strategy.
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
A greedy construction heuristic for the liner service network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard
is challenging due to the size of a global liner shipping operation and due to the hub-and-spoke network design, where a high percentage of the total cargo is transshipped. We present the first construction heuristic for large scale instances of the LSN-DP. The heuristic is able to find a solution for a real...
Pavlásek, J
1998-12-01
A hypothesis is presented that coherent oscillatory discharges of spatially distributed neuronal groups (the supposed binding mechanism) are the result of the convergence of stimulus-dependent activity in modality-specific afferent pathways with oscillatory activity generated in unspecific sensory systems. This view is supported by simulation experiments on model networks.
Directory of Open Access Journals (Sweden)
Hugo Opazo Mora
2008-06-01
Full Text Available This paper approaches the minimal loss reconfiguration problem, taking into account the load variations of the systems, through a stochastic reconfiguration process. The Monte Carlo method is used to consider the natural load variation. A normal probability function is used to generate aleatory load levels in the nodes. The results of this work show the existence of a set of branches that are frequently eliminated. This generates a tree branch set that best represents the universal randomness of the load. We call it "Expected Branch Set (EBS". The topology associated to the EBS coincides with that obtained using the average demand values. This makes it unnecessary to generate a considerable number of tests to find that topology that best considers the load variation. The proposed algorithm was applied to two test networks and to a large real network.Este trabajo se plantea la reconfiguración a mínimas pérdidas, tomando en cuenta las variaciones de carga del sistema, a través de un proceso de reconfiguración estocástico. El Método de Monte Carlo es usado para considerar las variaciones naturales de la carga, utilizando una función de probabilidad normal para generar niveles aleatorios de carga en los nudos. Los resultados de este trabajo muestran la existencia de un conjunto de ramas que son frecuentemente eliminadas en el proceso de reconfiguración. Esto genera un conjunto de ramas de un árbol, las que mejor representan aleatoriedad universal de la carga. La topología obtenida la denominamos "Conjunto de Ramas Esperadas" (Expected Branch Set, EBS. La topología asociada al EBS es casi similar a la topología obtenida usando los valores de demanda promedio. Esto hace innecesario el realizar un considerable número de pruebas para encontrar la topología que mejor considera las variaciones de carga. El algoritmo propuesto fue aplicado a dos sistemas de prueba y a un sistema real de gran envergadura.
DEFF Research Database (Denmark)
Chen, Peiyuan; Chen, Zhe; Bak-Jensen, Birgitte
2007-01-01
In order to assess the performance of distribution system under normal operating conditions with large integration of renewable energy based dispersed generation (DG) units, probabilistic modeling of the distribution system is necessary in order to take into consideration the stochastic behavior...... of load demands and DG units such as wind generation and combined heat and power plant generation. This paper classifies probabilistic models of load demands and DG units into summer and winter period, weekday and weekend as well as in 24 hours a day. The voltage results from the probabilistic load flow...
2012-09-13
supplier selection problem. 3.1 Introduction Multi-objective decision analysis (MODA) and multi-criteria decision making ( MCDM ) are very popular decision...xiv I. Introduction ...52 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.2 Decision
Directory of Open Access Journals (Sweden)
Osmar Viera Carcache
2017-03-01
Full Text Available This paper presents a computational proposal for the solution of the Cell Planning Problem. The importance of this problem in the area of Telecommunications imposes it as a reference in the search for new methods of optimization. Due to the complexity of the problem, this work uses a discrete relaxation and proposes a mathematical model for the application of the Meta-heuristic Ant Colony Optimization (ACO. For the analysis of the results, 5 instances of the problem of different sizes were selected and the Ants System (AS algorithm was applied. The results show that the proposal efficiently explores the search space, finding the optimal solution for each instance with a relatively low computational cost. These results are compared with 3 evolutionary alternatives of international reference that have been applied to the same study instances, showing a significant improvement by our proposal.
Directory of Open Access Journals (Sweden)
Ali Salmasnia
2012-01-01
Full Text Available An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables, called a multiple response optimization (MRO problem. Common optimization approaches often begin with estimating the relationship between the response variable with the process variables. Among these methods, response surface methodology (RSM, due to simplicity, has attracted most attention in recent years. However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem. Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization. In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses. The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.
National Research Council Canada - National Science Library
Y A Gatchin; O A Teploukhova
2016-01-01
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...
Known TCP Implementation Problems
Paxson, Vern (Editor); Allman, Mark; Dawson, Scott; Fenner, William; Griner, Jim; Heavens, Ian; Lahey, K.; Semke, J.; Volz, B.
1999-01-01
This memo catalogs a number of known TCP implementation problems. The goal in doing so is to improve conditions in the existing Internet by enhancing the quality of current TCP/IP implementations. It is hoped that both performance and correctness issues can be resolved by making implementors aware of the problems and their solutions. In the long term, it is hoped that this will provide a reduction in unnecessary traffic on the network, the rate of connection failures due to protocol errors, and load on network servers due to time spent processing both unsuccessful connections and retransmitted data. This will help to ensure the stability of the global Internet. Each problem is defined as follows: Name of Problem The name associated with the problem. In this memo, the name is given as a subsection heading. Classification one or more problem categories for which the problem is classified: "congestion control", "performance", "reliability", "resource management". Description A definition of the problem, succinct but including necessary background material. Significance A brief summary of the sorts of environments for which the problem is significant.
Solving the Dynamic Correlation Problem of the Susceptible-Infected-Susceptible Model on Networks
Cai, Chao-Ran; Chen, Michael Z Q; Holme, Petter; Guan, Jian-Yue
2016-01-01
The Susceptible-Infected-Susceptible model is a canonical model for emerging disease outbreaks. Such outbreaks are naturally modeled as taking place on networks. A theoretical challenge in network epidemiology is the dynamic correlations coming from that if one node is occupied, or infected (for disease spreading models), then its neighbors are likely to be occupied. By combining two theoretical approaches---the heterogeneous mean-field theory and the effective degree method---we are able to include these correlations in an analytical solution of the SIS model. We derive accurate expressions for the average prevalence (fraction of infected) and epidemic threshold. We also discuss how to generalize the approach to a larger class of stochastic population models.
Reliable Path Selection Problem in Uncertain Traffic Network after Natural Disaster
Directory of Open Access Journals (Sweden)
Jing Wang
2013-01-01
Full Text Available After natural disaster, especially for large-scale disasters and affected areas, vast relief materials are often needed. In the meantime, the traffic networks are always of uncertainty because of the disaster. In this paper, we assume that the edges in the network are either connected or blocked, and the connection probability of each edge is known. In order to ensure the arrival of these supplies at the affected areas, it is important to select a reliable path. A reliable path selection model is formulated, and two algorithms for solving this model are presented. Then, adjustable reliable path selection model is proposed when the edge of the selected reliable path is broken. And the corresponding algorithms are shown to be efficient both theoretically and numerically.
[IT safety in medical networks--current problems and approach to solutions].
Pommerening, K
2000-01-01
Designers and users of medical networks have to face strong requirements for data protection and security. Professional discretion and data protection laws allow the transfer of or access to patient data only in a therapeutic context. These data should also be protected from the network provider. Patients should be safe from any harm by faulty data or buggy procedures. On the other hand the security of the most used software products gets worse and worse. The use of the internet endangers more and more the integrity of the user's computer. The security requirements can be met only through strict care in planning, building, and configuring the infrastructure. Some concrete recommendations and guiding principles can immediately be realized. If these recommendations are followed, the internet can be of immense value for health care.
User Matching with Relation to the Stable Marriage Problem in Cognitive Radio Networks
Hamza, Doha R.
2017-03-20
We consider a network comprised of multiple primary users (PUs) and multiple secondary users (SUs), where the SUs seek access to a set of orthogonal channels each occupied by one PU. Only one SU is allowed to coexist with a given PU. We propose a distributed matching algorithm to pair the network users, where a Stackelberg game model is assumed for the interaction between the paired PU and SU. The selected secondary is given access in exchange for monetary compensation to the primary. The PU optimizes the interference price it charges to a given SU and the power allocation to maintain communication. The SU optimizes its power demand so as to maximize its utility. Our algorithm provides a unique stable matching. Numerical results indicate the advantage of the proposed algorithm over other reference schemes.
Leveraging socially networked mobile ICT platforms for the last-mile delivery problem.
Suh, Kyo; Smith, Timothy; Linhoff, Michelle
2012-09-04
Increasing numbers of people are managing their social networks on mobile information and communication technology (ICT) platforms. This study materializes these social relationships by leveraging spatial and networked information for sharing excess capacity to reduce the environmental impacts associated with "last-mile" package delivery systems from online purchases, particularly in low population density settings. Alternative package pickup location systems (PLS), such as a kiosk on a public transit platform or in a grocery store, have been suggested as effective strategies for reducing package travel miles and greenhouse gas emissions, compared to current door-to-door delivery models (CDS). However, our results suggest that a pickup location delivery system operating in a suburban setting may actually increase travel miles and emissions. Only once a social network is employed to assist in package pickup (SPLS) are significant reductions in the last-mile delivery distance and carbon emissions observed across both urban and suburban settings. Implications for logistics management's decades-long focus on improving efficiencies of dedicated distribution systems through specialization, as well as for public policy targeting carbon emissions of the transport sector are discussed.
Torrecilha, Rafaela Beatriz Pintor; Utsunomiya, Yuri Tani; Batista, Luís Fábio da Silva; Bosco, Anelise Maria; Nunes, Cáris Maroni; Ciarlini, Paulo César; Laurenti, Márcia Dalastra
2017-01-30
Quantification of Leishmania infantum load via real-time quantitative polymerase chain reaction (qPCR) in lymph node aspirates is an accurate tool for diagnostics, surveillance and therapeutics follow-up in dogs with leishmaniasis. However, qPCR requires infrastructure and technical training that is not always available commercially or in public services. Here, we used a machine learning technique, namely Radial Basis Artificial Neural Network, to assess whether parasite load could be learned from clinical data (serological test, biochemical markers and physical signs). By comparing 18 different combinations of input clinical data, we found that parasite load can be accurately predicted using a relatively small reference set of 35 naturally infected dogs and 20 controls. In the best case scenario (use of all clinical data), predictions presented no bias or inflation and an accuracy (i.e., correlation between true and predicted values) of 0.869, corresponding to an average error of ±38.2 parasites per unit of volume. We conclude that reasonable estimates of L. infantum load from lymph node aspirates can be obtained from clinical records when qPCR services are not available. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Lin Liu
2010-01-01
Full Text Available Cognitive radio (CR is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided.
A neural-network-based approach to the double traveling salesman problem.
Plebe, Alessio; Anile, Angelo Marcello
2002-02-01
The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This application poses further constraints, like a collision-avoidance function. The proposed solution is based on a self-organizing map structure, initialized with as many artificial neurons as the number of targets to be reached. One of the key components of the process is the combination of competitive relaxation with a mechanism for deleting and creating artificial neurons. Moreover, in the competitive relaxation process, information about the trajectory connecting the neurons is combined with the distance of neurons from the target. This strategy prevents tangles in the trajectory and collisions between the two tours. Results of tests indicate that the proposed approach is efficient and reliable for harvest sequence planning. Moreover, the enhancements added to the pure self-organizing map concept are of wider importance, as proved by a traveling salesman problem version of the program, simplified from the double version for comparison.
DEFF Research Database (Denmark)
Quaglia, Alberto; Gargalo, Carina L.; Chairakwongsa, Siwanat
2015-01-01
The developments obtained in recent years in the field of mathematical programming considerably reduced the computational time and resources needed to solve large and complex Mixed Integer Non Linear Programming (MINLP) problems. Nevertheless, the application of these methods in industrial practi...
ACTUAL PROBLEMS ABOUT IMPROVEMENT CAR FLEETS TO ABROAD THROUGH THE UKRAINIAN NETWORKS
Directory of Open Access Journals (Sweden)
Yu. M. Hermaniuk
2010-12-01
Full Text Available The article describes the basic problems of organization of international transit transportations. A statistical analysis of wagon delays at border stations has been done. Also some conclusions on the necessity of developing a mathematical model of pushing on the transit streams of wagons on the railway system in Ukraine have been done using the method of simulative modeling.
Sripada, Rebecca K; Bohnert, Amy S B; Teo, Alan R; Levine, Debra S; Pfeiffer, Paul N; Bowersox, Nicholas W; Mizruchi, Mark S; Chermack, Stephen T; Ganoczy, Dara; Walters, Heather; Valenstein, Marcia
2015-09-01
Low social support and small social network size have been associated with a variety of negative mental health outcomes, while their impact on mental health services use is less clear. To date, few studies have examined these associations in National Guard service members, where frequency of mental health problems is high, social support may come from military as well as other sources, and services use may be suboptimal. Surveys were administered to 1448 recently returned National Guard members. Multivariable regression models assessed the associations between social support characteristics, probable mental health conditions, and service utilization. In bivariate analyses, large social network size, high social network diversity, high perceived social support, and high military unit support were each associated with lower likelihood of having a probable mental health condition (p social support (OR .90, CI .88-.92) and high unit support (OR .96, CI .94-.97) continued to be significantly associated with lower likelihood of mental health conditions. Two social support measures were associated with lower likelihood of receiving mental health services in bivariate analyses, but were not significant in adjusted models. General social support and military-specific support were robustly associated with reduced mental health symptoms in National Guard members. Policy makers, military leaders, and clinicians should attend to service members' level of support from both the community and their units and continue efforts to bolster these supports. Other strategies, such as focused outreach, may be needed to bring National Guard members with need into mental health care.
Supply chain network design problem for a new market opportunity in an agile manufacturing system
Babazadeh, Reza; Razmi, Jafar; Ghodsi, Reza
2012-08-01
The characteristics of today's competitive environment, such as the speed with which products are designed, manufactured, and distributed, and the need for higher responsiveness and lower operational cost, are forcing companies to search for innovative ways to do business. The concept of agile manufacturing has been proposed in response to these challenges for companies. This paper copes with the strategic and tactical level decisions in agile supply chain network design. An efficient mixed-integer linear programming model that is able to consider the key characteristics of agile supply chain such as direct shipments, outsourcing, different transportation modes, discount, alliance (process and information integration) between opened facilities, and maximum waiting time of customers for deliveries is developed. In addition, in the proposed model, the capacity of facilities is determined as decision variables, which are often assumed to be fixed. Computational results illustrate that the proposed model can be applied as a power tool in agile supply chain network design as well as in the integration of strategic decisions with tactical decisions.
A quick method to estimate low voltage problem
He, Yuqing; He, Hongbin; Liu, Cong; Jiang, Zhuohan; Liu, Bo
2017-05-01
In order to solve the problem in the prediction of low voltage problem in distribution network, a method of estimating low voltage problem is proposed from two aspects: network simplification and load simplification. In the basis of the difference construction of the backbone and branch line, a backbone-branch network simplified model is proposed, and also the large input parameters problem is solved through the parameter estimation. In the basis of the division of the trunk that a branch load model is structured to realize a rapid distribution of the load. And finally, by using the voltage droptheoretical, a simple and practical low voltage loss quick check is formed to make it easy for grassroots staffs to use.
Energy Technology Data Exchange (ETDEWEB)
Nose Filho, Kenji; Araujo, Klayton A.M.; Maeda, Jorge L.Y.; Lotufo, Anna Diva P. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil)], Emails: kenjinose@yahoo.com.br, klayton_ama@hotmail.com, jorge-maeda@hotmail.com, annadiva@dee.feis.unesp.br
2009-07-01
This paper presents a development and implementation of a program to electrical load forecasting with data from a Brazilian electrical company, using four different architectures of neural networks of the MATLAB toolboxes: multilayer backpropagation gradient descendent with momentum, multilayer backpropagation Levenberg-Marquardt, adaptive network based fuzzy inference system and general regression neural network. The program presented a satisfactory performance, guaranteeing very good results. (author)
National Research Council Canada - National Science Library
Beibei Wang; Xiaoqing Hu; Peifeng Shen; Wenlu Ji; Yang Cao; Jiaping Tang
2017-01-01
.... The existence of these factors has brought challenges to the stability of the power distribution network, as well as increasing the risk of exceeding transmission capacity of distribution lines...
Efficient load rebalancing for distributed file system in Clouds
Directory of Open Access Journals (Sweden)
Mr. Mohan S. Deshmukh
2016-05-01
Full Text Available Cloud computing is an upcoming era in software industry. It’s a very vast and developing technology. Distributed file systems play an important role in cloud computing applications based on map reduce techniques. While making use of distributed file systems for cloud computing, nodes serves computing and storage functions at the same time. Given file is divided into small parts to use map reduce algorithms in parallel. But the problem lies here since in cloud computing nodes may be added, deleted or modified any time and also operations on files may be done dynamically. This causes the unequal load distribution of load among the nodes which leads to load imbalance problem in distributed file system. Newly developed distributed file system mostly depends upon central node for load distribution but this method is not helpful in large-scale and where chances of failure are more. Use of central node for load distribution creates a problem of single point dependency and chances of performance of bottleneck are more. As well as issues like movement cost and network traffic caused due to migration of nodes and file chunks need to be resolved. So we are proposing algorithm which will overcome all these problems and helps to achieve uniform load distribution efficiently. To verify the feasibility and efficiency of our algorithm we will be using simulation setup and compare our algorithm with existing techniques for the factors like load imbalance factor, movement cost and network traffic.
PROBLEM OF ECOLOGICAL NETWORK DEVELOPMENT IN BIG CITIES, USING MOSCOW AS AN EXAMPLE
Directory of Open Access Journals (Sweden)
Boyko Valeriya Mikhaylovna
2014-09-01
Full Text Available Creating stable system of protected areas in the city is practically impossible. The optimal way out of this situation is, to our mind, connected with such ecosystem management strategy that ensures spontaneous development of preserved natural ecosystems with simultaneous effective urban planting. It should be noted that the problems of the recreational ecosystem exploitation, despite many years of research, are not fully solved, and, considering recreation in urban protected areas, especially in the city, get a new trend. It seems reasonable nowadays to try to shuffle off the burden of active recreation from forest ecosystems on buffer zones. These zones should be specially created or restored because of carrying out projects on ecological rehabilitation, planted areas or natural planted areas. For the staff of the protected area system it creates additional opportunities for shifting of forces in the solution of other problems on maintaining and restoring biodiversity.
SET COVER PROBLEM OF COVERAGE PLANNING IN LTE-ADVANCED RELAY NETWORKS
Fan-Hsun Tseng; Li-Der Chou; Han-Chieh Chao; Wei-Jen Yu
2014-01-01
Various mobile devices are developing rapidly in contemporary society, such as smart phones and tablet PCs. Users are able to acquire different multimedia services through wireless communication anytime and anywhere. However, the increased demand also gives rise to a problem of insufficient bandwidth. Therefore, a fourth generation mobile telecommunications (4G) technology was proposed and widely investigated. One of the popular technologies is Long Term Evolution Advanced (LTE-Advanced), whi...
Mulder, Samuel A; Wunsch, Donald C
2003-01-01
The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here. This paper presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allowed by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lower quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware.
Energy Technology Data Exchange (ETDEWEB)
Marcondes, Eduardo; Goldbarg, Elizabeth; Goldbarg, Marco; Cunha, Thatiana [Universidade Federal do Rio Grande do Norte (UFRN), Natal, RN (Brazil)
2008-07-01
A major problem about the planning of production in refinery is the determination of what should be done in each stage of production as a horizon of time. Among such problems, distribution of oil products through networks of pipelines is a very significant problem because of its economic importance. In this work, a problem of distribution of oil through a network of pipelines is modeled. The network studied is a simplification of a real network. There are several restrictions to be met, such as limits of storage, transmission or receipt of limits and limitations of transport. The model is adopted bi-goal where you want to minimize the fragmentation and the time of transmission, given the restrictions of demand and storage capacity. Whereas the occupancy rate of networks is increasingly high, is of great importance optimize its use. In this work, the technique of optimization by Cloud of particles is applied to the problem of distribution of oil products by networks of pipelines. (author)
Taxi pooling in New York City: a network-based approach to social sharing problems
Santi, Paolo; Szell, Michael; Sobolevsky, Stanislav; Strogatz, Steven; Ratti, Carlo
2013-01-01
Taxi services are a vital part of urban transportation, and a major contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing th...
A hybrid model using decision tree and neural network for credit scoring problem
Directory of Open Access Journals (Sweden)
Amir Arzy Soltan
2012-08-01
Full Text Available Nowadays credit scoring is an important issue for financial and monetary organizations that has substantial impact on reduction of customer attraction risks. Identification of high risk customer can reduce finished cost. An accurate classification of customer and low type 1 and type 2 errors have been investigated in many studies. The primary objective of this paper is to develop a new method, which chooses the best neural network architecture based on one column hidden layer MLP, multiple columns hidden layers MLP, RBFN and decision trees and ensembling them with voting methods. The proposed method of this paper is run on an Australian credit data and a private bank in Iran called Export Development Bank of Iran and the results are used for making solution in low customer attraction risks.
Simply Coded Evolutionary Artificial Neural Networks on a Mobile Robot Control Problem
Katada, Yoshiaki; Hidaka, Takuya
One of the advantages of evolutionary robotics over other approaches in embodied cognitive science would be its parallel population search. Due to the population search, it takes a long time to evaluate all robot in a real environment. Thus, such techniques as to shorten the time are required for real robots to evolve in a real environment. This paper proposes to use simply coded evolutionary artificial neural networks for mobile robot control to make genetic search space as small as possible and investigates the performance of them using simulated and real robots. Two types of genetic algorithm (GA) are employed, one is the standard GA and the other is an extended GA, to achieve higher final fitnesses. The results suggest the benefits of the proposed method.
Caquilpan, V.; Sáez, Doris; Hernández, Roberto; Llanos, Jacqueline; Roje, T.; Nunez Vicencio, Alfredo
2017-01-01
Microgrids are suitable electrical solutions for providing energy in rural zones. However, it is challenging to propose in advance a good design of the microgrid because the electrical load is difficult to estimate due to its highly dependence of the residential consumption. In this paper, a novel
Gu, Senlin; Zhu, Leon; Mercier, Claude; Li, Yongjin
2017-05-31
Polypropylene (PP)/glass fiber (GF) composites showing excellent antistatic performance were prepared by a simple melt process blending PP with GF and a small amount of organic salts (OSs). Two types of OSs, tribuyl(octyl)phosphonium bis(trifloromethanesulfonyl)imide (TBOP-TFSI) and lithium bis(trifloromethanesulfonyl)imide (Li-TFSI), with equivalent anions were used as antistatic agents for the composites. It was found that the GF and OSs exhibited significant synergistic effects on the antistatic performance as well as the mechanical properties of the composites. On the one hand, the incorporation of GF significantly enhanced the electric conductivity of the composites at a constant OS loading. On the other hand, the two types of OSs improved the interfacial adhesion between the GF and the PP matrix, which led to an enhancement of the mechanical properties. This study showed that OSs had specific interactions with GFs and were absorbed exclusively on the GF surface. The GF network in the PP matrix provided perfect orbits for the movement of ions, inducing the excellent antistatic performance exhibited by the PP/GF composites at an OS loading of as low as 0.25 wt % when the GF formed a network in the PP matrix.
Maupome, G; McConnell, W R; Perry, B L
2016-12-01
To examine the influence of collectivist orientation (often called familismo when applied to the Latino sub-group in the United States) in oral health discussion networks. Through respondent-driven sampling and face-to-face interviews, we identified respondents' (egos) personal social network members (alters). Egos stated whom they talked with about oral health, and how often they discussed dental problems in the preceding 12 months. An urban community of adult Mexican-American immigrants in the Midwest United States. We interviewed 332 egos (90% born in Mexico); egos named an average of 3.9 alters in their networks, 1,299 in total. We applied egocentric network methods to examine the ego, alter, and network variables that characterize health discussion networks. Kin were most often leveraged when dental problems arose; egos relied on individuals whom they perceive to have better knowledge about dental matters. However, reliance on knowledgeable alters decreased among egos with greater behavioral acculturation. This paper developed a network-based conceptualization of familismo. We describe the structure of oral health networks, including kin, fictive kin, peers, and health professionals, and examine how networks and acculturation help shape oral health among these Mexican-Americans.
Directory of Open Access Journals (Sweden)
E. E. Tsiropoulou
2016-06-01
Full Text Available In this paper a joint resource allocation problem is studied in a multi-service Single Carrier FDMA (SC-FDMA wireless network. Mobile users request various services with different Quality of Service (QoS characteristics and they determine in a distributed and non-cooperative manner a joint subcarrier and power allocation towards fulfilling their QoS prerequisites. Initially, a well-designed utility function is formulated to appropriately represent users’ diverse QoS prerequisites with respect to their requested service. The subcarriers allocation problem is solved based on a multilateral bargaining model, where users are able to select different discount factors to enter the bargaining game, thus better expressing their different needs in system resources with respect to their requested service. The subcarriers mapping is realized based either on the localized SC-FDMA method where the subcarriers are sequentially allocated to the users or the distributed SC-FDMA via considering the maximum channel gain policy, where each subcarrier is allocated to the user with the maximum channel gain. Given the subcarriers assignment, an optimization problem with respect to users’ uplink transmission power is formulated and solved, in order to determine the optimal power allocation per subcarrier assigned to each user. Finally, the performance of the proposed framework is evaluated via modeling and simulation and extensive numerical results are presented.
A Space-Based Generic Pattern for Self-Initiative Load Balancing Agents
Kühn, Eva; Sesum-Cavic, Vesna
Load-Balancing is a significant problem in heterogeneous distributed systems. There exist many load balancing algorithms, however, most approaches are very problem specific oriented and a comparison is therefore complex. This paper proposes a generic architectural pattern for a load balancing framework that allows for the plugging of different load balancing algorithms, reaching from unintelligent to intelligent ones, to ease the selection of the best algorithm for a certain problem scenario. As in complex network environments there is no "one-fits-all solution", also the integration of several different algorithms shall be supported. The presented pattern assumes autonomous agents and decentralized control. It can be composed towards arbitrary network topologies, foresees exchangeable policies for load-balancing, and uses a black-board based communication mechanism to achieve high software architecture agility. The pattern has been implemented and first instantiations of it with three algorithms have been benchmarked.
Grover, Jeff
2016-01-01
This book is an extension of the author’s first book and serves as a guide and manual on how to specify and compute 2-, 3-, & 4-Event Bayesian Belief Networks (BBN). It walks the learner through the steps of fitting and solving fifty BBN numerically, using mathematical proof. The author wrote this book primarily for naïve learners and professionals, with a proof-based academic rigor. The author's first book on this topic, a primer introducing learners to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time, was meant for non-statisticians unfamiliar with the theorem - as is this book. This new book expands upon that approach and is meant to be a prescriptive guide for building BBN and executive decision-making for students and professionals; intended so that decision-makers can invest their time and start using this inductive reasoning principle in their decision-making processes. It highlights the utility of an algorithm that served as ...