Distributed Robust Optimization in Networked System.
Wang, Shengnan; Li, Chunguang
2016-10-11
In this paper, we consider a distributed robust optimization (DRO) problem, where multiple agents in a networked system cooperatively minimize a global convex objective function with respect to a global variable under the global constraints. The objective function can be represented by a sum of local objective functions. The global constraints contain some uncertain parameters which are partially known, and can be characterized by some inequality constraints. After problem transformation, we adopt the Lagrangian primal-dual method to solve this problem. We prove that the primal and dual optimal solutions of the problem are restricted in some specific sets, and we give a method to construct these sets. Then, we propose a DRO algorithm to find the primal-dual optimal solutions of the Lagrangian function, which consists of a subgradient step, a projection step, and a diffusion step, and in the projection step of the algorithm, the optimized variables are projected onto the specific sets to guarantee the boundedness of the subgradients. Convergence analysis and numerical simulations verifying the performance of the proposed algorithm are then provided. Further, for nonconvex DRO problem, the corresponding approach and algorithm framework are also provided.
Optimal design of network distribution systems
Directory of Open Access Journals (Sweden)
U. Passy
2003-12-01
Full Text Available The problem of finding the optimal distribution of pressure drop over a network is solved via an unconstrained gradient type algorithm. The developed algorithm is computationally attractive. Problems with several hundred variables and constraints were solved.
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Optimal Planning of Communication System of CPS for Distribution Network
Directory of Open Access Journals (Sweden)
Ting Yang
2017-01-01
Full Text Available IoT is the technical basis to realize the CPS (Cyber Physical System for distribution networks, with which the complex system becomes more intelligent and controllable. Because of the multihop and self-organization characteristics, the large-scale heterogeneous CPS network becomes more difficult to plan. Using topological potential theory, one of typical big data analysis technologies, this paper proposed a novel optimal CPS planning model. Topological potential equalization is considered as the optimization objective function in heterogeneous CPS network with the constraints of communication requirements, physical infrastructures, and network reliability. An improved binary particle swarm optimization algorithm is proposed to solve this complex optimal problem. Two IEEE classic examples are adopted in the simulation, and the results show that, compared with benchmark algorithms, our proposed method can provide an effective topology optimization scheme to improve the network reliability and transmitting performance.
Network anomaly detection system with optimized DS evidence theory.
Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu
2014-01-01
Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.
Stochastic network optimization with application to communication and queueing systems
Neely, Michael
2010-01-01
This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are prov
Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems
Patan, Maciej
2012-01-01
Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...
Control and Optimization of Network in Networked Control System
Directory of Open Access Journals (Sweden)
Wang Zhiwen
2014-01-01
Full Text Available In order to avoid quality of performance (QoP degradation resulting from quality of service (QoS, the solution to network congestion from the point of control theory, which marks departure of our results from the existing methods, is proposed in this paper. The congestion and bandwidth are regarded as state and control variables, respectively; then, the linear time-invariant (LTI model between congestion state and bandwidth of network is established. Consequently, linear quadratic method is used to eliminate the network congestion by allocating bandwidth dynamically. At last, numerical simulation results are given to illustrate the effectiveness of this modeling approach.
Neural network based optimal control of HVAC&R systems
Ning, Min
Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the
Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks
Directory of Open Access Journals (Sweden)
S. Vukmirović
2012-04-01
Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.
CFD Optimization on Network-Based Parallel Computer System
Cheung, Samson H.; VanDalsem, William (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Service network design of bike sharing systems analysis and optimization
Vogel, Patrick
2016-01-01
This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
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.
Optimal Configuration of Virtual Links for Avionics Network Systems
Directory of Open Access Journals (Sweden)
Dongha An
2015-01-01
Full Text Available As the bandwidth and scalability constraints become important design concerns in airborne networks, a new technology, called Avionics Full Duplex Switched Ethernet (AFDX, has been introduced and standardized as a part 7 in ARNIC 664. However, since previous research interests for AFDX are mainly bounded for analyzing the response time where flows information is given, configuration problem for both Maximum Transmission Unit (MTU and Bandwidth Allocation Gap (BAG over virtual links in AFDX networks has not been addressed yet even though it has great impact on required bandwidth. Thus, in this paper, we present two configuration approaches to set MTU and BAG values on virtual links efficiently while meeting the requirement of AFDX. The first is to search available feasible configuration (MTU, BAG pairs to satisfy application requirements as well as AFDX switch constraints, and the second is to get an optimal pair to minimize required bandwidth through well-known branch-and-bound algorithm. We analyze the complexity of the proposed algorithm and then evaluate the proposed algorithm by simulation. Finally, we prove that the proposed schemes are superior to general approach in the aspects of speed and required bandwidth in AFDX networks.
Optimization of stochastic discrete systems and control on complex networks computational networks
Lozovanu, Dmitrii
2014-01-01
This book presents the latest findings on stochastic dynamic programming models and on solving optimal control problems in networks. It includes the authors' new findings on determining the optimal solution of discrete optimal control problems in networks and on solving game variants of Markov decision problems in the context of computational networks. First, the book studies the finite state space of Markov processes and reviews the existing methods and algorithms for determining the main characteristics in Markov chains, before proposing new approaches based on dynamic programming and combinatorial methods. Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems. In closing, the book's final chapter is devoted to finite horizon stochastic con...
Effective Network Management via System-Wide Coordination and Optimization
2010-08-01
capacity of ISPs and socializes the performance benefits to all end-to-end traffic [30]. However, extending single-vantage solutions to a network-wide...O. Spatscheck, and V. Shkapenyuk. Gigascope: A stream database for network applications. In Proc. ACM SIGMOD, 2003. 185 [42] C. Kruegel, F. Valeur
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Optimizing Targeting of Intrusion Detection Systems in Social Networks
Puzis, Rami; Tubi, Meytal; Elovici, Yuval
Internet users communicate with each other in various ways: by Emails, instant messaging, social networking, accessing Web sites, etc. In the course of communicating, users may unintentionally copy files contaminated with computer viruses and worms [1, 2] to their computers and spread them to other users [3]. (Hereafter we will use the term "threats", rather than computer viruses and computer worms). The Internet is the chief source of these threats [4].
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Optimizing the Heat Exchanger Network of a Steam Reforming System
DEFF Research Database (Denmark)
Nielsen, Mads Pagh; Korsgaard, Anders Risum; Kær, Søren Knudsen
2004-01-01
gas steam reformer along with gas purification reactors to generate clean hydrogen suited for a PEM stack. The temperatures in the various reactors in the fuel processing system vary from around 1000°C to the stack temperature at 80°C. Furthermore, external heating must be supplied to the endothermic...... steam reforming reaction and steam must be generated. The dependence of the temperature profiles on conversion in shift reactors for gas purification is also significant. The optimum heat integration in the system is thus imperative in order to minimize the need for hot and cold utilities. A rigorous 1D......Proton Exchange Membrane (PEM) based combined heat and power production systems are highly integrated energy systems. They may include a hydrogen production system and fuel cell stacks along with post combustion units optionally coupled with gas turbines. The considered system is based on a natural...
Urban Traffic Signal System Control Structural Optimization Based on Network Analysis
Directory of Open Access Journals (Sweden)
Li Wang
2013-01-01
Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.
Optimization of steel casting feeding system based on BP neural network and genetic algorithm
Directory of Open Access Journals (Sweden)
Xue-dan Gong
2016-05-01
Full Text Available The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus inefficient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation (BP neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×106 mm3.
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Miao, Zhidong; Liu, Dake; Gong, Chen
2017-10-01
Inductive wireless power transfer (IWPT) is a promising power technology for implantable biomedical devices, where the power consumption is low and the efficiency is the most important consideration. In this paper, we propose an optimization method of impedance matching networks (IMN) to maximize the IWPT efficiency. The IMN at the load side is designed to achieve the optimal load, and the IMN at the source side is designed to deliver the required amount of power (no-more-no-less) from the power source to the load. The theoretical analyses and design procedure are given. An IWPT system for an implantable glaucoma therapeutic prototype is designed as an example. Compared with the efficiency of the resonant IWPT system, the efficiency of our optimized system increases with a factor of 1.73. Besides, the efficiency of our optimized IWPT system is 1.97 times higher than that of the IWPT system optimized by the traditional maximum power transfer method. All the discussions indicate that the optimization method proposed in this paper could achieve a high efficiency and long working time when the system is powered by a battery.
Jakovetic, Dusan; Xavier, João; Moura, José M. F.
2011-08-01
We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.
Wu, Jie; Zhou, Zhu-Jun; Zhan, Xi-Sheng; Yan, Huai-Cheng; Ge, Ming-Feng
2017-05-01
This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
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.
Optimization of hydrometric monitoring network in urban drainage systems using information theory.
Yazdi, J
2017-10-01
Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.
Directory of Open Access Journals (Sweden)
Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
NETWORK CENTRISM OPTIMIZATION OF EXPEDITIOUS SERVICE OF ELEMENTS OF THE POWER SUPPLY SYSTEM
Directory of Open Access Journals (Sweden)
Ye.I. Sokol
2016-06-01
Full Text Available Purpose. Development of precision selection criteria of options of technical realization of effective active and adaptive system of expeditious service of elements of a power supply system in the conditions of network-centric management. Methodology. In development of power supply systems their evolution from the elementary forms using elementary network technologies and models of interactions in power to more irregular shapes within the concept of Smart Grid with elements of network-centric character is observed. This direction is based on Internet-technologies of the last generation, and realize models of power activity which couldn't be realized before. Results. The number of possible options of active and adaptive system of expeditious service of elements of a power supply system is usually rather big and it is difficult to choose the acceptable option by direct search. Elimination of admissible options of the technical realization constructed on the principles of a network centrism means application of the theory of multicriteria optimization from a position of discrete programming. The basis of procedure of elimination is made by algorithm of an assessment of system by criterion of accuracy. Originality. The case of an assessment of the precision characteristic of system at restrictions for the set accuracy is connected with need of decomposition of requirements of all system in general and on separate subsystems. For such decomposition the ratios connecting the accuracy of functioning of a separate subsystem with variations of parameters of all system, and also with precision characteristics of subsystems of the lower levels influencing this subsystem are received. Practical value. In the conditions of the network-centric organization of management of expeditious service of elements of a power supply system elimination of options of subsystems when using precision criterion allows to receive the maximum number of essentially possible
Towards Optimal Transport Networks
Directory of Open Access Journals (Sweden)
Erik P. Vargo
2010-08-01
Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Marqués Sánchez, Pilar; Fernández Peña, Rosario; Cabrera León, Andrés; Muñoz Doyague, María F; Llopis Cañameras, Jaime; Arias Ramos, Natalia
2013-01-01
The search of new health management formulas focused to give wide services is one of the priorities of our present health policies. Those formulas examine the optimization of the links between the main actors involved in public health, ie, users, professionals, local socio-political and corporate agents. This paper is aimed to introduce the Social Network Analysis as a method for analyzing, measuring and interpreting those connections. The knowledge of people's relationships (what is called social networks) in the field of public health is becoming increasingly important at an international level. In fact, countries such as UK, Netherlands, Italy, Australia and U.S. are looking formulas to apply this knowledge to their health departments. With this work we show the utility of the ARS on topics related to sustainability of the health system, particularly those related with health habits and social support, topics included in the 2020 health strategies that underline the importance of the collaborative aspects in networks.
Power prediction in mobile communication systems using an optimal neural-network structure.
Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J
1997-01-01
Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.
Sahoo, Avimanyu; Jagannathan, Sarangapani
2017-02-01
In this paper, an event-driven stochastic adaptive dynamic programming (ADP)-based technique is introduced for nonlinear systems with a communication network within its feedback loop. A near optimal control policy is designed using an actor-critic framework and ADP with event sampled state vector. First, the system dynamics are approximated by using a novel neural network (NN) identifier with event sampled state vector. The optimal control policy is generated via an actor NN by using the NN identifier and value function approximated by a critic NN through ADP. The stochastic NN identifier, actor, and critic NN weights are tuned at the event sampled instants leading to aperiodic weight tuning laws. Above all, an adaptive event sampling condition based on estimated NN weights is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals along with the approximation accuracy. The net result is event-driven stochastic ADP technique that can significantly reduce the computation and network transmissions. Finally, the analytical design is substantiated with simulation results.
Synthesis and optimization of steam system networks. 2. Multiple steam levels
CSIR Research Space (South Africa)
Price, T
2010-08-01
Full Text Available stream_source_info Majozi_2010-ABSTRACT ONLY.pdf.txt stream_content_type text/plain stream_size 1539 Content-Encoding UTF-8 stream_name Majozi_2010-ABSTRACT ONLY.pdf.txt Content-Type text/plain; charset=UTF-8 Industrial... & Engineering Chemistry Research Vol. 49(19), pp. 9154–9164 Synthesis and Optimization of Steam System Networks. 2. Multiple Steam Levels Tim Price† and Thokozani Majozi*,†,‡ Department of Chemical Engineering, UniVersity of Pretoria, South Africa...
Taha, Ahmad Fayez
Transportation networks, wearable devices, energy systems, and the book you are reading now are all ubiquitous cyber-physical systems (CPS). These inherently uncertain systems combine physical phenomena with communication, data processing, control and optimization. Many CPSs are controlled and monitored by real-time control systems that use communication networks to transmit and receive data from systems modeled by physical processes. Existing studies have addressed a breadth of challenges related to the design of CPSs. However, there is a lack of studies on uncertain CPSs subject to dynamic unknown inputs and cyber-attacks---an artifact of the insertion of communication networks and the growing complexity of CPSs. The objective of this dissertation is to create secure, computational foundations for uncertain CPSs by establishing a framework to control, estimate and optimize the operation of these systems. With major emphasis on power networks, the dissertation deals with the design of secure computational methods for uncertain CPSs, focusing on three crucial issues---(1) cyber-security and risk-mitigation, (2) network-induced time-delays and perturbations and (3) the encompassed extreme time-scales. The dissertation consists of four parts. In the first part, we investigate dynamic state estimation (DSE) methods and rigorously examine the strengths and weaknesses of the proposed routines under dynamic attack-vectors and unknown inputs. In the second part, and utilizing high-frequency measurements in smart grids and the developed DSE methods in the first part, we present a risk mitigation strategy that minimizes the encountered threat levels, while ensuring the continual observability of the system through available, safe measurements. The developed methods in the first two parts rely on the assumption that the uncertain CPS is not experiencing time-delays, an assumption that might fail under certain conditions. To overcome this challenge, networked unknown input
DEFF Research Database (Denmark)
Hansen, C. T.; Madsen, Kaj; Nielsen, Hans Bruun
1991-01-01
algorithm using successive linear programming is presented. The performance of the algorithm is illustrated by optimizing a network with 201 pipes and 172 nodes. It is concluded that the new algorithm seems to be very efficient and stable, and that it always finds a solution with a cost near the best...
Zhou, Shiqiong; Kang, Longyun; Cheng, Miaomiao; Cao, Binggang
Owing to sun's rays distributing randomly and discontinuously and load fluctuation, energy storage system is very important in Solar Energy Electric Vehicle (SEEV). The combinatorial optimization by genetic algorithm and neural network was used to optimize the energy storage system (including storage batteries and flywheel).In the optimization design, the operation strategy of the system was fixed and used to instruct the simulation about the system's operation. And the optimal objective was selected as minimizing the total capital cost of the energy storage system, subject to the main constraint of the Loss of Power Supply Probability (LPSP). Studies have proved that the combinatorial optimization by genetic algorithm and neural network converges well, lessen calculation time and it is feasible.
Luitel, Bipul; Venayagamoorthy, Ganesh Kumar
2010-06-01
Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach. Copyright 2009 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Young-Bo Sim
2017-11-01
Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Design of the smart home system based on the optimal routing algorithm and ZigBee network
Xie, Xiaoxia
2017-01-01
To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA) to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system. PMID:29131868
Design of the smart home system based on the optimal routing algorithm and ZigBee network.
Jiang, Dengying; Yu, Ling; Wang, Fei; Xie, Xiaoxia; Yu, Yongsheng
2017-01-01
To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA) to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system.
Network design optimization of fuel cell systems and distributed energy devices.
Energy Technology Data Exchange (ETDEWEB)
Colella, Whitney G.
2010-07-01
This research explores the thermodynamics, economics, and environmental impacts of innovative, stationary, polygenerative fuel cell systems (FCSs). Each main report section is split into four subsections. The first subsection, 'Potential Greenhouse Gas (GHG) Impact of Stationary FCSs,' quantifies the degree to which GHG emissions can be reduced at a U.S. regional level with the implementation of different FCS designs. The second subsection, 'Optimizing the Design of Combined Heat and Power (CHP) FCSs,' discusses energy network optimization models that evaluate novel strategies for operating CHP FCSs so as to minimize (1) electricity and heating costs for building owners and (2) emissions of the primary GHG - carbon dioxide (CO{sub 2}). The third subsection, 'Optimizing the Design of Combined Cooling, Heating, and Electric Power (CCHP) FCSs,' is similar to the second subsection but is expanded to include capturing FCS heat with absorptive cooling cycles to produce cooling energy. The fourth subsection, - Thermodynamic and Chemical Engineering Models of CCHP FCSs,' discusses the physics and thermodynamic limits of CCHP FCSs.
Directory of Open Access Journals (Sweden)
Rongxin Tang
2015-10-01
Full Text Available Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one of the most popular deployment methods, in recent years the virtual force algorithm has been studied in detail by many scientists. In this paper, we focus on the virtual force algorithm and present a corresponding parameter investigation for mobile sensor deployment. We introduce an optimized virtual force algorithm based on the exchange force, in which a new shielding rule grounded in Delaunay triangulation is adopted. The algorithm employs a new performance metric called ‘pair-correlation diversion', designed to evaluate the uniformity and topology of the sensor distribution. We also discuss the implementation of the algorithm's computation and analyse the influence of experimental parameters on the algorithm. Our results indicate that the area ratio, φs, and the exchange force constant, G, influence the final performance of the sensor deployment in terms of the coverage rate, the convergence time and topology uniformity. Using simulations, we were able to verify the effectiveness of our algorithm and we obtained an optimal region for the (φs, G-parameter space which, in the future, could be utilized as an aid for experiments in robotic sensor deployment.
Zhou, Zhi; de Bedout, Juan Manuel; Kern, John Michael; Biyik, Emrah; Chandra, Ramu Sharat
2013-01-22
A system for optimizing customer utility usage in a utility network of customer sites, each having one or more utility devices, where customer site is communicated between each of the customer sites and an optimization server having software for optimizing customer utility usage over one or more networks, including private and public networks. A customer site model for each of the customer sites is generated based upon the customer site information, and the customer utility usage is optimized based upon the customer site information and the customer site model. The optimization server can be hosted by an external source or within the customer site. In addition, the optimization processing can be partitioned between the customer site and an external source.
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Zineb Simeu-Abazi
2009-11-01
Full Text Available The optimal management of a potable water distribution system requires the control of the reference (standard data, the control points, control of the drainage parameters (pressure, flow, etc. and maintenance parameters. The control of the mentioned data defines the network learning process [1]. Besides classic IT functions of acquisition, storage and data processing, a geographical information system (GIS can be used as the basis for an alarm system, allowing one to identify and to localize the presence of water leaks in the network [2]. In this article we propose an algorithm coupling the various drainage parameters for the management of the network. The algorithm leads to an optimal management of leaks. An application is in progress on the primary network in the region of Bonaberi in Douala, the largest city of Cameroon.
Optimal monitoring of computer networks
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.; Flanagan, D.
1997-08-01
The authors apply the ideas from optimal design theory to the very specific area of monitoring large computer networks. The behavior of these networks is so complex and uncertain that it is quite natural to use the statistical methods of experimental design which were originated in such areas as biology, behavioral sciences and agriculture, where the random character of phenomena is a crucial component and systems are too complicated to be described by some sophisticated deterministic models. They want to emphasize that only the first steps have been completed, and relatively simple underlying concepts about network functions have been used. Their immediate goal is to initiate studies focused on developing efficient experimental design techniques which can be used by practitioners working with large networks operating and evolving in a random environment.
Optimization on a Network-based Parallel Computer System for Supersonic Laminar Wing Design
Garcia, Joseph A.; Cheung, Samson; Holst, Terry L. (Technical Monitor)
1995-01-01
A set of Computational Fluid Dynamics (CFD) routines and flow transition prediction tools are integrated into a network based parallel numerical optimization routine. Through this optimization routine, the design of a 2-D airfoil and an infinitely swept wing will be studied in order to advance the design cycle capability of supersonic laminar flow wings. The goal of advancing supersonic laminar flow wing design is achieved by wisely choosing the design variables used in the optimization routine. The design variables are represented by the theory of Fourier series and potential theory. These theories, combined with the parallel CFD flow routines and flow transition prediction tools, provide a design space for a global optimal point to be searched. Finally, the parallel optimization routine enables gradient evaluations to be performed in a fast and parallel fashion.
On synthesis and optimization of steam system networks. 1. Sustained boiler efficiency
CSIR Research Space (South Africa)
Majozi, T
2010-08-01
Full Text Available The traditional steam system comprises a steam boiler and the associated heat exchanger network (HEN). Most research published in literature tends to address both the elements of the steam system as separate entities instead of analyzing...
Directory of Open Access Journals (Sweden)
C. G. Shi
2015-04-01
Full Text Available In this paper, the problem of low probability of identification (LPID improvement for radar network systems is investigated. Firstly, the security information is derived to evaluate the LPID performance for radar network. Then, without any prior knowledge of hostile intercept receiver, a novel fuzzy chance-constrained programming (FCCP based security information optimization scheme is presented to achieve enhanced LPID performance in radar network systems, which focuses on minimizing the achievable mutual information (MI at interceptor, while the attainable MI outage probability at radar network is enforced to be greater than a specified confidence level. Regarding to the complexity and uncertainty of electromagnetic environment in the modern battlefield, the trapezoidal fuzzy number is used to describe the threshold of achievable MI at radar network based on the credibility theory. Finally, the FCCP model is transformed to a crisp equivalent form with the property of trapezoidal fuzzy number. Numerical simulation results demonstrating the performance of the proposed strategy are provided.
Mathematical Aspects of Network Routing Optimization
Oliveira, Carlos AS
2011-01-01
Before the appearance of broadband links and wireless systems, networks have been used to connect people in new ways. Now, the modern world is connected through large-scale, computational networked systems such as the Internet. Because of the ever-advancing technology of networking, efficient algorithms have become increasingly necessary to solve some of the problems developing in this area. "Mathematical Aspects of Network Routing Optimization" focuses on computational issues arising from the process of optimizing network routes, such as quality of the resulting links and their reli
Sensitive Dependence of Optimal Network Dynamics on Network Structure
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Takashi Nishikawa
2017-11-01
Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
A swarm optimized neural network system for classification of microcalcification in mammograms.
Dheeba, J; Selvi, S Tamil
2012-10-01
Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A ( z )) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer.
Study on the evolutionary optimization of the topology of network control systems
DEFF Research Database (Denmark)
Zhou, Z.; Chen, B.; Wang, H.
2010-01-01
Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology...... optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition...... control network are considered in the optimisation process. In respect to the evolutionary algorithm design, an improved arena algorithm is proposed for the construction of the non-dominated set of the population. In addition, for the evaluation of individuals, the integrated use of the dominative...
Railway optimal network simulation for the development of regional transport-logistics system
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Mikhail Borisovich Petrov
2013-12-01
Full Text Available The dependence of logistics on mineral fuel is a stable tendency of regions development, though when making strategic plans of logistics in the regions, it is necessary to provide the alternative possibilities of power-supply sources change together with population density, transport infrastructure peculiarities, and demographic changes forecast. On the example of timber processing complex of the Sverdlovsk region, the authors suggest the algorithm of decision of the optimal logistics infrastructure allocation. The problem of regional railway network organization at the stage of slow transition from the prolonged stagnation to the new development is carried out. The transport networks’ configurations of countries on the Pacific Rim, which successfully developed nowadays, are analyzed. The authors offer some results of regional transport network simulation on the basis of artificial intelligence method. These methods let to solve the task with incomplete data. The ways of the transport network improvement in the Sverdlovsk region are offered.
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Shuai Zeng
2013-01-01
Full Text Available With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.
Optimal Design of the Feeder-Bus Network Based on the Transfer System
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Lianbo Deng
2013-01-01
Full Text Available This paper studied the classic feeder-bus network design problem (FBNDP, which can be described as follows: for the passenger travel demand between rail stations and bus stops on a given urban transit network, it designs the optimal feeder bus routes and frequencies so as to minimize the passengers’ travel expense and the operator’s cost. We extended the demand pattern of M-to-1 in most existing researches to M-to-M. We comprehensively considered the passenger travel cost, which includes the waiting and riding cost on the bus, riding cost on rail, and transfer cost between these two transportation modes, and presented a new genetic algorithm that determines the optimal feeder-bus operating frequencies under strict constraint conditions. The numerical examples under different demand patterns have been experienced and analysed, which showed the robustness and efficiency of the presented algorithm. We also found that the distribution pattern of the travel demand has a significant influence on the feeder-bus network construction.
Location based Network Optimizations for Mobile Wireless Networks
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen
The availability of location information in mobile devices, e.g., through built-in GPS receivers in smart phones, has motivated the investigation of the usefulness of location based network optimizations. Since the quality of input information is important for network optimizations, a main focus...... of this work is to evaluate how location based network optimizations are affected by varying quality of input information such as location information and user movements. The first contribution in this thesis concerns cooperative network-based localization systems. The investigations focus on assessing...... the achievable accuracy of future localization system in mobile settings, as well as quantifying the impact of having a realistic model of the required measurement exchanges. Secondly, this work has considered different large scale and small scale location based network optimizations, namely centralized relay...
Optimal Fragile Financial Networks
Castiglionesi, F.; Navarro, N.
2007-01-01
We study a financial network characterized by the presence of depositors, banks and their shareholders. Belonging to a financial network is beneficial for both the depositors and banks' shareholders since the return to investment increases with the number of banks connected. However, the network is
Optimal Sizing and Placement of Power-to-Gas Systems in Future Active Distribution Networks
DEFF Research Database (Denmark)
Diaz de Cerio Mendaza, Iker; Bhattarai, Bishnu Prasad; Kouzelis, Konstantinos
2015-01-01
of medium voltage distribution networks does not normally follow a common pattern, finding a singular and very particular layouts in each case. This fact, makes the placement and dimensioning of such flexible loads a complicated task for the distribution system operator in the future. This paper describes...
Directory of Open Access Journals (Sweden)
Yuqing Yang
2015-09-01
Full Text Available With global conventional energy depletion, as well as environmental pollution, utilizing renewable energy for power supply is the only way for human beings to survive. Currently, distributed generation incorporated into a distribution network has become the new trend, with the advantages of controllability, flexibility and tremendous potential. However, the fluctuation of distributed energy resources (DERs is still the main concern for accurate deployment. Thus, a battery energy storage system (BESS has to be involved to mitigate the bad effects of DERs’ integration. In this paper, optimal scheduling strategies for BESS operation have been proposed, to assist with consuming the renewable energy, reduce the active power loss, alleviate the voltage fluctuation and minimize the electricity cost. Besides, the electric vehicles (EVs considered as the auxiliary technique are also introduced to attenuate the DERs’ influence. Moreover, both day-ahead and real-time operation scheduling strategies were presented under the consideration with the constraints of BESS and the EVs’ operation, and the optimization was tackled by a fuzzy mathematical method and an improved particle swarm optimization (IPSO algorithm. Furthermore, the test system for the proposed strategies is a real distribution network with renewable energy integration. After simulation, the proposed scheduling strategies have been verified to be extremely effective for the enhancement of the distribution network characteristics.
ETRANS: an energy transport system optimization code for distributed networks of solar collectors
Energy Technology Data Exchange (ETDEWEB)
Barnhart, J.S.
1980-09-01
The optimization code ETRANS was developed at the Pacific Northwest Laboratory to design and estimate the costs associated with energy transport systems for distributed fields of solar collectors. The code uses frequently cited layouts for dish and trough collectors and optimizes them on a section-by-section basis. The optimal section design is that combination of pipe diameter and insulation thickness that yields the minimum annualized system-resultant cost. Among the quantities included in the costing algorithm are (1) labor and materials costs associated with initial plant construction, (2) operating expenses due to daytime and nighttime heat losses, and (3) operating expenses due to pumping power requirements. Two preliminary series of simulations were conducted to exercise the code. The results indicate that transport system costs for both dish and trough collector fields increase with field size and receiver exit temperature. Furthermore, dish collector transport systems were found to be much more expensive to build and operate than trough transport systems. ETRANS itself is stable and fast-running and shows promise of being a highly effective tool for the analysis of distributed solar thermal systems.
Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks.
Pérez-Solano, Juan J; Claver, Jose M; Ezpeleta, Santiago
2017-07-06
Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.
Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks
Directory of Open Access Journals (Sweden)
Juan J. Pérez-Solano
2017-07-01
Full Text Available Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.
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.
Combinatorial optimization networks and matroids
Lawler, Eugene
2011-01-01
Perceptively written text examines optimization problems that can be formulated in terms of networks and algebraic structures called matroids. Chapters cover shortest paths, network flows, bipartite matching, nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. A suitable text or reference for courses in combinatorial computing and concrete computational complexity in departments of computer science and mathematics.
Hamdi, Hadiwardoyo, Sigit P.; Correia, A. Gomes; Pereira, Paulo
2017-06-01
A road network requires timely maintenance to keep the road surface in good condition onward better services to improve accessibility and mobility. Strategies and maintenance techniques must be chosen in order to maximize road service level through cost-effective interventions. This approach requires an updated database, which the road network in Indonesia is supported by a manual and visual survey, also using NAASRA profiler. Furthermore, in this paper, the deterministic model of deterioration was used. This optimization model uses life cycle cost analysis (LCCA), applied in an integrated manner, using IRI indicator, and allows determining the priority of treatment, type of treatment and its relation to the cost. The purpose of this paper was focussed on the aspects of road maintenance management, i.e., maintenance optimization models for different levels of traffic and various initial of road distress conditions on the national road network in Indonesia. The implementation of Integrated Road Management System (IRMS) can provide a solution to the problem of cost constraints in the maintenance of the national road network. The results from this study found that as the lowest as agency cost, it will affect the increasing of user cost. With the achievement of the target plan scenario Pl000 with initial value IRI 2, it was found that the routine management throughout the year and in early reconstruction and periodic maintenance with a 30 mm thick overlay, will simultaneously provide a higher net benefit value and has the lowest total cost of transportation.
Optimization of Power Distribution Networks in Megacities
Manusov, V. Z.; Matrenin, P. V.; Ahyoev, J. S.; Atabaeva, L. Sh
2017-06-01
The study deals with the problem of city electrical networks optimization in big towns and megacities to increase electrical energy quality and decrease real and active power losses in the networks as well as in domestic consumers. The optimization is carried out according to the location selection and separate reactive power source in 10 kW networks of Swarm Intelligence algorithms, in particular, of Particle Swarm one. The problem solution based on Particle Swarm algorithm is determined by variables being discrete quantities and, in addition, there are several local minimums (troughs) to be available for a global minimum to be found. It is proved that the city power supply system optimization is carried out by the additional reactive power source to be installed at consumers location reducing reactive power flow, thereby, ensuring increase of power supply system quality and decrease of power losses in city networks.
Optimal system size for complex dynamics in random neural networks near criticality
Energy Technology Data Exchange (ETDEWEB)
Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)
2013-12-15
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Optimizations in Heterogeneous Mobile Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana
Heterogeneous Mobile Networks bring advantages over homogeneous deployments in achieving the demand for mobile network capacity and coverage not just outdoor rural and urban areas, but also to homes and enterprises where the large portion of the mobile traffic is generated. However......, the heterogeneity in the mobile networks bring many challenges that are discusses with this dissertation. More focus is placed on specific issues with indifferent areas of heterogeneity by proposing optimizations in order to overcome the considered problems.The heterogeneity of mobile networks, together...... with the densification of the base stations, bring into a very complex network management and operation control for the mobile operators. Furthermore, the need to provide always best connection and service with high quality demands for a joint overall network resource management. This thesis addresses this challenge...
Feasibility study on rehabilitation and optimization of gas pipeline network/system
Energy Technology Data Exchange (ETDEWEB)
NONE
2000-03-01
For the purpose of reducing greenhouse effect gas emissions, a survey was conducted on repairs and optimization of gas pipeline net/system in Bangladesh. In the survey, the measurement of methane gas concentration, wind direction/velocity and temperature was made for 16 stations of BC pipeline and BD pipeline including Ring Line. As a result of the measurement, the amount of methane leakage totaled 5,300 tons/year including 1,300 tons in BD pipeline, 2,500 tons in BC pipeline and 1,500 tons in Ring Line. For repairs/optimization of the pipeline net/system, the necessity of the following was pointed out: exchanges of gaskets, piping and valves; repairs of portions of the pipeline exposure; exchanges of pressure control valves and flowmeters; repair of the corrosion prevention system. In this improvement project, the reduction amount of greenhouse effect gas emissions will be 5,300 tons/year and approximately 106,000 tons in 20 years. The conservation will amount to 0.66 MMUS$/year. (NEDO)
Memory-optimal neural network approximation
Bölcskei, Helmut; Grohs, Philipp; Kutyniok, Gitta; Petersen, Philipp
2017-08-01
We summarize the main results of a recent theory-developed by the authors-establishing fundamental lower bounds on the connectivity and memory requirements of deep neural networks as a function of the complexity of the function class to be approximated by the network. These bounds are shown to be achievable. Specifically, all function classes that are optimally approximated by a general class of representation systems-so-called affine systems-can be approximated by deep neural networks with minimal connectivity and memory requirements. Affine systems encompass a wealth of representation systems from applied harmonic analysis such as wavelets, shearlets, ridgelets, α-shearlets, and more generally α-molecules. This result elucidates a remarkable universality property of deep neural networks and shows that they achieve the optimum approximation properties of all affine systems combined. Finally, we present numerical experiments demonstrating that the standard stochastic gradient descent algorithm generates deep neural networks which provide close-to-optimal approximation rates at minimal connectivity. Moreover, stochastic gradient descent is found to actually learn approximations that are sparse in the representation system optimally sparsifying the function class the network is trained on.
Energy Technology Data Exchange (ETDEWEB)
Kiesel, U. [HEW AG, Hamburg (Germany); Korri, P. [Tekla Qyj, Eschborn (Germany). Energieversorgung
2000-12-01
In June 200, the German electric utility Hamburgische Electricitaets-Werke AG (HEW) has bought and installed a high-performance, PC-based information system for all purposes of distribution network management tasks for their medium and low-voltage grids. The standard software Xpower of the Finnish software house Tekla Oyj has been streamlined with the requirements of the utility and is described with respect to the applications on site. (orig./CB) [German] Mit der Einfuehrung eines Netzinformationssystems (NIS) steht der Hamburgischen Electricitaets-Werke AG (HEW) seit Juni 2000 ein leistungsstarkes PC-basiertes Informationssystem zur Verfuegung, das langfristig die Projektierung und die Betriebsfuehrung der Mittel- und Niederspannungsnetze unterstuetzt und dokumentiert. Die Standardsoftware Xpower des finnischen Spezialisten Tekla Oyj wurde an die Anforderungen der HEW angepasst. (orig./CB)
DEFF Research Database (Denmark)
Gong, Hui; Olsen, Flemming Ove
acquisition card - DAQCard-700, and a self-learning mechanism - Neural Network. The optimization procedure starts with the welding process being carried out by continuously moving the focal point position from above a welding plate to below the plate, thus the process is ensured to be shifted from initially...... surface welding to deep/full penetration welding and back to surface welding again. A clear change on plasma brightness from the process is monitored by the photo diode on the front side of the plate with a viewing angle of 45o. The photo diode signal is acquired with the A/D converter card and installed......-learning mechanism - neural network as the essence of the control system is trained with the photo diode signals extracted from various welding processes with the changes on the laser power, translation speed, material and thickness of the plate, shielding gas type and flow rate, and welding configuration...
Optimization of spatial complex networks
Guillier, S.; Muñoz, V.; Rogan, J.; Zarama, R.; Valdivia, J. A.
2017-02-01
First, we estimate the connectivity properties of a predefined (fixed node locations) spatial network which optimizes a connectivity functional that balances construction and transportation costs. In this case we obtain a Gaussian distribution for the connectivity. However, when we consider these spatial networks in a growing process, we obtain a power law distribution for the connectivity. If the transportation costs in the functional involve the shortest geometrical path, we obtain a scaling exponent γ = 2.5. However, if the transportation costs in the functional involve just the shortest path, we obtain γ = 2.2. Both cases may be useful to analyze in some real networks.
Directory of Open Access Journals (Sweden)
Thang Trung Nguyen
2018-01-01
Full Text Available This paper proposes an effective novel cuckoo search algorithm (ENCSA in order to enhance the operation capacity of hydrothermal power systems, considering the constraints in the transmission network, and especially to overcome optimal power flow (OPF problems. This proposed algorithm is developed on the basis of the conventional cuckoo search algorithm (CSA by two modified techniques: the first is the self-adaptive technique for generating the second new solutions via discovery of alien eggs, and the second is the high-quality solutions based on a selection technique to keep the best solutions among all new and old solutions. These techniques are able to expand the search zone to overcome the local optimum trap and are able to improve the optimal solution quality and convergence speed as well. Therefore, the proposed method has significant impacts on the searching performances. The efficacy of the proposed method is investigated and verified using IEEE 30 and 118 buses systems via numerical simulation. The obtained results are compared with the conventional cuckoo search algorithm (CCSA and the modified cuckoo search algorithm (MCSA. As a result, the proposed method can overcome the OPF of hydrothermal power systems better than the conventional ones in terms of the optimal solution quality, convergence speed, and high success rate.
Directory of Open Access Journals (Sweden)
Yu Li
2017-05-01
Full Text Available With the incremental introduction of solar photovoltaic (PV generators into existing power systems, and their fast-growing share in the gross electricity generation, system voltage stability has become a critical issue. One of the major concerns is voltage fluctuation, due to large and random penetration of solar PV generators. To suppress severe system voltage deviation, reactive power control of the photovoltaic system inverter has been widely proposed in recent works; however, excessive use of reactive power control would increase both initial and operating costs. In this paper, a method for efficient allocation and control of reactive power injection using the sparse optimization technique is proposed. Based on a constrained linearized model describing the influence of reactive power injection on voltage magnitude change, the objective of this study is formulated as an optimization problem, which aims to find the best reactive power injection that minimizes the whole system voltage variation. Two types of formulations are compared: the first one is the conventional least-square optimization, while the second one is adopted from a sparse optimization technique, called the constrained least absolute shrinkage and selection operator (LASSO method. The constrained LASSO method adds ℓ 1 -norm penalty to the total reactive power injection, which contributes to the suppression of the number of control nodes with non-zero reactive power injection. The authors analyzed the effectiveness of the constrained LASSO method using the IEEE 39-bus and 57-bus power network as benchmark examples, under various PV power generation and allocation patterns. The simulation results show that the constrained LASSO method automatically selects the minimum number of inverters required for voltage regulation at the current operating point.
Contrast research of CDMA and GSM network optimization
Wu, Yanwen; Liu, Zehong; Zhou, Guangyue
2004-03-01
With the development of mobile telecommunication network, users of CDMA advanced their request of network service quality. While the operators also change their network management object from signal coverage to performance improvement. In that case, reasonably layout & optimization of mobile telecommunication network, reasonably configuration of network resource, improvement of the service quality, and increase the enterprise's core competition ability, all those have been concerned by the operator companies. This paper firstly looked into the flow of CDMA network optimization. Then it dissertated to some keystones in the CDMA network optimization, like PN code assignment, calculation of soft handover, etc. As GSM is also the similar cellular mobile telecommunication system like CDMA, so this paper also made a contrast research of CDMA and GSM network optimization in details, including the similarity and the different. In conclusion, network optimization is a long time job; it will run through the whole process of network construct. By the adjustment of network hardware (like BTS equipments, RF systems, etc.) and network software (like parameter optimized, configuration optimized, capacity optimized, etc.), network optimization work can improve the performance and service quality of the network.
Optimization of power system operation
Zhu, Jizhong
2015-01-01
This book applies the latest applications of new technologies topower system operation and analysis, including new and importantareas that are not covered in the previous edition. Optimization of Power System Operation covers both traditional andmodern technologies, including power flow analysis, steady-statesecurity region analysis, security constrained economic dispatch,multi-area system economic dispatch, unit commitment, optimal powerflow, smart grid operation, optimal load shed, optimalreconfiguration of distribution network, power system uncertaintyanalysis, power system sensitivity analysis, analytic hierarchicalprocess, neural network, fuzzy theory, genetic algorithm,evolutionary programming, and particle swarm optimization, amongothers. New topics such as the wheeling model, multi-areawheeling, the total transfer capability computation in multipleareas, are also addressed. The new edition of this book continues to provide engineers andac demics with a complete picture of the optimization of techn...
Optimization in a Networked Economy
Directory of Open Access Journals (Sweden)
Ahmet Sekreter
2017-10-01
Full Text Available An age of network has been living for the last decades. The information technologies have been used by hundreds of millions of users. These technologies are enabling to connect businesses and economic activities. One of the characteristics of the networked economy is the amount of data that produced due to the interlinking of firms, individuals, processes by businesses, and economic activities. Another issue with the networked economy is the complexity of the data. Extraction of the knowledge from the networked economy has challenges by the traditional approach since data is large scale, second decentralized, and third they connect many heterogeneous agents. The challenges can be overcome by the new optimization methods including human element or the social interactions with technological infrastructure.
Park, Kihong
2013-04-01
In this paper, we study a multiple-input single-output cognitive radio (CR) system where only the primary base station (BS) has multiple antennas. We consider a rate maximization problem of the secondary network under signal-to-interference-plus-noise-ratio constraints on the primary network in order to guarantee the quality-of-service for the latter network. While the interference due to the secondary transmission in the conventional underlay CR approach may severely degrade the performance of the primary network, we propose a primary BS-aided approach in which the primary BS helps relay the secondary users\\' signals instead of allowing them to communicate with each other via a direct path between them. In addition, an algorithm to find a near-optimal beamforming solution at the primary BS is proposed. Finally, based on some selected numerical results, we show that the proposed scheme outperforms the conventional underlay CR configuration over a wide transmit power range. © 2013 IEEE.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply....... The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
WiMax network planning and optimization
Zhang, Yan
2009-01-01
This book offers a comprehensive explanation on how to dimension, plan, and optimize WiMAX networks. The first part of the text introduces WiMAX networks architecture, physical layer, standard, protocols, security mechanisms, and highly related radio access technologies. It covers system framework, topology, capacity, mobility management, handoff management, congestion control, medium access control (MAC), scheduling, Quality of Service (QoS), and WiMAX mesh networks and security. Enabling easy understanding of key concepts and technologies, the second part presents practical examples and illu
Directory of Open Access Journals (Sweden)
C. H. López-Caraballo
2015-01-01
Full Text Available An artificial neural network (ANN based on particle swarm optimization (PSO was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term xt+6. The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the study of chaotic behaviour obtained from the predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with a Gaussian stochastic procedure (called stochastic hybrid ANN+PSO in order to obtain a new estimator of the predictions, which also allowed us to compute the uncertainties of predictions for noisy Mackey-Glass chaotic time series. Thus, we studied the impact of noise for several cases with a white noise level σN from 0.01 to 0.1.
Directory of Open Access Journals (Sweden)
Daniel J. Garcia
2015-07-01
Full Text Available The water footprint of energy systems must be considered, as future water scarcity has been identified as a major concern. This work presents a general life cycle network modeling and optimization framework for energy-based products and processes using a functional unit of liters of water consumed in the processing pathway. We analyze and optimize the water-energy nexus over the objectives of water footprint minimization, maximization of economic output per liter of water consumed (economic efficiency of water, and maximization of energy output per liter of water consumed (energy efficiency of water. A mixed integer, multiobjective nonlinear fractional programming (MINLFP model is formulated. A mixed integer linear programing (MILP-based branch and refine algorithm that incorporates both the parametric algorithm and nonlinear programming (NLP subproblems is developed to boost solving efficiency. A case study in bioenergy is presented, and the water footprint is considered from biomass cultivation to biofuel production, providing a novel perspective into the consumption of water throughout the value chain. The case study, optimized successively over the three aforementioned objectives, utilizes a variety of candidate biomass feedstocks to meet primary fuel products demand (ethanol, diesel, and gasoline. A minimum water footprint of 55.1 ML/year was found, economic efficiencies of water range from −$1.31/L to $0.76/L, and energy efficiencies of water ranged from 15.32 MJ/L to 27.98 MJ/L. These results show optimization provides avenues for process improvement, as reported values for the energy efficiency of bioethanol range from 0.62 MJ/L to 3.18 MJ/L. Furthermore, the proposed solution approach was shown to be an order of magnitude more efficient than directly solving the original MINLFP problem with general purpose solvers.
Phase transitions in Pareto optimal complex networks.
Seoane, Luís F; Solé, Ricard
2015-09-01
The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.
Design Optimization of Cyber-Physical Distributed Systems using IEEE Time-sensitive Networks (TSN)
DEFF Research Database (Denmark)
Pop, Paul; Lander Raagaard, Michael; Craciunas, Silviu S.
2016-01-01
In this paper we are interested in safety-critical real-time applications implemented on distributed architectures supporting the Time-SensitiveNetworking (TSN) standard. The ongoing standardization of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet...
Becus, Georges A.; Chan, Alistair K.
1993-01-01
Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.
Directory of Open Access Journals (Sweden)
Jian Chen
2018-02-01
Full Text Available In this paper, a hierarchical optimal operation strategy for a hybrid energy storage system (HESS is proposed, which is suitable to be utilized in distribution networks (DNs with high photovoltaic (PV penetration to achieve PV power smoothing, voltage regulation and price arbitrage. Firstly, a fuzzy-logic based variable step-size control strategy for an ultracapacitor (UC with the improvement of the lifetime of UC and tracking performance is adopted to smooth PV power fluctuations. The impact of PV forecasting errors is eliminated by adjusting the UC power in real time. Secondly, a coordinated control strategy, which includes centralized and local controls, is proposed for lithium-ion batteries. The centralized control is structured to determine the optimal battery unit for voltage regulation or price arbitrage according to lithium-ion battery performance indices. A modified lithium-ion battery aging model with better accuracy is proposed and the coupling relationship between the lifetime and the effective capacity is also considered. Additionally, the local control of the selected lithium-ion battery unit determines the charging/discharging power. A case study is used to validate the operation strategy and the results show that the lifetime equilibrium among different lithium-ion battery units can be achieved using the proposed strategy.
Optimization with Potts Neural Networks
Söderberg, Bo
The Potts Neural Network approach to non-binary discrete optimization problems is described. It applies to problems that can be described as a set of elementary `multiple choice' options. Instead of the conventional binary (Ising) neurons, mean field Potts neurons, having several available states, are used to describe the elementary degrees of freedom of such problems. The dynamics consists of iterating the mean field equations with annealing until convergence. Due to its deterministic character, the method is quite fast. When applied to problems of Graph Partition and scheduling types, it produces very good solutions also for problems of considerable size.
On synthesis and optimization of steam system networks. 3. Pressure drop consideration
CSIR Research Space (South Africa)
Price, T
2010-08-01
Full Text Available the likes of pipes and heat exchangers. Pressure drop correlations are therefore needed in the form of constraints so as to incorporate this into the optimization framework. Many correlations exist in litera- ture; however, those used by Kim and Smith2....2. Piping Pressure Drop. Kim and Smith2 define the piping pressure drop according to constraint 5. This is derived from commonly used pressure drop correlations, as well as a friction factor by Hewit et al.12 to approximate the Fanning Figure 1. Typical...
Optimal content delivery with network coding
Leong, Derek; Ho, Tracey; Cathey, Rebecca
2009-01-01
We present a unified linear program formulation for optimal content delivery in content delivery networks (CDNs), taking into account various costs and constraints associated with content dissemination from the origin server to storage nodes, data storage, and the eventual fetching of content from storage nodes by end users. Our formulation can be used to achieve a variety of performance goals and system behavior, including the bounding of fetch delay, load balancing, and robustness against...
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
National Aeronautics and Space Administration — An optimal alarm system is simply an optimal level-crossing predictor that can be designed to elicit the fewest false alarms for a fixed detection probability. It...
Airborne Network Optimization with Dynamic Network Update
2015-03-26
Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University...require small amounts of network bandwidth to perform routing. This thesis advocates the use of Kalman filters to predict network congestion in...airborne networks. Intelligent agents can make use of Kalman filter predictions to make informed decisions to manage communication in airborne networks. The
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Optimization for communications and networks
Saengudomlert, Poompat
2011-01-01
PrefaceList of FiguresIntroductionComponents of Optimization ProblemsClasses of Optimization ProblemsConvex OptimizationConvex Sets and Convex FunctionsProperties of Convex OptimizationFurther Properties of Convex SetsDual ProblemsLagrange MultipliersPrimal-Dual Optimality ConditionsSensitivity AnalysisNotes on Maximization ProblemsNumerical Algorithms for Unconstrained OptimizationNumerical Algorithms for Constrained OptimizationApplication: Transmit Power AllocationApplication: Minimum Delay RoutingExercise ProblemsLinear OptimizationIllustrative ExampleProperties of Linear Optimization Prob
Optimizing the next generation optical access networks
DEFF Research Database (Denmark)
Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso
2009-01-01
Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect of...
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Neural Networks for Synthesis and Optimization of Antenna Arrays
Directory of Open Access Journals (Sweden)
S. A. Djennas
2007-04-01
Full Text Available This paper describes a usual application of back-propagation neural networks for synthesis and optimization of antenna array. The neural network is able to model and to optimize the antennas arrays, by acting on radioelectric or geometric parameters and by taking into account predetermined general criteria. The neural network allows not only establishing important analytical equations for the optimization step, but also a great flexibility between the system parameters in input and output. This step of optimization becomes then possible due to the explicit relation given by the neural network. According to different formulations of the synthesis problem such as acting on the feed law (amplitude and/or phase and/or space position of the radiating sources, results on antennas arrays synthesis and optimization by neural networks are presented and discussed. However ANN is able to generate very fast the results of synthesis comparing to other approaches.
Control and optimization system
Xinsheng, Lou
2013-02-12
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Optimization of temporal networks under uncertainty
Wiesemann, Wolfram
2012-01-01
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization probl
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Intelligent Network Flow Optimization (INFLO) prototype acceptance test summary.
2015-05-01
This report summarizes the results of System Acceptance Testing for the implementation of the Intelligent Network : Flow Optimization (INFLO) Prototype bundle within the Dynamic Mobility Applications (DMA) portion of the Connected : Vehicle Program. ...
Hooghiemstra, P.B.; Krol, M.C.; Meirink, J.F.; Bergamaschi, P.; van der Werf, G.R.; Novelli, P.C.; Aben, I.; Rockmann, T.
2011-01-01
We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large
A Projection Neural Network for Constrained Quadratic Minimax Optimization.
Liu, Qingshan; Wang, Jun
2015-11-01
This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.
Neural network optimization, components, and design selection
Weller, Scott W.
1991-01-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and non-contrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of routing and classification types of optimization problems. It was their use in optimization that got me involved with Neural Networks. As it turned out, "optimization" used in this context was somewhat misleading, because while some network configurations could indeed solve certain kinds of optimization problems, the configuring or "training" of a Neural Network itself is an optimization problem, and most of the literature which talked about Neural Nets and optimization in the same breath did not speak to my goal of using Neural Nets to help solve lens optimization problems. I did eventually apply Neural Network to lens optimization, and I will touch on those results. The application of Neural Nets to the problem of lens selection was much more successful, and those results will dominate this paper.
Speed Optimization in Liner Shipping Network Design
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Karsten, Christian Vad; Pisinger, David
for the bunker consumption in the network as well as the transit time of cargo. Speed optimization has been considered for tramp shipping showing significant reductions in fuel consumption. However, variable speeds has not been considered for post optimization of the LSNDP, where speed optimization could result...
Perspective Application of Passive Optical Network with Optimized Bus Topology
Directory of Open Access Journals (Sweden)
P. Lafata
2012-06-01
Full Text Available Passive optical networks (PONs represent a promising solution for modern access telecommunication networks.These networks are able to meet the increasing demands on transmission rate for demanding multimedia services,while they can offer typical shared transmission speed of 1.25 or 2.5 Gbps. The major role in deploying opticaldistribution networks ODNs plays the maximum attenuable loss, which is caused mainly by passive optical splitters.This paper proposes an innovative application of passive optical networks with optimized bus topology especially forlocal backbone data networks. Due to using only passive components, it is necessary to optimize certain parameters,especially an overall attenuation balance. Considering the possibility of such optimization, the passive optical networkwith optimized bus topology provides several interesting opportunities for specific applications. This paper will presentselected aspects of passive optical networks and splitters with asymmetric splitting ratio. The essential part is focusedon the practical demonstration of their use to optimize the passive optical network with bus topology, which acts as alocal backbone network for structured cabling systems, and for local data networks in large buildings.
Energy Technology Data Exchange (ETDEWEB)
Costa, Geraldo R.M. da [Sao Paulo Univ., Sao Carlos, SP (Brazil). Escola de Engenharia
1994-12-31
This paper discusses, partially, the advantages and the disadvantages of the optimal power flow. It shows some of the difficulties of implementation and proposes solutions. An analysis is made comparing the power flow, BIGPOWER/CESP, and the optimal power flow, FPO/SEL, developed by the author, when applied to the CEPEL-ELETRONORTE and CESP systems. (author) 8 refs., 5 tabs.
Optimal channel efficiency in a sensory network
Mosqueiro, Thiago S.; Maia, Leonardo P.
2013-07-01
Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the
Power consumption optimization strategy for wireless networks
DEFF Research Database (Denmark)
Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola
2011-01-01
In this paper, we focus on the optimization of the total power utilization in a communication network. The utility function used in this paper aims for the maximization of joint network capacity in an interference limited environment. This paper outlines the various approaches currently being used...... in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...
Directory of Open Access Journals (Sweden)
Yan Geng
2017-01-01
Full Text Available This paper develops a type of data-driven networked optimal iterative learning control strategy for a class of discrete linear time-varying systems with one-operation Bernoulli-type communication delays. In terms of the stochastic Bernoulli-type one-operation communication delayed inputs and outputs, the previous-iteration synchronous compensations are adopted. By means of deriving gradients of two types of objective functions that express the optimal approximation of the system matrix and the minimal tracking error, the strategy approximates the system matrix and upgrades the control inputs in an interact mode as the iteration evolves. By taking advantage of matrix theory and statistical technique, it is derived that the approximation discrepancy of the system matrix is bounded and the mathematical expectation of the tracking error vanishes as the iteration goes on. Numerical simulations manifest the validity and effectiveness.
Dynamical System Approaches to Combinatorial Optimization
DEFF Research Database (Denmark)
Starke, Jens
2013-01-01
of large times as an asymptotically stable point of the dynamics. The obtained solutions are often not globally optimal but good approximations of it. Dynamical system and neural network approaches are appropriate methods for distributed and parallel processing. Because of the parallelization......Several dynamical system approaches to combinatorial optimization problems are described and compared. These include dynamical systems derived from penalty methods; the approach of Hopfield and Tank; self-organizing maps, that is, Kohonen networks; coupled selection equations; and hybrid methods...... thereof can be used as models for many industrial problems like manufacturing planning and optimization of flexible manufacturing systems. This is illustrated for an example in distributed robotic systems....
Optimization of OSPF Routing in IP Networks
Bley, Andreas; Fortz, Bernard; Gourdin, Eric; Holmberg, Kaj; Klopfenstein, Olivier; Pióro, Michał; Tomaszewski, Artur; Ümit, Hakan
The Internet is a huge world-wide packet switching network comprised of more than 13,000 distinct subnetworks, referred to as Autonomous Systems (ASs) autonomous system AS . They all rely on the Internet Protocol (IP) internet protocol IP for transport of packets across the network. And most of them use shortest path routing protocols shortest path routing!protocols , such as OSPF or IS-IS, to control the routing of IP packets routing!of IP packets within an AS. The idea of the routing is extremely simple — every packet is forwarded on IP links along the shortest route between its source and destination nodes of the AS. The AS network administrator can manage the routing of packets in the AS by supplying the so-called administrative weights of IP links, which specify the link lengths that are used by the routing protocols for their shortest path computations. The main advantage of the shortest path routing policy is its simplicity, allowing for little administrative overhead. From the network engineering perspective, however, shortest path routing can pose problems in achieving satisfactory traffic handling efficiency. As all routing paths depend on the same routing metric routing!metric , it is not possible to configure the routing paths for the communication demands between different pairs of nodes explicitly or individually; the routing can be controlled only indirectly and only as a whole by modifying the routing metric. Thus, one of the main tasks when planning such networks is to find administrative link weights that induce a globally efficient traffic routing traffic!routing configuration of an AS. It turns out that this task leads to very difficult mathematical optimization problems. In this chapter, we discuss and describe exact integer programming models and solution approaches as well as practically efficient smart heuristics for such shortest path routing problems shortest path routing!problems .
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Epidemiologically optimal static networks from temporal network data
Holme, Petter
2013-01-01
Network epidemiology's most important assumption is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We ...
Optimal scope of supply chain network & operations design
Ma, N.
2014-01-01
The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are
WiMAX network performance monitoring & optimization
DEFF Research Database (Denmark)
Zhang, Qi; Dam, H
2008-01-01
In this paper we present our WiMAX (worldwide interoperability for microwave access) network performance monitoring and optimization solution. As a new and small WiMAX network operator, there are many demanding issues that we have to deal with, such as limited available frequency resource, tight ...
Brocade: Optimal flow placement in SDN networks
CERN. Geneva
2015-01-01
Today' network poses several challanges to network providers. These challanges fall in to a variety of areas ranging from determining efficient utilization of network bandwidth to finding out which user applications consume majority of network resources. Also, how to protect a given network from volumetric and botnet attacks. Optimal placement of flows deal with identifying network issues and addressing them in a real-time. The overall solution helps in building new services where a network is more secure and more efficient. Benefits derived as a result are increased network efficiency due to better capacity and resource planning, better security with real-time threat mitigation, and improved user experience as a result of increased service velocity.
Optimal Power Flow Solution Using Ant Manners for Electrical Network
Directory of Open Access Journals (Sweden)
ALLAOUA, B.
2009-02-01
Full Text Available This paper presents ant manners and the collective intelligence for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.
Optimizing sentinel surveillance in temporal network epidemiology
Bai, Yuan; Yang, Bo; LIN, LIJUAN; Jose L Herrera; Du, Zhanwei; Holme, Petter
2017-01-01
To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but...
Distributed Optimization System
Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.
2004-11-30
A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.
Optimal hub location in pipeline networks
Energy Technology Data Exchange (ETDEWEB)
Dott, D.R.; Wirasinghe, S.C.; Chakma, A. [Univ. of Calgary, Alberta (Canada)
1996-12-31
This paper discusses optimization strategies and techniques for the location of natural gas marketing hubs in the North American gas pipeline network. A hub is a facility at which inbound and outbound network links meet and freight is redirected towards their destinations. Common examples of hubs used in the gas pipeline industry include gas plants, interconnects and market centers. Characteristics of the gas pipeline industry which are relevant to the optimization of transportation costs using hubs are presented. Allocation techniques for solving location-allocation problems are discussed. An outline of the research in process by the authors in the field of optimal gas hub location concludes the paper.
System floorplanning optimization
Browning, David W.
2013-01-10
Notebook and Laptop Original Equipment Manufacturers (OEMs) place great emphasis on creating unique system designs to differentiate themselves in the mobile market. These systems are developed from the \\'outside in\\' with the focus on how the system is perceived by the end-user. As a consequence, very little consideration is given to the interconnections or power of the devices within the system with a mentality of \\'just make it fit\\'. In this paper we discuss the challenges of Notebook system design and the steps by which system floor-planning tools and algorithms can be used to provide an automated method to optimize this process to ensure all required components most optimally fit inside the Notebook system.
System floorplanning optimization
Browning, David W.
2012-12-01
Notebook and Laptop Original Equipment Manufacturers (OEMs) place great emphasis on creating unique system designs to differentiate themselves in the mobile market. These systems are developed from the \\'outside in\\' with the focus on how the system is perceived by the end-user. As a consequence, very little consideration is given to the interconnections or power of the devices within the system with a mentality of \\'just make it fit\\'. In this paper we discuss the challenges of Notebook system design and the steps by which system floor-planning tools and algorithms can be used to provide an automated method to optimize this process to ensure all required components most optimally fit inside the Notebook system. © 2012 IEEE.
Optimization with PDE constraints ESF networking program 'OPTPDE'
2014-01-01
This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).
Small cell networks deployment, management, and optimization
Claussen, Holger; Ho, Lester; Razavi, Rouzbeh; Kucera, Stepan
2018-01-01
Small Cell Networks: Deployment, Management, and Optimization addresses key problems of the cellular network evolution towards HetNets. It focuses on the latest developments in heterogeneous and small cell networks, as well as their deployment, operation, and maintenance. It also covers the full spectrum of the topic, from academic, research, and business to the practice of HetNets in a coherent manner. Additionally, it provides complete and practical guidelines to vendors and operators interested in deploying small cells. The first comprehensive book written by well-known researchers and engineers from Nokia Bell Labs, Small Cell Networks begins with an introduction to the subject--offering chapters on capacity scaling and key requirements of future networks. It then moves on to sections on coverage and capacity optimization, and interference management. From there, the book covers mobility management, energy efficiency, and small cell deployment, ending with a section devoted to future trends and applicat...
Optimal configuration of digital communication network
Hwang, Yong G.
1990-12-01
As the costs for maintaining computer communication networks are rapidly rising, it is particularly important to design the network efficiently. The objective of this thesis is to model the minimum cost design of digital communications networks and propose a heuristical solution approach to the formulated model. The minimum cost design has been modeled as a zero-one integer programming problem. The Lagrangian relaxation method and subgradient optimization procedure have been used to find reasonably good feasible solutions. Although the reliability for computer communication networks is as important as the cost factor, only the cost factor is considered in the context of this thesis.
Optimal Channel Efficiency in a Sensory Network
Mosqueiro, Thiago S
2012-01-01
We show that the entropy of the distribution of avalanche lifetimes in the Kinouchi-Copelli model always achieves a maximum jointly with the dynamic range. This is noteworthy and nontrivial because while the dynamic range is an equilibrium average measure of the sensibility of a sensory system to a stimulus, the entropy of relaxation times is a purely dynamical quantity, independent of the stimulus rate, that can be interpreted as the efficiency of the network seen as a communication channel. The newly found optimization occurs for all topologies we tested, even when the distribution of avalanche lifetimes itself is not a power-law and when the entropy of the size distribution of avalanches is not concomitantly maximized, strongly suggesting that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.
Parallel Evolutionary Optimization for Neuromorphic Network Training
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)
2016-01-01
One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.
MEKELLE TRANSMISSION NETWORK USING OPTIMAL ...
African Journals Online (AJOL)
lab
In this paper, constraint dependent mutation function is implemented. The above mentioned operations of selection, crossover and mutation are repeated until the best solution is obtained. Genetic Algorithm Tool of MATLAB. The Optimization Tool of MATLAB is a graphical user interface (GUI) that can be used to solve an.
Performance of Networked Control Systems
Directory of Open Access Journals (Sweden)
Yingwei Zhang
2013-01-01
Full Text Available Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs with dual-network are designed by model predictive control method. The contributions are as follows. (1 The predictive control problem of the dual-network is considered. (2 The predictive performance of the dual-network is evaluated. (3 Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-loop system stability. Finally, the simulation results show the feasibility and effectiveness of the controllers designed.
Computer network defense system
Urias, Vincent; Stout, William M. S.; Loverro, Caleb
2017-08-22
A method and apparatus for protecting virtual machines. A computer system creates a copy of a group of the virtual machines in an operating network in a deception network to form a group of cloned virtual machines in the deception network when the group of the virtual machines is accessed by an adversary. The computer system creates an emulation of components from the operating network in the deception network. The components are accessible by the group of the cloned virtual machines as if the group of the cloned virtual machines was in the operating network. The computer system moves network connections for the group of the virtual machines in the operating network used by the adversary from the group of the virtual machines in the operating network to the group of the cloned virtual machines, enabling protecting the group of the virtual machines from actions performed by the adversary.
On sparsely connected optimal neural networks
Energy Technology Data Exchange (ETDEWEB)
Beiu, V. [Los Alamos National Lab., NM (United States); Draghici, S. [Wayne State Univ., Detroit, MI (United States)
1997-10-01
This paper uses two different approaches to show that VLSI- and size-optimal discrete neural networks are obtained for small fan-in values. These have applications to hardware implementations of neural networks, but also reveal an intrinsic limitation of digital VLSI technology: its inability to cope with highly connected structures. The first approach is based on implementing F{sub n,m} functions. The authors show that this class of functions can be implemented in VLSI-optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan-ins. In order to estimate the area (A) and the delay (T) of such networks, the following cost functions will be used: (i) the connectivity and the number-of-bits for representing the weights and thresholds--for good estimates of the area; and (ii) the fan-ins and the length of the wires--for good approximates of the delay. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on the size of fan-in 2 neural networks. They will generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan-in values. Finally, a size-optimal neural network of small constant fan-ins will be suggested for F{sub n,m} functions.
Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro
2015-01-01
Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.
DEFF Research Database (Denmark)
Limal, Emmanuel; Hjelme, Dag Roar; Stubkjær, Kristian Elmholdt
1998-01-01
We study the influence of the number of wavelengths per fibre and the line bit rate on network dimensions and transmission capacity utilisation for wavelength routed optical transport networks. The networks considered are of European scale, and the traffic matrices used are projections of a traffic...
Macke, Bruno; Ségard, Bernard; Wielonsky, Franck
2005-09-01
We demonstrate that significant effects in the "superluminal propagation" of light pulses cannot be observed without involving systems whose gain explodes outside the pulse spectrum. We explicitly determine the minimum norm of the gain to attain given superluminal effects and the transfer function of the corresponding optimal system. The gain norms, which would be required with the most efficient systems considered up to now (dispersive media, photonic barriers) to attain the same effects, are shown to exceed the minimum by several orders of magnitude. We finally estimate the largest superluminal advances which could be attained in a realistic experiment.
Topological Effects and Performance Optimization in Transportation Continuous Network Design
Directory of Open Access Journals (Sweden)
Jianjun Wu
2014-01-01
Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.
Determining optimal speed limits in traffic networks
Directory of Open Access Journals (Sweden)
Mansour Hadji Hosseinlou
2015-07-01
Full Text Available Determining the speed limit of road transport systems has a significant role in the speed management of vehicles. In most cases, setting a speed limit is considered as a trade-off between reducing travel time on one hand and reducing road accidents on the other, and the two factors of vehicle fuel consumption and emission rate of air pollutants have been neglected. This paper aims to evaluate optimal speed limits in traffic networks in a way that economized societal costs are incurred. In this study, experimental and field data as well as data from simulations are used to determine how speed is related to the emission of pollutants, fuel consumption, travel time, and the number of accidents. This paper also proposes a simple model to calculate the societal costs of travel and relate them to speed. As a case study, using emission test results on cars manufactured domestically and by simulating the suburban traffic flow by Aimsun software, the total societal costs of the Shiraz-Marvdasht motorway, which is one of the most traversed routes in Iran, have been estimated. The results of the study show that from a societal perspective, the optimal speed would be 73 km/h, and from a road user perspective, it would be 82 km/h (in 2011, the average speed of the passing vehicles on that motorway was 82 km/h. The experiments in this paper were run on three different vehicles with different types of fuel. In a comparative study, the results show that the calculated speed limit is lower than the optimal speed limits in Sweden, Norway, and Australia.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-11-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
Resilience-based optimal design of water distribution network
Suribabu, C. R.
2017-04-01
Optimal design of water distribution network is generally aimed to minimize the capital cost of the investments on tanks, pipes, pumps, and other appurtenances. Minimizing the cost of pipes is usually considered as a prime objective as its proportion in capital cost of the water distribution system project is very high. However, minimizing the capital cost of the pipeline alone may result in economical network configuration, but it may not be a promising solution in terms of resilience point of view. Resilience of the water distribution network has been considered as one of the popular surrogate measures to address ability of network to withstand failure scenarios. To improve the resiliency of the network, the pipe network optimization can be performed with two objectives, namely minimizing the capital cost as first objective and maximizing resilience measure of the configuration as secondary objective. In the present work, these two objectives are combined as single objective and optimization problem is solved by differential evolution technique. The paper illustrates the procedure for normalizing the objective functions having distinct metrics. Two of the existing resilience indices and power efficiency are considered for optimal design of water distribution network. The proposed normalized objective function is found to be efficient under weighted method of handling multi-objective water distribution design problem. The numerical results of the design indicate the importance of sizing pipe telescopically along shortest path of flow to have enhanced resiliency indices.
Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo
We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.
Optimal Sales Schemes for Network Goods
DEFF Research Database (Denmark)
Parakhonyak, Alexei; Vikander, Nick
This paper examines the optimal sequencing of sales in the presence of network externalities. A firm sells a good to a group of consumers whose payoff from buying is increasing in total quantity sold. The firm selects the order to serve consumers so as to maximize expected sales. It can serve all...
Optimizing Key Updates in Sensor Networks
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
2011-01-01
restrict the amount of data that may be exposed when a key is compromised. In this paper, we propose novel key update methods, and benefiting from stochastic model checking we propose a novel method for determining optimal key update strategies for custom network scenarios. We also present a case study...... where an application in commercial building automation is considered....
1985-01-01
Long-term and short-term objectives for the development of a network operating system for the Space Station are stated. The short-term objective is to develop a prototype network operating system for a 100 megabit/second fiber optic data bus. The long-term objective is to establish guidelines for writing a detailed specification for a Space Station network operating system. Major milestones are noted. Information is given in outline form.
Operational predictive optimal control of Barcelona water transport network
Pascual, J.; Romera, J.; Puig, V.; Cembrano, G.; Creus, R.; Minoves, M.
2013-01-01
This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Energy Technology Data Exchange (ETDEWEB)
Arjona, D.; Lay, R.K.; Harrington, R.J. [George Washington Univ., Washington, DC (United States)
1996-12-31
This paper is intended to present an approach to decision making in the operation of electrical power systems that will use a simple genetic algorithm as a teacher for the process of supervised learning of a feedforward, backpropagation artificial neural network. The fitness function used in the genetic algorithm is based on a load flow program and used to determine the optimal condition of the critical switches of the system. Reward and penalty functions are applied to it in order to emphasize environmental, economic, security, robustness, public policy and other considerations as they are predetermined by the philosophy of operation of the utility. These considerations (policies) become a part of the training set and operation of the neural network. The fitness function used by the genetic algorithm in order to rank the possible solutions is based on a load flow program. The binary nature of the genetic algorithm is particularly appropriate for the operation of switches. The result of the methodology is the equivalent of an on-line implicit load flow program used to redesign the configuration of the system in real time by opening and closing critical switches that are placed along the power system. Experiments leading towards the development of this methodology using real data from the Peninsular Control Area (the Yucatan Peninsula) of the National Mexican Interconnected Power Grid are presented.
Examination of operational optimization at Kemi district heating network
Directory of Open Access Journals (Sweden)
Ikonen Enso
2016-01-01
Full Text Available Model-based minimization of short term operational costs for energy distribution systems is examined. Based on the analogies between mass and energy distribution systems, a direct application of a stochastic optimal control approach was considered, previously developed and applied by the authors to water distribution systems. This paper examines the feasibility of the approach for district heating systems under certainty equivalence, i.e., the uncertain quantities are replaced by their nominal values. Simulations, based on a rough model of a part of the Kemi district heating network, are used to illustrate and validate the modeling and optimization approach. The outcomes show that optimal network loading can be designed with the considered tools.
Optimization of demand assigned SCPC satellite networks
Laborde, E.
1985-09-01
This paper investigates various system aspects and price tradeoffs involved in providing cost-effective Demand Assignment (DA) satellite channel service. Those network characteristics which significantly affect the ultimate cost-based decision are discussed. The number of stations participating in the DA or PA system, the number of satellite channels, and the traffic are kept parametric within expected limits, covering most of the present and future applications. In particular, the interrelationships between the network requirements (e.g., grade of service) and network elements, and the impacts of different blocking assignment allocations on the number of modems in the network is examined. A cost model is then derived that allows the evaluation and comparison of both DA and PA networks. Absolute and differential costing of PA and DA networks is permitted using economic quantities available to the system planner. These include modem cost, satellite channel cost, network size, and defined efficiency factors. Based on the differential cost comparisons for several DA and PA network strategies, tradeoffs have been derived to aid the system designer in configuring the most cost-effective DA network.
Optimizing online social networks for information propagation.
Directory of Open Access Journals (Sweden)
Duan-Bing Chen
Full Text Available Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Optimizing online social networks for information propagation.
Chen, Duan-Bing; Wang, Guan-Nan; Zeng, An; Fu, Yan; Zhang, Yi-Cheng
2014-01-01
Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
Generalized networking engineering: optimal pricing and routing in multiservice networks
Mitra, Debasis; Wang, Qiong
2002-07-01
One of the functions of network engineering is to allocate resources optimally to forecasted demand. We generalize the mechanism by incorporating price-demand relationships into the problem formulation, and optimizing pricing and routing jointly to maximize total revenue. We consider a network, with fixed topology and link bandwidths, that offers multiple services, such as voice and data, each having characteristic price elasticity of demand, and quality of service and policy requirements on routing. Prices, which depend on service type and origin-destination, determine demands, that are routed, subject to their constraints, so as to maximize revenue. We study the basic properties of the optimal solution and prove that link shadow costs provide the basis for both optimal prices and optimal routing policies. We investigate the impact of input parameters, such as link capacities and price elasticities, on prices, demand growth, and routing policies. Asymptotic analyses, in which network bandwidth is scaled to grow, give results that are noteworthy for their qualitative insights. Several numerical examples illustrate the analyses.
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Directory of Open Access Journals (Sweden)
Chien-Lin Huang
2015-01-01
Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.
Optimized evolution of networks for principal eigenvector localization
Pradhan, Priodyuti; Yadav, Alok; Dwivedi, Sanjiv K.; Jalan, Sarika
2017-08-01
Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating the localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having a localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of a few special structural features of such optimized networks, for instance, the presence of a set of edges which are necessary for localization, and rewiring only one of them leads to complete delocalization of the principal eigenvector. Furthermore, we report that principal eigenvector localization is not a consequence of changes in a single network property and, preferably, requires the collective influence of various distinct structural as well as spectral features.
Neural network optimization, components, and design selection
Weller, Scott W.
1990-07-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following
Optimized null model for protein structure networks.
Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa
2009-06-26
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by
Optimized null model for protein structure networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model
Congestion Relief of Contingent Power Network with Evolutionary Optimization Algorithm
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Abhinandan De
2012-03-01
Full Text Available This paper presents a differential evolution optimization technique based methodology for congestion management cost optimization of contingent power networks. In Deregulated systems, line congestion apart from causing stability problems can increase the cost of electricity. Restraining line flow to a particular level of congestion is quite imperative from stability as well as economy point of view. Employing Congestion Sensitivity Index proposed in this paper, the algorithm proposed can be adopted for selecting the congested lines in a power networks and then to search for a congestion constrained optimal generation schedule at the cost of a minimum congestion management charge without any load curtailment and installation of FACTS devices. It has been depicted that the methodology on application can provide better operating conditions in terms of improvement of bus voltage and loss profile of the system. The efficiency of the proposed methodology has been tested on an IEEE 30 bus benchmark system and the results look promising.
5G heterogeneous networks self-organizing and optimization
Rong, Bo; Kadoch, Michel; Sun, Songlin; Li, Wenjing
2016-01-01
This SpringerBrief provides state-of-the-art technical reviews on self-organizing and optimization in 5G systems. It covers the latest research results from physical-layer channel modeling to software defined network (SDN) architecture. This book focuses on the cutting-edge wireless technologies such as heterogeneous networks (HetNets), self-organizing network (SON), smart low power node (LPN), 3D-MIMO, and more. It will help researchers from both the academic and industrial worlds to better understand the technical momentum of 5G key technologies.
Seamless integrated network system for wireless communication systems
Wu, Gang; Mizuno, Mitsuhiko; Hase, Yoshihiro; Havinga, Paul J.M.
2006-01-01
To create a network that connects a plurality of wireless communication systems to create optimal systems for various environments, and that seamlessly integrates the resulting systems together in order to provide more efficient and advanced service in general. A network system that can seamlessly
Seamless integrated network system for wireless communication systems
Wu, Gang; Mizuno, Mitsuhiko; Hase, Yoshihiro; Havinga, Paul J.M.
2002-01-01
To create a network that connects a plurality of wireless communication systems to create optimal systems for various environments, and that seamlessly integrates the resulting systems together in order to provide more efficient and advanced service in general. A network system that can seamlessly
Optimal network modularity for information diffusion.
Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol
2014-08-22
We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.
Optimal Power Flow of the Algerian Electrical Network using an Ant Colony Optimization Method
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Tarek BOUKTIR
2005-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via an Ant Colony Optimization Meta-heuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the Algerian Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.
Nonlinear Non-convex Optimization of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef
2013-01-01
Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in p....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced....
Influence maximization in complex networks through optimal percolation
Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)
Network optimization including gas lift and network parameters under subsurface uncertainty
Energy Technology Data Exchange (ETDEWEB)
Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)
2013-08-01
Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A
Medical Optimization Network for Space Telemedicine Resources
Shah, R. V.; Mulcahy, R.; Rubin, D.; Antonsen, E. L.; Kerstman, E. L.; Reyes, D.
2017-01-01
INTRODUCTION: Long-duration missions beyond low Earth orbit introduce new constraints to the space medical system such as the inability to evacuate to Earth, communication delays, and limitations in clinical skillsets. NASA recognizes the need to improve capabilities for autonomous care on such missions. As the medical system is developed, it is important to have an ability to evaluate the trade space of what resources will be most important. The Medical Optimization Network for Space Telemedicine Resources was developed for this reason, and is now a system to gauge the relative importance of medical resources in addressing medical conditions. METHODS: A list of medical conditions of potential concern for an exploration mission was referenced from the Integrated Medical Model, a probabilistic model designed to quantify in-flight medical risk. The diagnostic and treatment modalities required to address best and worst-case scenarios of each medical condition, at the terrestrial standard of care, were entered into a database. This list included tangible assets (e.g. medications) and intangible assets (e.g. clinical skills to perform a procedure). A team of physicians working within the Exploration Medical Capability Element of NASA's Human Research Program ranked each of the items listed according to its criticality. Data was then obtained from the IMM for the probability of occurrence of the medical conditions, including a breakdown of best case and worst case, during a Mars reference mission. The probability of occurrence information and criticality for each resource were taken into account during analytics performed using Tableau software. RESULTS: A database and weighting system to evaluate all the diagnostic and treatment modalities was created by combining the probability of condition occurrence data with the criticalities assigned by the physician team. DISCUSSION: Exploration Medical Capabilities research at NASA is focused on providing a medical system to
Triangulation positioning system network
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Sfendourakis Marios
2017-01-01
Full Text Available This paper presents ongoing work on localization and positioning through triangulation procedure for a Fixed Sensors Network - FSN.The FSN has to work as a system.As the triangulation problem becomes high complicated in a case with large numbers of sensors and transmitters, an adequate grid topology is needed in order to tackle the detection complexity.For that reason a Network grid topology is presented and areas that are problematic and need further analysis are analyzed.The Network System in order to deal with problems of saturation and False Triangulations - FTRNs will have to find adequate methods in every sub-area of the Area Of Interest - AOI.Also, concepts like Sensor blindness and overall Network blindness, are presented. All these concepts affect the Network detection rate and its performance and ought to be considered in a way that the network overall performance won’t be degraded.Network performance should be monitored contentiously, with right algorithms and methods.It is also shown that as the number of TRNs and FTRNs is increased Detection Complexity - DC is increased.It is hoped that with further research all the characteristics of a triangulation system network for positioning will be gained and the system will be able to perform autonomously with a high detection rate.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Functional optimization of the arterial network
Mauroy, Benjamin
2014-01-01
We build an evolutionary scenario that explains how some crucial physiological constraints in the arterial network of mammals - i.e. hematocrit, vessels diameters and arterial pressure drops - could have been selected by evolution. We propose that the arterial network evolved while being constrained by its function as an organ. To support this hypothesis, we focus our study on one of the main function of blood network: oxygen supply to the organs. We consider an idealized organ with a given oxygen need and we optimize blood network geometry and hematocrit with the constraint that it must fulfill the organ oxygen need. Our model accounts for the non-Newtonian behavior of blood, its maintenance cost and F\\aa hr\\ae us effects (decrease in average concentration of red blood cells as the vessel diameters decrease). We show that the mean shear rates (relative velocities of fluid layers) in the tree vessels follow a scaling law related to the multi-scale property of the tree network, and we show that this scaling la...
Shahsavari, Shadab; Rezaie Shirmard, Leila; Amini, Mohsen; Abedin Dokoosh, Farid
2017-01-01
Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
An optimal routing strategy on scale-free networks
Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Qi, Zhaohui; Zhao, Yongbin
Traffic is one of the most fundamental dynamical processes in networked systems. With the traditional shortest path routing (SPR) protocol, traffic congestion is likely to occur on the hub nodes on scale-free networks. In this paper, we propose an improved optimal routing (IOR) strategy which is based on the betweenness centrality and the degree centrality of nodes in the scale-free networks. With the proposed strategy, the routing paths can accurately bypass hub nodes in the network to enhance the transport efficiency. Simulation results show that the traffic capacity as well as some other indexes reflecting transportation efficiency are further improved with the IOR strategy. Owing to the significantly improved traffic performance, this study is helpful to design more efficient routing strategies in communication or transportation systems.
Optimizing use of course management systems.
Wink, Diane M
2011-01-01
In this bimonthly series, the author examines how nurse educators can use Internet and Web-based computer technologies such as search, communication, and collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. The focus of this article is optimizing the use of a course management system.
Superstructure-based optimization of biorefinery networks: Production of biodiesel
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Orsi, Albert; Gani, Rafiqul
2015-01-01
industrial system based on biomass, an inexpensive, abundant and renewable raw material, is being established with sustainability as the main driving force [1]. The processing facilities for the production of multiple products (including biofuels and chemicals) from biomass are referred as biorefineries [2......]. The optimal synthesis of biorefinery networks problem is defined as: given a set of biomass derived feedstock and a set of desired final products and specifications, determine a flexible, sustainable and innovative processing network with the targets of minimum cost and sustainable development taking...... into account the available technologies, geographical location, future technological developments and global market changes. The problem of optimal design of biorefinery networks is solved in this work through three different stages: (i) synthesis stage, (ii) design stage, and (iii) innovation stage...
Optimal flux patterns in cellular metabolic networks
Energy Technology Data Exchange (ETDEWEB)
Almaas, E
2007-01-20
The availability of whole-cell level metabolic networks of high quality has made it possible to develop a predictive understanding of bacterial metabolism. Using the optimization framework of flux balance analysis, I investigate metabolic response and activity patterns to variations in the availability of nutrient and chemical factors such as oxygen and ammonia by simulating 30,000 random cellular environments. The distribution of reaction fluxes is heavy-tailed for the bacteria H. pylori and E. coli, and the eukaryote S. cerevisiae. While the majority of flux balance investigations have relied on implementations of the simplex method, it is necessary to use interior-point optimization algorithms to adequately characterize the full range of activity patterns on metabolic networks. The interior-point activity pattern is bimodal for E. coli and S. cerevisiae, suggesting that most metabolic reaction are either in frequent use or are rarely active. The trimodal activity pattern of H. pylori indicates that a group of its metabolic reactions (20%) are active in approximately half of the simulated environments. Constructing the high-flux backbone of the network for every environment, there is a clear trend that the more frequently a reaction is active, the more likely it is a part of the backbone. Finally, I briefly discuss the predicted activity patterns of the central-carbon metabolic pathways for the sample of random environments.
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Topology optimized permanent magnet systems
Bjørk, R.; Bahl, C. R. H.; Insinga, A. R.
2017-09-01
Topology optimization of permanent magnet systems consisting of permanent magnets, high permeability iron and air is presented. An implementation of topology optimization for magnetostatics is discussed and three examples are considered. The Halbach cylinder is topology optimized with iron and an increase of 15% in magnetic efficiency is shown. A topology optimized structure to concentrate a homogeneous field is shown to increase the magnitude of the field by 111%. Finally, a permanent magnet with alternating high and low field regions is topology optimized and a Λcool figure of merit of 0.472 is reached, which is an increase of 100% compared to a previous optimized design.
Directory of Open Access Journals (Sweden)
Shin-Hyung Kim
2013-09-01
Full Text Available An automatic pipe routing system is proposed and implemented. Generally, the pipe routing design as a part of the shipbuilding process requires a considerable number of man hours due to the complexity which comes from physical and operational constraints and the crucial influence on outfitting construction productivity. Therefore, the automation of pipe routing design operations and processes has always been one of the most important goals for improvements in shipbuilding design. The proposed system is applied to a pipe routing design in the engine room space of a commercial ship. The effectiveness of this system is verified as a reasonable form of support for pipe routing design jobs. The automatic routing result of this system can serve as a good basis model in the initial stages of pipe routing design, allowing the designer to reduce their design lead time significantly. As a result, the design productivity overall can be improved with this automatic pipe routing system.
Kim, Shin-Hyung; Ruy, Won-Sun; Jang, Beom Seon
2013-09-01
An automatic pipe routing system is proposed and implemented. Generally, the pipe routing design as a part of the shipbuilding process requires a considerable number of man hours due to the complexity which comes from physical and operational constraints and the crucial influence on outfitting construction productivity. Therefore, the automation of pipe routing design operations and processes has always been one of the most important goals for improvements in shipbuilding design. The proposed system is applied to a pipe routing design in the engine room space of a commercial ship. The effectiveness of this system is verified as a reasonable form of support for pipe routing design jobs. The automatic routing result of this system can serve as a good basis model in the initial stages of pipe routing design, allowing the designer to reduce their design lead time significantly. As a result, the design productivity overall can be improved with this automatic pipe routing system
Optimal Control of Mechanical Systems
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Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.
Sohn, Insoo; Liu, Huaping; Ansari, Nirwan
2015-01-01
An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.
Network systems security analysis
Yilmaz, Ä.°smail
2015-05-01
Network Systems Security Analysis has utmost importance in today's world. Many companies, like banks which give priority to data management, test their own data security systems with "Penetration Tests" by time to time. In this context, companies must also test their own network/server systems and take precautions, as the data security draws attention. Based on this idea, the study cyber-attacks are researched throughoutly and Penetration Test technics are examined. With these information on, classification is made for the cyber-attacks and later network systems' security is tested systematically. After the testing period, all data is reported and filed for future reference. Consequently, it is found out that human beings are the weakest circle of the chain and simple mistakes may unintentionally cause huge problems. Thus, it is clear that some precautions must be taken to avoid such threats like updating the security software.
Neural networks, penalty logic and optimality theory
Blutner, R.; Benz, A.; Blutner, R.
2009-01-01
Ever since the discovery of neural networks, there has been a controversy between two modes of information processing. On the one hand, symbolic systems have proven indispensable for our understanding of higher intelligence, especially when cognitive domains like language and reasoning are examined.
Optimizing cooperation on complex networks in the presence of failure.
Chen, Yu-Zhong; Lai, Ying-Cheng
2012-10-01
Cooperation has been recognized as a fundamental driving force in many natural, social, and economic systems. We investigate whether, given a complex-networked system in which agents (nodes) interact with one another according to the rules of evolutionary games and are subject to failure or death, cooperation can prevail and be optimized. We articulate a control scheme to maximize cooperation by introducing a time tolerance, a time duration that sustains an agent even if its payoff falls below a threshold. Strikingly, we find that a significant cooperation cluster can emerge when the time tolerance is approximately uniformly distributed over the network. A heuristic theory is derived to understand the optimization mechanism, which emphasizes the role played by medium-degree nodes. Implications for policy making to prevent or mitigate large-scale cascading breakdown are pointed out.
Particle swarm optimization based space debris surveillance network scheduling
Jiang, Hai; Liu, Jing; Cheng, Hao-Wen; Zhang, Yao
2017-02-01
The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.
Optimal Brain Surgeon on Artificial Neural Networks in
DEFF Research Database (Denmark)
Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine
2012-01-01
It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...
Optimizing the spatial pattern of networks for monitoring radioactive releases
Melles, S.J.; Heuvelink, G.B.M.; Twenhofel, C.J.W.; Dijk, van A.; Hiemstra, P.H.; Baume, O.P.; Stohlker, U.
2011-01-01
This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines
Transmission network expansion planning with simulation optimization
Energy Technology Data Exchange (ETDEWEB)
Bent, Russell W [Los Alamos National Laboratory; Berscheid, Alan [Los Alamos National Laboratory; Toole, G. Loren [Los Alamos National Laboratory
2010-01-01
Within the electric power literatW''e the transmi ssion expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models. Often, their approaches are tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (i.e. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a generalization of the powerful Limited Discrepancy Search (LDS) that encapsulates the complexity in a black box that may be queJied for information about the quality of a proposed expansion. This allows the development of a new optimization algOlitlun that is independent of the underlying power model.
Hybrid Intelligent Systems in Manufacturing Optimization
Gelgele, Hirpa Lemu
2002-01-01
The main objective of the work reported in this thesis has been to study and develop methodologies that can improve the communication gap between design and manufacturing systems. The emphasis has been on searching for the possible means of modeling and optimizing processes in an integrated design and manufacturing system environment using the combined capabilities (hybrids) of computational intelligence tools particularly that of artificial neural networks and genetic algorithms. Within...
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Topology optimized permanent magnet systems
DEFF Research Database (Denmark)
Bjørk, Rasmus; Bahl, Christian; Insinga, Andrea Roberto
2017-01-01
Topology optimization of permanent magnet systems consisting of permanent magnets, high permeability iron and air is presented. An implementation of topology optimization for magnetostatics is discussed and three examples are considered. The Halbach cylinder is topology optimized with iron...... and an increase of 15% in magnetic efficiency is shown. A topology optimized structure to concentrate a homogeneous field is shown to increase the magnitude of the field by 111%. Finally, a permanent magnet with alternating high and low field regions is topology optimized and a ΛcoolΛcool figure of merit of 0...
Directory of Open Access Journals (Sweden)
L. Krichen
2007-03-01
Full Text Available In this paper, we develop a method to maintain an acceptable voltages profile and minimization of active losses of a power system including wind generators in real time. These tasks are ensured by acting on capacitor and inductance benches implemented in the consuming nodes. To solve this problem, we minimize an objective function associated to active losses under constraints imposed on the voltages and the reactive productions of the various benches. The minimization procedure was realised by the use of genetic algorithms (GA. The major disadvantage of this technique is that it requires a significant computing time thus not making it possible to deal with the problem in real time. After a training phase, a neural model has the capacity to provide a good estimation of the voltages, the reactive productions and the losses for forecast curves of the load and the wind speed, in real time.
Directory of Open Access Journals (Sweden)
Zhongrong Zhang
2016-01-01
Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Yang, Yongheng
2017-01-01
Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most...... significant impact on the network voltage level. Following, it ensures the optimal set-points of both active power and reactive power for the selected inverters, guaranteeing the entire system operating constraints (e.g., the network voltage magnitude) within reasonable ranges. However, the intermittent...
Optimizing queries in distributed systems
Directory of Open Access Journals (Sweden)
Ion LUNGU
2006-01-01
Full Text Available This research presents the main elements of query optimizations in distributed systems. First, data architecture according with system level architecture in a distributed environment is presented. Then the architecture of a distributed database management system (DDBMS is described on conceptual level followed by the presentation of the distributed query execution steps on these information systems. The research ends with presentation of some aspects of distributed database query optimization and strategies used for that.
Broadband Access Network Planning Optimization Considering Real Copper Cable Lengths
Peternel, Blaž Kos, Andrej
Broadband access network planning strategies with techno-economic calculations are important topics, when optimal broadband network deployments are considered. This paper analyzes optimal deployment combination of digital subscriber line technologies (xDSL) and fiber to the home technologies (FTTx), following different user bandwidth demand scenarios. For this reason, optimal placement of remote digital subscriber line multiplexer (RDSLAM) is examined. Furthermore, the article also discusses the economy of investments, depending on certain investment threshold and the reach of different xDSL technologies. Finally, the difference between broadband network deployment in a characteristic urban and rural area in Republic of Slovenia, in terms of required optical cable dig length per household is shown. A tree structure network model of a traditional copper access network is introduced. A dynamic programming logic, with recursion as a basis of a tree structure examination and evaluation of optimal network elements placement is used. The tree structure network model considers several real network parameters (e. g.: copper cable lengths, user coordinates, node coordinates). The main input for the optimization is a local loop distance between each user and a candidate node for RDSLAM placement. Modelling of copper access networks with a tree structure makes new extensions in planning optimization of broadband access networks. Optimization of network elements placement has direct influence on efficiency and profitability of broadband access telecommunication networks.
Optimization Models for Flexible and Adaptive SDN Network Virtualization Layers
Zerwas, Johannes; Blenk, Andreas; Kellerer, Wolfgang
2016-01-01
Network hypervisors provide the network virtualization layer for Software Defined Networking (SDN). They enable virtual network (VN) tenants to bring their SDN controllers to program their logical networks individually according to their demands. In order to make use of the high flexibility of virtual SDN networks and to provide high performance, the deployment of the virtualization layer needs to adapt to changing VN demands. This paper initializes the study of the optimization of dynamic SD...
Optimal Dynamical Range of Excitable Networks at Criticality
Kinouchi, Osame
2006-01-01
A recurrent idea in the study of complex systems is that optimal information processing is to be found near bifurcation points or phase transitions. However, this heuristic hypothesis has few (if any) concrete realizations where a standard and biologically relevant quantity is optimized at criticality. Here we give a clear example of such a phenomenon: a network of excitable elements has its sensitivity and dynamic range maximized at the critical point of a non-equilibrium phase transition. Our results are compatible with the essential role of gap junctions in olfactory glomeruli and retinal ganglionar cell output. Synchronization and global oscillations also appear in the network dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement of dynamic range, therefore allowing the coding of information spanning several orders of magnitude. The mechanism could provide a microscopic neural basis for psychophysical laws.
Embedded Systems Design: Optimization Challenges
DEFF Research Database (Denmark)
Pop, Paul
2005-01-01
-to-market, and reduce development and manufacturing costs. In this paper, the author introduces several embedded systems design problems, and shows how they can be formulated as optimization problems. Solving such challenging design optimization problems are the key to the success of the embedded systems design...... of designing such systems is becoming increasingly important and difficult at the same time. New automated design optimization techniques are needed, which are able to: successfully manage the complexity of embedded systems, meet the constraints imposed by the application domain, shorten the time...
Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen
2013-02-01
This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.
Nazempour, Rezvan; Monfared, Mohammad Ali Saniee; Zio, Enrico
2016-01-01
Drinking water for human health and well-being is crucial. Accidental and intentional water contamination can pose great danger to consumers. Optimal design of a system that can quickly detect the presence of contamination in a water distribution network is very challenging for technical and operational reasons. However, on the one hand improvement in chemical and biological sensor technology has created the possibility of designing efficient contamination detection systems. On the other hand...
Topology Optimization for Energy Management in Underwater Sensor Networks
2015-02-01
and communication. This multi-objective cost functional leads to non-dominant optimization. Such a problem is formulated as a Pareto -optimal trade...K. Jha1 Thomas A. Wettergren2 Asok Ray1 Kushal Mukherjee3 Keywords: Underwater Sensor Network, Energy Management, Pareto Optimization, Adaptation...communication models for under- water environment. The approximate Pareto -optimal surface is obtained as a trade-off between network lifetime and probability
Optimizing intermittent water supply in urban pipe distribution networks
Lieb, Anna M; Wilkening, Jon
2015-01-01
In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets, and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. We demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure gradients.
INTERVALS OPTIMIZATION OF SYSTEMS INFORMATION SECURITY INSPECTION
Directory of Open Access Journals (Sweden)
V. A. Bogatyrev
2014-09-01
Full Text Available A Markov model is suggested for secure information systems, functioning under conditions of destructive impacts, which aftereffects are found by on-line and test control. It is assumed that on-line control, in contrast to the test one, is char- acterized by the limited control completeness, but does not require the stopping of computational process. The aim of re- search is to create models that optimize intervals of test control initialization by the criterion of probability maximization for system stay in the ready state to secure fulfillment of the functional requests and minimization of the dangerous system states in view of the uncertainty and intensity variance of the destructive impacts. Variants of testing intervals optimization are con- sidered depending on the intensity of destructive impacts by the criterion of the maximum system availability for the safe execution of queries. Optimization is carried out with and without adaptation to the actual intensity change of destructive impacts. The efficiency of adaptive change for testing periods is shown depending on the observed activity of destructive impacts. The solution of optimization problem is obtained by built-in tools of computer mathematics Mathcad 15, including symbolic mathematics for solution of systems of algebraic equations. The proposed models and methods of determining the optimal testing intervals can find their application in the system design of computer systems and networks of critical applications, working under conditions of destabilizing actions with the increased requirements for their safety.
Structural brain network: What is the effect of LiFE optimization of whole brain tractography?
Directory of Open Access Journals (Sweden)
Shouliang eQi
2016-02-01
Full Text Available Structural brain networks constructed based on diffusion-weighted MRI (dMRI have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE method, and study the differences between the networks with and without LiFE optimization. For a cohort of 9 healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75-0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The
An artificial immune system algorithm approach for reconfiguring distribution network
Syahputra, Ramadoni; Soesanti, Indah
2017-08-01
This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Li
2013-12-01
Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.
Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access
Zafar, Ammar
2012-09-16
Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources. © 2012 Ammar Zafar et al.
Neural network for constrained nonsmooth optimization using Tikhonov regularization.
Qin, Sitian; Fan, Dejun; Wu, Guangxi; Zhao, Lijun
2015-03-01
This paper presents a one-layer neural network to solve nonsmooth convex optimization problems based on the Tikhonov regularization method. Firstly, it is shown that the optimal solution of the original problem can be approximated by the optimal solution of a strongly convex optimization problems. Then, it is proved that for any initial point, the state of the proposed neural network enters the equality feasible region in finite time, and is globally convergent to the unique optimal solution of the related strongly convex optimization problems. Compared with the existing neural networks, the proposed neural network has lower model complexity and does not need penalty parameters. In the end, some numerical examples and application are given to illustrate the effectiveness and improvement of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
Towards Optimal Event Detection and Localization in Acyclic Flow Networks
Agumbe Suresh, Mahima
2012-01-03
Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.
Optimization of neural network algorithm of the land market description
Directory of Open Access Journals (Sweden)
M. A. Karpovich
2016-01-01
Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.
The Structural Optimization System CAOS
DEFF Research Database (Denmark)
Rasmussen, John
1990-01-01
CAOS is a system for structural shape optimization. It is closely integrated in a Computer Aided Design environment and controlled entirely from the CAD-system AutoCAD. The mathematical foundation of the system is briefly presented and a description of the CAD-integration strategy is given together...
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
Optimization and Control of Electric Power Systems
Energy Technology Data Exchange (ETDEWEB)
Lesieutre, Bernard C. [Univ. of Wisconsin, Madison, WI (United States); Molzahn, Daniel K. [Univ. of Wisconsin, Madison, WI (United States)
2014-10-17
The analysis and optimization needs for planning and operation of the electric power system are challenging due to the scale and the form of model representations. The connected network spans the continent and the mathematical models are inherently nonlinear. Traditionally, computational limits have necessitated the use of very simplified models for grid analysis, and this has resulted in either less secure operation, or less efficient operation, or both. The research conducted in this project advances techniques for power system optimization problems that will enhance reliable and efficient operation. The results of this work appear in numerous publications and address different application problems include optimal power flow (OPF), unit commitment, demand response, reliability margins, planning, transmission expansion, as well as general tools and algorithms.
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
Directory of Open Access Journals (Sweden)
Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Self-Optimization of LTE Networks Utilizing Celnet Xplorer
Buvaneswari, A; Polakos, Paul; Buvaneswari, Arumugam
2010-01-01
In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performan...
Scalable Optimization Methods for Distribution Networks With High PV Integration
Energy Technology Data Exchange (ETDEWEB)
Guggilam, Swaroop S.; Dall' Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.; Giannakis, Georgios B.
2016-07-01
This paper proposes a suite of algorithms to determine the active- and reactive-power setpoints for photovoltaic (PV) inverters in distribution networks. The objective is to optimize the operation of the distribution feeder according to a variety of performance objectives and ensure voltage regulation. In general, these algorithms take a form of the widely studied ac optimal power flow (OPF) problem. For the envisioned application domain, nonlinear power-flow constraints render pertinent OPF problems nonconvex and computationally intensive for large systems. To address these concerns, we formulate a quadratic constrained quadratic program (QCQP) by leveraging a linear approximation of the algebraic power-flow equations. Furthermore, simplification from QCQP to a linearly constrained quadratic program is provided under certain conditions. The merits of the proposed approach are demonstrated with simulation results that utilize realistic PV-generation and load-profile data for illustrative distribution-system test feeders.
UMTS network planning, optimization, and inter-operation with GSM
Rahnema, Moe
2008-01-01
UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle
Context-Aware Local Optimization of Sensor Network Deployment
Directory of Open Access Journals (Sweden)
Meysam Argany
2015-07-01
Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.
Optimization of Neuro-Fuzzy System
Directory of Open Access Journals (Sweden)
M. Sarosa
2007-05-01
Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.
Optimal resource allocation for efficient transport on complex networks
Gong, Xiaofeng; Kun, Li; Lai, C.-H.
2008-07-01
The problem of efficient transport on a complex network is studied in this paper. We find that there exists an optimal way to allocate resources for information processing on each node to achieve the best transport capacity of the network, or the largest input information rate which does not cause jamming in network traffic, provided that the network structure and routing strategy are given. More interestingly, this achievable network capacity limit is closely related to the topological structure of the network, and is actually inversely proportional to the average distance of the network, measured according to the same routing rule.
Directory of Open Access Journals (Sweden)
Li Ran
2017-01-01
Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.
Outage Analysis and Optimization of SWIPT in Network-Coded Two-Way Relay Networks
Directory of Open Access Journals (Sweden)
Ruihong Jiang
2017-01-01
Full Text Available This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT in network-coded two-way relay systems, where a relay first harvests energy from the signals transmitted by two sources and then uses the harvested energy to forward the received information to the two sources. We consider two transmission protocols, power splitting two-way relay (PS-TWR and time switching two-way relay (TS-TWR protocols. We present two explicit expressions for the system outage probability of the two protocols and further derive approximate expressions for them in high and low SNR cases. To explore the system performance limits, two optimization problems are formulated to minimize the system outage probability. Since the problems are nonconvex and have no known solution methods, a genetic algorithm- (GA- based algorithm is designed. Numerical and simulation results validate our theoretical analysis. It is shown that, by jointly optimizing the time assignment and SWIPT receiver parameters, a great performance gain can be achieved for both PS-TWR and TS-TWR. Moreover, the optimized PS-TWR always outperforms the optimized TS-TWR in terms of outage performance. Additionally, the effects of parameters including relay location and transmit powers are also discussed, which provide some insights for the SWIPT-enabled two-way relay networks.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
Directory of Open Access Journals (Sweden)
Trung Dung Ngo
2011-08-01
Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.
LinkMind: Link Optimization in Swarming Mobile Sensor Networks
DEFF Research Database (Denmark)
Ngo, Trung Dung
2012-01-01
optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm......A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One...... of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high ...
Optimizing Seismic Monitoring Networks for EGS and Conventional Geothermal Projects
Kraft, Toni; Herrmann, Marcus; Bethmann, Falko; Stefan, Wiemer
2013-04-01
In the past several years, geological energy technologies receive growing attention and have been initiated in or close to urban areas. Some of these technologies involve injecting fluids into the subsurface (e.g., oil and gas development, waste disposal, and geothermal energy development) and have been found or suspected to cause small to moderate sized earthquakes. These earthquakes, which may have gone unnoticed in the past when they occurred in remote sparsely populated areas, are now posing a considerable risk for the public acceptance of these technologies in urban areas. The permanent termination of the EGS project in Basel, Switzerland after a number of induced ML~3 (minor) earthquakes in 2006 is one prominent example. It is therefore essential for the future development and success of these geological energy technologies to develop strategies for managing induced seismicity and keeping the size of induced earthquakes at a level that is acceptable to all stakeholders. Most guidelines and recommendations on induced seismicity published since the 1970ies conclude that an indispensable component of such a strategy is the establishment of seismic monitoring in an early stage of a project. This is because an appropriate seismic monitoring is the only way to detect and locate induced microearthquakes with sufficient certainty to develop an understanding of the seismic and geomechanical response of the reservoir to the geotechnical operation. In addition, seismic monitoring lays the foundation for the establishment of advanced traffic light systems and is therefore an important confidence building measure towards the local population and authorities. We have developed an optimization algorithm for seismic monitoring networks in urban areas that allows to design and evaluate seismic network geometries for arbitrary geotechnical operation layouts. The algorithm is based on the D-optimal experimental design that aims to minimize the error ellipsoid of the linearized
Boussalis, Dhemetrios; Wang, Shyh J.
1992-01-01
This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Distributed optimization of a multisubchannel Ad Hoc cognitive radio network
Leith, Alex
2012-05-01
In this paper, we study the distributed-duality-based optimization of a multisubchannel ad hoc cognitive radio network (CRN) that coexists with a multicell primary radio network (PRN). For radio resource allocation in multiuser orthogonal frequency-division multiplexing (MU-OFDM) systems, the orthogonal-access-based exclusive subchannel assignment (ESA) technique has been a popular method, but it is suboptimal in ad hoc networks, because nonorthogonal access between multiple secondary-user links by using shared subchannel assignment (SSA) can bring a higher weighted sum rate. We utilize the Lagrangian dual composition tool and design low-complexity near-optimal SSA resource allocation methods, assuming practical discrete-rate modulation and that the CRN-to-PRN interference constraint has to strictly be satisfied. However, available SSA methods for CRNs are either suboptimal or involve high complexity and suffer from slow convergence. To address this problem, we design fast-convergence SSA duality schemes and introduce several novel methods to increase the speed of convergence and to satisfy various system constraints with low complexity. For practical implementation in ad hoc CRNs, we design distributed-duality schemes that involve only a small number of CRN local information exchanges for dual update. The effects of many system parameters are presented through simulation results, which show that the near-optimal SSA duality scheme can perform significantly better than the suboptimal ESA duality and SSA-iterative waterfilling schemes and that the performance loss of the distributed schemes is small, compared with their centralized counterparts. © 2012 IEEE.
Truss systems and shape optimization
Pricop, Mihai Victor; Bunea, Marian; Nedelcu, Roxana
2017-07-01
Structure optimization is an important topic because of its benefits and wide applicability range, from civil engineering to aerospace and automotive industries, contributing to a more green industry and life. Truss finite elements are still in use in many research/industrial codesfor their simple stiffness matrixand are naturally matching the requirements for cellular materials especially considering various 3D printing technologies. Optimality Criteria combined with Solid Isotropic Material with Penalization is the optimization method of choice, particularized for truss systems. Global locked structures areobtainedusinglocally locked lattice local organization, corresponding to structured or unstructured meshes. Post processing is important for downstream application of the method, to make a faster link to the CAD systems. To export the optimal structure in CATIA, a CATScript file is automatically generated. Results, findings and conclusions are given for two and three-dimensional cases.
Proposal for optimal placement platform of bikes using queueing networks.
Mizuno, Shinya; Iwamoto, Shogo; Seki, Mutsumi; Yamaki, Naokazu
2016-01-01
In recent social experiments, rental motorbikes and rental bicycles have been arranged at nodes, and environments where users can ride these bikes have been improved. When people borrow bikes, they return them to nearby nodes. Some experiments have been conducted using the models of Hamachari of Yokohama, the Niigata Rental Cycle, and Bicing. However, from these experiments, the effectiveness of distributing bikes was unclear, and many models were discontinued midway. Thus, we need to consider whether these models are effectively designed to represent the distribution system. Therefore, we construct a model to arrange the nodes for distributing bikes using a queueing network. To adopt realistic values for our model, we use the Google Maps application program interface. Thus, we can easily obtain values of distance and transit time between nodes in various places in the world. Moreover, we apply the distribution of a population to a gravity model and we compute the effective transition probability for this queueing network. If the arrangement of the nodes and number of bikes at each node is known, we can precisely design the system. We illustrate our system using convenience stores as nodes and optimize the node configuration. As a result, we can optimize simultaneously the number of nodes, node places, and number of bikes for each node, and we can construct a base for a rental cycle business to use our system.
Distributed optimization system and method
Hurtado, John E.; Dohrmann, Clark R.; Robinett, III, Rush D.
2003-06-10
A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.
Optimization theory for large systems
Lasdon, Leon S
2002-01-01
Important text examines most significant algorithms for optimizing large systems and clarifying relations between optimization procedures. Much data appear as charts and graphs and will be highly valuable to readers in selecting a method and estimating computer time and cost in problem-solving. Initial chapter on linear and nonlinear programming presents all necessary background for subjects covered in rest of book. Second chapter illustrates how large-scale mathematical programs arise from real-world problems. Appendixes. List of Symbols.
Perotto, E.
1987-08-01
The Network Operating System is an addition to CMS designed to allow multitasking operation, while conserving all the facilities of CMS: file system, interactivity, high level language environment. Multitasking is useful for server virtual machines, e.g. Network Transport Managers, File Managers, Disk space Managers, Tape Unit Managers, where the execution of a task involves long waits due to I/O completion, VCMF communication delays or human responses, during which the task status stays as a control block in memory, while the virtual machine serves other users executing the same lines of code. Multitasking is not only for multi-user service: a big data reduction program may run as a main task, while a side task, connected to the virtual console, gives reports on the ongoing work of the main task in response to user commands and steers the main task through common data. All the service routines (Wait, Create and Delete Task, Get and Release Buffer, VMCF Open and Close Link, Send and Receive, I/O and Console Routines) are FORTRAN callable, and may be used from any language environment consistent with the same parameter passing conventions. The outstanding feature of this system is efficiency, no user defined SVC are used, and the use of other privileged instructions as LPSW or SSM is the bare necessary, so that CP (with the associated overhead) is not too involved. System code and read-only data are write-protected with a different storage key from CMS and user program.
High-speed, intra-system networks
Energy Technology Data Exchange (ETDEWEB)
Quinn, Heather M [Los Alamos National Laboratory; Graham, Paul S [Los Alamos National Laboratory; Manuzzato, Andrea [Los Alamos National Laboratory; Fairbanks, Tom [Los Alamos National Laboratory; Dallmann, Nicholas [Los Alamos National Laboratory; Desgeorges, Rose [Los Alamos National Laboratory
2010-06-28
Recently, engineers have been studying on-payload networks for fast communication paths. Using intra-system networks as a means to connect devices together allows for a flexible payload design that does not rely on dedicated communication paths between devices. In this manner, the data flow architecture of the system can be dynamically reconfigured to allow data routes to be optimized for the application or configured to route around devices that are temporarily or permanently unavailable. To use intra-system networks, devices will need network controllers and switches. These devices are likely to be affected by single-event effects, which could affect data communication. In this paper we will present radiation data and performance analysis for using a Broadcom network controller in a neutron environment.
Simulated Annealing in Optimization of Energy Production in a Water Supply Network
Almeida Samora, Irene; Franca, Mário J.; Schleiss, Anton; Helena M. Ramos
2016-01-01
In water supply systems, the potential exists for micro-hydropower that uses the pressure excess in the networks to produce electricity. However, because urban drinking water networks are complex systems in which flows and pressure vary constantly, identification of the ideal locations for turbines is not straightforward, and assessment implies the need for simulation. In this paper, an optimization algorithm is proposed to provide a selection of optimal locations for the installation of a gi...
Submodularity in dynamics and control of networked systems
Clark, Andrew; Bushnell, Linda; Poovendran, Radha
2016-01-01
This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllabilit...
Numerical Analysis on the Optimization of Hydraulic Fracture Networks
Directory of Open Access Journals (Sweden)
Zhaobin Zhang
2015-10-01
Full Text Available The clear understanding of hydraulic fracture network complexity and the optimization of fracture network configuration are important to the hydraulic fracturing treatment of shale gas reservoirs. For the prediction of hydraulic fracture network configuration, one of the problems is the accurate representation of natural fractures. In this work, a real natural fracture network is reconstructed from shale samples. Moreover, a virtual fracture system is proposed to simulate the large number of small fractures that are difficult to identify. A numerical model based on the displacement discontinuity method is developed to simulate the fluid-rock coupling system. A dimensionless stress difference that is normalized by rock strength is proposed to quantify the anisotropy of crustal stress. The hydraulic fracturing processes under different stress conditions are simulated. The most complex fracture configurations are obtained when the maximum principle stress direction is perpendicular to the principle natural fracture direction. In contrast, the worst results are obtained when these two directions are parallel to each other. Moreover, the side effects of the unfavorable geological conditions caused by crustal stress anisotropy can be partly suppressed by increasing the viscous effect of the fluid.
Optimization of Hierarchical System for Data Acquisition
Directory of Open Access Journals (Sweden)
V. Novotny
2011-04-01
Full Text Available Television broadcasting over IP networks (IPTV is one of a number of network applications that are except of media distribution also interested in data acquisition from group of information resources of variable size. IP-TV uses Real-time Transport Protocol (RTP protocol for media streaming and RTP Control Protocol (RTCP protocol for session quality feedback. Other applications, for example sensor networks, have data acquisition as the main task. Current solutions have mostly problem with scalability - how to collect and process information from large amount of end nodes quickly and effectively? The article deals with optimization of hierarchical system of data acquisition. Problem is mathematically described, delay minima are searched and results are proved by simulations.
Optimization of RFID network planning using Zigbee and WSN
Hasnan, Khalid; Ahmed, Aftab; Badrul-aisham, Bakhsh, Qadir
2015-05-01
Everyone wants to be ease in their life. Radio frequency identification (RFID) wireless technology is used to make our life easier. RFID technology increases productivity, accuracy and convenience in delivery of service in supply chain. It is used for various applications such as preventing theft of automobiles, tolls collection without stopping, no checkout lines at grocery stores, managing traffic, hospital management, corporate campuses and airports, mobile asset tracking, warehousing, tracking library books, and to track a wealth of assets in supply chain management. Efficiency of RFID can be enhanced by integrating with wireless sensor network (WSN), zigbee mesh network and internet of things (IOT). The proposed system is used for identifying, sensing and real-time locating system (RTLS) of items in an indoor heterogeneous region. The system gives real-time richer information of object's characteristics, location and their environmental parameters like temperature, noise and humidity etc. RTLS reduce human error, optimize inventory management, increase productivity and information accuracy at indoor heterogeneous network. The power consumption and the data transmission rate of the system can be minimized by using low power hardware design.
Global Optimization for Transport Network Expansion and Signal Setting
Directory of Open Access Journals (Sweden)
Haoxiang Liu
2015-01-01
Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.
Optimal interval for major maintenance actions in electricity distribution networks
Energy Technology Data Exchange (ETDEWEB)
Louit, Darko; Pascual, Rodrigo [Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna MacKenna, 4860 Santiago (Chile); Banjevic, Dragan [Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, 5 King' s College Rd., Toronto, Ontario (Canada)
2009-09-15
Many systems require the periodic undertaking of major (preventive) maintenance actions (MMAs) such as overhauls in mechanical equipment, reconditioning of train lines, resurfacing of roads, etc. In the long term, these actions contribute to achieving a lower rate of occurrence of failures, though in many cases they increase the intensity of the failure process shortly after performed, resulting in a non-monotonic trend for failure intensity. Also, in the special case of distributed assets such as communications and energy networks, pipelines, etc., it is likely that the maintenance action takes place sequentially over an extended period of time, implying that different sections of the network underwent the MMAs at different periods. This forces the development of a model based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme. Additionally, extended maintenance times and sequential execution of the MMAs make it difficult to identify failures occurring before and after the preventive maintenance action. This results in the loss of important information for the characterization of the failure process. A simple model is introduced to determine the optimal MMA interval considering such restrictions. Furthermore, a case study illustrates the optimal tree trimming interval around an electricity distribution network. (author)
Information spread in networks: Games, optimal control, and stabilization
Khanafer, Ali
This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack
Hardware Abstraction and Protocol Optimization for Coded Sensor Networks
DEFF Research Database (Denmark)
Nistor, Maricica; Lucani Rötter, Daniel Enrique; Barros, joao
2014-01-01
The design of the communication protocols in wireless sensor networks (WSNs) often neglects several key characteristics of the sensor's hardware, while assuming that the number of transmitted bits is the dominating factor behind the system's energy consumption. A closer look at the hardware...... specifications of common sensors reveals, however, that other equally important culprits exist, such as the reception and processing energy. Hence, there is a need for a more complete hardware abstraction of a sensor node to reduce effectively the total energy consumption of the network by designing energy......-efficient protocols that use such an abstraction, as well as mechanisms to optimize a communication protocol in terms of energy consumption. The problem is modeled for different feedback-based techniques, where sensors are connected to a base station, either directly or through relays. We show that for four example...
Logistics systems optimization under competition
DEFF Research Database (Denmark)
Choi, Tsan Ming; Govindan, Kannan; Ma, Lijun
2015-01-01
numerical analysis and reveal that their proposed method significantly outperforms the classical method. They conduct their analysis from the manufacturer?s perspective. Technically, they convert the proposed FDEA model into a crisp linear programming optimization problem. As a result, the problem......Nowadays, optimization on logistics and supply chain systems is a crucial and critical issue in industrial and systems engineering. Important areas of logistics and supply chain systems include transportation control, inventory management, and facility location planning. Under a competitive market...... environment, decision making for all these critical areas requires more sophisticated mathematical modeling and analysis. Since finding the optimal solution of MCVRP is computationally expensive, they design a few guiding rules, which employ the searching history, to enhance the searching. They conduct...
Discrete optimization in architecture extremely modular systems
Zawidzki, Machi
2017-01-01
This book is comprised of two parts, both of which explore modular systems: Pipe-Z (PZ) and Truss-Z (TZ), respectively. It presents several methods of creating PZ and TZ structures subjected to discrete optimization. The algorithms presented employ graph-theoretic and heuristic methods. The underlying idea of both systems is to create free-form structures using the minimal number of types of modular elements. PZ is more conceptual, as it forms single-branch mathematical knots with a single type of module. Conversely, TZ is a skeletal system for creating free-form pedestrian ramps and ramp networks among any number of terminals in space. In physical space, TZ uses two types of modules that are mirror reflections of each other. The optimization criteria discussed include: the minimal number of units, maximal adherence to the given guide paths, etc.
Inferring biomolecular interaction networks based on convex optimization.
Han, Soohee; Yoon, Yeoin; Cho, Kwang-Hyun
2007-10-01
We present an optimization-based inference scheme to unravel the functional interaction structure of biomolecular components within a cell. The regulatory network of a cell is inferred from the data obtained by perturbation of adjustable parameters or initial concentrations of specific components. It turns out that the identification procedure leads to a convex optimization problem with regularization as we have to achieve the sparsity of a network and also reflect any a priori information on the network structure. Since the convex optimization has been well studied for a long time, a variety of efficient algorithms were developed and many numerical solvers are freely available. In order to estimate time derivatives from discrete-time samples, a cubic spline fitting is incorporated into the proposed optimization procedure. Throughout simulation studies on several examples, it is shown that the proposed convex optimization scheme can effectively uncover the functional interaction structure of a biomolecular regulatory network with reasonable accuracy.
Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Davide Caputo
2010-01-01
Full Text Available In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO and genetic algorithms (GA. This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications.
Xia, Li
2014-11-20
This paper studies the joint optimization problem of energy and delay in a multi-hop wireless network. The optimization variables are the transmission rates, which are adjustable according to the packet queueing length in the buffer. The optimization goal is to minimize the energy consumption of energy-critical nodes and the packet transmission delay throughout the network. In this paper, we aim at understanding the well-known decentralized algorithms which are threshold based from a different research angle. By using a simplified network model, we show that we can adopt the semi-open Jackson network model and study this optimization problem in closed form. This simplified network model further allows us to establish some significant optimality properties. We prove that the system performance is monotonic with respect to (w.r.t.) the transmission rate. We also prove that the threshold-type policy is optimal, i.e., when the number of packets in the buffer is larger than a threshold, transmit with the maximal rate (power); otherwise, no transmission. With these optimality properties, we develop a heuristic algorithm to iteratively find the optimal threshold. Finally, we conduct some simulation experiments to demonstrate the main idea of this paper.
Optimal network solution for proactive risk assessment and emergency response
Cai, Tianxing
Coupled with the continuous development in the field industrial operation management, the requirement for operation optimization in large scale manufacturing network has provoked more interest in the research field of engineering. Compared with the traditional way to take the remedial measure after the occurrence of the emergency event or abnormal situation, the current operation control calls for more proactive risk assessment to set up early warning system and comprehensive emergency response planning. Among all the industries, chemical industry and energy industry have higher opportunity to face with the abnormal and emergency situations due to their own industry characterization. Therefore the purpose of the study is to develop methodologies to give aid in emergency response planning and proactive risk assessment in the above two industries. The efficacy of the developed methodologies is demonstrated via two industrial real problems. The first case is to handle energy network dispatch optimization under emergency of local energy shortage under extreme conditions such as earthquake, tsunami, and hurricane, which may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. The second case is a scheduling methodology to coordinate multiple chemical plants' start-ups in order to minimize regional air quality impacts under extreme meteorological conditions. The objective is to reschedule multi-plant start-up sequence to achieve the minimum sum of delay time compared to the expected start-up time of each plant. All these approaches can provide quantitative decision support for multiple stake holders, including government and environment agencies, chemical industry, energy industry and local
Hopfield neural network implementation of the optimal CDMA multiuser detector.
Kechriotis, G I; Manolakos, E S
1996-01-01
We investigate the application of Hopfield neural networks (HNN's) to the problem of multiuser detection in spread spectrum/CDMA (code division multiple access) communication systems. It is shown that the NP-complete problem of minimizing the objective function of the optimal multiuser detector (OMD) can be translated into minimizing an HNN "energy" function, thus allowing to take advantage of the ability of HNN's to perform very fast gradient descent algorithms in analog hardware and produce in real-time suboptimal solutions to hard combinatorial optimization problems. The performance of the proposed HNN receiver is evaluated via computer simulations and compared to that of other suboptimal schemes as well as to that of the OMD for both the synchronous and the asynchronous CDMA transmission cases. It is shown that the HNN detector exhibits a number of attractive properties and that it provides a powerful generalization of a well-known and extensively studied suboptimal scheme, namely the multistage detector.
Cellular Neural Networks for NP-Hard Optimization
Directory of Open Access Journals (Sweden)
Mária Ercsey-Ravasz
2009-02-01
Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.
Optimizing information processing in neuronal networks beyond critical states.
Ferraz, Mariana Sacrini Ayres; Melo-Silva, Hiago Lucas Cardeal; Kihara, Alexandre Hiroaki
2017-01-01
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
Optimizing information processing in neuronal networks beyond critical states.
Directory of Open Access Journals (Sweden)
Mariana Sacrini Ayres Ferraz
Full Text Available Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
Network Optimization for Induced Seismicity Monitoring in Urban Areas
Kraft, T.; Husen, S.; Wiemer, S.
2012-12-01
With the global challenge to satisfy an increasing demand for energy, geological energy technologies receive growing attention and have been initiated in or close to urban areas in the past several years. Some of these technologies involve injecting fluids into the subsurface (e.g., oil and gas development, waste disposal, and geothermal energy development) and have been found or suspected to cause small to moderate sized earthquakes. These earthquakes, which may have gone unnoticed in the past when they occurred in remote sparsely populated areas, are now posing a considerable risk for the public acceptance of these technologies in urban areas. The permanent termination of the EGS project in Basel, Switzerland after a number of induced ML~3 (minor) earthquakes in 2006 is one prominent example. It is therefore essential to the future development and success of these geological energy technologies to develop strategies for managing induced seismicity and keeping the size of induced earthquake at a level that is acceptable to all stakeholders. Most guidelines and recommendations on induced seismicity published since the 1970ies conclude that an indispensable component of such a strategy is the establishment of seismic monitoring in an early stage of a project. This is because an appropriate seismic monitoring is the only way to detect and locate induced microearthquakes with sufficient certainty to develop an understanding of the seismic and geomechanical response of the reservoir to the geotechnical operation. In addition, seismic monitoring lays the foundation for the establishment of advanced traffic light systems and is therefore an important confidence building measure towards the local population and authorities. We have developed an optimization algorithm for seismic monitoring networks in urban areas that allows to design and evaluate seismic network geometries for arbitrary geotechnical operation layouts. The algorithm is based on the D-optimal experimental
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-01-01
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271
Wireless Sensor Network Optimization: Multi-Objective Paradigm.
Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad
2015-07-20
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Wireless Sensor Network Optimization: Multi-Objective Paradigm
Directory of Open Access Journals (Sweden)
Muhammad Iqbal
2015-07-01
Full Text Available Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.
Computer Networks A Systems Approach
Peterson, Larry L
2011-01-01
This best-selling and classic book teaches you the key principles of computer networks with examples drawn from the real world of network and protocol design. Using the Internet as the primary example, the authors explain various protocols and networking technologies. Their systems-oriented approach encourages you to think about how individual network components fit into a larger, complex system of interactions. Whatever your perspective, whether it be that of an application developer, network administrator, or a designer of network equipment or protocols, you will come away with a "big pictur
Iterative free-energy optimization for recurrent neural networks (INFERNO)
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439
Iterative free-energy optimization for recurrent neural networks (INFERNO).
Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias
2017-01-01
The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.
Iterative free-energy optimization for recurrent neural networks (INFERNO.
Directory of Open Access Journals (Sweden)
Alexandre Pitti
Full Text Available The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.
Simultaneous optimization of water and heat exchange networks
Energy Technology Data Exchange (ETDEWEB)
Chen, Zhiyou; Hou, Yanlong; Li, Xiaoduan; Wang, Jingtao [Tianjin University, Tianjin (China)
2014-04-15
This paper focuses on the simultaneous optimization of the heat-integrated water allocation networks. A mathematic model is established to illustrate the modified state-space representation of this problem. An easy logical method is employed to help identify the streams of hot or cold ones. In this model, the water exchange networks (WEN), heat exchange networks (HEN), and the interactions between the WEN and HEN combine together as one unity. Thus, the whole network can be solved at one time, which enhances the possibility to get a global optimal result. Examples from the literature and a PVC plant are analyzed to illustrate the accuracy and applicability of this method.
METHODS OF INTEGRATED OPTIMIZATION MAGLEV TRANSPORT SYSTEMS
Directory of Open Access Journals (Sweden)
A. Lasher
2013-09-01
example, this research proved the sustainability of the proposed integrated optimization parameters of transport systems. This approach could be applied not only for MTS, but also for other transport systems. Originality. The bases of the complex optimization of transport presented are the new system of universal scientific methods and approaches that ensure high accuracy and authenticity of calculations with the simulation of transport systems and transport networks taking into account the dynamics of their development. Practical value. The development of the theoretical and technological bases of conducting the complex optimization of transport makes it possible to create the scientific tool, which ensures the fulfillment of the automated simulation and calculating of technical and economic structure and technology of the work of different objects of transport, including its infrastructure.
Optimal power flow for distribution networks with distributed generation
Directory of Open Access Journals (Sweden)
Radosavljević Jordan
2015-01-01
Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046
ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2014-04-01
Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...
Designing optimal greenhouse gas monitoring networks for Australia
Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.
2016-01-01
Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
Online Algorithms for Adaptive Optimization in Heterogeneous Delay Tolerant Networks
Directory of Open Access Journals (Sweden)
Wissam Chahin
2013-12-01
Full Text Available Delay Tolerant Networks (DTNs are an emerging type of networks which do not need a predefined infrastructure. In fact, data forwarding in DTNs relies on the contacts among nodes which may possess different features, radio range, battery consumption and radio interfaces. On the other hand, efficient message delivery under limited resources, e.g., battery or storage, requires to optimize forwarding policies. We tackle optimal forwarding control for a DTN composed of nodes of different types, forming a so-called heterogeneous network. Using our model, we characterize the optimal policies and provide a suitable framework to design a new class of multi-dimensional stochastic approximation algorithms working for heterogeneous DTNs. Crucially, our proposed algorithms drive online the source node to the optimal operating point without requiring explicit estimation of network parameters. A thorough analysis of the convergence properties and stability of our algorithms is presented.
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Energy Technology Data Exchange (ETDEWEB)
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
Optimization model for the design of distributed wastewater treatment networks
Directory of Open Access Journals (Sweden)
Ibrić Nidret
2012-01-01
Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.
Optimal Redesign of the Dutch Road Network
Snelder, M.; Wagelmans, A.P.M.; Schrijver, J.M.; Van Zuylen, H.J.; Immers, L.H.
2007-01-01
The Dutch national road network has been developed over several decades. In the past, roads were constructed according to the then current spatial and transportation planning philosophies. Because the existing road network is a result of a long process of successive developments, the question can be
Design Optimization of Structural Health Monitoring Systems
Energy Technology Data Exchange (ETDEWEB)
Flynn, Eric B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-03-06
Sensor networks drive decisions. Approach: Design networks to minimize the expected total cost (in a statistical sense, i.e. Bayes Risk) associated with making wrong decisions and with installing maintaining and running the sensor network itself. Search for optimal solutions using Monte-Carlo-Sampling-Adapted Genetic Algorithm. Applications include structural health monitoring and surveillance.
A Medical Center Network for Optimized Lung Cancer Biospecimen Banking
2015-10-01
1 Award Number: W81XWH-10-1-0818 TITLE: “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” PRINCIPAL INVESTIGATOR: Christopher...To) 20Sep2014 - 19Sep2015 4. TITLE AND SUBTITLE “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” 5a. CONTRACT NUMBER 5b...Although new subject enrollments and specimen collection have ceased, the LCBRN is committed to the outcome of this project, which is a bank of
High-resolution optimal quantization for stochastic pooling networks
McDonnell, Mark D.; Amblard, Pierre-Olivier; Stocks, Nigel G.; Zozor, Steeve; Abbott, Derek
2007-01-01
Pooling networks of noisy threshold devices are good models for natural networks (e.g. neural networks in some parts of sensory pathways in vertebrates, networks of mossy fibers in the hippothalamus, . . . ) as well as for artificial networks (e.g. digital beamformers for sonar arrays, flash analog-to-digital converters, rate-constrained distributed sensor networks, . . . ). Such pooling networks exhibit the curious effect of suprathreshold stochastic resonance, which means that an optimal stochastic control of the network exists. Recently, some progress has been made in understanding pooling networks of identical, but independently noisy, threshold devices. One aspect concerns the behavior of information processing in the asymptotic limit of large networks, which is a limit of high relevance for neuroscience applications. The mutual information between the input and the output of the network has been evaluated, and its extremization has been performed. The aim of the present work is to extend these asymptotic results to study the more general case when the threshold values are no longer identical. In this situation, the values of thresholds can be described by a density, rather than by exact locations. We present a derivation of Shannon's mutual information between the input and output of these networks. The result is an approximation that relies a weak version of the law of large numbers, and a version of the central limit theorem. Optimization of the mutual information is then discussed.
Network of networks in Linux operating system
Wang, Haoqin; Chen, Zhen; Xiao, Guanping; Zheng, Zheng
2016-04-01
Operating system represents one of the most complex man-made systems. In this paper, we analyze Linux Operating System (LOS) as a complex network via modeling functions as nodes and function calls as edges. It is found that for the LOS network and modularized components within it, the out-degree follows an exponential distribution and the in-degree follows a power-law distribution. For better understanding the underlying design principles of LOS, we explore the coupling correlations of components in LOS from aspects of topology and function. The result shows that the component for device drivers has a strong manifestation in topology while a weak manifestation in function. However, the component for process management shows the contrary phenomenon. Moreover, in an effort to investigate the impact of system failures on networks, we make a comparison between the networks traced from normal and failure status of LOS. This leads to a conclusion that the failure will change function calls which should be executed in normal status and introduce new function calls in the meanwhile.
Optimization of Multicast Protocols for Heterogeneous Satellite Networks
Ehlert, Sven; Firrincieli, Rosario; Corazza, Giovanni E.
2003-07-01
With the growing need for context aware information delivery to groups and the increasing importance of streaming media distribution, the telecommunications research community is examining and evaluating different means for point-to- multipoint content delivery to end users, exploiting multicasting-broadcasting transport means. Traditionally, terrestrial networks are the main carriers for point-to-point content delivery, but satellite systems are at a prime when it comes to efficiently broadcast data to a wide user population. In this paper, we evaluate a heterogeneous satellite/terrestrial network in terms of content distribution capacity, using different multicast transport protocols. We focus our attention on reliable point- to-multipoint data delivery, our goal being to optimize quality of service while preserving capacity. We considered Scalable Reliable Multicast (SRM) and Pragmatic General Multicast (PGM) protocols. The results show that SRM does not perform very well, especially at high BER. On the other hand, PGM improves bandwidth exploitation by minimizing retransmission redundancy. However, PGM works optimally if and only if all routers are PGM-aware, which may be an unreasonable assumption when using a part of the public Internet for transportation. Therefore, we have augmented the PGM protocol by adding a Designated Local Repairer (DLR) node in order to counteract the effects of a mixed PGM-aware/unaware environment.
Optimal Bidding Strategy for Renewable Microgrid with Active Network Management
Directory of Open Access Journals (Sweden)
Seung Wan Kim
2016-01-01
Full Text Available Active Network Management (ANM enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG and Battery Energy Storage System (BESS units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.
Optimal control of metabolic networks with saturable enzyme kinetics.
Oyarzuun, D A
2011-03-01
This note addresses the optimal control of non-linear metabolic networks by means of time-dependent enzyme synthesis rates. The authors consider networks with general topologies described by a control-affine dynamical system coupled with a linear model for enzyme synthesis and degradation. The problem formulation accounts for transitions between two metabolic equilibria, which typically arise in metabolic adaptations to environmental changes, and the minimisation of a quadratic functional that weights the cost/benefit relation between the transcriptional effort required for enzyme synthesis and the transition to the new phenotype. Using a linear time-variant approximation of the non-linear dynamics, the problem is recast as a sequence of linear-quadratic problems, the solution of which involves a sequence of differential Lyapunov equations. The authors provide conditions for convergence to an approximate solution of the original problem, which are naturally satisfied by a wide class of models for saturable enzyme kinetics. As a case study the authors use the method to examine the robustness of an optimal just-in-time gene expression pattern with respect to heterogeneity in the biosynthetic costs of individual proteins.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...
Views of wireless network systems.
Energy Technology Data Exchange (ETDEWEB)
Young, William Frederick; Duggan, David Patrick
2003-10-01
Wireless networking is becoming a common element of industrial, corporate, and home networks. Commercial wireless network systems have become reliable, while the cost of these solutions has become more affordable than equivalent wired network solutions. The security risks of wireless systems are higher than wired and have not been studied in depth. This report starts to bring together information on wireless architectures and their connection to wired networks. We detail information contained on the many different views of a wireless network system. The method of using multiple views of a system to assist in the determination of vulnerabilities comes from the Information Design Assurance Red Team (IDART{trademark}) Methodology of system analysis developed at Sandia National Laboratories.
Mapping biological systems to network systems
Rathore, Heena
2016-01-01
The book presents the challenges inherent in the paradigm shift of network systems from static to highly dynamic distributed systems – it proposes solutions that the symbiotic nature of biological systems can provide into altering networking systems to adapt to these changes. The author discuss how biological systems – which have the inherent capabilities of evolving, self-organizing, self-repairing and flourishing with time – are inspiring researchers to take opportunities from the biology domain and map them with the problems faced in network domain. The book revolves around the central idea of bio-inspired systems -- it begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems -- it is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully ...
Optimization of space manufacturing systems
Akin, D. L.
1979-01-01
Four separate analyses are detailed: transportation to low earth orbit, orbit-to-orbit optimization, parametric analysis of SPS logistics based on earth and lunar source locations, and an overall program option optimization implemented with linear programming. It is found that smaller vehicles are favored for earth launch, with the current Space Shuttle being right at optimum payload size. Fully reusable launch vehicles represent a savings of 50% over the Space Shuttle; increased reliability with less maintenance could further double the savings. An optimization of orbit-to-orbit propulsion systems using lunar oxygen for propellants shows that ion propulsion is preferable by a 3:1 cost margin over a mass driver reaction engine at optimum values; however, ion engines cannot yet operate in the lower exhaust velocity range where the optimum lies, and total program costs between the two systems are ambiguous. Heavier payloads favor the use of a MDRE. A parametric model of a space manufacturing facility is proposed, and used to analyze recurring costs, total costs, and net present value discounted cash flows. Parameters studied include productivity, effects of discounting, materials source tradeoffs, economic viability of closed-cycle habitats, and effects of varying degrees of nonterrestrial SPS materials needed from earth. Finally, candidate optimal scenarios are chosen, and implemented in a linear program with external constraints in order to arrive at an optimum blend of SPS production strategies in order to maximize returns.
Robust adaptive learning of feedforward neural networks via LMI optimizations.
Jing, Xingjian
2012-07-01
Feedforward neural networks (FNNs) have been extensively applied to various areas such as control, system identification, function approximation, pattern recognition etc. A novel robust control approach to the learning problems of FNNs is further investigated in this study in order to develop efficient learning algorithms which can be implemented with optimal parameter settings and considering noise effect in the data. To this aim, the learning problem of a FNN is cast into a robust output feedback control problem of a discrete time-varying linear dynamic system. New robust learning algorithms with adaptive learning rate are therefore developed, using linear matrix inequality (LMI) techniques to find the appropriate learning rates and to guarantee the fast and robust convergence. Theoretical analysis and examples are given to illustrate the theoretical results. Copyright © 2012 Elsevier Ltd. All rights reserved.
Optimizing and Understanding Network Structure for Diffusion
Zhang, Yao
2017-01-01
Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagion...
Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming
2017-05-01
To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.
Trajectory Based Optimal Segment Computation in Road Network Databases
DEFF Research Database (Denmark)
Li, Xiaohui; Ceikute, Vaida; Jensen, Christian S.
2013-01-01
that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route...... that adopt different approaches to computing the query. Algorithm AUG uses graph augmentation, and ITE uses iterative road-network partitioning. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings....
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
2012-01-01
Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
2016-01-01
In this paper, we propose a generic computer-aided methodology for synthesis of different processing networks using superstructure optimization. The methodology can handle different network optimization problems of various application fields. It integrates databases with a common data architecture......, a generic model to represent the processing steps, and appropriate optimization tools. A special software interface has been created to automate the steps in the methodology workflow, allow the transfer of data between tools and obtain the mathematical representation of the problem as required...
Quasi-Lagrangian neural network for convex quadratic optimization.
Costantini, Giovanni; Perfetti, Renzo; Todisco, Massimiliano
2008-10-01
A new neural network for convex quadratic optimization is presented in this brief. The proposed network can handle both equality and inequality constraints, as well as bound constraints on the optimization variables. It is based on the Lagrangian approach, but exploits a partial dual method in order to keep the number of variables at minimum. The dynamic evolution is globally convergent and the steady-state solutions satisfy the necessary and sufficient conditions of optimality. The circuit implementation is simpler with respect to existing solutions for the same class of problems. The validity of the proposed approach is verified through some simulation examples.
Risks in Networked Computer Systems
Klingsheim, André N.
2008-01-01
Networked computer systems yield great value to businesses and governments, but also create risks. The eight papers in this thesis highlight vulnerabilities in computer systems that lead to security and privacy risks. A broad range of systems is discussed in this thesis: Norwegian online banking systems, the Norwegian Automated Teller Machine (ATM) system during the 90's, mobile phones, web applications, and wireless networks. One paper also comments on legal risks to bank cust...
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Resource Optimization Scheme for Multimedia-Enabled Wireless Mesh Networks
Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md. Jalil; Suh, Doug Young
2014-01-01
Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment. PMID:25111241
Resource optimization scheme for multimedia-enabled wireless mesh networks.
Ali, Amjad; Ahmed, Muhammad Ejaz; Piran, Md Jalil; Suh, Doug Young
2014-08-08
Wireless mesh networking is a promising technology that can support numerous multimedia applications. Multimedia applications have stringent quality of service (QoS) requirements, i.e., bandwidth, delay, jitter, and packet loss ratio. Enabling such QoS-demanding applications over wireless mesh networks (WMNs) require QoS provisioning routing protocols that lead to the network resource underutilization problem. Moreover, random topology deployment leads to have some unused network resources. Therefore, resource optimization is one of the most critical design issues in multi-hop, multi-radio WMNs enabled with multimedia applications. Resource optimization has been studied extensively in the literature for wireless Ad Hoc and sensor networks, but existing studies have not considered resource underutilization issues caused by QoS provisioning routing and random topology deployment. Finding a QoS-provisioned path in wireless mesh networks is an NP complete problem. In this paper, we propose a novel Integer Linear Programming (ILP) optimization model to reconstruct the optimal connected mesh backbone topology with a minimum number of links and relay nodes which satisfies the given end-to-end QoS demands for multimedia traffic and identification of extra resources, while maintaining redundancy. We further propose a polynomial time heuristic algorithm called Link and Node Removal Considering Residual Capacity and Traffic Demands (LNR-RCTD). Simulation studies prove that our heuristic algorithm provides near-optimal results and saves about 20% of resources from being wasted by QoS provisioning routing and random topology deployment.
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, Janus; Zhang, Qi; Fitzek, Frank
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...
Wavelength Converters Placement in Optical Networks Using Bee Colony Optimization
Directory of Open Access Journals (Sweden)
MARKOVIC, G. Z.
2016-02-01
Full Text Available Wavelength converters placement (WCP in all-optical WDM networks belongs to the class of hard combinatorial optimization problems. So far, this problem has been solved by various heuristic strategies or by application of metaheuristic approaches such as genetic algorithms (GA, particle swarm optimization (PSO, differential evolution (DE, etc. In this paper, we introduce the application of Bee Colony Optimization (BCO metaheuristic to solve the WCP problem in all-optical WDM networks. Numerous studies prove that BCO is a fast, robust and computationally efficient tool in tackling complex optimization problems. The objective of the proposed BCO-WCP algorithm is to find the best placement of limited number of wavelength converters in given optical network such that the overall network blocking probability is minimized. To evaluate the performances of the BCO-WCP algorithm, numerous simulation experiments have been performed over some realistic optical network examples. The blocking probability performance and computational complexity are compared with optimal solution obtained by exhaustive search (ES approach as well as with DE and PSO metaheuristics. It will be shown that the BCO-WCP algorithm is not only be able to produce high quality (optimal solution, but significantly outperforms the computational efficiency of other considered approaches.
[Network structures in biological systems].
Oleskin, A V
2013-01-01
Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.
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.
Profile-driven regression for modeling and runtime optimization of mobile networks
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2010-01-01
Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...... others, the throughput model accounts for node speed. The resulting optimization is very effective; locally optimizing the network factors at runtime results in throughput as much as six times higher than that achieved with the factors at their default levels....
Optimal satisfaction degree in energy harvesting cognitive radio networks
Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui
2015-12-01
A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).
Autonomous Optimization of Targeted Stimulation of Neuronal Networks.
Kumar, Sreedhar S; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin; Egert, Ulrich
2016-08-01
Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative
Language Networks as Complex Systems
Lee, Max Kueiming; Ou, Sheue-Jen
2008-01-01
Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…
Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2015-01-01
We propose the realized systemic risk beta as a measure of financial companies' contribution to systemic risk, given network interdependence between firms' tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define
Optimal Control and Forecasting of Complex Dynamical Systems
Grigorenko, Ilya
2006-01-01
This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance. The theory presented here can be efficiently applied to various problems, such as the determination of the optimal shape of a laser pulse to induce certain excitations in quantum systems, the optimal design of nanostructured materials and devices, or the control of chaotic systems and minimization of the forecast error for a given forecasting model (for example, artificial neural networks). Starting from a brief review of the history of variational calcul
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...... networks is proposed. The tool allows for assessment of the length of the effe ctive memory of previous inputs built up in the recurrent network during application. Time series modeling is also treated from a more general point of view, namely modeling of the joint probability distribution function...
Optimization of wireless sensor networks based on chicken swarm optimization algorithm
Wang, Qingxi; Zhu, Lihua
2017-05-01
In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.
Optimizing of Passive Optical Network Deployment Using Algorithm with Metrics
Directory of Open Access Journals (Sweden)
Tomas Pehnelt
2017-01-01
Full Text Available Various approaches and methods are used for designing of optimum deployment of Passive Optical Networks (PON according to selected optimization criteria, such as optimal trenching distance, endpoint attenuation and overall installed fibre length. This article describes the ideas and possibilities for an algorithm with the application of graph algorithms for finding the shortest path from Optical Line Termination to Optical Network Terminal unit. This algorithm uses a combination of different methods for generating of an optimal metric, thus creating the optimized tree topology mainly focused on summary trenching distance. Furthermore, it deals with algorithms for finding an optimal placement of optical splitter with the help of K-Means clustering method and hierarchical clustering technique. The results of the proposed algorithm are compared with existing methods.
Optimization of TTEthernet Networks to Support Best-Effort Traffic
DEFF Research Database (Denmark)
Tamas-Selicean, Domitian; Pop, Paul
2014-01-01
This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet......’s strong points, there is little research on this topic. However, in this paper, we extend our DOTTS optimization approach to optimize TTEthernet networks, such that not only the TT and RC messages are schedulable, but we also maximize the available bandwidth for BE messages. The proposed optimization has...
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Energy Technology Data Exchange (ETDEWEB)
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
An Optimal Routing Algorithm in Service Customized 5G Networks
Directory of Open Access Journals (Sweden)
Haipeng Yao
2016-01-01
Full Text Available With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN, Content-Centric Networking (CCN, and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.
MILP model for energy optimization in EIP water networks
Energy Technology Data Exchange (ETDEWEB)
Taskhiri, Mohammad Sadegh [De La Salle University, Industrial Engineering Department, Manila (Philippines); Tan, Raymond R. [De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines); Chiu, Anthony S.F. [De La Salle University, Industrial Engineering Department, Manila (Philippines); De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines)
2011-10-15
The eco-industrial park (EIP) concept provides a framework in which several plants can cooperate with each other and exchange their wastewater to minimize total freshwater consumption. Energy analysis is a methodology that considers the total, cumulative energy which has been consumed within a system; thus, by minimizing energy, an environmentally optimal EIP can be designed. This article presents a mixed-integer linear programming (MILP) model for minimizing energy of an interplant water network in an EIP. The methodology accounts for the environmental impacts of water use, energy consumption, and capital goods within the EIP in a balanced manner. The proposed technique is then demonstrated by solving a case study from literature. (orig.)
Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks.
Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet
2017-06-01
This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model.
Optimal Systems for Information and Decision,
INFORMATION THEORY, OPTIMIZATION), CODING, STATISTICAL ANALYSIS, SYSTEMS ENGINEERING, DECISION THEORY, SOCIAL COMMUNICATION, DATA STORAGE SYSTEMS, DATA TRANSMISSION SYSTEMS , MATHEMATICAL MODELS, DECODING, COSTS
A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Shibo He
2010-01-01
Full Text Available In wireless sensor networks (WSNs, there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.
Networking systems design and development
Chao, Lee
2009-01-01
Effectively integrating theory and hands-on practice, Networking Systems Design and Development provides students and IT professionals with the knowledge and skills needed to design, implement, and manage fully functioning network systems using readily available Linux networking tools. Recognizing that most students are beginners in the field of networking, the text provides step-by-step instruction for setting up a virtual lab environment at home. Grounded in real-world applications, this book provides the ideal blend of conceptual instruction and lab work to give students and IT professional
Fluid Limits of Optimally Controlled Queueing Networks
Guodong Pang; Day, Martin V.
2007-01-01
We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes. Peer Reviewed
Fluid Limits of Optimally Controlled Queueing Networks
Directory of Open Access Journals (Sweden)
Guodong Pang
2007-01-01
Full Text Available We consider a class of queueing processes represented by a Skorokhod problem coupled with a controlled point process. Posing a discounted control problem for such processes, we show that the optimal value functions converge, in the fluid limit, to the value of an analogous deterministic control problem for fluid processes.
LTE-Advanced Radio and Network Optimization
DEFF Research Database (Denmark)
Velez, Fernando J.; Sousa, Sofia; Flores, Jessica Acevedo
2015-01-01
In cellular optimization, the UL and DL the values from carrier-to-noise-plus-interference ratio (CNIR) from/at the mobile station are very important parameters. From a detailed analysis of its variation with the coverage and reuse distances for different values of the Channel Quality Indicator (...
Communicating embedded systems networks applications
Krief, Francine
2013-01-01
Embedded systems become more and more complex and require having some knowledge in various disciplines such as electronics, data processing, telecommunications and networks. Without detailing all the aspects related to the design of embedded systems, this book, which was written by specialists in electronics, data processing and telecommunications and networks, gives an interesting point of view of communication techniques and problems in embedded systems. This choice is easily justified by the fact that embedded systems are today massively communicating and that telecommunications and network
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-08-01
Full Text Available It is envisaged that in future Cognitive Radio (CR networks deployment, multiple radio access networks may coexist. The networks may have different characteristics in terms of multiple attributes. CRs will have choices of selecting the optimal network out of the available networks. Optimal network selection is a challenging task that can be performed by spectrum handoff with Multiple Attribute Decision Making (MADM. The spectrum handoff decision with MADM provides wider and optimal choice with quality of service. This motivates the devolopment of a spectrum handoff scheme with MADM methods such as simple additive weighting, a technique for order preference by similarity to the ideal solution, a grey relational analysis and a cost function based method, which is the objective of this study. The CR preferences are based on voice, video and data services, called triple play services. The numerical results show that all MADM methods are effective for selecting the optimal network for spectrum handoff with a reduced complexity for the spectrum handoff decision. The paper shows that the proposed spectrum handoff scheme can be effectively implemented to select the optimal network according to triple play services in CR networks.
Directory of Open Access Journals (Sweden)
KHANBABAZADEH Javad
2016-10-01
Full Text Available Distribution network designers and operators are trying to deliver electrical energy with high reliability and quality to their subscribers. Due to high losses in the distribution systems, using distributed generation can improves reliability, reduces losses and improves voltage profile of distribution network. Therefore, the choice of the location of these resources and also determining the amount of their generated power to maximize the benefits of this type of resource is an important issue which is discussed from different points of view today. In this paper, a new multi-objective optimal location and sizing of distributed generation resources is performed to maximize its benefits on the 33 bus distribution test network considering reliability and using a new Antlion Optimizer (ALO. The benefits for DG are considered as system losses reduction, system reliability improvement and benefits from the sale electricity and voltage profile improvement. For each of the mentioned benefits, the ALO algorithm is used to optimize the location and sizing of distributed generation resources. In order to verify the proposed approach, the obtained results have been analyzed and compared with the results of particle swarm optimization (PSO algorithm. The results show that the ALO has shown better performance in optimization problem solution versus PSO.
Economic and environmental optimization of a multi-site utility network for an industrial complex.
Kim, Sang Hun; Yoon, Sung-Geun; Chae, Song Hwa; Park, Sunwon
2010-01-01
Most chemical companies consume a lot of steam, water and electrical resources in the production process. Given recent record fuel costs, utility networks must be optimized to reduce the overall cost of production. Environmental concerns must also be considered when preparing modifications to satisfy the requirements for industrial utilities, since wastes discharged from the utility networks are restricted by environmental regulations. Construction of Eco-Industrial Parks (EIPs) has drawn attention as a promising approach for retrofitting existing industrial parks to improve energy efficiency. The optimization of the utility network within an industrial complex is one of the most important undertakings to minimize energy consumption and waste loads in the EIP. In this work, a systematic approach to optimize the utility network of an industrial complex is presented. An important issue in the optimization of a utility network is the desire of the companies to achieve high profits while complying with the environmental regulations. Therefore, the proposed optimization was performed with consideration of both economic and environmental factors. The proposed approach consists of unit modeling using thermodynamic principles, mass and energy balances, development of a multi-period Mixed Integer Linear Programming (MILP) model for the integration of utility systems in an industrial complex, and an economic/environmental analysis of the results. This approach is applied to the Yeosu Industrial Complex, considering seasonal utility demands. The results show that both the total utility cost and waste load are reduced by optimizing the utility network of an industrial complex. 2009 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Yang Sun
2018-01-01
Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
Optimal Band Allocation for Cognitive Cellular Networks
Liu,Tingting; Jiang, Chengling
2011-01-01
FCC new regulation for cognitive use of the TV white space spectrum provides a new means for improving traditional cellular network performance. But it also introduces a number of technical challenges. This letter studies one of the challenges, that is, given the significant differences in the propagation property and the transmit power limitations between the cellular band and the TV white space, how to jointly utilize both bands such that the benefit from the TV white space for improving ce...
Optimal localization of diffusion sources in complex networks
Hu, Zhao-Long; Han, Xiao; Lai, Ying-Cheng
2017-01-01
Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications. PMID:28484635
Robust Optimization of Fourth Party Logistics Network Design under Disruptions
Directory of Open Access Journals (Sweden)
Jia Li
2015-01-01
Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
Singularities in minimax optimization of networks
DEFF Research Database (Denmark)
Madsen, Kaj; Schjær-Jacobsen, Hans
1976-01-01
A theoretical treatment of singularities in nonlinear minimax optimization problems, which allows for a classification in regular and singular problems, is presented. A theorem for determining a singularity that is present in a given problem is formulated. A group of problems often used...... in the literature to test nonlinear minimax algorithms, i.e., minimax design of multisection quarter-wave transformers, is shown to exhibit singularities and the reason for this is pointed out. Based on the theoretical results presented an algorithm for nonlinear minimax optimization is developed. The new algorithm...... maintains the quadratic convergence property of a recent algorithm by Madsen et al. when applied to regular problems and it is demonstrated to significantly improve the final convergence on singular problems....
Online Advertisement, Optimization and Stochastic Networks
Tan, Bo; Srikant, R.
2010-01-01
In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click...
Delays and networked control systems
Hetel, Laurentiu; Daafouz, Jamal; Johansson, Karl
2016-01-01
This edited monograph includes state-of-the-art contributions on continuous time dynamical networks with delays. The book is divided into four parts. The first part presents tools and methods for the analysis of time-delay systems with a particular attention on control problems of large scale or infinite-dimensional systems with delays. The second part of the book is dedicated to the use of time-delay models for the analysis and design of Networked Control Systems. The third part of the book focuses on the analysis and design of systems with asynchronous sampling intervals which occur in Networked Control Systems. The last part of the book exposes several contributions dealing with the design of cooperative control and observation laws for networked control systems. The target audience primarily comprises researchers and experts in the field of control theory, but the book may also be beneficial for graduate students. .
Domotics – A Cost Effective Smart Home Automation System Using Wifi as Network Infrastructure
National Research Council Canada - National Science Library
Abhinav Talgeri; Abheesh Kumar B A
2014-01-01
... (also called as Domotics) systems. It considers problems with their implementation, discusses possible solutions through various network technologies and indicates how to optimize the use of such systems...
Optimality principles in the regulation of metabolic networks
Berkhout, J.; Bruggeman, F.J.; Teusink, B.
2012-01-01
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks
Optimal phasing of district heating network investments using multi-stage stochastic programming
Directory of Open Access Journals (Sweden)
Romain Stephane Claude Lambert
2016-06-01
Full Text Available Most design optimisation studies for district heating systems have focused on the optimal sizing of network assets and on the location of production units. However, the strategic value of the flexibility in phasing of the inherently modular heat networks, which is an important aspect in many feasibility studies for district heating schemes in the UK, is almost always neglected in the scientific literature. This paper considers the sequential problem faced by a decision-maker in the phasing of long-term investments into district heating networks and their expansions. The problem is formulated as a multi-stage stochastic programme to determine the annual capital expenditure that maximises the expected net present value of the project. The optimisation approach is illustrated by applying it to the hypothetical case of the UK’s Marston Vale eco town. It was found that the approach is capable of simulating the optimal growth of a network, from both a single heat source or separate islands of growth, as well as the optimal marginal expansion of an existing district heating network. The proposed approach can be used by decision makers as a framework to determine both the optimal phasing and extension of district heating networks and can be adapted simply to various, more complex real-life situations by introducing additional constraints and parameters. The versatility of the base formulation also makes it a powerful approach regardless of the size of the network and also potentially applicable to cooling networks.
Optimal capacitor placement in smart distribution systems to improve ...
African Journals Online (AJOL)
An energy efficient power distribution network can provide cost-effective and collaborative platform for supporting present and future smart distribution system requirements. Energy efficiency in distribution systems is achieved through reconfiguration of distributed generation and optimal capacitor placement. Though several ...
Optimal dimensioning model of water distribution systems | Gomes ...
African Journals Online (AJOL)
This study is aimed at developing a pipe-sizing model for a water distribution system. The optimal solution minimises the system's total cost, which comprises the hydraulic network capital cost, plus the capitalised cost of pumping energy. The developed model, called Lenhsnet, may also be used for economical design when ...
Stochastic Modelling and Optimization of Complex Infrastructure Systems
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown that recent progress in stochastic modelling and optimization in combination with advanced computer systems has now made it possible to improve the design and the maintenance strategies for infrastructure systems. The paper concentrates on highway networks and single large...
Optimal Design of Piping Systems for District Heating,
1995-08-01
First, a method for determining the optimal size for a single pipe segment in a district heating system is developed. The method is general enough to...excessive throttling losses in the consumer’s control valves. The method developed here should be feasible for designing the piping networks for district ... heating systems of moderate size, and its major advantage is its flexibility. (MM)
RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks
Energy Technology Data Exchange (ETDEWEB)
Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)
2013-12-25
The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need
Optimal paths on the road network as directed polymers
Solon, A. P.; Bunin, G.; Chu, S.; Kardar, M.
2017-11-01
We analyze the statistics of the shortest and fastest paths on the road network between randomly sampled end points. We find that, to a good approximation, the optimal paths can be described as directed polymers in a disordered medium, which belong to the Kardar-Parisi-Zhang universality class of interface roughening. Comparing the scaling behavior of our data with simulations of directed polymers and previous theoretical results, we are able to point out the few characteristics of the road network that are relevant to the large-scale statistics of optimal paths. Indeed, we show that the local structure is akin to a disordered environment with a power-law distribution which become less important at large scales where long-ranged correlations in the network control the scaling behavior of the optimal paths.
Optimal Renewable Energy Systems for Regions
Directory of Open Access Journals (Sweden)
Karl-Heinz Kettl
2014-03-01
Full Text Available Most sources for renewable energy can be deduced from solar radiation as the main natural income of society. Contrary to conventional fossil and radioactive energy resources that are mined or pumped out from central point sources, solar energy is a de-central resource that requires area for its conversion to useful products and services. This requires a new technological as well as logistical concept for energy systems where regions play a key role as providers of energy and goods. The contribution will provide the conceptual framework for renewable energy system generation on a regional level, taking into account the responsibility of regions to provide goods and services to the larger society and to support urban centres. It will show how optimal resource-technology-demand networks may be constructed, using process network synthesis approaches and how the ecological efficiency of such regional systems can be measured. Application of these methods to real life case studies (in particular the region of Muehlviertel in Austria will on the one hand prove the versatility of the methods presented and on the other hand will provide insight into the scope of necessary change if society moves towards a low carbon sustainable energy system.
Hayashi, Yasuhiro; Takano, Hirotaka; Matsuki, Junya; Nishikawa, Yuji
Distribution network has huge number of configuration candidates because the network configuration is determined by state of many sectionalizing switches (opened or closed) installing in terms of keeping power quality, reliability and so on. Since feeder current and voltage depends on the network configuration, distribution loss, voltage imbalance and bank efficiency can be controlled by changing state of these switches. In addition, feeder current and voltage change by out put of distributed generators (DGs) such as photovoltaic generation system, wind turbine generation system and so on, connected to the feeder. Recently, total number of DGs connected to distribution network increases drastically. Therefore, many configuration candidates of the distribution network must be evaluated multiply from various viewpoints such as distribution loss, voltage imbalance, bank efficiency and so on, considering power supply from connected DGs. In this paper, the authors propose a multi-objective optimization method from three evaluation viewpoints ((1) distribution loss, (2) voltage imbalance and (3) bank efficiency) using pareto optimal solution. In the proposed method, after several high-ranking candidates with small distribution loss are extracted by combinatorial optimization method, each candidate are evaluated from the viewpoints of voltage imbalance and bank efficiency using pareto optimal solution, then loss minimum configuration is determined as the best configuration among these solutions. Numerical simulations are carried out for a real scale system model consists of 72 distribution feeders and 234 sectionalizing switches in order to examine the validity of the proposed method.
Interorganizational Innovation in Systemic Networks
DEFF Research Database (Denmark)
Seemann, Janne; Dinesen, Birthe; Gustafsson, Jeppe
2013-01-01
that linear n-stage models by reducing complexity and flux end up focusing only on the surface of the network and are thus unable to grasp important aspects of network dynamics. The paper suggests that there is a need for a more dynamic innovation model able to grasp the whole picture of dynamics in systemic...... patients with chronic obstructive pulmonary disease (COPD) to avoid readmission, perform self monitoring and to maintain rehabilitation in their homes. The aim of the paper is to identify, analyze and discuss innovation dynamics in the COPD network and on a preliminary basis to identify implications...... for managing innovations in systemic networks. The main argument of this paper is that innovation dynamics in systemic networks should be understood as a complex interplay of four logics: 1) Fragmented innovation, 2) Interface innovation, 3) Competing innovation, 4) Co-innovation. The findings indicate...
Asynchronous control for networked systems
Rubio, Francisco; Bencomo, Sebastián
2015-01-01
This book sheds light on networked control systems; it describes different techniques for asynchronous control, moving away from the periodic actions of classical control, replacing them with state-based decisions and reducing the frequency with which communication between subsystems is required. The text focuses specially on event-based control. Split into two parts, Asynchronous Control for Networked Systems begins by addressing the problems of single-loop networked control systems, laying out various solutions which include two alternative model-based control schemes (anticipatory and predictive) and the use of H2/H∞ robust control to deal with network delays and packet losses. Results on self-triggering and send-on-delta sampling are presented to reduce the need for feedback in the loop. In Part II, the authors present solutions for distributed estimation and control. They deal first with reliable networks and then extend their results to scenarios in which delays and packet losses may occur. The novel ...
High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil
Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)
1998-01-01
The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.
On the Optimality of Trust Network Analysis with Subjective Logic
Directory of Open Access Journals (Sweden)
PARK, Y.
2014-08-01
Full Text Available Building and measuring trust is one of crucial aspects in e-commerce, social networking and computer security. Trust networks are widely used to formalize trust relationships and to conduct formal reasoning of trust values. Diverse trust network analysis methods have been developed so far and one of the most widely used schemes is TNA-SL (Trust Network Analysis with Subjective Logic. Recent papers claimed that TNA-SL always finds the optimal solution by producing the least uncertainty. In this paper, we present some counter-examples, which imply that TNA-SL is not an optimal algorithm. Furthermore, we present a probabilistic algorithm in edge splitting to minimize uncertainty.
Multi-objective optimization in computer networks using metaheuristics
Donoso, Yezid
2007-01-01
Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...
On limited fan-in optimal neural networks
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.; Makaruk, H.E. [Los Alamos National Lab., NM (United States); Draghici, S. [Wayne State Univ., Detroit, MI (United States). Vision and Neural Networks Lab.
1998-03-01
Because VLSI implementations do not cope well with highly interconnected nets the area of a chip growing as the cube of the fan-in--this paper analyses the influence of limited fan in on the size and VLSI optimality of such nets. Two different approaches will show that VLSI- and size-optimal discrete neural networks can be obtained for small (i.e. lower than linear) fan-in values. They have applications to hardware implementations of neural networks. The first approach is based on implementing a certain sub class of Boolean functions, IF{sub n,m} functions. The authors will show that this class of functions can be implemented in VLSI optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan ins. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on neural networks with fan-ins limited to 2. They generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan in values, while relative minimum size solutions can be obtained for fan-ins strictly lower than linear. Finally, a size-optimal neural network having small constant fan-ins will be suggested for IF{sub n,m} functions.
Directory of Open Access Journals (Sweden)
Haichao Wang
2017-07-01
Full Text Available A district heating (DH system is one of the most important components of infrastructures in cold areas. Proper DH network design should balance the initial investment and the heat distribution cost of the DH network. Currently, this design is often based on a recommended value for specific pressure loss (R = ∆P/L in the main lines. This will result in a feasible network design, but probably not be optimal in most cases. The paper develops a novel optimization model to facilitate the design by considering the initial investment in the pipes and the heat distribution costs. The model will generate all possible network scenarios consisting of different series of diameters for each pipe in the flow direction of the network. Then, the annuity on the initial investment, the heat distribution cost, and the total annual cost will be calculated for each network scenario, taking into account the uncertainties of the material prices and the yearly operating time levels. The model is applied to a sample DH network and the results indicate that the model works quite well, clearly identifying the optimal network design and demonstrating that the heat distribution cost is more important than the initial investment in DH network design.
Ant colony optimization and neural networks applied to nuclear power plant monitoring
Energy Technology Data Exchange (ETDEWEB)
Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2015-07-01
A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)
Coherent Network Optimizing of Rail-Based Urban Mass Transit
Directory of Open Access Journals (Sweden)
Ying Zhang
2012-01-01
Full Text Available An efficient public transport is more than ever a crucial factor when it comes to the quality of life and competitiveness of many cities and regions in Asia. In recent years, the rail-based urban mass transit has been regarded as one of the key means to overcoming the great challenges in Chinese megacities. The purpose of this study is going to develop a coherent network optimizing for rail-based urban mass transit to find the best alternatives for the user and to demonstrate how to meet sustainable development needs and to match the enormous capacity requirements simultaneously. This paper presents an introduction to the current situation of the important lines, and transfer points in the metro system Shanghai. The insufficient aspects are analyzed and evaluated; while the optimizing ideas and measurements are developed and concreted. A group of examples are used to illustrate the approach. The whole study could be used for the latest reference for other megacities which have to be confronted with the similar situations and processes with enormous dynamic travel and transport demands.
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
RECOMMENDER SYSTEMS IN SOCIAL NETWORKS
Directory of Open Access Journals (Sweden)
Cleomar Valois Batista Jr
2011-12-01
Full Text Available The continued and diversified growth of social networks has changed the way in which users interact with them. With these changes, what once was limited to social contact is now used for exchanging ideas and opinions, creating the need for new features. Users have so much information at their fingertips that they are unable to process it by themselves; hence, the need to develop new tools. Recommender systems were developed to address this need and many techniques were used for different approaches to the problem. To make relevant recommendations, these systems use large sets of data, not taking the social network of the user into consideration. Developing a recommender system that takes into account the social network of the user is another way of tackling the problem. The purpose of this project is to use the theory of six degrees of separation (Watts 2003 amongst users of a social network to enhance existing recommender systems.
Directory of Open Access Journals (Sweden)
N. Hooda
2017-06-01
Full Text Available The classic problem of the capital cost optimization of branched piped networks consists of choosing pipe diameters for each pipe in the network from a discrete set of commercially available pipe diameters. Each pipe in the network can consist of multiple segments of differing diameters. Water networks also consist of intermediate tanks that act as buffers between incoming flow from the primary source and the outgoing flow to the demand nodes. The network from the primary source to the tanks is called the primary network, and the network from the tanks to the demand nodes is called the secondary network. During the design stage, the primary and secondary networks are optimized separately, with the tanks acting as demand nodes for the primary network. Typically the choice of tank locations, their elevations, and the set of demand nodes to be served by different tanks is manually made in an ad hoc fashion before any optimization is done. It is desirable therefore to include this tank configuration choice in the cost optimization process itself. In this work, we explain why the choice of tank configuration is important to the design of a network and describe an integer linear program model that integrates the tank configuration to the standard pipe diameter selection problem. In order to aid the designers of piped-water networks, the improved cost optimization formulation is incorporated into our existing network design system called JalTantra.
Hooda, Nikhil; Damani, Om
2017-06-01
The classic problem of the capital cost optimization of branched piped networks consists of choosing pipe diameters for each pipe in the network from a discrete set of commercially available pipe diameters. Each pipe in the network can consist of multiple segments of differing diameters. Water networks also consist of intermediate tanks that act as buffers between incoming flow from the primary source and the outgoing flow to the demand nodes. The network from the primary source to the tanks is called the primary network, and the network from the tanks to the demand nodes is called the secondary network. During the design stage, the primary and secondary networks are optimized separately, with the tanks acting as demand nodes for the primary network. Typically the choice of tank locations, their elevations, and the set of demand nodes to be served by different tanks is manually made in an ad hoc fashion before any optimization is done. It is desirable therefore to include this tank configuration choice in the cost optimization process itself. In this work, we explain why the choice of tank configuration is important to the design of a network and describe an integer linear program model that integrates the tank configuration to the standard pipe diameter selection problem. In order to aid the designers of piped-water networks, the improved cost optimization formulation is incorporated into our existing network design system called JalTantra.
An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures
Energy Technology Data Exchange (ETDEWEB)
Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2016-01-01
As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....
Directory of Open Access Journals (Sweden)
Zhengyu Xie
2015-01-01
Full Text Available We consider the sensor networks hierarchical optimization problem in high-speed railway transport hub (HRTH. The sensor networks are optimized from three hierarchies which are key area sensors optimization, passenger line sensors optimization, and whole area sensors optimization. Case study on a specific HRTH in China showed that the hierarchical optimization method is effective to optimize the sensor networks for security monitoring in HRTH.
Bui-Xuan, Binh-Minh; Jones, Nick S
2014-10-08
By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2 n ), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.
A multiobjective optimization framework for multicontaminant industrial water network design.
Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge
2011-07-01
The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.
Self-organization in neural networks - Applications in structural optimization
Hajela, Prabhat; Fu, B.; Berke, Laszlo
1993-01-01
The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.
Optimizing Signal Behavior of Femtocells for Improved Network
Directory of Open Access Journals (Sweden)
Meera Joseph
2016-10-01
Full Text Available The high demand for network coverage in an indoor setting brought about the acceptance of femtocell technology as a solution using the backhaul connectivity in the existing network. The quality of signal, voice calling, Internet, security and data are improved through the use femtocell at the indoor environment. Here the service provider attempts to reduce their operation cost by presenting self-organizing mechanisms for optimization of the network. The remarkable part is that, femtocells improves coverage, enhances the data rate at the indoor environment. Therefore, the challenges of the femtocell also known as interference deteriorates the capacity and quality performance of the whole cellular network. In this paper we simulate the bit error rate against signal behaviour at the indoor environment and we also simulate the transmitting power over signal for both macrocells and femtocells. We focus on the transmitting power that might cause interference within the cellular network.
Optimizing Soil Moisture Sampling Locations for Validation Networks for SMAP
Roshani, E.; Berg, A. A.; Lindsay, J.
2013-12-01
Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories
Discrete particle swarm optimization for identifying community structures in signed social networks.
Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng
2014-10-01
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Dall' Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.
2015-07-01
This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the control of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Energy Technology Data Exchange (ETDEWEB)
Magnier, Laurent; Haghighat, Fariborz [Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., BE-351, Montreal, Quebec H3G 1M8 (Canada)
2010-03-15
Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption. (author)
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as ... A multi- objective optimization of hard turning using neural network modelling and swarm intelligence ... being used in this study), and these activated values in turn become the starting signals for the next adjacent ...
Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. (2009). Optimizing Knowledge Sharing in Learning Networks through Peer Tutoring. Presentation at the IADIS international conference on Cognition and Exploratory in Digital Age (CELDA 2009). November, 20-22, 2009, Rome, Italy.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning in Digital Age (CELDA 2009) (pp. 550-551). November, 20-22, 2009, Rome, Italy: Springer.
Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring
Hsiao, Amy; Brouns, Francis; Kester, Liesbeth; Sloep, Peter
2009-01-01
Hsiao, Y. P., Brouns, F., Kester, L., & Sloep, P. B. (2009). Optimizing Knowledge Sharing In Learning Networks Through Peer Tutoring. In D. Kinshuk, J. Sampson, J. Spector, P. Isaías, P. Barbosa & D. Ifenthaler (Eds.). Proceedings of IADIS International Conference Cognition and Exploratory Learning
Optimal server scheduling in hybrid P2P networks
B. Zhang (Bo); S.C. Borst (Sem); M.I. Reiman
2010-01-01
htmlabstractWe consider the server scheduling problem in hybrid P2P networks in the context of a fluid model. Specifically, we examine how to allocate the limited amount of server upload capacity among competing swarms over time in order to optimize the download performance experienced by users. For
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
This paper is concerned with the problem of optimizing the distribution of water in Kigali City at a minimum cost. The mathematical formulation is a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price of pipes, the hydraulic gradient ...
Optimization of Gas Flow Network using the Traveling Salesman ...
African Journals Online (AJOL)
The overall goal of this paper is to develop a general formulation for an optimal infrastructure layout design of gas pipeline distribution networks using algorithm developed from the application of two industrial engineering concepts: the traveling salesman problem (TSP) and the nearest neighbor (NN). The focus is on the ...
Synchronization-optimized networks for coupled nearly identical ...
Indian Academy of Sciences (India)
173–182. Synchronization-optimized networks for coupled nearly identical oscillators and their structural analysis. SUMAN ACHARYYA1,∗ and R E AMRITKAR1,2. 1Theoretical Physics Division, Physical Research Laboratory, Ahmedabad 380 009, India. 2Institute of Infrastructure Technology Research and Management, ...
Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network
Directory of Open Access Journals (Sweden)
Matti Mantere
2013-09-01
Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.
Multilevel Complex Networks and Systems
Caldarelli, Guido
2014-03-01
Network theory has been a powerful tool to model isolated complex systems. However, the classical approach does not take into account the interactions often present among different systems. Hence, the scientific community is nowadays concentrating the efforts on the foundations of new mathematical tools for understanding what happens when multiple networks interact. The case of economic and financial networks represents a paramount example of multilevel networks. In the case of trade, trade among countries the different levels can be described by the different granularity of the trading relations. Indeed, we have now data from the scale of consumers to that of the country level. In the case of financial institutions, we have a variety of levels at the same scale. For example one bank can appear in the interbank networks, ownership network and cds networks in which the same institution can take place. In both cases the systemically important vertices need to be determined by different procedures of centrality definition and community detection. In this talk I will present some specific cases of study related to these topics and present the regularities found. Acknowledged support from EU FET Project ``Multiplex'' 317532.
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
A New Optimized GA-RBF Neural Network Algorithm
Directory of Open Access Journals (Sweden)
Weikuan Jia
2014-01-01
Full Text Available When confronting the complex problems, radial basis function (RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm, which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
A new optimized GA-RBF neural network algorithm.
Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
Bode, F.; Reuschen, S.; Nowak, W.
2015-12-01
Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.
Evolutionary optimization of network reconstruction from derivative-variable correlations
Leguia, Marc G.; Andrzejak, Ralph G.; Levnajić, Zoran
2017-08-01
Topologies of real-world complex networks are rarely accessible, but can often be reconstructed from experimentally obtained time series via suitable network reconstruction methods. Extending our earlier work on methods based on statistics of derivative-variable correlations, we here present a new method built on integrating an evolutionary optimization algorithm into the derivative-variable correlation method. Results obtained from our modification of the method in general outperform the original results, demonstrating the suitability of evolutionary optimization logic in network reconstruction problems. We show the method’s usefulness in realistic scenarios where the reconstruction precision can be limited by the nature of the time series. We also discuss important limitations coming from various dynamical regimes that time series can belong to.
Optimization Program for Drinking Water Systems
The Area-Wide Optimization Program (AWOP) provides tools and approaches for drinking water systems to meet water quality optimization goals and provide an increased – and sustainable – level of public health protection to their consumers.
Robust Optimization of Uncertain Logistics Networks
Hosseini, Sara; Dullaert, Wout
2011-01-01
Decision makers should concentrate on managing any probable risk of the logistics system, starting from the design phase. As a result, unforeseen conditions during implementation will be less likely to invalidate the basic design plan or disturb the performance targets. One of the main difficulties
Optimizing Neural Network Architectures Using Generalization Error Estimators
DEFF Research Database (Denmark)
Larsen, Jan
1994-01-01
This paper addresses the optimization of neural network architectures. It is suggested to optimize the architecture by selecting the model with minimal estimated averaged generalization error. We consider a least-squares (LS) criterion for estimating neural network models, i.e., the associated...... model weights are estimated by minimizing the LS criterion. The quality of a particular estimated model is measured by the average generalization error. This is defined as the expected squared prediction error on a novel input-output sample averaged over all possible training sets. An essential part...... of the suggested architecture optimization scheme is to calculate an estimate of the average generalization error. We suggest using the GEN-estimator which allows for dealing with nonlinear, incomplete models, i.e., models which are not capable of modeling the underlying nonlinear relationship perfectly. In most...
Situational Awareness of Network System Roles (SANSR)
Energy Technology Data Exchange (ETDEWEB)
Huffer, Kelly M [ORNL; Reed, Joel W [ORNL
2017-01-01
In a large enterprise it is difficult for cyber security analysts to know what services and roles every machine on the network is performing (e.g., file server, domain name server, email server). Using network flow data, already collected by most enterprises, we developed a proof-of-concept tool that discovers the roles of a system using both clustering and categorization techniques. The tool's role information would allow cyber analysts to detect consequential changes in the network, initiate incident response plans, and optimize their security posture. The results of this proof-of-concept tool proved to be quite accurate on three real data sets. We will present the algorithms used in the tool, describe the results of preliminary testing, provide visualizations of the results, and discuss areas for future work. Without this kind of situational awareness, cyber analysts cannot quickly diagnose an attack or prioritize remedial actions.
Optimal network topologies: expanders, cages, Ramanujan graphs, entangled networks and all that
Donetti, Luca; Neri, Franco; Muñoz, Miguel A.
2006-08-01
We report on some recent developments in the search for optimal network topologies. First we review some basic concepts on spectral graph theory, including adjacency and Laplacian matrices, paying special attention to the topological implications of having large spectral gaps. We also introduce related concepts such as 'expanders', Ramanujan, and Cage graphs. Afterwards, we discuss two different dynamical features of Networks, synchronizability and flow of random walkers, so that they are optimized if the corresponding Laplacian matrix has a large spectral gap. From this, we show, by developing a numerical optimization algorithm, that maximum synchronizability and fast random walk spreading are obtained for a particular type of extremely homogeneous regular networks, with long loops and poor modular structure, that we call entangled networks. These turn out to be related to Ramanujan and Cage graphs. We argue also that these graphs are very good finite-size approximations to Bethe lattices, and provide optimal or almost optimal solutions to many other problems, for instance searchability in the presence of congestion or performance of neural networks. Finally, we study how these results are modified when studying dynamical processes controlled by a normalized (weighted and directed) dynamics; much more heterogeneous graphs are optimal in this case. Finally, a critical discussion of the limitations and possible extensions of this work is presented.
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Kolosionis, Konstantinos; Papadopoulou, Maria P.
2017-04-01
Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.
Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen
2017-02-01
Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.
Directory of Open Access Journals (Sweden)
Krishan Kumar
2017-01-01
Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.
Directory of Open Access Journals (Sweden)
Wenpeng Yu
2013-12-01
Full Text Available Due to the interconnection and active management of Distributed Generation (DG and Energy Storage Systems (ESSs, the traditional electrical distribution network has become an Active Distribution Network (ADN, posing challenges to the operation optimization of the network. The power supply and storage capacity indexes of a Local Autonomy Control Region (LACR, which consists of DGs, ESSs and the network, are proposed in this paper to quantify the power regulating range of a LACR. DG/ESS and the network are considered as a whole in the model of the indexes, considering both network constraints and power constraints of the DG/ESS. The index quantifies the maximum LACR power supplied to or received from ADN lines. Similarly, power supply and storage capacity indexes of the ADN line are also proposed to quantify the maximum power exchanged between ADN lines. Then a practical algorithm to calculate the indexes is presented, and an operation optimization model is proposed based on the indexes to maximum the economic benefit of DG/ESS. In the optimization model, the power supply reliability of the ADN line is also considered. Finally, the indexes of power supply and storage capacity and the optimization are demonstrated in a case study.
TRADING-OFF CONSTRAINTS IN THE PUMP SCHEDULING OPTIMIZATION OF WATER DISTRIBUTION NETWORKS
Directory of Open Access Journals (Sweden)
Gencer Genço\\u011Flu
2016-01-01
Full Text Available Pumps are one of the essential components of water supply systems. Depending of the topography, a water supply system may completely rely on pumping. They may consume non-negligible amount of water authorities' budgets during operation. Besides their energy costs, maintaining the healthiness of pumping systems is another concern for authorities. This study represents a multi-objective optimization method for pump scheduling problem. The optimization objective contains hydraulic and operational constraints. Switching of pumps and usage of electricity tariff are assumed to be key factors for operational reliability and energy consumption and costs of pumping systems. The local optimals for systems operational reliability, energy consumptions and energy costs are investigated resulting from trading-off pump switch and electricity tariff constraints within given set of boundary conditions. In the study, a custom made program is employed that combines genetic algorithm based optimization module with hydraulic network simulation software -EPANET. Developed method is applied on the case study network; N8-3 pressure zone of the Northern Supply of Ankara (Turkey Water Distribution Network. This work offers an efficient method for water authorities aiming to optimize pumping schedules considering expenditures and operational reliability mutually.
Directory of Open Access Journals (Sweden)
Murad Khan
2014-01-01
Full Text Available Optimizing the balance between different handover parameters for network selection is one of the challenging tasks for seamless communication in heterogeneous networks. Traditional approaches for network selection are mostly based on the Receive Signal Strength (RSS from the Point of Attachment (PoA of a network. Most of these schemes are suffered from high handover delay, false handover indications, and inappropriate network selection for handover. To address these problems, we present an optimized network selection scheme based on the speed of a mobile node. A mechanism based on two different thresholds on the speed of a Mobile Node (MN is integrated in the proposed scheme. If the speed of an MN is greater than any of the threshold, the MN performs handover to a particular network. We employ Grey Relational Analysis (GRA in the proposed scheme to select the best PoA of the selected network. Similarly, to deal with false handover indications, we proposed an optimized handover triggering technique. We compare our proposed scheme with existing schemes in context of energy consumption for scanning, frequent and failed handovers, packet loss ratio, and handover delay. The proposed scheme shows superior performance and it outperforms existing schemes used for similar purpose. Moreover, simulation results show the accuracy and performance of the proposed scheme.
Stochastic Optimization of Complex Systems
Energy Technology Data Exchange (ETDEWEB)
Birge, John R. [University of Chicago
2014-03-20
This project focused on methodologies for the solution of stochastic optimization problems based on relaxation and penalty methods, Monte Carlo simulation, parallel processing, and inverse optimization. The main results of the project were the development of a convergent method for the solution of models that include expectation constraints as in equilibrium models, improvement of Monte Carlo convergence through the use of a new method of sample batch optimization, the development of new parallel processing methods for stochastic unit commitment models, and the development of improved methods in combination with parallel processing for incorporating automatic differentiation methods into optimization.
Schubert, Martin
2012-01-01
This book develops a mathematical framework for modeling and optimizing interference-coupled multiuser systems. At the core of this framework is the concept of general interference functions, which provides a simple means of characterizing interdependencies between users. The entire analysis builds on the two core axioms scale-invariance and monotonicity. The proposed network calculus has its roots in power control theory and wireless communications. It adds theoretical tools for analyzing the typical behavior of interference-coupled networks. In this way it complements existing game-theoretic approaches. The framework should also be viewed in conjunction with optimization theory. There is a fruitful interplay between the theory of interference functions and convex optimization theory. By jointly exploiting the properties of interference functions, it is possible to design algorithms that outperform general-purpose techniques that only exploit convexity. The title “network calculus” refers to the fact tha...
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
Promoting Social Network Awareness: A Social Network Monitoring System
Cadima, Rita; Ferreira, Carlos; Monguet, Josep; Ojeda, Jordi; Fernandez, Joaquin
2010-01-01
To increase communication and collaboration opportunities, members of a community must be aware of the social networks that exist within that community. This paper describes a social network monitoring system--the KIWI system--that enables users to register their interactions and visualize their social networks. The system was implemented in a…
Optimal dynamics for quality control in spatially distributed mitochondrial networks.
Directory of Open Access Journals (Sweden)
Pinkesh K Patel
Full Text Available Recent imaging studies of mitochondrial dynamics have implicated a cycle of fusion, fission, and autophagy in the quality control of mitochondrial function by selectively increasing the membrane potential of some mitochondria at the expense of the turnover of others. This complex, dynamical system creates spatially distributed networks that are dependent on active transport along cytoskeletal networks and on protein import leading to biogenesis. To study the relative impacts of local interactions between neighboring mitochondria and their reorganization via transport, we have developed a spatiotemporal mathematical model encompassing all of these processes in which we focus on the dynamics of a health parameter meant to mimic the functional state of mitochondria. In agreement with previous models, we show that both autophagy and the generation of membrane potential asymmetry following a fusion/fission cycle are required for maintaining a healthy mitochondrial population. This health maintenance is affected by mitochondrial density and motility primarily through changes in the frequency of fusion events. Health is optimized when the selectivity thresholds for fusion and fission are matched, providing a mechanistic basis for the observed coupling of the two processes through the protein OPA1. We also demonstrate that the discreteness of the components exchanged during fusion is critical for quality control, and that the effects of limiting total amounts of autophagy and biogenesis have distinct consequences on health and population size, respectively. Taken together, our results show that several general principles emerge from the complexity of the quality control cycle that can be used to focus and interpret future experimental studies, and our modeling framework provides a road-map for deconstructing the functional importance of local interactions in communities of cells as well as organelles.
2015-03-01
biometric data collection. Capture role- player mock biometric data including finger prints, iris scans, and facial recognition photos. (MOC training...boarded vessel used the SEEK II to collect biometrics including fingerprints, iris scans, and facial recognition photos. Following system setup and...MARITIME INFORMATION DOMINANCE: OPTIMIZING TACTICAL NETWORK FOR BIOMETRIC DATA SHARING IN MARITIME INTERDICTION OPERATIONS by Adam R. Sinsel
Dependability of self-optimizing mechatronic systems
Rammig, Franz; Schäfer, Wilhelm; Sextro, Walter
2014-01-01
Intelligent technical systems, which combine mechanical, electrical and software engineering with control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. Self-optimizing systems create high value for example in terms of energy and resource efficiency as well as reliability. The Collaborative Research Center 614 "Self-optimizing Concepts and Structures in Mechanical Engineering" pursued the long-term aim to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop self-optimizing systems. This book is directed to researchers and practitioners alike. It provides a design methodology for the development of self-optimizing systems consisting of a reference process, methods, and tools. The reference process is divided into two phase...
Optimization of wireless Bluetooth sensor systems.
Lonnblad, J; Castano, J; Ekstrom, M; Linden, M; Backlund, Y
2004-01-01
Within this study, three different Bluetooth sensor systems, replacing cables for transmission of biomedical sensor data, have been designed and evaluated. The three sensor architectures are built on 1-, 2- and 3-chip solutions and depending on the monitoring situation and signal character, different solutions are optimal. Essential parameters for all systems have been low physical weight and small size, resistance to interference and interoperability with other technologies as global- or local networks, PC's and mobile phones. Two different biomedical input signals, ECG and PPG (photoplethysmography), have been used to evaluate the three solutions. The study shows that it is possibly to continuously transmit an analogue signal. At low sampling rates and slowly varying parameters, as monitoring the heart rate with PPG, the 1-chip solution is the most suitable, offering low power consumption and thus a longer battery lifetime or a smaller battery, minimizing the weight of the sensor system. On the other hand, when a higher sampling rate is required, as an ECG, the 3-chip architecture, with a FPGA or micro-controller, offers the best solution and performance. Our conclusion is that Bluetooth might be useful in replacing cables of medical monitoring systems.
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.
Kobayashi, Koichi; Hiraishi, Kunihiko
2014-01-01
One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs.
MenuOptimizer: interactive optimization of menu systems
Bailly, Gilles; Oulasvirta, Antti; Kötzing, Timo; Hoppe, Sabrina
2013-01-01
International audience; Menu systems are challenging to design because design spaces are immense, and several human factors affect user behavior. This paper contributes to the design of menus with the goal of interactively assisting designers with an optimizer in the loop. To reach this goal, 1) we extend a predictive model of user performance to account for expectations as to item groupings; 2) we adapt an ant colony optimizer that has been proven efficient for this class of problems; and 3)...
Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.
Directory of Open Access Journals (Sweden)
Saket Navlakha
2015-07-01
Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.
Optimality principles in the regulation of metabolic networks.
Berkhout, Jan; Bruggeman, Frank J; Teusink, Bas
2012-08-29
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Ren, Danping; Wu, Shanshan; Zhang, Lijing
2016-09-01
In view of the characteristics of the global control and flexible monitor of software-defined networks (SDN), we proposes a new optical access network architecture dedicated to Wavelength Division Multiplexing-Passive Optical Network (WDM-PON) systems based on SDN. The network coding (NC) technology is also applied into this architecture to enhance the utilization of wavelength resource and reduce the costs of light source. Simulation results show that this scheme can optimize the throughput of the WDM-PON network, greatly reduce the system time delay and energy consumption.
Plant systems biology: network matters.
Lucas, Mikaël; Laplaze, Laurent; Bennett, Malcolm J
2011-04-01
Systems biology is all about networks. A recent trend has been to associate systems biology exclusively with the study of gene regulatory or protein-interaction networks. However, systems biology approaches can be applied at many other scales, from the subatomic to the ecosystem scales. In this review, we describe studies at the sub-cellular, tissue, whole plant and crop scales and highlight how these studies can be related to systems biology. We discuss the properties of system approaches at each scale as well as their current limits, and pinpoint in each case advances unique to the considered scale but representing potential for the other scales. We conclude by examining plant models bridging different scales and considering the future prospects of plant systems biology. © 2011 Blackwell Publishing Ltd.
Directory of Open Access Journals (Sweden)
Heryanto M Ary
2015-01-01
Full Text Available UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.
Efficient Network Monitoring for Large Data Acquisition Systems
Savu, DO; The ATLAS collaboration; Al-Shabibi, A; Sjoen, R; Batraneanu, SM; Stancu, SN
2011-01-01
Though constantly evolving and improving, the available network monitoring solutions have limitations when applied to the infrastructure of a high speed real-time data acquisition (DAQ) system. DAQ networks are particular computer networks where experts have to pay attention to both individual subsections as well as system wide traffic flows while monitoring the network. The ATLAS Network at the Large Hadron Collider (LHC) has more than 200 switches interconnecting 3500 hosts and totaling 8500 high speed links. The use of heterogeneous tools for monitoring various infrastructure parameters, in order to assure optimal DAQ system performance, proved to be a tedious and time consuming task for experts. To alleviate this problem we used our networking and DAQ expertise to build a flexible and scalable monitoring system providing an intuitive user interface with the same look and feel irrespective of the data provider that is used. Our system uses custom developed components for critical performance monitoring and...
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Store layout optimization using indoor positioning system
National Research Council Canada - National Science Library
Hwangbo, Hyunwoo; Kim, Jonghyuk; Lee, Zoonky; Kim, Soyean
2017-01-01
Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability...
Network synthesis and parameter optimization for vehicle suspension with inerter
Directory of Open Access Journals (Sweden)
Long Chen
2016-12-01
Full Text Available In order to design a comfortable-oriented vehicle suspension structure, the network synthesis method was utilized to transfer the problem into solving a timing robust control problem and determine the structure of “inerter–spring–damper” suspension. Bilinear Matrix Inequality was utilized to obtain the timing transfer function. Then, the transfer function of suspension system can be physically implemented by passive elements such as spring, damper, and inerter. By analyzing the sensitivity and quantum genetic algorithm, the optimized parameters of inerter–spring–damper suspension were determined. A quarter-car model was established. The performance of the inerter–spring–damper suspension was verified under random input. The simulation results manifested that the dynamic performance of the proposed suspension was enhanced in contrast with traditional suspension. The root mean square of vehicle body acceleration decreases by 18.9%. The inerter–spring–damper suspension can inhibit the vertical vibration within the frequency of 1–3 Hz effectively and enhance the performance of ride comfort significantly.
Networks, linkages, and migration systems.
Fawcett, J T
1989-01-01
Recent theoretical interest in migration systems calls attention to the functions of diverse linkages between countries in stimulating, directing,and maintaining international flows of people. This article proposes a conceptual framework for the non-people linkages in international migration systems and discusses the implications for population movement of the 4 categories and 3 types of linkages that define the network. The 4 categories include 1) state to state relations, 2) mass culture connections, 3) family and personal networks, and 4) migrant agency activities. The 3 types of linkages are 1) tangible linkages, 2) regulatory linkages, and 3) relational linkages.
Network operating system focus technology
1985-01-01
An activity structured to provide specific design requirements and specifications for the Space Station Data Management System (DMS) Network Operating System (NOS) is outlined. Examples are given of the types of supporting studies and implementation tasks presently underway to realize a DMS test bed capability to develop hands-on understanding of NOS requirements as driven by actual subsystem test beds participating in the overall Johnson Space Center test bed program. Classical operating system elements and principal NOS functions are listed.
Loss optimization in distribution networks with distributed generation
DEFF Research Database (Denmark)
Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte
2017-01-01
This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators in...
Optimal placement of distributed generation in distribution networks
African Journals Online (AJOL)
user
loss formula and in the second segment the optimal location of DG is found by using the loss sensitivity factor. The analytical expression is based on exact loss ..... A computer program is written in MATLAB 7 to find the optimal size of .... Proceedings of IEEE PES Power Systems Conference and Exposition-PSCE 2006, pp.
Optimal placement of distributed generation in distribution networks
African Journals Online (AJOL)
user
This paper proposes the application of Particle Swarm Optimization (PSO) technique to find the optimal size and optimum ... DG also has several benefits like energy costs through combined ... and high capacity of DG can be explained by the fact that the distribution system was initially designed such that power flows.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.
2014-12-01
The report documents policy considerations for the Intelligent Network Flow Optimization (INFLO) connected vehicle applications : bundle. INFLO aims to optimize network flow on freeways and arterials by informing motorists of existing and impen...
2013-09-20
... Strategic Network Optimization (SNO) Program Environmental Assessment. SUMMARY: The Defense Logistics Agency... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF DEFENSE Office of the Secretary Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program...
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
Optimal Constrained Stationary Intervention in Gene Regulatory Networks
Directory of Open Access Journals (Sweden)
Golnaz Vahedi
2008-05-01
Full Text Available A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study.
Networked control of microgrid system of systems
Mahmoud, Magdi S.; Rahman, Mohamed Saif Ur; AL-Sunni, Fouad M.
2016-08-01
The microgrid has made its mark in distributed generation and has attracted widespread research. However, microgrid is a complex system which needs to be viewed from an intelligent system of systems perspective. In this paper, a network control system of systems is designed for the islanded microgrid system consisting of three distributed generation units as three subsystems supplying a load. The controller stabilises the microgrid system in the presence of communication infractions such as packet dropouts and delays. Simulation results are included to elucidate the effectiveness of the proposed control strategy.
Distributed Optimization of Multi Beam Directional Communication Networks
2017-06-30
spatial processing strategies for multi-beam transmission with a simple MAC layer in a simulation study. Full-duplex communications ease the burden on...Distributed Optimization of Multi-Beam Directional Communication Networks Theodoros Tsiligkaridis MIT Lincoln Laboratory Lexington, MA 02141, USA...based routing. I. INTRODUCTION Missions where multiple communication goals are of in- terest are becoming more prevalent in military applications
An Extended Microcomputer-Based Network Optimization Package.
1982-10-01
for New Cycle ..... ............ 53 * 19. Four Node Cycle ...... ... .. ... .. ... .. 54 20. All Artificial Start .................. 61 21. Micronet ...package. For the sake of brevity, the microcomputer-based network optimization package herein described will be referred to as Micronet . The host...computer for Micronet is an Apple II Plus with 64K of memory. This is certainly not the most powerful microcomputer (with some competitors featuring
PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN
Directory of Open Access Journals (Sweden)
Arie Rachmad Syulistyo
2016-02-01
Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.
Synchrony-optimized networks of Kuramoto oscillators with inertia
Pinto, Rafael S.; Saa, Alberto
2016-12-01
We investigate synchronization in networks of Kuramoto oscillators with inertia. More specifically, we introduce a rewiring algorithm consisting basically in a hill climb scheme in which the edges of the network are swapped in order to enhance its synchronization capacity. We show that the synchrony-optimized networks generated by our algorithm have some interesting topological and dynamical properties. In particular, they typically exhibit an anticipation of the synchronization onset and are more robust against certain types of perturbations. We consider synthetic random networks and also a network with a topology based on an approximated model of the (high voltage) power grid of Spain, since networks of Kuramoto oscillators with inertia have been used recently as simplified models for power grids, for which synchronization is obviously a crucial issue. Despite the extreme simplifications adopted in these models, our results, among others recently obtained in the literature, may provide interesting principles to guide the future growth and development of real-world grids, specially in the case of a change of the current paradigm of centralized towards distributed generation power grids.
Optimality Principles in the Regulation of Metabolic Networks
Directory of Open Access Journals (Sweden)
Jan Berkhout
2012-08-01
Full Text Available One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.
Directory of Open Access Journals (Sweden)
JongHyup Lee
2016-08-01
Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks.
Emary, E; Zawbaa, Hossam M; Grosan, Crina
2017-01-10
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent's own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents' states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.
DEFF Research Database (Denmark)
Dalla Rosa, A.; Boulter, R.; Church, K.
2012-01-01
better energy delivery performance than high-temperature district heating (HTDH) (Tsupply> 100 C), decreasing the heat loss by approximately 40%. The low-temperature networks (Tsupplyinvestment. The implementation...... in Canada. The paper discusses critical issues and quantifies the performance of design concepts for DH supply to low heat density areas. DH is a fundamental energy infrastructure and is part of the solution for sustainable energy planning in Canadian communities....
Analysis and Optimization of Heterogeneous Real-Time Embedded Systems
DEFF Research Database (Denmark)
Pop, Paul; Eles, Petru; Peng, Zebo
2005-01-01
An increasing number of real-time applications are today implemented using distributed heterogeneous architectures composed of interconnected networks of processors. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling...... policies. Each network has its own communication protocol, each processor in the architecture can have its own scheduling policy, and several scheduling policies can share a processor. In this context, the task of designing such systems is becoming increasingly important and difficult at the same time....... The success of such new design methods depends on the availability of analysis and optimization techniques. In this paper, we present analysis and optimization techniques for heterogeneous real-time embedded systems. We address in more detail a particular class of such systems called multi-clusters, composed...
A Generic Methodology for Superstructure Optimization of Different Processing Networks
DEFF Research Database (Denmark)
Bertran, Maria-Ona; Frauzem, Rebecca; Zhang, Lei
problem.The proposed methodology involves the use of additional methods and tools, such as a database and an external software for solving the network optimization problem. The database has been created using an ontology-based knowledge representation consisting in various layers of data...... in a software interface that guides the user through the problem formulation and solution steps and integrates the various methods and tools for efficient flow of information between them. By using this interface, the user can retrieve and/or modify existing networks and alternatives from the database, as well...... of sustainable processing networks containing three stages: (i) synthesis stage, (ii) design stage, and (iii) innovation stage. In this work, a focus is placed on the first stage, the synthesis stage. Process synthesis becomes necessary in determining the appropriate processing routes to produce a selection...
Architecting Communication Network of Networks for Space System of Systems
Bhasin, Kul B.; Hayden, Jeffrey L.
2008-01-01
The National Aeronautics and Space Administration (NASA) and the Department of Defense (DoD) are planning Space System of Systems (SoS) to address the new challenges of space exploration, defense, communications, navigation, Earth observation, and science. In addition, these complex systems must provide interoperability, enhanced reliability, common interfaces, dynamic operations, and autonomy in system management. Both NASA and the DoD have chosen to meet the new demands with high data rate communication systems and space Internet technologies that bring Internet Protocols (IP), routers, servers, software, and interfaces to space networks to enable as much autonomous operation of those networks as possible. These technologies reduce the cost of operations and, with higher bandwidths, support the expected voice, video, and data needed to coordinate activities at each stage of an exploration mission. In this paper, we discuss, in a generic fashion, how the architectural approaches and processes are being developed and used for defining a hypothetical communication and navigation networks infrastructure to support lunar exploration. Examples are given of the products generated by the architecture development process.
Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.
Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao
2017-05-05
This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.
Network centrality of metro systems.
Directory of Open Access Journals (Sweden)
Sybil Derrible
Full Text Available Whilst being hailed as the remedy to the world's ills, cities will need to adapt in the 21(st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no "winner takes all" unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node, but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48. Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc. to develop more sustainable cities.
Directory of Open Access Journals (Sweden)
Leśko Michał
2017-12-01
Full Text Available The aim of this document is to present the topic of modeling district heating systems in order to enable optimization of their operation, with special focus on thermal energy storage in the pipelines. Two mathematical models for simulation of transient behavior of district heating networks have been described, and their results have been compared in a case study. The operational optimization in a DH system, especially if this system is supplied from a combined heat and power plant, is a difficult and complicated task. Finding a global financial optimum requires considering long periods of time and including thermal energy storage possibilities into consideration. One of the most interesting options for thermal energy storage is utilization of thermal inertia of the network itself. This approach requires no additional investment, while providing significant possibilities for heat load shifting. It is not feasible to use full topological models of the networks, comprising thousands of substations and network sections, for the purpose of operational optimization with thermal energy storage, because such models require long calculation times. In order to optimize planned thermal energy storage actions, it is necessary to model the transient behavior of the network in a very simple way - allowing for fast and reliable calculations. Two approaches to building such models have been presented. Both have been tested by comparing the results of simulation of the behavior of the same network. The characteristic features, advantages and disadvantages of both kinds of models have been identified. The results can prove useful for district heating system operators in the near future.
Leśko, Michał; Bujalski, Wojciech
2017-12-01
The aim of this document is to present the topic of modeling district heating systems in order to enable optimization of their operation, with special focus on thermal energy storage in the pipelines. Two mathematical models for simulation of transient behavior of district heating networks have been described, and their results have been compared in a case study. The operational optimization in a DH system, especially if this system is supplied from a combined heat and power plant, is a difficult and complicated task. Finding a global financial optimum requires considering long periods of time and including thermal energy storage possibilities into consideration. One of the most interesting options for thermal energy storage is utilization of thermal inertia of the network itself. This approach requires no additional investment, while providing significant possibilities for heat load shifting. It is not feasible to use full topological models of the networks, comprising thousands of substations and network sections, for the purpose of operational optimization with thermal energy storage, because such models require long calculation times. In order to optimize planned thermal energy storage actions, it is necessary to model the transient behavior of the network in a very simple way - allowing for fast and reliable calculations. Two approaches to building such models have been presented. Both have been tested by comparing the results of simulation of the behavior of the same network. The characteristic features, advantages and disadvantages of both kinds of models have been identified. The results can prove useful for district heating system operators in the near future.
Increased Efficiency of Face Recognition System using Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Rajani Muraleedharan
2006-02-01
Full Text Available This research was inspired by the need of a flexible and cost effective biometric security system. The flexibility of the wireless sensor network makes it a natural choice for data transmission. Swarm intelligence (SI is used to optimize routing in distributed time varying network. In this paper, SI maintains the required bit error rate (BER for varied channel conditions while consuming minimal energy. A specific biometric, the face recognition system, is discussed as an example. Simulation shows that the wireless sensor network is efficient in energy consumption while keeping the transmission accuracy, and the wireless face recognition system is competitive to the traditional wired face recognition system in classification accuracy.
Pricing resources in LTE networks through multiobjective optimization.
Lai, Yung-Liang; Jiang, Jehn-Ruey
2014-01-01
The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
Traffic Based Optimization of Spectrum Sensing in Cognitive Radio Networks
Directory of Open Access Journals (Sweden)
Changhua Yao
2014-01-01
Full Text Available We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN, by considering the data traffic of second user (SU. Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the achievable throughput, based on the data traffic of SU. Our model is more general because the traditional Sensing-Throughput Tradeoff model can be seen as a special case of our model. We also prove that the throughput is a concave function of sensing time and there is only one optimal sensing time value which is determined by the data traffic. Simulation results show that the proposed approach outperforms existing methods.
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang
2016-01-01
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.
Electric power system applications of optimization
Momoh, James A
2008-01-01
Introduction Structure of a Generic Electric Power System Power System Models Power System Control Power System Security Assessment Power System Optimization as a Function of Time Review of Optimization Techniques Applicable to Power Systems Electric Power System Models Complex Power Concepts Three-Phase Systems Per Unit Representation Synchronous Machine Modeling Reactive Capability Limits Prime Movers and Governing Systems Automatic Gain Control Transmission Subsystems Y-Bus Incorporating the Transformer Effect Load Models Available Transfer Capability Illustrative Examples Power
Systems engineering technology for networks
1994-01-01
The report summarizes research pursued within the Systems Engineering Design Laboratory at Virginia Polytechnic Institute and State University between May 16, 1993 and January 31, 1994. The project was proposed in cooperation with the Computational Science and Engineering Research Center at Howard University. Its purpose was to investigate emerging systems engineering tools and their applicability in analyzing the NASA Network Control Center (NCC) on the basis of metrics and measures.
Practice-Oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms
M.O.W. Grond; N.H. Luong (Ngoc Hoang); J. Morren (Johan); P.A.N. Bosman (Peter); J.G. Slootweg; J.A. La Poutré (Han)
2014-01-01
htmlabstractDistribution network operators require more advanced planning tools to deal with the challenges of future network planning. An appropriate planning and optimization tool can identify which option for network extension should be selected from available alternatives. However, many
The LILARTI neural network system
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.
1992-10-01
The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.
PLIO: a generic tool for real-time operational predictive optimal control of water networks.
Cembrano, G; Quevedo, J; Puig, V; Pérez, R; Figueras, J; Verdejo, J M; Escaler, I; Ramón, G; Barnet, G; Rodríguez, P; Casas, M
2011-01-01
This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation).
Reliability Based Optimization of Structural Systems
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard
1987-01-01
The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement...
PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks
DEFF Research Database (Denmark)
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2013-01-01
our protocol is that each helper to a link in a multi-hop path reinforces that link by listening to coded packets transmitted in the link and by judiciously choosing when to start transmitting to make the data exchange faster and more efficient. This paper pays special attention to techniques......This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii...
Optimization of Bistable Viscoelastic Systems
DEFF Research Database (Denmark)
Jensen, Kristian Ejlebjærg; Szabo, Peter; Okkels, Fridolin
2014-01-01
the critical pressure gives rise to increased hydraulic resistance. We have combined a state-of-the-art implementation for viscoelastic flow modeling with topology optimization in a high level finite element package (COMSOL). We use this framework on the cross geometry with the aim to reduce the critical...
Directory of Open Access Journals (Sweden)
Haibo Zhang
2016-08-01
Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Energy Technology Data Exchange (ETDEWEB)
Hwangbo, Soonho; Lee, In-Beum [POSTECH, Pohang (Korea, Republic of); Han, Jeehoon [University of Wisconsin-Madison, Madison (United States)
2014-10-15
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.
Optimal Real-time Dispatch for Integrated Energy Systems
DEFF Research Database (Denmark)
Anvari-Moghaddam, Amjad; Guerrero, Josep M.; Rahimi-Kian, Ashkan
2016-01-01
With the emerging of small-scale integrated energy systems (IESs), there are significant potentials to increase the functionality of a typical demand-side management (DSM) strategy and typical implementation of building-level distributed energy resources (DERs). By integrating DSM and DERs...... into a cohesive, networked package that fully utilizes smart energy-efficient end-use devices, advanced building control/automation systems, and integrated communications architectures, it is possible to efficiently manage energy and comfort at the end-use location. In this paper, an ontology-driven multi......-agent control system with intelligent optimizers is proposed for optimal real-time dispatch of an integrated building and microgrid system considering coordinated demand response (DR) and DERs management. The optimal dispatch problem is formulated as a mixed integer nonlinear programing problem (MINLP...
Abdelhady, Amr, M.
2016-01-06
Multi-teir hetrogeneous networks have become an essential constituent for next generation cellular networks. Meanwhile, energy efficiency (EE) has been considered a critical design criterion along with the traditional spectral efficiency (SE) metric. In this context, we study power and spectrum allocation for the recently proposed two-teir architecture known as Phantom cellular networks. The optimization framework includes both EE and SE, where we propose an algorithm that computes the SE and EE resource allocation for Phantom cellular networks. Then, we compare the performance of both design strategies versus the number of users, and the ration of Phantom cellresource blocks to the total number or resource blocks. We aim to investigate the effect of some system parameters to acheive improved SE or EE performance at a non-significant loss in EE or SE performance, respectively. It was found that the system parameters can be tuned so that the EE solution does not yield a significant loss in the SE performance.
Shi, Shengchao; Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan
2017-09-04
This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach's method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.
Artificial intelligence in power system optimization
Ongsakul, Weerakorn
2013-01-01
With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also be useful for those who are interested in using artificial intelligence in power system optimization.
Liu, An; Liu,; Xiang, Haige; Luo, Wu
2010-01-01
The general MIMO interference network is considered and is named the B-MAC Network, for it is a combination of multiple interfering broadcast channels employing dirty paper coding and multiaccess channels employing successive interference cancellation. Included as special cases are interference channels, X channels, and most practical wireless networks. The optimization of this important class of networks has been hindered by that beyond the single antenna multiaccess channels, little is known about the optimal input structure, which is found here to be the polite water-filling, satisfied by all Pareto optimal input. This network version of water-filling is polite because it optimally balances between reducing interference to others and maximizing a link's own rate, offering a method to decompose a network into multiple equivalent single-user channels and thus, paving the way for designing/improving low-complexity centralized/distributed/game-theoretic algorithms for most network optimization problems. Deeply...
Fluid Analysis of Network Content Dissemination and Cloud Systems
2017-03-06
performance in complex network content dissemination and cloud systems. We employed tools of queueing theory , convex optimization and control theory , to...of time-to-live (TTL) caching, where the decision involves a choice of timer for each stored content, and its relative popularity must be...dissemination and cloud systems. We employed tools of queueing theory , convex optimization and control theory , to study how to disseminate content in
Exploiting node mobility for energy optimization in wireless sensor networks
El-Moukaddem, Fatme Mohammad
Wireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network. In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of data-intensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework. We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called max-data mobile relay configuration (MMRC ) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed
Wu, Kai; Liu, Jing; Wang, Shuai
2016-11-01
Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
Optimal control strategy for a novel computer virus propagation model on scale-free networks
Zhang, Chunming; Huang, Haitao
2016-06-01
This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.
Directory of Open Access Journals (Sweden)
T. Velmurugan
2016-03-01
Full Text Available Heterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO and Horizontal Handoff (HHO problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN. Constraints such as Receiver Signal Strength (RSS, battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods.
Noniterative convex optimization methods for network component analysis.
Jacklin, Neil; Ding, Zhi; Chen, Wei; Chang, Chunqi
2012-01-01
This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.
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.
Optimal design of distributed control and embedded systems
Çela, Arben; Li, Xu-Guang; Niculescu, Silviu-Iulian
2014-01-01
Optimal Design of Distributed Control and Embedded Systems focuses on the design of special control and scheduling algorithms based on system structural properties as well as on analysis of the influence of induced time-delay on systems performances. It treats the optimal design of distributed and embedded control systems (DCESs) with respect to communication and calculation-resource constraints, quantization aspects, and potential time-delays induced by the associated communication and calculation model. Particular emphasis is put on optimal control signal scheduling based on the system state. In order to render this complex optimization problem feasible in real time, a time decomposition is based on periodicity induced by the static scheduling is operated. The authors present a co-design approach which subsumes the synthesis of the optimal control laws and the generation of an optimal schedule of control signals on real-time networks as well as the execution of control tasks on a single processor. The a...
Liang, X B
2001-01-01
We propose a general recurrent neural-network (RNN) model for nonlinear optimization over a nonempty compact convex subset which includes the bound subset and spheroid subset as special cases. It is shown that the compact convex subset is a positive invariant and attractive set of the RNN system and that all the network trajectories starting from the compact convex subset converge to the equilibrium set of the RNN system. The above equilibrium set of the RNN system coincides with the optimum set of the minimization problem over the compact convex subset when the objective function is convex. The analysis of these qualitative properties for the RNN model is conducted by employing the properties of the projection operator of Euclidean space onto the general nonempty closed convex subset. A numerical simulation example is also given to illustrate the qualitative properties of the proposed general RNN model for solving an optimization problem over various compact convex subsets.
Model checking optimal finite-horizon control for probabilistic gene regulatory networks.
Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan
2017-12-14
Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.
A Software Tool for Optimal Sizing of PV Systems in Malaysia
Tamer Khatib; Azah Mohamed; K. Sopian
2012-01-01
This paper presents a MATLAB based user friendly software tool called as PV.MY for optimal sizing of photovoltaic (PV) systems. The software has the capabilities of predicting the metrological variables such as solar energy, ambient temperature and wind speed using artificial neural network (ANN), optimizes the PV module/ array tilt angle, optimizes the inverter size and calculate optimal capacities of PV array, battery, wind turbine and diesel generator in hybrid PV systems. The ANN based mo...
Optimal Control of Nonlinear Hydraulic Networks in the Presence of Disturbance
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Leth, John-Josef; Kallesøe, Carsten
2014-01-01
consumption. To this end, an optimal control strategy is proposed in this paper. In the water supply system model, the hydraulic resistance of the valve is estimated by the real data from a water supply system and it is considered to be a disturbance. The method which is used to solve the nonlinear optimal...... control problem is the interior point method. The method which is used in this paper can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users.......Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power...
Directory of Open Access Journals (Sweden)
Stella Kafetzoglou
2015-08-01
Full Text Available Among the key aspects of the Internet of Things (IoT is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.
Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon
2015-08-11
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.
Facsimile Network System in RCP
Muramatsu, Kazuya
Recruit Computer Print Corp. has compiled Weekly Housing Magazine by use of its own CTS (Computerized Type Setting) resulting in the real estate database as byproduct. Based on the database it has made the network linking with sponsors, real estate companies. It has transmitted lists for data cleaning directly from computer enabling to save manpower and reduce time in the compilation process. The author describes the objectives, details, functions and promise of the system.
An Approach to Optical Network Design using General Heuristic Optimization Framework
Directory of Open Access Journals (Sweden)
Marko Lacković
2010-12-01
Full Text Available The article tackles the problem of optimization methods in optical network design process, based on optimal traffic routing with the goal to minimize the utilized network resources for given topology and traffic demands. An optimization framework Nyx has been developed with the focus on flexibility in solving optimization problems by implementing general heuristic search techniques. Nyx modular organization has been described, including coding types for solutions and genetic algorithm as the optimization method. Optimal routing has been implemented to demonstrate the use of Nyx in the optical network design process. Optimal routing procedure has been applied to Pan-European optical network with variations of routing procedures and the number of wavelengths. The analysis included no protection scenario, 1+1 protection and path restoration. The routing was performed using shortest path routing and optimal routing which minimizes the use of optical network resources, being network multiplexers, amplifiers and fibers.
Network Centrality of Metro Systems
Derrible, Sybil
2012-01-01
Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities. PMID:22792373
Optimizing mission critical data dissemination in massive IoT networks
Farooq, Muhammad Junaid
2017-06-29
Mission critical data dissemination in massive Internet of things (IoT) networks imposes constraints on the message transfer delay between devices. Due to low power and communication range of IoT devices, data is foreseen to be relayed over multiple device-to-device (D2D) links before reaching the destination. The coexistence of a massive number of IoT devices poses a challenge in maximizing the successful transmission capacity of the overall network alongside reducing the multi-hop transmission delay in order to support mission critical applications. There is a delicate interplay between the carrier sensing threshold of the contention based medium access protocol and the choice of packet forwarding strategy selected at each hop by the devices. The fundamental problem in optimizing the performance of such networks is to balance the tradeoff between conflicting performance objectives such as the spatial frequency reuse, transmission quality, and packet progress towards the destination. In this paper, we use a stochastic geometry approach to quantify the performance of multi-hop massive IoT networks in terms of the spatial frequency reuse and the transmission quality under different packet forwarding schemes. We also develop a comprehensive performance metric that can be used to optimize the system to achieve the best performance. The results can be used to select the best forwarding scheme and tune the carrier sensing threshold to optimize the performance of the network according to the delay constraints and transmission quality requirements.
Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations
Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.
2011-12-01
HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of
Directory of Open Access Journals (Sweden)
P. V. Melyushin
2014-01-01
Full Text Available The paper considers problems on designing of medical information systems and proposes an approach to creation of a highly reliable automated system for processing electronic medical records on the basis of file allocation optimization in the network nodes. A mathematical model has been developed for optimal distribution of the files in the network nodes and an experimental investigation of two schemes of medical information systems has been executed in the paper.
Li, Yanning
2014-03-01
This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.
Hubble Systems Optimize Hospital Schedules
2009-01-01
Don Rosenthal, a former Ames Research Center computer scientist who helped design the Hubble Space Telescope's scheduling software, co-founded Allocade Inc. of Menlo Park, California, in 2004. Allocade's OnCue software helps hospitals reclaim unused capacity and optimize constantly changing schedules for imaging procedures. After starting to use the software, one medical center soon reported noticeable improvements in efficiency, including a 12 percent increase in procedure volume, 35 percent reduction in staff overtime, and significant reductions in backlog and technician phone time. Allocade now offers versions for outpatient and inpatient magnetic resonance imaging (MRI), ultrasound, interventional radiology, nuclear medicine, Positron Emission Tomography (PET), radiography, radiography-fluoroscopy, and mammography.
Optimalization of selected RFID systems Parameters
Directory of Open Access Journals (Sweden)
Peter Vestenicky
2004-01-01
Full Text Available This paper describes procedure for maximization of RFID transponder read range. This is done by optimalization of magnetics field intensity at transponder place and by optimalization of antenna and transponder coils coupling factor. Results of this paper can be used for RFID with inductive loop, i.e. system working in near electromagnetic field.
Optimal sensor configuration for complex systems
DEFF Research Database (Denmark)
Sadegh, Payman; Spall, J. C.
1998-01-01
The paper considers the problem of sensor configuration for complex systems with the aim of maximizing the useful information about certain quantities of interest. Our approach involves: 1) definition of an appropriate optimality criterion or performance measure; and 2) description of an efficient...... illustrate the approach with the optimal placement of acoustic sensors for signal detection in structures...
Optimal Node Placement in Underwater Wireless Sensor Networks
Felamban, M.
2013-03-25
Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.
Quantum-based algorithm for optimizing artificial neural networks.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
2013-08-01
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
Optimal concentrations in transport systems
Kim, Wonjung; Bush, John W. M.; Jensen, Kaare H.; Holbrook, N. Michele
2013-01-01
Many biological and man-made systems rely on transport systems for the distribution of material, for example matter and energy. Material transfer in these systems is determined by the flow rate and the concentration of material. While the most concentrated solutions offer the greatest potential in terms of material transfer, impedance typically increases with concentration, thus making them the most difficult to transport. We develop a general framework for describing systems for which impeda...
Improved mine blast algorithm for optimal cost design of water distribution systems
Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon
2015-12-01
The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.
Directory of Open Access Journals (Sweden)
Ming-Ta Yang
2013-01-01
Full Text Available In power systems, determining the values of time dial setting (TDS and the plug setting (PS for directional overcurrent relays (DOCRs is an extremely constrained optimization problem that has been previously described and solved as a nonlinear programming problem. Optimization coordination problems of near-end faults and far-end faults occurring simultaneously in circuits with various topologies, including fixed and variable network topologies, are considered in this study. The aim of this study was to apply the Nelder-Mead (NM simplex search method and particle swarm optimization (PSO to solve this optimization problem. The proposed NM-PSO method has the advantage of NM algorithm, with a quicker movement toward optimal solution, as well as the advantage of PSO algorithm in the ability to obtain globally optimal solution. Neither a conventional PSO nor the proposed NM-PSO method is capable of dealing with constrained optimization problems. Therefore, we use the gradient-based repair method embedded in a conventional PSO and the proposed NM-PSO. This study used an IEEE 8-bus test system as a case study to compare the convergence performance of the proposed NM-PSO method and a conventional PSO approach. The results demonstrate that a robust and optimal solution can be obtained efficiently by implementing the proposal.
Optimizing a Drone Network to Deliver Automated External Defibrillators.
Boutilier, Justin J; Brooks, Steven C; Janmohamed, Alyf; Byers, Adam; Buick, Jason E; Zhan, Cathy; Schoellig, Angela P; Cheskes, Sheldon; Morrison, Laurie J; Chan, Timothy C Y
2017-06-20
Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times. An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event. © 2017 American Heart Association, Inc.