Community-Based Social Networks: Generation of Power Law Degree Distribution and IP Solutions to the KPP

ERIC Educational Resources Information Center

Wu, Wentao

2012-01-01

The objective of this thesis is two-fold: (1) to investigate the degree distribution property of community-based social networks (CSNs) and (2) to provide solutions to a pertinent problem, the Key Player Problem. In the first part of this thesis, we consider a growing community-based network in which the ability of nodes competing for links to new…

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm

PubMed Central

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate. PMID:28991919

Optimal coordinated voltage control in active distribution networks using backtracking search algorithm.

PubMed

Tengku Hashim, Tengku Juhana; Mohamed, Azah

2017-01-01

The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate.

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Distributed Large Data-Object Environments: End-to-End Performance Analysis of High Speed Distributed Storage Systems in Wide Area ATM Networks

NASA Technical Reports Server (NTRS)

Johnston, William; Tierney, Brian; Lee, Jason; Hoo, Gary; Thompson, Mary

1996-01-01

We have developed and deployed a distributed-parallel storage system (DPSS) in several high speed asynchronous transfer mode (ATM) wide area networks (WAN) testbeds to support several different types of data-intensive applications. Architecturally, the DPSS is a network striped disk array, but is fairly unique in that its implementation allows applications complete freedom to determine optimal data layout, replication and/or coding redundancy strategy, security policy, and dynamic reconfiguration. In conjunction with the DPSS, we have developed a 'top-to-bottom, end-to-end' performance monitoring and analysis methodology that has allowed us to characterize all aspects of the DPSS operating in high speed ATM networks. In particular, we have run a variety of performance monitoring experiments involving the DPSS in the MAGIC testbed, which is a large scale, high speed, ATM network and we describe our experience using the monitoring methodology to identify and correct problems that limit the performance of high speed distributed applications. Finally, the DPSS is part of an overall architecture for using high speed, WAN's for enabling the routine, location independent use of large data-objects. Since this is part of the motivation for a distributed storage system, we describe this architecture.

Fundamentally Distributed Information Processing Integrates the Motor Network into the Mental Workspace during Mental Rotation.

PubMed

Schlegel, Alexander; Konuthula, Dedeepya; Alexander, Prescott; Blackwood, Ethan; Tse, Peter U

2016-08-01

The manipulation of mental representations in the human brain appears to share similarities with the physical manipulation of real-world objects. In particular, some neuroimaging studies have found increased activity in motor regions during mental rotation, suggesting that mental and physical operations may involve overlapping neural populations. Does the motor network contribute information processing to mental rotation? If so, does it play a similar computational role in both mental and manual rotation, and how does it communicate with the wider network of areas involved in the mental workspace? Here we used multivariate methods and fMRI to study 24 participants as they mentally rotated 3-D objects or manually rotated their hands in one of four directions. We find that information processing related to mental rotations is distributed widely among many cortical and subcortical regions, that the motor network becomes tightly integrated into a wider mental workspace network during mental rotation, and that motor network activity during mental rotation only partially resembles that involved in manual rotation. Additionally, these findings provide evidence that the mental workspace is organized as a distributed core network that dynamically recruits specialized subnetworks for specific tasks as needed.

A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

NASA Astrophysics Data System (ADS)

Pawlicki, Ted

1988-03-01

Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions of hierarchical indexing. (i.e. the specificity, adjunct, and parent indices) It supports the notion that multiple canonical views of an object may have to be stored in memory to enable its efficient identification. The use of variable fields in the state space vectors appears to keep the number of required nodes in the network down to a tractable number while imposing a semantic value on different areas of the state space. This semantic imposition supports an interface between the analogical aspects of neural networks and the propositional paradigms of symbolic processing.

Incentive-Based Voltage Regulation in Distribution Networks: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Xinyang; Chen, Lijun; Dall'Anese, Emiliano

This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltagemore » constraints. Stability of the proposed schemes is analytically established and numerically corroborated.« less

Incentive-Based Voltage Regulation in Distribution Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Baker, Kyri A; Zhou, Xinyang

This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltagemore » constraints. Stability of the proposed schemes is analytically established and numerically corroborated.« less

The Importance of Distributed Broadband Networks to Academic Biomedical Research and Education Programs

ERIC Educational Resources Information Center

Yellowlees, Peter M.; Hogarth, Michael; Hilty, Donald M.

2006-01-01

Objective: This article highlights the importance of distributed broadband networks as part of the core infrastructure necessary to deliver academic research and education programs. Method: The authors review recent developments in the field and present the University of California, Davis, environment as a case study of a future virtual regional…

Location and Size Planning of Distributed Photovoltaic Generation in Distribution network System Based on K-means Clustering Analysis

NASA Astrophysics Data System (ADS)

Lu, Siqi; Wang, Xiaorong; Wu, Junyong

2018-01-01

The paper presents a method to generate the planning scenarios, which is based on K-means clustering analysis algorithm driven by data, for the location and size planning of distributed photovoltaic (PV) units in the network. Taken the power losses of the network, the installation and maintenance costs of distributed PV, the profit of distributed PV and the voltage offset as objectives and the locations and sizes of distributed PV as decision variables, Pareto optimal front is obtained through the self-adaptive genetic algorithm (GA) and solutions are ranked by a method called technique for order preference by similarity to an ideal solution (TOPSIS). Finally, select the planning schemes at the top of the ranking list based on different planning emphasis after the analysis in detail. The proposed method is applied to a 10-kV distribution network in Gansu Province, China and the results are discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhao, Changhong; Guggilam, Swaroop

Power networks have to withstand a variety of disturbances that affect system frequency, and the problem is compounded with the increasing integration of intermittent renewable generation. Following a large-signal generation or load disturbance, system frequency is arrested leveraging primary frequency control provided by governor action in synchronous generators. In this work, we propose a framework for distributed energy resources (DERs) deployed in distribution networks to provide (supplemental) primary frequency response. Particularly, we demonstrate how power-frequency droop slopes for individual DERs can be designed so that the distribution feeder presents a guaranteed frequency-regulation characteristic at the feeder head. Furthermore, the droopmore » slopes are engineered such that injections of individual DERs conform to a well-defined fairness objective that does not penalize them for their location on the distribution feeder. Time-domain simulations for an illustrative network composed of a combined transmission network and distribution network with frequency-responsive DERs are provided to validate the approach.« less

An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Lancaster, Stephen T.; Gasparini, Nicole M.; Bras, Rafael L.; Rybarczyk, Scott M.

2001-10-01

We describe a new set of data structures and algorithms for dynamic terrain modeling using a triangulated irregular network (TINs). The framework provides an efficient method for storing, accessing, and updating a Delaunay triangulation and its associated Voronoi diagram. The basic data structure consists of three interconnected data objects: triangles, nodes, and directed edges. Encapsulating each of these geometric elements within a data object makes it possible to essentially decouple the TIN representation from the modeling applications that make use of it. Both the triangulation and its corresponding Voronoi diagram can be rapidly retrieved or updated, making these methods well suited to adaptive remeshing schemes. We develop a set of algorithms for defining drainage networks and identifying closed depressions (e.g., lakes) for hydrologic and geomorphic modeling applications. We also outline simple numerical algorithms for solving network routing and 2D transport equations within the TIN framework. The methods are illustrated with two example applications, a landscape evolution model and a distributed rainfall-runoff model.

A novel strategy for load balancing of distributed medical applications.

PubMed

Logeswaran, Rajasvaran; Chen, Li-Choo

2012-04-01

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.

Network placement optimization for large-scale distributed system

NASA Astrophysics Data System (ADS)

Ren, Yu; Liu, Fangfang; Fu, Yunxia; Zhou, Zheng

2018-01-01

The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

a Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration: Using Fuzzy Sets in Normalization of Objective Functions

NASA Astrophysics Data System (ADS)

Milani, Armin Ebrahimi; Haghifam, Mahmood Reza

2008-10-01

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. In this paper each objectives is normalized with inspiration from fuzzy sets-to cause optimization more flexible- and formulized as a unique multi-objective function. The genetic algorithm is used for solving the suggested model, in which there is no risk of non-liner objective functions and constraints. The effectiveness of the proposed method is demonstrated through the examples.

Primary Frequency Response with Aggregated DERs: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guggilam, Swaroop S.; Dhople, Sairaj V.; Zhao, Changhong

2017-03-03

Power networks have to withstand a variety of disturbances that affect system frequency, and the problem is compounded with the increasing integration of intermittent renewable generation. Following a large-signal generation or load disturbance, system frequency is arrested leveraging primary frequency control provided by governor action in synchronous generators. In this work, we propose a framework for distributed energy resources (DERs) deployed in distribution networks to provide (supplemental) primary frequency response. Particularly, we demonstrate how power-frequency droop slopes for individual DERs can be designed so that the distribution feeder presents a guaranteed frequency-regulation characteristic at the feeder head. Furthermore, the droopmore » slopes are engineered such that injections of individual DERs conform to a well-defined fairness objective that does not penalize them for their location on the distribution feeder. Time-domain simulations for an illustrative network composed of a combined transmission network and distribution network with frequency-responsive DERs are provided to validate the approach.« less

Network inference using informative priors

PubMed Central

Mukherjee, Sach; Speed, Terence P.

2008-01-01

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of “network inference” is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling. PMID:18799736

Network inference using informative priors.

PubMed

Mukherjee, Sach; Speed, Terence P

2008-09-23

Recent years have seen much interest in the study of systems characterized by multiple interacting components. A class of statistical models called graphical models, in which graphs are used to represent probabilistic relationships between variables, provides a framework for formal inference regarding such systems. In many settings, the object of inference is the network structure itself. This problem of "network inference" is well known to be a challenging one. However, in scientific settings there is very often existing information regarding network connectivity. A natural idea then is to take account of such information during inference. This article addresses the question of incorporating prior information into network inference. We focus on directed models called Bayesian networks, and use Markov chain Monte Carlo to draw samples from posterior distributions over network structures. We introduce prior distributions on graphs capable of capturing information regarding network features including edges, classes of edges, degree distributions, and sparsity. We illustrate our approach in the context of systems biology, applying our methods to network inference in cancer signaling.

GUEST EDITORS' INTRODUCTION: Guest Editors' introduction

NASA Astrophysics Data System (ADS)

Guerraoui, Rachid; Vinoski, Steve

1997-09-01

The organization of a distributed system can have a tremendous impact on its capabilities, its performance, and its ability to evolve to meet changing requirements. For example, the client - server organization model has proven to be adequate for organizing a distributed system as a number of distributed servers that offer various functions to client processes across the network. However, it lacks peer-to-peer capabilities, and experience with the model has been predominantly in the context of local networks. To achieve peer-to-peer cooperation in a more global context, systems issues of scale, heterogeneity, configuration management, accounting and sharing are crucial, and the complexity of migrating from locally distributed to more global systems demands new tools and techniques. An emphasis on interfaces and modules leads to the modelling of a complex distributed system as a collection of interacting objects that communicate with each other only using requests sent to well defined interfaces. Although object granularity typically varies at different levels of a system architecture, the same object abstraction can be applied to various levels of a computing architecture. Since 1989, the Object Management Group (OMG), an international software consortium, has been defining an architecture for distributed object systems called the Object Management Architecture (OMA). At the core of the OMA is a `software bus' called an Object Request Broker (ORB), which is specified by the OMG Common Object Request Broker Architecture (CORBA) specification. The OMA distributed object model fits the structure of heterogeneous distributed applications, and is applied in all layers of the OMA. For example, each of the OMG Object Services, such as the OMG Naming Service, is structured as a set of distributed objects that communicate using the ORB. Similarly, higher-level OMA components such as Common Facilities and Domain Interfaces are also organized as distributed objects that can be layered over both Object Services and the ORB. The OMG creates specifications, not code, but the interfaces it standardizes are always derived from demonstrated technology submitted by member companies. The specified interfaces are written in a neutral Interface Definition Language (IDL) that defines contractual interfaces with potential clients. Interfaces written in IDL can be translated to a number of programming languages via OMG standard language mappings so that they can be used to develop components. The resulting components can transparently communicate with other components written in different languages and running on different operating systems and machine types. The ORB is responsible for providing the illusion of `virtual homogeneity' regardless of the programming languages, tools, operating systems and networks used to realize and support these components. With the adoption of the CORBA 2.0 specification in 1995, these components are able to interoperate across multi-vendor CORBA-based products. More than 700 member companies have joined the OMG, including Hewlett-Packard, Digital, Siemens, IONA Technologies, Netscape, Sun Microsystems, Microsoft and IBM, which makes it the largest standards body in existence. These companies continue to work together within the OMG to refine and enhance the OMA and its components. This special issue of Distributed Systems Engineering publishes five papers that were originally presented at the `Distributed Object-Based Platforms' track of the 30th Hawaii International Conference on System Sciences (HICSS), which was held in Wailea on Maui on 6 - 10 January 1997. The papers, which were selected based on their quality and the range of topics they cover, address different aspects of CORBA, including advanced aspects such as fault tolerance and transactions. These papers discuss the use of CORBA and evaluate CORBA-based development for different types of distributed object systems and architectures. The first paper, by S Rahkila and S Stenberg, discusses the application of CORBA to telecommunication management networks. In the second paper, P Narasimhan, L E Moser and P M Melliar-Smith present a fault-tolerant extension of an ORB. The third paper, by J Liang, S Sédillot and B Traverson, provides an overview of the CORBA Transaction Service and its integration with the ISO Distributed Transaction Processing protocol. In the fourth paper, D Sherer, T Murer and A Würtz discuss the evolution of a cooperative software engineering infrastructure to a CORBA-based framework. The fifth paper, by R Fatoohi, evaluates the communication performance of a commercially-available Object Request Broker (Orbix from IONA Technologies) on several networks, and compares the performance with that of more traditional communication primitives (e.g., BSD UNIX sockets and PVM). We wish to thank both the referees and the authors of these papers, as their cooperation was fundamental in ensuring timely publication.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

WaveJava: Wavelet-based network computing

NASA Astrophysics Data System (ADS)

Ma, Kun; Jiao, Licheng; Shi, Zhuoer

1997-04-01

Wavelet is a powerful theory, but its successful application still needs suitable programming tools. Java is a simple, object-oriented, distributed, interpreted, robust, secure, architecture-neutral, portable, high-performance, multi- threaded, dynamic language. This paper addresses the design and development of a cross-platform software environment for experimenting and applying wavelet theory. WaveJava, a wavelet class library designed by the object-orient programming, is developed to take advantage of the wavelets features, such as multi-resolution analysis and parallel processing in the networking computing. A new application architecture is designed for the net-wide distributed client-server environment. The data are transmitted with multi-resolution packets. At the distributed sites around the net, these data packets are done the matching or recognition processing in parallel. The results are fed back to determine the next operation. So, the more robust results can be arrived quickly. The WaveJava is easy to use and expand for special application. This paper gives a solution for the distributed fingerprint information processing system. It also fits for some other net-base multimedia information processing, such as network library, remote teaching and filmless picture archiving and communications.

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks.

PubMed

Li, Zhijun; Ge, Shuzhi Sam; Liu, Sibang

2014-08-01

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

OXC management and control system architecture with scalability, maintenance, and distributed managing environment

NASA Astrophysics Data System (ADS)

Park, Soomyung; Joo, Seong-Soon; Yae, Byung-Ho; Lee, Jong-Hyun

2002-07-01

In this paper, we present the Optical Cross-Connect (OXC) Management Control System Architecture, which has the scalability and robust maintenance and provides the distributed managing environment in the optical transport network. The OXC system we are developing, which is divided into the hardware and the internal and external software for the OXC system, is made up the OXC subsystem with the Optical Transport Network (OTN) sub layers-hardware and the optical switch control system, the signaling control protocol subsystem performing the User-to-Network Interface (UNI) and Network-to-Network Interface (NNI) signaling control, the Operation Administration Maintenance & Provisioning (OAM&P) subsystem, and the network management subsystem. And the OXC management control system has the features that can support the flexible expansion of the optical transport network, provide the connectivity to heterogeneous external network elements, be added or deleted without interrupting OAM&P services, be remotely operated, provide the global view and detail information for network planner and operator, and have Common Object Request Broker Architecture (CORBA) based the open system architecture adding and deleting the intelligent service networking functions easily in future. To meet these considerations, we adopt the object oriented development method in the whole developing steps of the system analysis, design, and implementation to build the OXC management control system with the scalability, the maintenance, and the distributed managing environment. As a consequently, the componentification for the OXC operation management functions of each subsystem makes the robust maintenance, and increases code reusability. Also, the component based OXC management control system architecture will have the flexibility and scalability in nature.

An operating system for future aerospace vehicle computer systems

NASA Technical Reports Server (NTRS)

Foudriat, E. C.; Berman, W. J.; Will, R. W.; Bynum, W. L.

1984-01-01

The requirements for future aerospace vehicle computer operating systems are examined in this paper. The computer architecture is assumed to be distributed with a local area network connecting the nodes. Each node is assumed to provide a specific functionality. The network provides for communication so that the overall tasks of the vehicle are accomplished. The O/S structure is based upon the concept of objects. The mechanisms for integrating node unique objects with node common objects in order to implement both the autonomy and the cooperation between nodes is developed. The requirements for time critical performance and reliability and recovery are discussed. Time critical performance impacts all parts of the distributed operating system; e.g., its structure, the functional design of its objects, the language structure, etc. Throughout the paper the tradeoffs - concurrency, language structure, object recovery, binding, file structure, communication protocol, programmer freedom, etc. - are considered to arrive at a feasible, maximum performance design. Reliability of the network system is considered. A parallel multipath bus structure is proposed for the control of delivery time for time critical messages. The architecture also supports immediate recovery for the time critical message system after a communication failure.

Statistical approaches used to assess and redesign surface water-quality-monitoring networks.

PubMed

Khalil, B; Ouarda, T B M J

2009-11-01

An up-to-date review of the statistical approaches utilized for the assessment and redesign of surface water quality monitoring (WQM) networks is presented. The main technical aspects of network design are covered in four sections, addressing monitoring objectives, water quality variables, sampling frequency and spatial distribution of sampling locations. This paper discusses various monitoring objectives and related procedures used for the assessment and redesign of long-term surface WQM networks. The appropriateness of each approach for the design, contraction or expansion of monitoring networks is also discussed. For each statistical approach, its advantages and disadvantages are examined from a network design perspective. Possible methods to overcome disadvantages and deficiencies in the statistical approaches that are currently in use are recommended.

Smart sensing surveillance system

NASA Astrophysics Data System (ADS)

Hsu, Charles; Chu, Kai-Dee; O'Looney, James; Blake, Michael; Rutar, Colleen

2010-04-01

An effective public safety sensor system for heavily-populated applications requires sophisticated and geographically-distributed infrastructures, centralized supervision, and deployment of large-scale security and surveillance networks. Artificial intelligence in sensor systems is a critical design to raise awareness levels, improve the performance of the system and adapt to a changing scenario and environment. In this paper, a highly-distributed, fault-tolerant, and energy-efficient Smart Sensing Surveillance System (S4) is presented to efficiently provide a 24/7 and all weather security operation in crowded environments or restricted areas. Technically, the S4 consists of a number of distributed sensor nodes integrated with specific passive sensors to rapidly collect, process, and disseminate heterogeneous sensor data from near omni-directions. These distributed sensor nodes can cooperatively work to send immediate security information when new objects appear. When the new objects are detected, the S4 will smartly select the available node with a Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR camera to track the objects and capture associated imagery. The S4 provides applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. Other imaging processes can be updated to meet specific requirements and operations. In the S4, all the sensor nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology. This UWB RF technology can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The Service Oriented Architecture of S4 enables remote applications to interact with the S4 network and use the specific presentation methods. In addition, the S4 is compliant with Open Geospatial Consortium - Sensor Web Enablement (OGC-SWE) standards to efficiently discover, access, use, and control heterogeneous sensors and their metadata. These S4 capabilities and technologies have great potential for both military and civilian applications, enabling highly effective security support tools for improving surveillance activities in densely crowded environments. The S4 system is directly applicable to solutions for emergency response personnel, law enforcement, and other homeland security missions, as well as in applications requiring the interoperation of sensor networks with handheld or body-worn interface devices.

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

PubMed

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Network-based Modeling of Mesoscale Catchments - The Hydrology Perspective of Glowa-danube

NASA Astrophysics Data System (ADS)

Ludwig, R.; Escher-Vetter, H.; Hennicker, R.; Mauser, W.; Niemeyer, S.; Reichstein, M.; Tenhunen, J.

Within the GLOWA initiative of the German Ministry for Research and Educa- tion (BMBF), the project GLOWA-Danube is funded to establish a transdisciplinary network-based decision support tool for water related issues in the Upper Danube wa- tershed. It aims to develop and validate integration techniques, integrated models and integrated monitoring procedures and to implement them in the network-based De- cision Support System DANUBIA. An accurate description of processes involved in energy, water and matter fluxes and turnovers requires an intense collaboration and exchange of water related expertise of different scientific disciplines. DANUBIA is conceived as a distributed expert network and is developed on the basis of re-useable, refineable, and documented sub-models. In order to synthesize a common understand- ing between the project partners, a standardized notation of parameters and functions and a platform-independent structure of computational methods and interfaces has been established using the Unified Modeling Language UML. DANUBIA is object- oriented, spatially distributed and raster-based at its core. It applies the concept of "proxels" (Process Pixel) as its basic object, which has different dimensions depend- ing on the viewing scale and connects to its environment through fluxes. The presented study excerpts the hydrological view point of GLOWA-Danube, its approach of model coupling and network based communication (using the Remote Method Invocation RMI), the object-oriented technology to simulate physical processes and interactions at the land surface and the methodology to treat the issue of spatial and temporal scal- ing in large, heterogeneous catchments. The mechanisms applied to communicate data and model parameters across the typical discipline borders will be demonstrated from the perspective of a land-surface object, which comprises the capabilities of interde- pendent expert models for snowmelt, soil water movement, runoff formation, plant growth and radiation balance in a distributed JAVA-based modeling environment. The coupling to the adjacent physical objects of atmosphere, groundwater and river net- work will also be addressed.

Multiobjective assessment of distributed energy storage location in electricity networks

NASA Astrophysics Data System (ADS)

Ribeiro Gonçalves, José António; Neves, Luís Pires; Martins, António Gomes

2017-07-01

This paper presents a methodology to provide information to a decision maker on the associated impacts, both of economic and technical nature, of possible management schemes of storage units for choosing the best location of distributed storage devices, with a multiobjective optimisation approach based on genetic algorithms. The methodology was applied to a case study, a known distribution network model in which the installation of distributed storage units was tested, using lithium-ion batteries. The obtained results show a significant influence of the charging/discharging profile of batteries on the choice of their best location, as well as the relevance that these choices may have for the different network management objectives, for example, for reducing network energy losses or minimising voltage deviations. Results also show a difficult cost-effectiveness of an energy-only service, with the tested systems, both due to capital cost and due to the efficiency of conversion.

Studying the Impact of Distributed Solar PV on Power Systems using Integrated Transmission and Distribution Models: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jain, Himanshu; Palmintier, Bryan S; Krad, Ibrahim

This paper presents the results of a distributed solar PV impact assessment study that was performed using a synthetic integrated transmission (T) and distribution (D) model. The primary objective of the study was to present a new approach for distributed solar PV impact assessment, where along with detailed models of transmission and distribution networks, consumer loads were modeled using the physics of end-use equipment, and distributed solar PV was geographically dispersed and connected to the secondary distribution networks. The highlights of the study results were (i) increase in the Area Control Error (ACE) at high penetration levels of distributed solarmore » PV; and (ii) differences in distribution voltages profiles and voltage regulator operations between integrated T&D and distribution only simulations.« less

Origin of the cosmic network in ΛCDM: Nature vs nurture

NASA Astrophysics Data System (ADS)

Shandarin, Sergei; Habib, Salman; Heitmann, Katrin

2010-05-01

The large-scale structure of the Universe, as traced by the distribution of galaxies, is now being revealed by large-volume cosmological surveys. The structure is characterized by galaxies distributed along filaments, the filaments connecting in turn to form a percolating network. Our objective here is to quantitatively specify the underlying mechanisms that drive the formation of the cosmic network: By combining percolation-based analyses with N-body simulations of gravitational structure formation, we elucidate how the network has its origin in the properties of the initial density field (nature) and how its contrast is then amplified by the nonlinear mapping induced by the gravitational instability (nurture).

Performance evaluation of NASA/KSC CAD/CAE graphics local area network

NASA Technical Reports Server (NTRS)

Zobrist, George

1988-01-01

This study had as an objective the performance evaluation of the existing CAD/CAE graphics network at NASA/KSC. This evaluation will also aid in projecting planned expansions, such as the Space Station project on the existing CAD/CAE network. The objectives were achieved by collecting packet traffic on the various integrated sub-networks. This included items, such as total number of packets on the various subnetworks, source/destination of packets, percent utilization of network capacity, peak traffic rates, and packet size distribution. The NASA/KSC LAN was stressed to determine the useable bandwidth of the Ethernet network and an average design station workload was used to project the increased traffic on the existing network and the planned T1 link. This performance evaluation of the network will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the existing network.

Data quality assessment for comparative effectiveness research in distributed data networks

PubMed Central

Brown, Jeffrey; Kahn, Michael; Toh, Sengwee

2015-01-01

Background Electronic health information routinely collected during healthcare delivery and reimbursement can help address the need for evidence about the real-world effectiveness, safety, and quality of medical care. Often, distributed networks that combine information from multiple sources are needed to generate this real-world evidence. Objective We provide a set of field-tested best practices and a set of recommendations for data quality checking for comparative effectiveness research (CER) in distributed data networks. Methods Explore the requirements for data quality checking and describe data quality approaches undertaken by several existing multi-site networks. Results There are no established standards regarding how to evaluate the quality of electronic health data for CER within distributed networks. Data checks of increasing complexity are often employed, ranging from consistency with syntactic rules to evaluation of semantics and consistency within and across sites. Temporal trends within and across sites are widely used, as are checks of each data refresh or update. Rates of specific events and exposures by age group, sex, and month are also common. Discussion Secondary use of electronic health data for CER holds promise but is complex, especially in distributed data networks that incorporate periodic data refreshes. The viability of a learning health system is dependent on a robust understanding of the quality, validity, and optimal secondary uses of routinely collected electronic health data within distributed health data networks. Robust data quality checking can strengthen confidence in findings based on distributed data network. PMID:23793049

An evolving model of online bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

2013-12-01

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

Real-time physiological monitoring with distributed networks of sensors and object-oriented programming techniques

NASA Astrophysics Data System (ADS)

Wiesmann, William P.; Pranger, L. Alex; Bogucki, Mary S.

1998-05-01

Remote monitoring of physiologic data from individual high- risk workers distributed over time and space is a considerable challenge. This is often due to an inadequate capability to accurately integrate large amounts of data into usable information in real time. In this report, we have used the vertical and horizontal organization of the 'fireground' as a framework to design a distributed network of sensors. In this system, sensor output is linked through a hierarchical object oriented programing process to accurately interpret physiological data, incorporate these data into a synchronous model and relay processed data, trends and predictions to members of the fire incident command structure. There are several unique aspects to this approach. The first includes a process to account for variability in vital parameter values for each individual's normal physiologic response by including an adaptive network in each data process. This information is used by the model in an iterative process to baseline a 'normal' physiologic response to a given stress for each individual and to detect deviations that indicate dysfunction or a significant insult. The second unique capability of the system orders the information for each user including the subject, local company officers, medical personnel and the incident commanders. Information can be retrieved and used for training exercises and after action analysis. Finally this system can easily be adapted to existing communication and processing links along with incorporating the best parts of current models through the use of object oriented programming techniques. These modern software techniques are well suited to handling multiple data processes independently over time in a distributed network.

Adaptive multi-sensor biomimetics for unsupervised submarine hunt (AMBUSH): Early results

NASA Astrophysics Data System (ADS)

Blouin, Stéphane

2014-10-01

Underwater surveillance is inherently difficult because acoustic wave propagation and transmission are limited and unpredictable when targets and sensors move around in the communication-opaque undersea environment. Today's Navy underwater sensors enable the collection of a massive amount of data, often analyzed offtine. The Navy of tomorrow will dominate by making sense of that data in real-time. DRDC's AMBUSH project proposes a new undersea-surveillance network paradigm that will enable such a real-time operation. Nature abounds with examples of collaborative tasks taking place despite limited communication and computational capabilities. This publication describes a year's worth of research efforts finding inspiration in Nature's collaborative tasks such as wolves hunting in packs. This project proposes the utilization of a heterogeneous network combining both static and mobile network nodes. The military objective is to enable an unsupervised surveillance capability while maximizing target localization performance and endurance. The scientific objective is to develop the necessary technology to acoustically and passively localize a noise-source of interest in shallow waters. The project fulfills these objectives via distributed computing and adaptation to changing undersea conditions. Specific research interests discussed here relate to approaches for performing: (a) network self-discovery, (b) network connectivity self-assessment, (c) opportunistic network routing, (d) distributed data-aggregation, and (e) simulation of underwater acoustic propagation. We present early results then followed by a discussion about future work.

A Rendering System Independent High Level Architecture Implementation for Networked Virtual Environments

DTIC Science & Technology

2002-09-01

Management .........................15 5. Time Management ..............................16 6. Data Distribution Management .................16 D...50 b. Ownership Management .....................51 c. Data Distribution Management .............51 2. Additional Objects and Interactions...16 Figure 6. Data Distribution Management . (From: ref. 2) ...16 Figure 7. RTI and Federate Code Responsibilities. (From: ref. 2

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring

PubMed Central

Gharavi, Hamid; Hu, Bin

2018-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network. PMID:29503505

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring.

PubMed

Gharavi, Hamid; Hu, Bin

2017-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1997-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1998-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Intelligent, self-contained robotic hand

DOEpatents

Krutik, Vitaliy; Doo, Burt; Townsend, William T.; Hauptman, Traveler; Crowell, Adam; Zenowich, Brian; Lawson, John

2007-01-30

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

Process for anodizing a robotic device

DOEpatents

Townsend, William T [Weston, MA

2011-11-08

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

A component-based, distributed object services architecture for a clinical workstation.

PubMed

Chueh, H C; Raila, W F; Pappas, J J; Ford, M; Zatsman, P; Tu, J; Barnett, G O

1996-01-01

Attention to an architectural framework in the development of clinical applications can promote reusability of both legacy systems as well as newly designed software. We describe one approach to an architecture for a clinical workstation application which is based on a critical middle tier of distributed object-oriented services. This tier of network-based services provides flexibility in the creation of both the user interface and the database tiers. We developed a clinical workstation for ambulatory care using this architecture, defining a number of core services including those for vocabulary, patient index, documents, charting, security, and encounter management. These services can be implemented through proprietary or more standard distributed object interfaces such as CORBA and OLE. Services are accessed over the network by a collection of user interface components which can be mixed and matched to form a variety of interface styles. These services have also been reused with several applications based on World Wide Web browser interfaces.

A component-based, distributed object services architecture for a clinical workstation.

PubMed Central

Chueh, H. C.; Raila, W. F.; Pappas, J. J.; Ford, M.; Zatsman, P.; Tu, J.; Barnett, G. O.

1996-01-01

Attention to an architectural framework in the development of clinical applications can promote reusability of both legacy systems as well as newly designed software. We describe one approach to an architecture for a clinical workstation application which is based on a critical middle tier of distributed object-oriented services. This tier of network-based services provides flexibility in the creation of both the user interface and the database tiers. We developed a clinical workstation for ambulatory care using this architecture, defining a number of core services including those for vocabulary, patient index, documents, charting, security, and encounter management. These services can be implemented through proprietary or more standard distributed object interfaces such as CORBA and OLE. Services are accessed over the network by a collection of user interface components which can be mixed and matched to form a variety of interface styles. These services have also been reused with several applications based on World Wide Web browser interfaces. PMID:8947744

Network Design for Reliability and Resilience to Attack

DTIC Science & Technology

2014-03-01

attacker can destroy n arcs in the network SPNI Shortest-Path Network-Interdiction problem TSP Traveling Salesman Problem UB upper bound UKR Ukraine...elimination from the traveling salesman problem (TSP). Literature calls a walk that does not contain a cycle a path [19]. The objective function in...arc lengths as random variables with known probability distributions. The m-median problem seeks to design a network with minimum average travel cost

Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications.

PubMed

van Mierlo, Trevor; Hyatt, Douglas; Ching, Andrew T

2015-06-25

Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) Technology Infrastructure for a Distributed Data Network

PubMed Central

Schilling, Lisa M.; Kwan, Bethany M.; Drolshagen, Charles T.; Hosokawa, Patrick W.; Brandt, Elias; Pace, Wilson D.; Uhrich, Christopher; Kamerick, Michael; Bunting, Aidan; Payne, Philip R.O.; Stephens, William E.; George, Joseph M.; Vance, Mark; Giacomini, Kelli; Braddy, Jason; Green, Mika K.; Kahn, Michael G.

2013-01-01

Introduction: Distributed Data Networks (DDNs) offer infrastructure solutions for sharing electronic health data from across disparate data sources to support comparative effectiveness research. Data sharing mechanisms must address technical and governance concerns stemming from network security and data disclosure laws and best practices, such as HIPAA. Methods: The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) deploys TRIAD grid technology, a common data model, detailed technical documentation, and custom software for data harmonization to facilitate data sharing in collaboration with stakeholders in the care of safety net populations. Data sharing partners host TRIAD grid nodes containing harmonized clinical data within their internal or hosted network environments. Authorized users can use a central web-based query system to request analytic data sets. Discussion: SAFTINet DDN infrastructure achieved a number of data sharing objectives, including scalable and sustainable systems for ensuring harmonized data structures and terminologies and secure distributed queries. Initial implementation challenges were resolved through iterative discussions, development and implementation of technical documentation, governance, and technology solutions. PMID:25848567

Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) Technology Infrastructure for a Distributed Data Network.

PubMed

Schilling, Lisa M; Kwan, Bethany M; Drolshagen, Charles T; Hosokawa, Patrick W; Brandt, Elias; Pace, Wilson D; Uhrich, Christopher; Kamerick, Michael; Bunting, Aidan; Payne, Philip R O; Stephens, William E; George, Joseph M; Vance, Mark; Giacomini, Kelli; Braddy, Jason; Green, Mika K; Kahn, Michael G

2013-01-01

Distributed Data Networks (DDNs) offer infrastructure solutions for sharing electronic health data from across disparate data sources to support comparative effectiveness research. Data sharing mechanisms must address technical and governance concerns stemming from network security and data disclosure laws and best practices, such as HIPAA. The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) deploys TRIAD grid technology, a common data model, detailed technical documentation, and custom software for data harmonization to facilitate data sharing in collaboration with stakeholders in the care of safety net populations. Data sharing partners host TRIAD grid nodes containing harmonized clinical data within their internal or hosted network environments. Authorized users can use a central web-based query system to request analytic data sets. SAFTINet DDN infrastructure achieved a number of data sharing objectives, including scalable and sustainable systems for ensuring harmonized data structures and terminologies and secure distributed queries. Initial implementation challenges were resolved through iterative discussions, development and implementation of technical documentation, governance, and technology solutions.

Child Pornography in Peer-to-Peer Networks

ERIC Educational Resources Information Center

Steel, Chad M. S.

2009-01-01

Objective: The presence of child pornography in peer-to-peer networks is not disputed, but there has been little effort done to quantify and analyze the distribution and nature of that content to-date. By performing an analysis of queries and query hits on the largest peer-to-peer network, we are able to both quantify and describe the nature of…

Insertion algorithms for network model database management systems

NASA Astrophysics Data System (ADS)

Mamadolimov, Abdurashid; Khikmat, Saburov

2017-12-01

The network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, forms partial order. When a database is large and a query comparison is expensive then the efficiency requirement of managing algorithms is minimizing the number of query comparisons. We consider updating operation for network model database management systems. We develop a new sequantial algorithm for updating operation. Also we suggest a distributed version of the algorithm.

Dome: Distributed Object Migration Environment

DTIC Science & Technology

1994-05-01

Best Available Copy AD-A281 134 Computer Science Dome: Distributed object migration environment Adam Beguelin Erik Seligman Michael Starkey May 1994...Beguelin Erik Seligman Michael Starkey May 1994 CMU-CS-94-153 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Dome... Linda [4], Isis [2], and Express [6] allow a pro- grammer to treat a heterogeneous network of computers as a parallel machine. These tools allow the

LDCM Ground System. Network Lesson Learned

NASA Technical Reports Server (NTRS)

Gal-Edd, Jonathan

2010-01-01

This slide presentation reviews the Landsat Data Continuity Mission (LDCM) and the lessons learned in implementing the network that was assembled to allow for the acquisition, archiving and distribution of the data from the Landsat mission. The objective of the LDCM is to continue the acquisition, archiving, and distribution of moderate-resolution multispectral imagery affording global, synoptic, and repetitive coverage of the earth's land surface at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. It includes a review of the ground network, including a block diagram of the ground network elements (GNE) and a review of the RF design and testing. Also included is a listing of the lessons learned.

Origin of the cosmic network in {Lambda}CDM: Nature vs nurture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shandarin, Sergei; Habib, Salman; Heitmann, Katrin

The large-scale structure of the Universe, as traced by the distribution of galaxies, is now being revealed by large-volume cosmological surveys. The structure is characterized by galaxies distributed along filaments, the filaments connecting in turn to form a percolating network. Our objective here is to quantitatively specify the underlying mechanisms that drive the formation of the cosmic network: By combining percolation-based analyses with N-body simulations of gravitational structure formation, we elucidate how the network has its origin in the properties of the initial density field (nature) and how its contrast is then amplified by the nonlinear mapping induced by themore » gravitational instability (nurture).« less

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

DTIC Science & Technology

2007-03-01

Intelligence AIS Artificial Immune System ANN Artificial Neural Networks API Application Programming Interface BFS Breadth-First Search BIS Biological...problem domain is too large for only one algorithm’s application . It ranges from network - based sniffer systems, responsible for Enterprise-wide coverage...options to network administrators in choosing detectors to employ in future ID applications . Objectives Our hypothesis validity is based on a set

Software To Secure Distributed Propulsion Simulations

NASA Technical Reports Server (NTRS)

Blaser, Tammy M.

2003-01-01

Distributed-object computing systems are presented with many security threats, including network eavesdropping, message tampering, and communications middleware masquerading. NASA Glenn Research Center, and its industry partners, has taken an active role in mitigating the security threats associated with developing and operating their proprietary aerospace propulsion simulations. In particular, they are developing a collaborative Common Object Request Broker Architecture (CORBA) Security (CORBASec) test bed to secure their distributed aerospace propulsion simulations. Glenn has been working with its aerospace propulsion industry partners to deploy the Numerical Propulsion System Simulation (NPSS) object-based technology. NPSS is a program focused on reducing the cost and time in developing aerospace propulsion engines

Clock Synchronization for Multihop Wireless Sensor Networks

ERIC Educational Resources Information Center

Solis Robles, Roberto

2009-01-01

In wireless sensor networks, more so generally than in other types of distributed systems, clock synchronization is crucial since by having this service available, several applications such as media access protocols, object tracking, or data fusion, would improve their performance. In this dissertation, we propose a set of algorithms to achieve…

Evaluation of the U.S. Geological Survey Ground-Water Data-Collection Program in Hawaii, 1992

USGS Publications Warehouse

Anthony, Stephen S.

1997-01-01

In 1992, the U.S. Geological Survey ground-water data-collection program in the State of Hawaii consisted of 188 wells distributed among the islands of Oahu, Kauai, Maui, Molokai, and Hawaii. Water-level and water-quality (temperature, specific conductance, and chloride concentration) data were collected from observation wells, deep monitoring wells that penetrate the zone of transition between freshwater and saltwater, free-flowing wells, and pumped wells. The objective of the program was to collect sufficient spatial and temporal data to define seasonal and long-term changes in ground-water levels and chloride concentrations induced by natural and human-made stresses for different climatic and hydrogeologic settings. Wells needed to meet this objective can be divided into two types of networks: (1) a water-management network to determine the response of ground-water flow systems to human-induced stresses, such as pumpage, and (2) a baseline network to determine the response of ground-water flow systems to natural stresses for different climatic and hydrogeologic settings. Maps showing the distribution and magnitude of pumpage and the distribution of proposed pumped wells are presented to identify areas in need of water-management networks. Wells in the 1992 U.S. Geological Survey ground-water data-collection program were classified as either water-management or baseline network wells. In addition, locations where additional water-management network wells are needed for water-level and water-quality data were identified.

Integrated Distributed Directory Service for KSC

NASA Technical Reports Server (NTRS)

Ghansah, Isaac

1997-01-01

This paper describes an integrated distributed directory services (DDS) architecture as a fundamental component of KSC distributed computing systems. Specifically, an architecture for an integrated directory service based on DNS and X.500/LDAP has been suggested. The architecture supports using DNS in its traditional role as a name service and X.500 for other services. Specific designs were made in the integration of X.500 DDS for Public Key Certificates, Kerberos Security Services, Network-wide Login, Electronic Mail, WWW URLS, Servers, and other diverse network objects. Issues involved in incorporating the emerging Microsoft Active Directory Service MADS in KSC's X.500 were discussed.

Monitoring Design for Source Identification in Water Distribution Systems

EPA Science Inventory

The design of sensor networks for the purpose of monitoring for contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection problem and the expression of public health protection objective...

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

NASA Astrophysics Data System (ADS)

Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid

2011-10-01

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.

Technologies for network-centric C4ISR

NASA Astrophysics Data System (ADS)

Dunkelberger, Kirk A.

2003-07-01

Three technologies form the heart of any network-centric command, control, communication, intelligence, surveillance, and reconnaissance (C4ISR) system: distributed processing, reconfigurable networking, and distributed resource management. Distributed processing, enabled by automated federation, mobile code, intelligent process allocation, dynamic multiprocessing groups, check pointing, and other capabilities creates a virtual peer-to-peer computing network across the force. Reconfigurable networking, consisting of content-based information exchange, dynamic ad-hoc routing, information operations (perception management) and other component technologies forms the interconnect fabric for fault tolerant inter processor and node communication. Distributed resource management, which provides the means for distributed cooperative sensor management, foe sensor utilization, opportunistic collection, symbiotic inductive/deductive reasoning and other applications provides the canonical algorithms for network-centric enterprises and warfare. This paper introduces these three core technologies and briefly discusses a sampling of their component technologies and their individual contributions to network-centric enterprises and warfare. Based on the implied requirements, two new algorithms are defined and characterized which provide critical building blocks for network centricity: distributed asynchronous auctioning and predictive dynamic source routing. The first provides a reliable, efficient, effective approach for near-optimal assignment problems; the algorithm has been demonstrated to be a viable implementation for ad-hoc command and control, object/sensor pairing, and weapon/target assignment. The second is founded on traditional dynamic source routing (from mobile ad-hoc networking), but leverages the results of ad-hoc command and control (from the contributed auctioning algorithm) into significant increases in connection reliability through forward prediction. Emphasis is placed on the advantages gained from the closed-loop interaction of the multiple technologies in the network-centric application environment.

Self-powered information measuring wireless networks using the distribution of tasks within multicore processors

NASA Astrophysics Data System (ADS)

Zhuravska, Iryna M.; Koretska, Oleksandra O.; Musiyenko, Maksym P.; Surtel, Wojciech; Assembay, Azat; Kovalev, Vladimir; Tleshova, Akmaral

2017-08-01

The article contains basic approaches to develop the self-powered information measuring wireless networks (SPIM-WN) using the distribution of tasks within multicore processors critical applying based on the interaction of movable components - as in the direction of data transmission as wireless transfer of energy coming from polymetric sensors. Base mathematic model of scheduling tasks within multiprocessor systems was modernized to schedule and allocate tasks between cores of one-crystal computer (SoC) to increase energy efficiency SPIM-WN objects.

Compensation of distributed delays in integrated communication and control systems

NASA Technical Reports Server (NTRS)

Ray, Asok; Luck, Rogelio

1991-01-01

The concept, analysis, implementation, and verification of a method for compensating delays that are distributed between the sensors, controller, and actuators within a control loop are discussed. With the objective of mitigating the detrimental effects of these network induced delays, a predictor-controller algorithm was formulated and analyzed. Robustness of the delay compensation algorithm was investigated relative to parametric uncertainties in plant modeling. The delay compensator was experimentally verified on an IEEE 802.4 network testbed for velocity control of a DC servomotor.

OpenSim: A Flexible Distributed Neural Network Simulator with Automatic Interactive Graphics.

PubMed

Jarosch, Andreas; Leber, Jean Francois

1997-06-01

An object-oriented simulator called OpenSim is presented that achieves a high degree of flexibility by relying on a small set of building blocks. The state variables and algorithms put in this framework can easily be accessed through a command shell. This allows one to distribute a large-scale simulation over several workstations and to generate the interactive graphics automatically. OpenSim opens new possibilities for cooperation among Neural Network researchers. Copyright 1997 Elsevier Science Ltd.

A Distributed Cache Update Deployment Strategy in CDN

NASA Astrophysics Data System (ADS)

E, Xinhua; Zhu, Binjie

2018-04-01

The CDN management system distributes content objects to the edge of the internet to achieve the user's near access. Cache strategy is an important problem in network content distribution. A cache strategy was designed in which the content effective diffusion in the cache group, so more content was storage in the cache, and it improved the group hit rate.

The Network Configuration of an Object Relational Database Management System

NASA Technical Reports Server (NTRS)

Diaz, Philip; Harris, W. C.

2000-01-01

The networking and implementation of the Oracle Database Management System (ODBMS) requires developers to have knowledge of the UNIX operating system as well as all the features of the Oracle Server. The server is an object relational database management system (DBMS). By using distributed processing, processes are split up between the database server and client application programs. The DBMS handles all the responsibilities of the server. The workstations running the database application concentrate on the interpretation and display of data.

Maximizing Information Diffusion in the Cyber-physical Integrated Network †

PubMed Central

Lu, Hongliang; Lv, Shaohe; Jiao, Xianlong; Wang, Xiaodong; Liu, Juan

2015-01-01

Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, forming a cyber-physical integrated network. In order to maximize information diffusion in such a network, a group of objects are selected as the forwarding points. To optimize the selection, a minimum connected dominating set (CDS) strategy is adopted. However, existing approaches focus on minimizing the size of the CDS, neglecting an important factor: the weight of links. In this paper, we propose a distributed maximizing the probability of information diffusion (DMPID) algorithm in the cyber-physical integrated network. Unlike previous approaches that only consider the size of CDS selection, DMPID also considers the information spread probability that depends on the weight of links. To weaken the effects of excessively-weighted links, we also present an optimization strategy that can properly balance the two factors. The results of extensive simulation show that DMPID can nearly double the information diffusion probability, while keeping a reasonable size of selection with low overhead in different distributed networks. PMID:26569254

Distributed health care imaging information systems

NASA Astrophysics Data System (ADS)

Thompson, Mary R.; Johnston, William E.; Guojun, Jin; Lee, Jason; Tierney, Brian; Terdiman, Joseph F.

1997-05-01

We have developed an ATM network-based system to collect and catalogue cardio-angiogram videos from the source at a Kaiser central facility and make them available for viewing by doctors at primary care Kaiser facilities. This an example of the general problem of diagnostic data being generated at tertiary facilities, while the images, or other large data objects they produce, need to be used from a variety of other locations such as doctor's offices or local hospitals. We describe the use of a highly distributed computing and storage architecture to provide all aspects of collecting, storing, analyzing, and accessing such large data-objects in a metropolitan area ATM network. Our large data-object management system provides network interface between the object sources, the data management system and the user of the data. As the data is being stored, a cataloguing system automatically creates and stores condensed versions of the data, textural metadata and pointers to the original data. The catalogue system provides a Web-based graphical interface to the data. The user is able the view the low-resolution data with a standard Internet connection and Web browser. If high-resolution is required, a high-speed connection and special application programs can be used to view the high-resolution original data.

Pruning Neural Networks with Distribution Estimation Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less

Distributed Hypothesis Testing in Distributed Sensor Networks

DTIC Science & Technology

1984-07-01

single structure(, object Is Itself an important task in many applica- tions. At least at he conceptual level, there is no dlffculty in treating targets...First, we need to provide a modeling framwork within which the models of the various nodes, constructed as discussed above, can be embedded. It is within

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying.

PubMed

López-Matencio, Pablo

2016-02-06

Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (³He), a potential enabling element for fusion reactors, on the Moon. ³He is abundant on the Moon's surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases.

Strategic rehabilitation planning of piped water networks using multi-criteria decision analysis.

PubMed

Scholten, Lisa; Scheidegger, Andreas; Reichert, Peter; Maurer, Max; Mauer, Max; Lienert, Judit

2014-02-01

To overcome the difficulties of strategic asset management of water distribution networks, a pipe failure and a rehabilitation model are combined to predict the long-term performance of rehabilitation strategies. Bayesian parameter estimation is performed to calibrate the failure and replacement model based on a prior distribution inferred from three large water utilities in Switzerland. Multi-criteria decision analysis (MCDA) and scenario planning build the framework for evaluating 18 strategic rehabilitation alternatives under future uncertainty. Outcomes for three fundamental objectives (low costs, high reliability, and high intergenerational equity) are assessed. Exploitation of stochastic dominance concepts helps to identify twelve non-dominated alternatives and local sensitivity analysis of stakeholder preferences is used to rank them under four scenarios. Strategies with annual replacement of 1.5-2% of the network perform reasonably well under all scenarios. In contrast, the commonly used reactive replacement is not recommendable unless cost is the only relevant objective. Exemplified for a small Swiss water utility, this approach can readily be adapted to support strategic asset management for any utility size and based on objectives and preferences that matter to the respective decision makers. Copyright © 2013 Elsevier Ltd. All rights reserved.

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

NASA Technical Reports Server (NTRS)

Aretskin-Hariton, Eliot; Thomas, George; Culley, Dennis; Kratz, Jonathan

2017-01-01

Distributed engine control (DEC) systems alter aircraft engine design constraints because of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

NASA Technical Reports Server (NTRS)

Aretskin-Hariton, Eliot D.; Thomas, George Lindsey; Culley, Dennis E.; Kratz, Jonathan L.

2017-01-01

Distributed engine control (DEC) systems alter aircraft engine design constraints be- cause of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

A Digital Library for Education: The PEN-DOR Project.

ERIC Educational Resources Information Center

Fullerton, Karen; Greenberg, Jane; McClure, Maureen; Rasmussen, Edie; Stewart, Darin

1999-01-01

Describes Pen-DOR (Pennsylvania Education Network Digital Object Repository), a digital library designed to provide K-12 educators with access to multimedia resources and tools to create new lesson plans and modify existing ones via the World Wide Web. Discusses design problems of a distributed, object-oriented database architecture and describes…

HONTIOR - HIGHER-ORDER NEURAL NETWORK FOR TRANSFORMATION INVARIANT OBJECT RECOGNITION

NASA Technical Reports Server (NTRS)

Spirkovska, L.

1994-01-01

Neural networks have been applied in numerous fields, including transformation invariant object recognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant object recognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant object recognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the first, but it also contains a number of functions to display and edit training and test images. Finally, the third program is an auxiliary program which calculates the included angles for a given input field size. HONTIOR is written in C language, and was originally developed for Sun3 and Sun4 series computers. Both graphic and command line versions of the program are provided. The command line version has been successfully compiled and executed both on computers running the UNIX operating system and on DEC VAX series computer running VMS. The graphic version requires the SunTools windowing environment, and therefore runs only on Sun series computers. The executable for the graphics version of HONTIOR requires 1Mb of RAM. The standard distribution medium for HONTIOR is a .25 inch streaming magnetic tape cartridge in UNIX tar format. It is also available on a 3.5 inch diskette in UNIX tar format. The package includes sample input and output data. HONTIOR was developed in 1991. Sun, Sun3 and Sun4 are trademarks of Sun Microsystems, Inc. UNIX is a registered trademark of AT&T Bell Laboratories. DEC, VAX, and VMS are trademarks of Digital Equipment Corporation.

Performance evaluation of the NASA/KSC CAD/CAE and office automation LAN's

NASA Technical Reports Server (NTRS)

Zobrist, George W.

1994-01-01

This study's objective is the performance evaluation of the existing CAD/CAE (Computer Aided Design/Computer Aided Engineering) network at NASA/KSC. This evaluation also includes a similar study of the Office Automation network, since it is being planned to integrate this network into the CAD/CAE network. The Microsoft mail facility which is presently on the CAD/CAE network was monitored to determine its present usage. This performance evaluation of the various networks will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the CAD/CAE network and determining the effectiveness of the planned FDDI (Fiber Distributed Data Interface) migration.

The Clouds distributed operating system - Functional description, implementation details and related work

NASA Technical Reports Server (NTRS)

Dasgupta, Partha; Leblanc, Richard J., Jr.; Appelbe, William F.

1988-01-01

Clouds is an operating system in a novel class of distributed operating systems providing the integration, reliability, and structure that makes a distributed system usable. Clouds is designed to run on a set of general purpose computers that are connected via a medium-of-high speed local area network. The system structuring paradigm chosen for the Clouds operating system, after substantial research, is an object/thread model. All instances of services, programs and data in Clouds are encapsulated in objects. The concept of persistent objects does away with the need for file systems, and replaces it with a more powerful concept, namely the object system. The facilities in Clouds include integration of resources through location transparency; support for various types of atomic operations, including conventional transactions; advanced support for achieving fault tolerance; and provisions for dynamic reconfiguration.

Unified web-based network management based on distributed object orientated software agents

NASA Astrophysics Data System (ADS)

Djalalian, Amir; Mukhtar, Rami; Zukerman, Moshe

2002-09-01

This paper presents an architecture that provides a unified web interface to managed network devices that support CORBA, OSI or Internet-based network management protocols. A client gains access to managed devices through a web browser, which is used to issue management operations and receive event notifications. The proposed architecture is compatible with both the OSI Management reference Model and CORBA. The steps required for designing the building blocks of such architecture are identified.

Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms With Directed Gossip Communication

NASA Astrophysics Data System (ADS)

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.

A network-based distributed, media-rich computing and information environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, R.L.

1995-12-31

Sunrise is a Los Alamos National Laboratory (LANL) project started in October 1993. It is intended to be a prototype National Information Infrastructure development project. A main focus of Sunrise is to tie together enabling technologies (networking, object-oriented distributed computing, graphical interfaces, security, multi-media technologies, and data-mining technologies) with several specific applications. A diverse set of application areas was chosen to ensure that the solutions developed in the project are as generic as possible. Some of the application areas are materials modeling, medical records and image analysis, transportation simulations, and K-12 education. This paper provides a description of Sunrise andmore » a view of the architecture and objectives of this evolving project. The primary objectives of Sunrise are three-fold: (1) To develop common information-enabling tools for advanced scientific research and its applications to industry; (2) To enhance the capabilities of important research programs at the Laboratory; (3) To define a new way of collaboration between computer science and industrially-relevant research.« less

Analysis of haptic information in the cerebral cortex

PubMed Central

2016-01-01

Haptic sensing of objects acquires information about a number of properties. This review summarizes current understanding about how these properties are processed in the cerebral cortex of macaques and humans. Nonnoxious somatosensory inputs, after initial processing in primary somatosensory cortex, are partially segregated into different pathways. A ventrally directed pathway carries information about surface texture into parietal opercular cortex and thence to medial occipital cortex. A dorsally directed pathway transmits information regarding the location of features on objects to the intraparietal sulcus and frontal eye fields. Shape processing occurs mainly in the intraparietal sulcus and lateral occipital complex, while orientation processing is distributed across primary somatosensory cortex, the parietal operculum, the anterior intraparietal sulcus, and a parieto-occipital region. For each of these properties, the respective areas outside primary somatosensory cortex also process corresponding visual information and are thus multisensory. Consistent with the distributed neural processing of haptic object properties, tactile spatial acuity depends on interaction between bottom-up tactile inputs and top-down attentional signals in a distributed neural network. Future work should clarify the roles of the various brain regions and how they interact at the network level. PMID:27440247

Adaptable Network Cooperate Catalog for Complex Information Objects. From Single Library to a Consortium: Sharing the Management and Distribution of Information Resources.

ERIC Educational Resources Information Center

Morag, Azriel

Libraries faced with the challenge of cooperative cataloging must maintain a high degree of unification within the library network (consortium) without compromising local libraries' independence. This paper compares a traditional model for cooperative catalogs achieved by means of a Union Catalog that depends entirely on replication of data…

Derivation of WECC Distributed PV System Model Parameters from Quasi-Static Time-Series Distribution System Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mather, Barry A; Boemer, Jens C.; Vittal, Eknath

The response of low voltage networks with high penetration of PV systems to transmission network faults will, in the future, determine the overall power system performance during certain hours of the year. The WECC distributed PV system model (PVD1) is designed to represent small-scale distribution-connected systems. Although default values are provided by WECC for the model parameters, tuning of those parameters seems to become important in order to accurately estimate the partial loss of distributed PV systems for bulk system studies. The objective of this paper is to describe a new methodology to determine the WECC distributed PV system (PVD1)more » model parameters and to derive parameter sets obtained for six distribution circuits of a Californian investor-owned utility with large amounts of distributed PV systems. The results indicate that the parameters for the partial loss of distributed PV systems may differ significantly from the default values provided by WECC.« less

Optimal Water-Power Flow Problem: Formulation and Distributed Optimal Solution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhao, Changhong; Zamzam, Admed S.

This paper formalizes an optimal water-power flow (OWPF) problem to optimize the use of controllable assets across power and water systems while accounting for the couplings between the two infrastructures. Tanks and pumps are optimally managed to satisfy water demand while improving power grid operations; {for the power network, an AC optimal power flow formulation is augmented to accommodate the controllability of water pumps.} Unfortunately, the physics governing the operation of the two infrastructures and coupling constraints lead to a nonconvex (and, in fact, NP-hard) problem; however, after reformulating OWPF as a nonconvex, quadratically-constrained quadratic problem, a feasible point pursuit-successivemore » convex approximation approach is used to identify feasible and optimal solutions. In addition, a distributed solver based on the alternating direction method of multipliers enables water and power operators to pursue individual objectives while respecting the couplings between the two networks. The merits of the proposed approach are demonstrated for the case of a distribution feeder coupled with a municipal water distribution network.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhou, Xinyang; Liu, Zhiyuan

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

Data aggregation in wireless sensor networks using the SOAP protocol

NASA Astrophysics Data System (ADS)

Al-Yasiri, A.; Sunley, A.

2007-07-01

Wireless sensor networks (WSN) offer an increasingly attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have physical and resource limitations, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. In this paper, a data aggregation framework using SOAP (Simple Object Access Protocol) on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. The aim of the study is to assess the suitability of the protocol in such environments where resources are limited compared to traditional networks.

The structure of the clouds distributed operating system

NASA Technical Reports Server (NTRS)

Dasgupta, Partha; Leblanc, Richard J., Jr.

1989-01-01

A novel system architecture, based on the object model, is the central structuring concept used in the Clouds distributed operating system. This architecture makes Clouds attractive over a wide class of machines and environments. Clouds is a native operating system, designed and implemented at Georgia Tech. and runs on a set of generated purpose computers connected via a local area network. The system architecture of Clouds is composed of a system-wide global set of persistent (long-lived) virtual address spaces, called objects that contain persistent data and code. The object concept is implemented at the operating system level, thus presenting a single level storage view to the user. Lightweight treads carry computational activity through the code stored in the objects. The persistent objects and threads gives rise to a programming environment composed of shared permanent memory, dispensing with the need for hardware-derived concepts such as the file systems and message systems. Though the hardware may be distributed and may have disks and networks, the Clouds provides the applications with a logically centralized system, based on a shared, structured, single level store. The current design of Clouds uses a minimalist philosophy with respect to both the kernel and the operating system. That is, the kernel and the operating system support a bare minimum of functionality. Clouds also adheres to the concept of separation of policy and mechanism. Most low-level operating system services are implemented above the kernel and most high level services are implemented at the user level. From the measured performance of using the kernel mechanisms, we are able to demonstrate that efficient implementations are feasible for the object model on commercially available hardware. Clouds provides a rich environment for conducting research in distributed systems. Some of the topics addressed in this paper include distributed programming environments, consistency of persistent data and fault-tolerance.

Dynamics of feature categorization.

PubMed

Martí, Daniel; Rinzel, John

2013-01-01

In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Dynamic ADMM for Real-Time Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearization of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of noncontrollable resources. Optimality and convergence of the proposed algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

Dynamic ADMM for Real-Time Optimal Power Flow: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

Distributed Attentional Deficits in Chronic Methamphetamine Abusers: Evidence from the Attentional Network Task (ANT)

ERIC Educational Resources Information Center

Salo, Ruth; Gabay, Shai; Fassbender, Catherine; Henik, Avishai

2011-01-01

Objective: The goal of the present study was to examine distributed attentional functions in long-term but currently abstinent methamphetamine (MA) abusers using a task that measures attentional alertness, orienting, and conflict resolution. Methods: Thirty currently abstinent MA abusers (1 month-5 years) and 22 healthy non-substance using adults…

Distributed cooperative control of AC microgrids

NASA Astrophysics Data System (ADS)

Bidram, Ali

In this dissertation, the comprehensive secondary control of electric power microgrids is of concern. Microgrid technical challenges are mainly realized through the hierarchical control structure, including primary, secondary, and tertiary control levels. Primary control level is locally implemented at each distributed generator (DG), while the secondary and tertiary control levels are conventionally implemented through a centralized control structure. The centralized structure requires a central controller which increases the reliability concerns by posing the single point of failure. In this dissertation, the distributed control structure using the distributed cooperative control of multi-agent systems is exploited to increase the secondary control reliability. The secondary control objectives are microgrid voltage and frequency, and distributed generators (DGs) active and reactive powers. Fully distributed control protocols are implemented through distributed communication networks. In the distributed control structure, each DG only requires its own information and the information of its neighbors on the communication network. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability. Since the DG dynamics are nonlinear and non-identical, input-output feedback linearization is used to transform the nonlinear dynamics of DGs to linear dynamics. Proposed control frameworks cover the control of microgrids containing inverter-based DGs. Typical microgrid test systems are used to verify the effectiveness of the proposed control protocols.

Self-organizing intelligent network of smart electrical heating devices as an alternative to traditional ways of heating

NASA Astrophysics Data System (ADS)

Zaslavsky, Aleksander M.; Tkachov, Viktor V.; Protsenko, Stanislav M.; Bublikov, Andrii V.; Suleimenov, Batyrbek; Orshubekov, Nurbek; Gromaszek, Konrad

2017-08-01

The paper considers the problem of automated decentralized distribution of the electric energy among unlimited-power electric heaters providing the given temperature distribution within the zones of monitored object heating in the context of maximum use of electric power which limiting level is time-dependent randomly. Principles of collective selforganization automata for solving the problem are analyzed. It has been shown that after all the automata make decision, equilibrium of Nash type is attained when unused power within the electric network is not more than a power of any non-energized electric heater.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations.

PubMed

Khalifa, Tarek; Abdrabou, Atef; Shaban, Khaled; Gaouda, A M

2018-05-11

Supporting a conventional power grid with advanced communication capabilities is a cornerstone to transferring it to a smart grid. A reliable communication infrastructure with a high throughput can lay the foundation towards the ultimate objective of a fully automated power grid with self-healing capabilities. In order to realize this objective, the communication infrastructure of a power distribution network needs to be extended to cover all substations including medium/low voltage ones. This shall enable information exchange among substations for a variety of system automation purposes with a low latency that suits time critical applications. This paper proposes the integration of two heterogeneous wireless technologies (such as WiFi and cellular 3G/4G) to provide reliable and fast communication among primary and secondary distribution substations. This integration allows the transmission of different data packets (not packet replicas) over two radio interfaces, making these interfaces act like a one data pipe. Thus, the paper investigates the applicability and effectiveness of employing heterogeneous wireless networks (HWNs) in achieving the desired reliability and timeliness requirements of future smart grids. We study the performance of HWNs in a realistic scenario under different data transfer loads and packet loss ratios. Our findings reveal that HWNs can be a viable data transfer option for smart grids.

Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations

PubMed Central

Khalifa, Tarek; Abdrabou, Atef; Gaouda, A. M.

2018-01-01

Supporting a conventional power grid with advanced communication capabilities is a cornerstone to transferring it to a smart grid. A reliable communication infrastructure with a high throughput can lay the foundation towards the ultimate objective of a fully automated power grid with self-healing capabilities. In order to realize this objective, the communication infrastructure of a power distribution network needs to be extended to cover all substations including medium/low voltage ones. This shall enable information exchange among substations for a variety of system automation purposes with a low latency that suits time critical applications. This paper proposes the integration of two heterogeneous wireless technologies (such as WiFi and cellular 3G/4G) to provide reliable and fast communication among primary and secondary distribution substations. This integration allows the transmission of different data packets (not packet replicas) over two radio interfaces, making these interfaces act like a one data pipe. Thus, the paper investigates the applicability and effectiveness of employing heterogeneous wireless networks (HWNs) in achieving the desired reliability and timeliness requirements of future smart grids. We study the performance of HWNs in a realistic scenario under different data transfer loads and packet loss ratios. Our findings reveal that HWNs can be a viable data transfer option for smart grids. PMID:29751633

Software Comparison for Renewable Energy Deployment in a Distribution Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, David Wenzhong; Muljadi, Eduard; Tian, Tian

The main objective of this report is to evaluate different software options for performing robust distributed generation (DG) power system modeling. The features and capabilities of four simulation tools, OpenDSS, GridLAB-D, CYMDIST, and PowerWorld Simulator, are compared to analyze their effectiveness in analyzing distribution networks with DG. OpenDSS and GridLAB-D, two open source software, have the capability to simulate networks with fluctuating data values. These packages allow the running of a simulation each time instant by iterating only the main script file. CYMDIST, a commercial software, allows for time-series simulation to study variations on network controls. PowerWorld Simulator, another commercialmore » tool, has a batch mode simulation function through the 'Time Step Simulation' tool, which obtains solutions for a list of specified time points. PowerWorld Simulator is intended for analysis of transmission-level systems, while the other three are designed for distribution systems. CYMDIST and PowerWorld Simulator feature easy-to-use graphical user interfaces (GUIs). OpenDSS and GridLAB-D, on the other hand, are based on command-line programs, which increase the time necessary to become familiar with the software packages.« less

Generative adversarial networks for brain lesion detection

NASA Astrophysics Data System (ADS)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Network dysfunction predicts speech production after left hemisphere stroke

PubMed Central

Leech, Robert; Wise, Richard J.S.

2016-01-01

Objective: To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. Methods: We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Results: Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Conclusions: Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. PMID:26962070

Distributed run of a one-dimensional model in a regional application using SOAP-based web services

NASA Astrophysics Data System (ADS)

Smiatek, Gerhard

This article describes the setup of a distributed computing system in Perl. It facilitates the parallel run of a one-dimensional environmental model on a number of simple network PC hosts. The system uses Simple Object Access Protocol (SOAP) driven web services offering the model run on remote hosts and a multi-thread environment distributing the work and accessing the web services. Its application is demonstrated in a regional run of a process-oriented biogenic emission model for the area of Germany. Within a network consisting of up to seven web services implemented on Linux and MS-Windows hosts, a performance increase of approximately 400% has been reached compared to a model run on the fastest single host.

Congestion Avoidance Testbed Experiments. Volume 2

NASA Technical Reports Server (NTRS)

Denny, Barbara A.; Lee, Diane S.; McKenney, Paul E., Sr.; Lee, Danny

1994-01-01

DARTnet provides an excellent environment for executing networking experiments. Since the network is private and spans the continental United States, it gives researchers a great opportunity to test network behavior under controlled conditions. However, this opportunity is not available very often, and therefore a support environment for such testing is lacking. To help remedy this situation, part of SRI's effort in this project was devoted to advancing the state of the art in the techniques used for benchmarking network performance. The second objective of SRI's effort in this project was to advance networking technology in the area of traffic control, and to test our ideas on DARTnet, using the tools we developed to improve benchmarking networks. Networks are becoming more common and are being used by more and more people. The applications, such as multimedia conferencing and distributed simulations, are also placing greater demand on the resources the networks provide. Hence, new mechanisms for traffic control must be created to enable their networks to serve the needs of their users. SRI's objective, therefore, was to investigate a new queueing and scheduling approach that will help to meet the needs of a large, diverse user population in a "fair" way.

A neural network approach for image reconstruction in electron magnetic resonance tomography.

PubMed

Durairaj, D Christopher; Krishna, Murali C; Murugesan, Ramachandran

2007-10-01

An object-oriented, artificial neural network (ANN) based, application system for reconstruction of two-dimensional spatial images in electron magnetic resonance (EMR) tomography is presented. The standard back propagation algorithm is utilized to train a three-layer sigmoidal feed-forward, supervised, ANN to perform the image reconstruction. The network learns the relationship between the 'ideal' images that are reconstructed using filtered back projection (FBP) technique and the corresponding projection data (sinograms). The input layer of the network is provided with a training set that contains projection data from various phantoms as well as in vivo objects, acquired from an EMR imager. Twenty five different network configurations are investigated to test the ability of the generalization of the network. The trained ANN then reconstructs two-dimensional temporal spatial images that present the distribution of free radicals in biological systems. Image reconstruction by the trained neural network shows better time complexity than the conventional iterative reconstruction algorithms such as multiplicative algebraic reconstruction technique (MART). The network is further explored for image reconstruction from 'noisy' EMR data and the results show better performance than the FBP method. The network is also tested for its ability to reconstruct from limited-angle EMR data set.

On the Biological Plausibility of Grandmother Cells: Implications for Neural Network Theories in Psychology and Neuroscience

ERIC Educational Resources Information Center

Bowers, Jeffrey S.

2009-01-01

A fundamental claim associated with parallel distributed processing (PDP) theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts (e.g. "dog"), that is, coded with their own dedicated…

Empirical analysis of web-based user-object bipartite networks

NASA Astrophysics Data System (ADS)

Shang, Ming-Sheng; Lü, Linyuan; Zhang, Yi-Cheng; Zhou, Tao

2010-05-01

Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative similarity, to quantify the diversity of tastes based on the collaborative selection. Accordingly, the correlation between degree and selection diversity is investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.

Simulation Methods for Design of Networked Power Electronics and Information Systems

DTIC Science & Technology

2014-07-01

Insertion of latency in every branch and at every node permits the system model to be efficiently distributed across many separate computing cores. An... the system . We demonstrated extensibility and generality of the Virtual Test Bed (VTB) framework to support multiple solvers and their associated...Information Systems Objectives The overarching objective of this program is to develop methods for fast

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

PubMed Central

Liang, Zhongwei; Zhou, Liang; Liu, Xiaochu; Wang, Xiaogang

2014-01-01

It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices. PMID:25143781

Development of Tool Representations in the Dorsal and Ventral Visual Object Processing Pathways

PubMed Central

Kersey, Alyssa J.; Clark, Tyia S.; Lussier, Courtney A.; Mahon, Bradford Z.; Cantlon, Jessica F.

2016-01-01

Tools represent a special class of objects, because they are processed across both the dorsal and ventral visual object processing pathways. Three core regions are known to be involved in tool processing: the left posterior middle temporal gyrus, the medial fusiform gyrus (bilaterally), and the left inferior parietal lobule. A critical and relatively unexplored issue concerns whether, in development, tool preferences emerge at the same time and to a similar degree across all regions of the tool-processing network. To test this issue, we used functional magnetic resonance imaging to measure the neural amplitude, peak location, and the dispersion of tool-related neural responses in the youngest sample of children tested to date in this domain (ages 4–8 years). We show that children recruit overlapping regions of the adult tool-processing network and also exhibit similar patterns of co-activation across the network to adults. The amplitude and co-activation data show that the core components of the tool-processing network are established by age 4. Our findings on the distributions of peak location and dispersion of activation indicate that the tool network undergoes refinement between ages 4 and 8 years. PMID:26108614

Distributed processing method for arbitrary view generation in camera sensor network

NASA Astrophysics Data System (ADS)

Tehrani, Mehrdad P.; Fujii, Toshiaki; Tanimoto, Masayuki

2003-05-01

Camera sensor network as a new advent of technology is a network that each sensor node can capture video signals, process and communicate them with other nodes. The processing task in this network is to generate arbitrary view, which can be requested from central node or user. To avoid unnecessary communication between nodes in camera sensor network and speed up the processing time, we have distributed the processing tasks between nodes. In this method, each sensor node processes part of interpolation algorithm to generate the interpolated image with local communication between nodes. The processing task in camera sensor network is ray-space interpolation, which is an object independent method and based on MSE minimization by using adaptive filtering. Two methods were proposed for distributing processing tasks, which are Fully Image Shared Decentralized Processing (FIS-DP), and Partially Image Shared Decentralized Processing (PIS-DP), to share image data locally. Comparison of the proposed methods with Centralized Processing (CP) method shows that PIS-DP has the highest processing speed after FIS-DP, and CP has the lowest processing speed. Communication rate of CP and PIS-DP is almost same and better than FIS-DP. So, PIS-DP is recommended because of its better performance than CP and FIS-DP.

Impact of Drainage Networks on Cholera Outbreaks in Lusaka, Zambia

PubMed Central

Suzuki, Hiroshi; Fujino, Yasuyuki; Kimura, Yoshinari; Cheelo, Meetwell

2009-01-01

Objectives. We investigated the association between precipitation patterns and cholera outbreaks and the preventative roles of drainage networks against outbreaks in Lusaka, Zambia. Methods. We collected data on 6542 registered cholera patients in the 2003–2004 outbreak season and on 6045 cholera patients in the 2005–2006 season. Correlations between monthly cholera incidences and amount of precipitation were examined. The distribution pattern of the disease was analyzed by a kriging spatial analysis method. We analyzed cholera case distribution and spatiotemporal cluster by using 2590 cholera cases traced with a global positioning system in the 2005–2006 season. The association between drainage networks and cholera cases was analyzed with regression analysis. Results. Increased precipitation was associated with the occurrence of cholera outbreaks, and insufficient drainage networks were statistically associated with cholera incidences. Conclusions. Insufficient coverage of drainage networks elevated the risk of cholera outbreaks. Integrated development is required to upgrade high-risk areas with sufficient infrastructure for a long-term cholera prevention strategy. PMID:19762668

Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network

DOE PAGES

Zhu, Zhenshan; Liu, Dichen; Liao, Qingfen; ...

2018-01-26

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); softmore » open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.« less

Optimal Power Scheduling for a Medium Voltage AC/DC Hybrid Distribution Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Zhenshan; Liu, Dichen; Liao, Qingfen

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); softmore » open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.« less

A Comparison of Techniques for Camera Selection and Hand-Off in a Video Network

NASA Astrophysics Data System (ADS)

Li, Yiming; Bhanu, Bir

Video networks are becoming increasingly important for solving many real-world problems. Multiple video sensors require collaboration when performing various tasks. One of the most basic tasks is the tracking of objects, which requires mechanisms to select a camera for a certain object and hand-off this object from one camera to another so as to accomplish seamless tracking. In this chapter, we provide a comprehensive comparison of current and emerging camera selection and hand-off techniques. We consider geometry-, statistics-, and game theory-based approaches and provide both theoretical and experimental comparison using centralized and distributed computational models. We provide simulation and experimental results using real data for various scenarios of a large number of cameras and objects for in-depth understanding of strengths and weaknesses of these techniques.

SSBRP User Operations Facility (UOF) Overview and Development Strategy

NASA Technical Reports Server (NTRS)

Picinich, Lou; Stone, Thom; Sun, Charles; Windrem, May; Givens, John J. (Technical Monitor)

1995-01-01

This paper will present the Space Station Biological Research Project (SSBRP) User Operations Facility (UOF) architecture and development strategy. A major element of the UOF at NASA Ames Research Center, the Communication and Data System (CDS) will be the primary focus of the discussions. CDS operational, telescience, security, and development objectives will be discussed along with CDS implementation strategy. The implementation strategy discussions will include: Object Oriented Analysis & Design, System & Software Prototyping, and Technology Utilization. A CDS design overview that includes: CDS Context Diagram, CDS Architecture, Object Models, Use Cases, and User Interfaces will also be presented. CDS development brings together "cutting edge" technologies and techniques such as: object oriented development, network security, multimedia networking, web-based data distribution, JAVA, and graphical user interfaces. Use of these "cutting edge" technologies and techniques translates directly to lower development and operations costs.

Selective visual attention in object detection processes

NASA Astrophysics Data System (ADS)

Paletta, Lucas; Goyal, Anurag; Greindl, Christian

2003-03-01

Object detection is an enabling technology that plays a key role in many application areas, such as content based media retrieval. Attentive cognitive vision systems are here proposed where the focus of attention is directed towards the most relevant target. The most promising information is interpreted in a sequential process that dynamically makes use of knowledge and that enables spatial reasoning on the local object information. The presented work proposes an innovative application of attention mechanisms for object detection which is most general in its understanding of information and action selection. The attentive detection system uses a cascade of increasingly complex classifiers for the stepwise identification of regions of interest (ROIs) and recursively refined object hypotheses. While the most coarse classifiers are used to determine first approximations on a region of interest in the input image, more complex classifiers are used for more refined ROIs to give more confident estimates. Objects are modelled by local appearance based representations and in terms of posterior distributions of the object samples in eigenspace. The discrimination function to discern between objects is modeled by a radial basis functions (RBF) network that has been compared with alternative networks and been proved consistent and superior to other artifical neural networks for appearance based object recognition. The experiments were led for the automatic detection of brand objects in Formula One broadcasts within the European Commission's cognitive vision project DETECT.

General practice and the Internet revolution. Use of an Internet social network to communicate information on prevention in France.

PubMed

Veuillotte, Isabelle; Morel, Gilles; Pitois, Stephane; Haler, Renaud; Mercier, Patricia; Aubry, Catherine; Cannet, Didier

2015-03-01

The popularity of social networks and the huge number of exchanges have made them immensely important for the communication of information. This French study explored prevention in hereditary breast cancer using a social Internet network to communicate information. The principal objective was to inform French women aged from 20 to 50 years, using the social network Facebook, about the warning signs of breast cancer in cases of a predisposition to the disease due to a genetic mutation. The secondary objectives were to inform people about screening. An information page entitled "hereditary breast cancer: and if I was concerned?" was distributed in 3 different ways: from friend to friend, via groups of persons, and by targeted advertising. Four articles and 11 messages were distributed over 27 days. The total number of visits for this period amounted to 1019. A total of 81 percent of the Internauts were women and 55 percent of the visitors were aged between 25 and 44 years. Other information campaigns concerning public health issues could be conducted using this tool. A legal framework is necessary to preserve the quality of the medical information provided. This new means of communication, used for prevention purposes, will add to other frequently used methods of communication. © The Author(s) 2013.

The company objects keep: Linking referents together during cross-situational word learning.

PubMed

Zettersten, Martin; Wojcik, Erica; Benitez, Viridiana L; Saffran, Jenny

2018-04-01

Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

Power System Information Delivering System Based on Distributed Object

NASA Astrophysics Data System (ADS)

Tanaka, Tatsuji; Tsuchiya, Takehiko; Tamura, Setsuo; Seki, Tomomichi; Kubota, Kenji

In recent years, improvement in computer performance and development of computer network technology or the distributed information processing technology has a remarkable thing. Moreover, the deregulation is starting and will be spreading in the electric power industry in Japan. Consequently, power suppliers are required to supply low cost power with high quality services to customers. Corresponding to these movements the authors have been proposed SCOPE (System Configuration Of PowEr control system) architecture for distributed EMS/SCADA (Energy Management Systems / Supervisory Control and Data Acquisition) system based on distributed object technology, which offers the flexibility and expandability adapting those movements. In this paper, the authors introduce a prototype of the power system information delivering system, which was developed based on SCOPE architecture. This paper describes the architecture and the evaluation results of this prototype system. The power system information delivering system supplies useful power systems information such as electric power failures to the customers using Internet and distributed object technology. This system is new type of SCADA system which monitors failure of power transmission system and power distribution system with geographic information integrated way.

Guest Editor's introduction: Selected papers from the 4th USENIX Conference on Object-Oriented Technologies and Systems

NASA Astrophysics Data System (ADS)

Sventek, Joe

1998-12-01

Hewlett-Packard Laboratories, 1501 Page Mill Road, Palo Alto, CA 94304, USA Introduction The USENIX Conference on Object-Oriented Technologies and Systems (COOTS) is held annually in the late spring. The conference evolved from a set of C++ workshops that were held under the auspices of USENIX, the first of which met in 1989. Given the growing diverse interest in object-oriented technologies, the C++ focus of the workshop eventually became too narrow, with the result that the scope was widened in 1995 to include object-oriented technologies and systems. COOTS is intended to showcase advanced R&D efforts in object-oriented technologies and software systems. The conference emphasizes experimental research and experience gained by using object-oriented techniques and languages to build complex software systems that meet real-world needs. COOTS solicits papers in the following general areas: application of, and experiences with, object-oriented technologies in particular domains (e.g. financial, medical, telecommunication); the architecture and implementation of distributed object systems (e.g. CORBA, DCOM, RMI); object-oriented programming and specification languages; object-oriented design and analysis. The 4th meeting of COOTS was held 27 - 30 April 1998 at the El Dorado Hotel, Santa Fe, New Mexico, USA. Several tutorials were given. The technical program proper consisted of a single track of six sessions, with three paper presentations per session. A keynote address and a provocative panel session rounded out the technical program. The program committee reviewed 56 papers, selecting the best 18 for presentation in the technical sessions. While we solicit papers across the spectrum of applications of object-oriented technologies, this year there was a predominance of distributed, object-oriented papers. The accepted papers reflected this asymmetry, with 15 papers on distributed objects and 3 papers on object-oriented languages. The papers in this special issue are the six best distributed object papers (in the opinion of the program committee). They represent the diversity of research in this particular area, and should give the reader a good idea of the types of papers presented at COOTS as well as the calibre of the work so presented. The papers The paper by Jain, Widoff and Schmidt explores the suitability of Java for writing performance-sensitive distributed applications. Despite the popularity of Java, there are many concerns about its efficiency; in particular, networking and computation performance are key concerns when considering the use of Java to develop performance-sensitive distributed applications. This paper makes three contributions to the study of Java for these applications: it describes an architecture using Java and the Web to develop MedJava, which is a distributed electronic medical imaging system with stringent networking and computation requirements; it presents benchmarks of MedJava image processing and compares the results to the performance of xv, which is an equivalent image processing application written in C; it presents performance benchmarks using Java as a transport interface to exchange large medical images over high-speed ATM networks. The paper by Little and Shrivastava covers the integration of several important topics: transactions, distributed systems, Java, the Internet and security. The usefulness of this paper lies in the synthesis of an effective solution applying work in different areas of computing to the Java environment. Securing applications constructed from distributed objects is important if these applications are to be used in mission-critical situations. Delegation is one aspect of distributed system security that is necessary for such applications. The paper by Nagaratnam and Lea describes a secure delegation model for Java-based, distributed object environments. The paper by Frølund and Koistinen addresses the topical issue of providing a common way for describing Quality-of-Service (QoS) features in distributed, object-oriented systems. They present a general QoS language, QML, that can be used to capture QoS properties as part of a design. They also show how to extend UML to support QML concepts. The paper by Szymaszek, Uszok and Zielinski discusses the important issue of efficient implementation and usage of fine-grained objects in CORBA-based applications. Fine-grained objects can have serious ramifications on overall application performance and scalability, and the paper suggests that such objects should not be treated as first-class CORBA objects, proposing instead the use of collections and smart proxies for efficient implementation. The paper by Milojicic, LaForge and Chauhan describes a mobile objects and agents infrastructure. Their particular research has focused on communication support across agent migration and extensive resource control. The paper also discusses issues regarding interoperation between agent systems. Acknowledgments The editor wishes to thank all of the authors, reviewers and publishers. Without their excellent work, and the contribution of their valuable time, this special issue would not have been possible.

Group-oriented coordination models for distributed client-server computing

NASA Technical Reports Server (NTRS)

Adler, Richard M.; Hughes, Craig S.

1994-01-01

This paper describes group-oriented control models for distributed client-server interactions. These models transparently coordinate requests for services that involve multiple servers, such as queries across distributed databases. Specific capabilities include: decomposing and replicating client requests; dispatching request subtasks or copies to independent, networked servers; and combining server results into a single response for the client. The control models were implemented by combining request broker and process group technologies with an object-oriented communication middleware tool. The models are illustrated in the context of a distributed operations support application for space-based systems.

Effects of the distribution density of a biomass combined heat and power plant network on heat utilisation efficiency in village-town systems.

PubMed

Zhang, Yifei; Kang, Jian

2017-11-01

The building of biomass combined heat and power (CHP) plants is an effective means of developing biomass energy because they can satisfy demands for winter heating and electricity consumption. The purpose of this study was to analyse the effect of the distribution density of a biomass CHP plant network on heat utilisation efficiency in a village-town system. The distribution density is determined based on the heat transmission threshold, and the heat utilisation efficiency is determined based on the heat demand distribution, heat output efficiency, and heat transmission loss. The objective of this study was to ascertain the optimal value for the heat transmission threshold using a multi-scheme comparison based on an analysis of these factors. To this end, a model of a biomass CHP plant network was built using geographic information system tools to simulate and generate three planning schemes with different heat transmission thresholds (6, 8, and 10 km) according to the heat demand distribution. The heat utilisation efficiencies of these planning schemes were then compared by calculating the gross power, heat output efficiency, and heat transmission loss of the biomass CHP plant for each scenario. This multi-scheme comparison yielded the following results: when the heat transmission threshold was low, the distribution density of the biomass CHP plant network was high and the biomass CHP plants tended to be relatively small. In contrast, when the heat transmission threshold was high, the distribution density of the network was low and the biomass CHP plants tended to be relatively large. When the heat transmission threshold was 8 km, the distribution density of the biomass CHP plant network was optimised for efficient heat utilisation. To promote the development of renewable energy sources, a planning scheme for a biomass CHP plant network that maximises heat utilisation efficiency can be obtained using the optimal heat transmission threshold and the nonlinearity coefficient for local roads. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of water quality and stability in the drinking water distribution network in the Azogues city, Ecuador.

PubMed

García-Ávila, Fernando; Ramos-Fernández, Lía; Pauta, Damián; Quezada, Diego

2018-06-01

This document presents the physical-chemical parameters with the objective of evaluating and analyzing the drinking water quality in the Azogues city applying the water quality index (WQI) and to research the water stability in the distribution network using corrosion indexes. Thirty samples were collected monthly for six months throughout the drinking water distribution network; turbidity, temperature, electric conductivity, pH, total dissolved solids, total hardness, calcium, magnesium, alkalinity, chlorides, nitrates, sulfates and phosphates were determined; the physical-chemical parameters were measured using standard methods. The processed data revealed that the average values ​​of LSI, RSI and PSI were 0.5 (±0.34), 6.76 (±0.6), 6.50 (±0.99) respectively. The WQI calculation indicated that 100% of the samples are considered excellent quality water. According to the Langelier, Ryznar and Pukorius indexes showed that drinking water in Azogues is corrosive. The quality of drinking water according to the WQI is in a good and excellent category.

Dynamic Power Distribution System Management With a Locally Connected Communication Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Kaiqing; Basar, Tamer

Coordinated optimization and control of distribution-level assets can enable a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitate distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, or line losses, to be close to a desired profile. By and large, the performance of the DSM algorithms has been challenged by two factors: i) the possibly non-strongly connected communication network over DERs that hinders the coordination; ii) the dynamics of the real system caused by themore » DERs with heterogeneous capabilities, time-varying operating conditions, and real-time measurement mismatches. In this paper, we investigate the modeling and algorithm design and analysis with the consideration of these two factors. In particular, a game theoretic characterization is first proposed to account for a locally connected communication network over DERs, along with the analysis of the existence and uniqueness of the Nash equilibrium (NE) therein. To achieve the equilibrium in a distributed fashion, a projected-gradient-based asynchronous DSM algorithm is then advocated. The algorithm performance, including the convergence speed and the tracking error, is analytically guaranteed under the dynamic setting. Extensive numerical tests on both synthetic and realistic cases corroborate the analytical results derived.« less

Multi-phenomenology Observation Network Evaluation Tool'' (MONET)

NASA Astrophysics Data System (ADS)

Oltrogge, D.; North, P.; Vallado, D.

2014-09-01

Evaluating overall performance of an SSA "system-of-systems" observational network collecting against thousands of Resident Space Objects (RSO) is very difficult for typical tasking or scheduling-based analysis tools. This is further complicated by networks that have a wide variety of sensor types and phenomena, to include optical, radar and passive RF types, each having unique resource, ops tempo, competing customer and detectability constraints. We present details of the Multi-phenomenology Observation Network Evaluation Tool (MONET), which circumvents these difficulties by assessing the ideal performance of such a network via a digitized supply-vs-demand approach. Cells of each sensors supply time are distributed among RSO targets of interest to determine the average performance of the network against that set of RSO targets. Orbit Determination heuristics are invoked to represent observation quantity and geometry notionally required to obtain the desired orbit estimation quality. To feed this approach, we derive the detectability and collection rate performance of optical, radar and passive RF sensor physical and performance characteristics. We then prioritize the selected RSO targets according to object size, active/inactive status, orbit regime, and/or other considerations. Finally, the OD-derived tracking demands of each RSO of interest are levied against remaining sensor supply until either (a) all sensor time is exhausted; or (b) the list of RSO targets is exhausted. The outputs from MONET include overall network performance metrics delineated by sensor type, objects and orbits tracked, along with likely orbit accuracies which might result from the conglomerate network tracking.

Power Allocation and Outage Probability Analysis for SDN-based Radio Access Networks

NASA Astrophysics Data System (ADS)

Zhao, Yongxu; Chen, Yueyun; Mai, Zhiyuan

2018-01-01

In this paper, performance of Access network Architecture based SDN (Software Defined Network) is analyzed with respect to the power allocation issue. A power allocation scheme PSO-PA (Particle Swarm Optimization-power allocation) algorithm is proposed, the proposed scheme is subjected to constant total power with the objective of minimizing system outage probability. The entire access network resource configuration is controlled by the SDN controller, then it sends the optimized power distribution factor to the base station source node (SN) and the relay node (RN). Simulation results show that the proposed scheme reduces the system outage probability at a low complexity.

The Feasibility of Implementing Multicommand Software Functions on a Microcomputer Network.

DTIC Science & Technology

1979-10-01

studies 20 ABSTRACT fConllnuo on revete &Ide it necessary and Identify by block number) ’This report presents the results of a study of design...considerations for hybrid monitor systems for distributed microcomputer networks. The objective of the study was to determine the feasibility of such monitor...Management Informa- tion and Computer Sciences. The study was one task on a project bentitled "The Feasibility of Implementing Multicommand Software

MPL-net at ARM Sites

NASA Technical Reports Server (NTRS)

Spinhirne, J. D.; Welton, E. J.; Campbell, J. R.; Berkoff, T. A.; Starr, David OC. (Technical Monitor)

2002-01-01

The NASA MPL-net project goal is consistent data products of the vertical distribution of clouds and aerosol from globally distributed lidar observation sites. The four ARM micro pulse lidars are a basis of the network to consist of over twelve sites. The science objective is ground truth for global satellite retrievals and accurate vertical distribution information in combination with surface radiation measurements for aerosol and cloud models. The project involves improvement in instruments and data processing and cooperation with ARM and other partners.

Distributed telemedicine for the National Information Infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forslund, D.W.; Lee, Seong H.; Reverbel, F.C.

1997-08-01

TeleMed is an advanced system that provides a distributed multimedia electronic medical record available over a wide area network. It uses object-based computing, distributed data repositories, advanced graphical user interfaces, and visualization tools along with innovative concept extraction of image information for storing and accessing medical records developed in a separate project from 1994-5. In 1996, we began the transition to Java, extended the infrastructure, and worked to begin deploying TeleMed-like technologies throughout the nation. Other applications are mentioned.

Planning for Information Resource Management by OSD.

DTIC Science & Technology

1987-12-01

9,129 Objective 1.2- DSS 21,648 23,702 3,893 4,090 4,309 Objective 1.3- Computing 8,197 7,590 435 459 483 Objective 1.4- Distribute messages 75 213 29 ...System DCOAR 26 Office Automation Computer 1,961 3,483 Systems DCOAR 27 Network Performance and 125 454 Capital Budget DCOAR 29 Requirements Analysis...Defense Personnel Analysis 124 167 System ASD(FM&P) 15 Mobilization Training Base 29 63 Cap ASD(FM&P) 16 Manpower Management/ i 8 33 Program Review 01

Accessing and distributing EMBL data using CORBA (common object request broker architecture).

PubMed

Wang, L; Rodriguez-Tomé, P; Redaschi, N; McNeil, P; Robinson, A; Lijnzaad, P

2000-01-01

The EMBL Nucleotide Sequence Database is a comprehensive database of DNA and RNA sequences and related information traditionally made available in flat-file format. Queries through tools such as SRS (Sequence Retrieval System) also return data in flat-file format. Flat files have a number of shortcomings, however, and the resources therefore currently lack a flexible environment to meet individual researchers' needs. The Object Management Group's common object request broker architecture (CORBA) is an industry standard that provides platform-independent programming interfaces and models for portable distributed object-oriented computing applications. Its independence from programming languages, computing platforms and network protocols makes it attractive for developing new applications for querying and distributing biological data. A CORBA infrastructure developed by EMBL-EBI provides an efficient means of accessing and distributing EMBL data. The EMBL object model is defined such that it provides a basis for specifying interfaces in interface definition language (IDL) and thus for developing the CORBA servers. The mapping from the object model to the relational schema in the underlying Oracle database uses the facilities provided by PersistenceTM, an object/relational tool. The techniques of developing loaders and 'live object caching' with persistent objects achieve a smart live object cache where objects are created on demand. The objects are managed by an evictor pattern mechanism. The CORBA interfaces to the EMBL database address some of the problems of traditional flat-file formats and provide an efficient means for accessing and distributing EMBL data. CORBA also provides a flexible environment for users to develop their applications by building clients to our CORBA servers, which can be integrated into existing systems.

Accessing and distributing EMBL data using CORBA (common object request broker architecture)

PubMed Central

Wang, Lichun; Rodriguez-Tomé, Patricia; Redaschi, Nicole; McNeil, Phil; Robinson, Alan; Lijnzaad, Philip

2000-01-01

Background: The EMBL Nucleotide Sequence Database is a comprehensive database of DNA and RNA sequences and related information traditionally made available in flat-file format. Queries through tools such as SRS (Sequence Retrieval System) also return data in flat-file format. Flat files have a number of shortcomings, however, and the resources therefore currently lack a flexible environment to meet individual researchers' needs. The Object Management Group's common object request broker architecture (CORBA) is an industry standard that provides platform-independent programming interfaces and models for portable distributed object-oriented computing applications. Its independence from programming languages, computing platforms and network protocols makes it attractive for developing new applications for querying and distributing biological data. Results: A CORBA infrastructure developed by EMBL-EBI provides an efficient means of accessing and distributing EMBL data. The EMBL object model is defined such that it provides a basis for specifying interfaces in interface definition language (IDL) and thus for developing the CORBA servers. The mapping from the object model to the relational schema in the underlying Oracle database uses the facilities provided by PersistenceTM, an object/relational tool. The techniques of developing loaders and 'live object caching' with persistent objects achieve a smart live object cache where objects are created on demand. The objects are managed by an evictor pattern mechanism. Conclusions: The CORBA interfaces to the EMBL database address some of the problems of traditional flat-file formats and provide an efficient means for accessing and distributing EMBL data. CORBA also provides a flexible environment for users to develop their applications by building clients to our CORBA servers, which can be integrated into existing systems. PMID:11178259

Quantization and training of object detection networks with low-precision weights and activations

NASA Astrophysics Data System (ADS)

Yang, Bo; Liu, Jian; Zhou, Li; Wang, Yun; Chen, Jie

2018-01-01

As convolutional neural networks have demonstrated state-of-the-art performance in object recognition and detection, there is a growing need for deploying these systems on resource-constrained mobile platforms. However, the computational burden and energy consumption of inference for these networks are significantly higher than what most low-power devices can afford. To address these limitations, this paper proposes a method to train object detection networks with low-precision weights and activations. The probability density functions of weights and activations of each layer are first directly estimated using piecewise Gaussian models. Then, the optimal quantization intervals and step sizes for each convolution layer are adaptively determined according to the distribution of weights and activations. As the most computationally expensive convolutions can be replaced by effective fixed point operations, the proposed method can drastically reduce computation complexity and memory footprint. Performing on the tiny you only look once (YOLO) and YOLO architectures, the proposed method achieves comparable accuracy to their 32-bit counterparts. As an illustration, the proposed 4-bit and 8-bit quantized versions of the YOLO model achieve a mean average precision of 62.6% and 63.9%, respectively, on the Pascal visual object classes 2012 test dataset. The mAP of the 32-bit full-precision baseline model is 64.0%.

The use of neural networks and texture analysis for rapid objective selection of regions of interest in cytoskeletal images.

PubMed

Derkacs, Amanda D Felder; Ward, Samuel R; Lieber, Richard L

2012-02-01

Understanding cytoskeletal dynamics in living tissue is prerequisite to understanding mechanisms of injury, mechanotransduction, and mechanical signaling. Real-time visualization is now possible using transfection with plasmids that encode fluorescent cytoskeletal proteins. Using this approach with the muscle-specific intermediate filament protein desmin, we found that a green fluorescent protein-desmin chimeric protein was unevenly distributed throughout the muscle fiber, resulting in some image areas that were saturated as well as others that lacked any signal. Our goal was to analyze the muscle fiber cytoskeletal network quantitatively in an unbiased fashion. To objectively select areas of the muscle fiber that are suitable for analysis, we devised a method that provides objective classification of regions of images of striated cytoskeletal structures into "usable" and "unusable" categories. This method consists of a combination of spatial analysis of the image using Fourier methods along with a boosted neural network that "decides" on the quality of the image based on previous training. We trained the neural network using the expert opinion of three scientists familiar with these types of images. We found that this method was over 300 times faster than manual classification and that it permitted objective and accurate classification of image regions.

Optical technologies for the Internet of Things era

NASA Astrophysics Data System (ADS)

Ji, Philip N.

2017-08-01

Internet of Things (IoT) is a network of interrelated physical objects that can collect and exchange data with one another through embedded electronics, software, sensors, over the Internet. It extends Internet connectivity beyond traditional networking devices to a diverse range of physical devices and everyday things that utilize embedded technologies to communicate and interact with the external environment. The IoT brings automation and efficiency improvement to everyday life, business, and society. Therefore IoT applications and market are growing rapidly. Contrary to common belief that IoT is only related to wireless technology, optical technologies actually play important roles in the growth of IoT and contribute to its advancement. Firstly, fiber optics provides the backbone for transporting large amount of data generated by IoT network in the core , metro and access networks, and in building or in the physical object. Secondly, optical switching technologies, including all-optical switching and hybrid optical-electrical switching, enable fast and high bandwidth routing in IoT data processing center. Thirdly, optical sensing and imaging delivers comprehensive information of multiple physical phenomena through monitoring various optical properties such as intensity, phase, wavelength, frequency, polarization, and spectral distribution. In particular, fiber optic sensor has the advantages of high sensitivity, low latency, and long distributed sensing range. It is also immune to electromagnetic interference, and can be implemented in harsh environment. In this paper, the architecture of IoT is described, and the optical technologies and their applications in the IoT networks are discussed with practical examples.

Network-Based Analysis of Software Change Propagation

PubMed Central

Wang, Rongcun; Qu, Binbin

2014-01-01

The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system. PMID:24790557

Network-based analysis of software change propagation.

PubMed

Wang, Rongcun; Huang, Rubing; Qu, Binbin

2014-01-01

The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Earthquake correlations and networks: A comparative study

NASA Astrophysics Data System (ADS)

Krishna Mohan, T. R.; Revathi, P. G.

2011-04-01

We quantify the correlation between earthquakes and use the same to extract causally connected earthquake pairs. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski [M. Baiesi and M. Paczuski, Phys. Rev. E EULEEJ1539-375510.1103/PhysRevE.69.06610669, 066106 (2004)]. A network of earthquakes is then constructed from the time-ordered catalog and with links between the more correlated ones. A list of recurrences to each of the earthquakes is identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions (viz., California, Japan, and the Himalayas) are comparatively analyzed using such a network model. The distribution of recurrence lengths and recurrence times are two of the key features analyzed to draw conclusions about the universal aspects of such a network model. We find that the unimodal feature of recurrence length distribution, which helps to associate typical rupture lengths with different magnitude earthquakes, is robust across the different seismic regions. The out-degree of the networks shows a hub structure rooted on the large magnitude earthquakes. In-degree distribution is seen to be dependent on the density of events in the neighborhood. Power laws, with two regimes having different exponents, are obtained with recurrence time distribution. The first regime confirms the Omori law for aftershocks while the second regime, with a faster falloff for the larger recurrence times, establishes that pure spatial recurrences also follow a power-law distribution. The crossover to the second power-law regime can be taken to be signaling the end of the aftershock regime in an objective fashion.

Distributed Finite Element Analysis Using a Transputer Network

NASA Technical Reports Server (NTRS)

Watson, James; Favenesi, James; Danial, Albert; Tombrello, Joseph; Yang, Dabby; Reynolds, Brian; Turrentine, Ronald; Shephard, Mark; Baehmann, Peggy

1989-01-01

The principal objective of this research effort was to demonstrate the extraordinarily cost effective acceleration of finite element structural analysis problems using a transputer-based parallel processing network. This objective was accomplished in the form of a commercially viable parallel processing workstation. The workstation is a desktop size, low-maintenance computing unit capable of supercomputer performance yet costs two orders of magnitude less. To achieve the principal research objective, a transputer based structural analysis workstation termed XPFEM was implemented with linear static structural analysis capabilities resembling commercially available NASTRAN. Finite element model files, generated using the on-line preprocessing module or external preprocessing packages, are downloaded to a network of 32 transputers for accelerated solution. The system currently executes at about one third Cray X-MP24 speed but additional acceleration appears likely. For the NASA selected demonstration problem of a Space Shuttle main engine turbine blade model with about 1500 nodes and 4500 independent degrees of freedom, the Cray X-MP24 required 23.9 seconds to obtain a solution while the transputer network, operated from an IBM PC-AT compatible host computer, required 71.7 seconds. Consequently, the $80,000 transputer network demonstrated a cost-performance ratio about 60 times better than the $15,000,000 Cray X-MP24 system.

Development of Tool Representations in the Dorsal and Ventral Visual Object Processing Pathways.

PubMed

Kersey, Alyssa J; Clark, Tyia S; Lussier, Courtney A; Mahon, Bradford Z; Cantlon, Jessica F

2016-07-01

Tools represent a special class of objects, because they are processed across both the dorsal and ventral visual object processing pathways. Three core regions are known to be involved in tool processing: the left posterior middle temporal gyrus, the medial fusiform gyrus (bilaterally), and the left inferior parietal lobule. A critical and relatively unexplored issue concerns whether, in development, tool preferences emerge at the same time and to a similar degree across all regions of the tool-processing network. To test this issue, we used functional magnetic resonance imaging to measure the neural amplitude, peak location, and the dispersion of tool-related neural responses in the youngest sample of children tested to date in this domain (ages 4-8 years). We show that children recruit overlapping regions of the adult tool-processing network and also exhibit similar patterns of co-activation across the network to adults. The amplitude and co-activation data show that the core components of the tool-processing network are established by age 4. Our findings on the distributions of peak location and dispersion of activation indicate that the tool network undergoes refinement between ages 4 and 8 years. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Models and algorithm of optimization launch and deployment of virtual network functions in the virtual data center

NASA Astrophysics Data System (ADS)

Bolodurina, I. P.; Parfenov, D. I.

2017-10-01

The goal of our investigation is optimization of network work in virtual data center. The advantage of modern infrastructure virtualization lies in the possibility to use software-defined networks. However, the existing optimization of algorithmic solutions does not take into account specific features working with multiple classes of virtual network functions. The current paper describes models characterizing the basic structures of object of virtual data center. They including: a level distribution model of software-defined infrastructure virtual data center, a generalized model of a virtual network function, a neural network model of the identification of virtual network functions. We also developed an efficient algorithm for the optimization technology of containerization of virtual network functions in virtual data center. We propose an efficient algorithm for placing virtual network functions. In our investigation we also generalize the well renowned heuristic and deterministic algorithms of Karmakar-Karp.

Reduction of water losses by rehabilitation of water distribution network.

PubMed

Güngör, Mahmud; Yarar, Ufuk; Firat, Mahmut

2017-09-11

Physical or real losses may be indicated as the most important component of the water losses occurring in a water distribution network (WDN). The objective of this study is to examine the effects of piping material management and network rehabilitation on the physical water losses and water losses management in a WDN. For this aim, the Denizli WDN consisting of very old pipes that have exhausted their economic life is selected as the study area. The fact that the current network is old results in the decrease of pressure strength, increase of failure intensity, and inefficient use of water resources thus leading to the application of the rehabilitation program. In Denizli, network renewal works have been carried out since the year 2009 under the rehabilitation program. It was determined that the failure rate at regions where network renewal constructions have been completed decreased down to zero level. Renewal of piping material enables the minimization of leakage losses as well as the failure rate. On the other hand, the system rehabilitation has the potential to amortize itself in a very short amount of time if the initial investment cost of network renewal is considered along with the operating costs of the old and new systems, as well as water loss costs. As a result, it can be stated that renewal of piping material in water distribution systems, enhancement of the physical properties of the system, provide significant contributions such as increase of water and energy efficiency and more effective use of resources.

Analysis of an algorithm for distributed recognition and accountability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ko, C.; Frincke, D.A.; Goan, T. Jr.

1993-08-01

Computer and network systems are available to attacks. Abandoning the existing huge infrastructure of possibly-insecure computer and network systems is impossible, and replacing them by totally secure systems may not be feasible or cost effective. A common element in many attacks is that a single user will often attempt to intrude upon multiple resources throughout a network. Detecting the attack can become significantly easier by compiling and integrating evidence of such intrusion attempts across the network rather than attempting to assess the situation from the vantage point of only a single host. To solve this problem, we suggest an approachmore » for distributed recognition and accountability (DRA), which consists of algorithms which ``process,`` at a central location, distributed and asynchronous ``reports`` generated by computers (or a subset thereof) throughout the network. Our highest-priority objectives are to observe ways by which an individual moves around in a network of computers, including changing user names to possibly hide his/her true identity, and to associate all activities of multiple instance of the same individual to the same network-wide user. We present the DRA algorithm and a sketch of its proof under an initial set of simplifying albeit realistic assumptions. Later, we relax these assumptions to accommodate pragmatic aspects such as missing or delayed ``reports,`` clock slew, tampered ``reports,`` etc. We believe that such algorithms will have widespread applications in the future, particularly in intrusion-detection system.« less

MAGIC: Model and Graphic Information Converter

NASA Technical Reports Server (NTRS)

Herbert, W. C.

2009-01-01

MAGIC is a software tool capable of converting highly detailed 3D models from an open, standard format, VRML 2.0/97, into the proprietary DTS file format used by the Torque Game Engine from GarageGames. MAGIC is used to convert 3D simulations from authoritative sources into the data needed to run the simulations in NASA's Distributed Observer Network. The Distributed Observer Network (DON) is a simulation presentation tool built by NASA to facilitate the simulation sharing requirements of the Data Presentation and Visualization effort within the Constellation Program. DON is built on top of the Torque Game Engine (TGE) and has chosen TGE's Dynamix Three Space (DTS) file format to represent 3D objects within simulations.

Mueller-matrix mapping of biological tissues in differential diagnosis of optical anisotropy mechanisms of protein networks

NASA Astrophysics Data System (ADS)

Ushenko, V. A.; Sidor, M. I.; Marchuk, Yu F.; Pashkovskaya, N. V.; Andreichuk, D. R.

2015-03-01

We report a model of Mueller-matrix description of optical anisotropy of protein networks in biological tissues with allowance for the linear birefringence and dichroism. The model is used to construct the reconstruction algorithms of coordinate distributions of phase shifts and the linear dichroism coefficient. In the statistical analysis of such distributions, we have found the objective criteria of differentiation between benign and malignant tissues of the female reproductive system. From the standpoint of evidence-based medicine, we have determined the operating characteristics (sensitivity, specificity and accuracy) of the Mueller-matrix reconstruction method of optical anisotropy parameters and demonstrated its effectiveness in the differentiation of benign and malignant tumours.

Enhancing source location protection in wireless sensor networks

NASA Astrophysics Data System (ADS)

Chen, Juan; Lin, Zhengkui; Wu, Di; Wang, Bailing

2015-12-01

Wireless sensor networks are widely deployed in the internet of things to monitor valuable objects. Once the object is monitored, the sensor nearest to the object which is known as the source informs the base station about the object's information periodically. It is obvious that attackers can capture the object successfully by localizing the source. Thus, many protocols have been proposed to secure the source location. However, in this paper, we examine that typical source location protection protocols generate not only near but also highly localized phantom locations. As a result, attackers can trace the source easily from these phantom locations. To address these limitations, we propose a protocol to enhance the source location protection (SLE). With phantom locations far away from the source and widely distributed, SLE improves source location anonymity significantly. Theory analysis and simulation results show that our SLE provides strong source location privacy preservation and the average safety period increases by nearly one order of magnitude compared with existing work with low communication cost.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Smart PV grid to reinforce the electrical network

NASA Astrophysics Data System (ADS)

AL-Hamad, Mohamed Y.; Qamber, Isa S.

2017-11-01

Photovoltaic (PV) became the new competitive energy resources of the planet and needs to be engaged in grid to break up the congestion in both Distribution and Transmission systems. The objective of this research is to reduce the load flow through the distribution and transmission equipment by 20%. This reduction will help in relief networks loaded equipment's in all networks. Many projects are starting to develop in the GCC countries and need to be organized to achieve maximum benefits from involving the Renewable Energy Sources (RES) in the network. The GCC countries have a good location for solar energy with high intensity of the solar radiation and clear sky along the year. The opportunities of the solar energy is to utilize and create a sustainable energy resource for this region. Moreover, the target of this research is to engage the PV technology in such a way to lower the over loaded equipment and increases the electricity demand at the consumer's side.

Adaptive architectures for resilient control of networked multiagent systems in the presence of misbehaving agents

NASA Astrophysics Data System (ADS)

Torre, Gerardo De La; Yucelen, Tansel

2018-03-01

Control algorithms of networked multiagent systems are generally computed distributively without having a centralised entity monitoring the activity of agents; and therefore, unforeseen adverse conditions such as uncertainties or attacks to the communication network and/or failure of agent-wise components can easily result in system instability and prohibit the accomplishment of system-level objectives. In this paper, we study resilient coordination of networked multiagent systems in the presence of misbehaving agents, i.e. agents that are subject to exogenous disturbances that represent a class of adverse conditions. In particular, a distributed adaptive control architecture is presented for directed and time-varying graph topologies to retrieve a desired networked multiagent system behaviour. Apart from the existing relevant literature that make specific assumptions on the graph topology and/or the fraction of misbehaving agents, we show that the considered class of adverse conditions can be mitigated by the proposed adaptive control approach that utilises a local state emulator - even if all agents are misbehaving. Illustrative numerical examples are provided to demonstrate the theoretical findings.

[Development and Use of Hidrosig

NASA Technical Reports Server (NTRS)

Gupta, Vijay K.; Milne, Bruce T.

2003-01-01

The NASA portion of this joint NSF-NASA grant consists of objective 2 and a part of objective 3. A major effort was made on objective 2, and it consisted of developing a numerical GIs environment called Hidrosig. This major research tool is being developed by the University of Colorado for conducting river-network-based scaling analyses of coupled water-energy-landform-vegetation interactions including water and energy balances, and floods and droughts, at multiple space-time scales.Objective 2: To analyze the relevant remotely sensed products from satellites, radars and ground measurements to compute the transported water mass for each complete Strahler stream using an 'assimilated water balance equation' at daily and other appropriate time scales. This objective requires analysis of concurrent data sets for Precipitation (PPT), Evapotranspiration (ET) and stream flows (Q) on river networks. To solve this major problem, our decision was to develop Hidrosig, a new Open-Source GIs software. A research group in Colombia, South America, developed the first version of Hidrosig, and Ricardo Mantilla was part of this effort as an undergraduate student before joining the graduate program at the University of Colorado in 2001. Hydrosig automatically extracts river networks from large DEMs and creates a "link-based" data structure, which is required to conduct a variety of analyses under objective 2. It is programmed in Java, which is a multi-platform programming language freely distributed by SUN under a GPL license. Some existent commercial tools like Arc-Info, RiverTools and others are not suitable for our purpose for two reasons. First, the source code is not available that is needed to build on the network data structure. Second, these tools use different programming languages that are not most versatile for our purposes. For example, RiverTools uses an IDL platform that is not very efficient for organizing diverse data sets on river networks. Hidrosig establishes a clear data organization framework that allows a simultaneous analysis of spatial fields along river network structures involving Horton- Strahler framework. Software tools for network extraction from DEMs and network-based analysis of geomorphologic and topologic variables were developed during the first year and a part of second year.

A Three-Phase Microgrid Restoration Model Considering Unbalanced Operation of Distributed Generation

DOE PAGES

Wang, Zeyu; Wang, Jianhui; Chen, Chen

2016-12-07

Recent severe outages highlight the urgency of improving grid resiliency in the U.S. Microgrid formation schemes are proposed to restore critical loads after outages occur. Most distribution networks have unbalanced configurations that are not represented in sufficient detail by single-phase models. This study provides a microgrid formation plan that adopts a three-phase network model to represent unbalanced distribution networks. The problem formulation has a quadratic objective function with mixed-integer linear constraints. The three-phase network model enables us to examine the three-phase power outputs of distributed generators (DGs), preventing unbalanced operation that might trip DGs. Because the DG unbalanced operation constraintmore » is non-convex, an iterative process is presented that checks whether the unbalanced operation limits for DGs are satisfied after each iteration of optimization. We also develop a relatively conservative linear approximation on the unbalanced operation constraint to handle larger networks. Compared with the iterative solution process, the conservative linear approximation is able to accelerate the solution process at the cost of sacrificing optimality to a limited extent. Simulation in the IEEE 34 node and IEEE 123 test feeders indicate that the proposed method yields more practical microgrid formations results. In addition, this paper explores the coordinated operation of DGs and energy storage (ES) installations. The unbalanced three-phase outputs of ESs combined with the relatively balanced outputs of DGs could supply unbalanced loads. In conclusion, the case study also validates the DG-ES coordination.« less

A Three-Phase Microgrid Restoration Model Considering Unbalanced Operation of Distributed Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zeyu; Wang, Jianhui; Chen, Chen

Recent severe outages highlight the urgency of improving grid resiliency in the U.S. Microgrid formation schemes are proposed to restore critical loads after outages occur. Most distribution networks have unbalanced configurations that are not represented in sufficient detail by single-phase models. This study provides a microgrid formation plan that adopts a three-phase network model to represent unbalanced distribution networks. The problem formulation has a quadratic objective function with mixed-integer linear constraints. The three-phase network model enables us to examine the three-phase power outputs of distributed generators (DGs), preventing unbalanced operation that might trip DGs. Because the DG unbalanced operation constraintmore » is non-convex, an iterative process is presented that checks whether the unbalanced operation limits for DGs are satisfied after each iteration of optimization. We also develop a relatively conservative linear approximation on the unbalanced operation constraint to handle larger networks. Compared with the iterative solution process, the conservative linear approximation is able to accelerate the solution process at the cost of sacrificing optimality to a limited extent. Simulation in the IEEE 34 node and IEEE 123 test feeders indicate that the proposed method yields more practical microgrid formations results. In addition, this paper explores the coordinated operation of DGs and energy storage (ES) installations. The unbalanced three-phase outputs of ESs combined with the relatively balanced outputs of DGs could supply unbalanced loads. In conclusion, the case study also validates the DG-ES coordination.« less

Water Distribution System Deficiencies and Gastrointestinal Illness: A Systematic Review and Meta-Analysis

PubMed Central

Gruber, Joshua S.; Colford, John M.

2014-01-01

Background: Water distribution systems are vulnerable to performance deficiencies that can cause (re)contamination of treated water and plausibly lead to increased risk of gastrointestinal illness (GII) in consumers. Objectives: It is well established that large system disruptions in piped water networks can cause GII outbreaks. We hypothesized that routine network problems can also contribute to background levels of waterborne illness and conducted a systematic review and meta-analysis to assess the impact of distribution system deficiencies on endemic GII. Methods: We reviewed published studies that compared direct tap water consumption to consumption of tap water re-treated at the point of use (POU) and studies of specific system deficiencies such as breach of physical or hydraulic pipe integrity and lack of disinfectant residual. Results: In settings with network malfunction, consumers of tap water versus POU-treated water had increased GII [incidence density ratio (IDR) = 1.34; 95% CI: 1.00, 1.79]. The subset of nonblinded studies showed a significant association between GII and tap water versus POU-treated water consumption (IDR = 1.52; 95% CI: 1.05, 2.20), but there was no association based on studies that blinded participants to their POU water treatment status (IDR = 0.98; 95% CI: 0.90, 1.08). Among studies focusing on specific network deficiencies, GII was associated with temporary water outages (relative risk = 3.26; 95% CI: 1.48, 7.19) as well as chronic outages in intermittently operated distribution systems (odds ratio = 1.61; 95% CI: 1.26, 2.07). Conclusions: Tap water consumption is associated with GII in malfunctioning distribution networks. System deficiencies such as water outages also are associated with increased GII, suggesting a potential health risk for consumers served by piped water networks. Citation: Ercumen A, Gruber JS, Colford JM Jr. 2014. Water distribution system deficiencies and gastrointestinal illness: a systematic review and meta-analysis. Environ Health Perspect 122:651–660; http://dx.doi.org/10.1289/ehp.1306912 PMID:24659576

Smart sensing surveillance system

NASA Astrophysics Data System (ADS)

Hsu, Charles; Chu, Kai-Dee; O'Looney, James; Blake, Michael; Rutar, Colleen

2010-04-01

Unattended ground sensor (UGS) networks have been widely used in remote battlefield and other tactical applications over the last few decades due to the advances of the digital signal processing. The UGS network can be applied in a variety of areas including border surveillance, special force operations, perimeter and building protection, target acquisition, situational awareness, and force protection. In this paper, a highly-distributed, fault-tolerant, and energyefficient Smart Sensing Surveillance System (S4) is presented to efficiently provide 24/7 and all weather security operation in a situation management environment. The S4 is composed of a number of distributed nodes to collect, process, and disseminate heterogeneous sensor data. Nearly all S4 nodes have passive sensors to provide rapid omnidirectional detection. In addition, Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR cameras are integrated to selected nodes to track the objects and capture associated imagery. These S4 camera-connected nodes will provide applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. In the S4, all the nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology, which can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The S4 utilizes a Service Oriented Architecture such that remote applications can interact with the S4 network and use the specific presentation methods. The S4 capabilities and technologies have great potential for both military and civilian applications, enabling highly effective security support tools for improving surveillance activities in densely crowded environments and near perimeters and borders. The S4 is compliant with Open Geospatial Consortium - Sensor Web Enablement (OGC-SWE®) standards. It would be directly applicable to solutions for emergency response personnel, law enforcement, and other homeland security missions, as well as in applications requiring the interoperation of sensor networks with handheld or body-worn interface devices.

Design and Implementation of Replicated Object Layer

NASA Technical Reports Server (NTRS)

Koka, Sudhir

1996-01-01

One of the widely used techniques for construction of fault tolerant applications is the replication of resources so that if one copy fails sufficient copies may still remain operational to allow the application to continue to function. This thesis involves the design and implementation of an object oriented framework for replicating data on multiple sites and across different platforms. Our approach, called the Replicated Object Layer (ROL) provides a mechanism for consistent replication of data over dynamic networks. ROL uses the Reliable Multicast Protocol (RMP) as a communication protocol that provides for reliable delivery, serialization and fault tolerance. Besides providing type registration, this layer facilitates distributed atomic transactions on replicated data. A novel algorithm called the RMP Commit Protocol, which commits transactions efficiently in reliable multicast environment is presented. ROL provides recovery procedures to ensure that site and communication failures do not corrupt persistent data, and male the system fault tolerant to network partitions. ROL will facilitate building distributed fault tolerant applications by performing the burdensome details of replica consistency operations, and making it completely transparent to the application.Replicated databases are a major class of applications which could be built on top of ROL.

Locating an imaging radar in Canada for identifying spaceborne objects

NASA Astrophysics Data System (ADS)

Schick, William G.

1992-12-01

This research presents a study of the maximal coverage p-median facility location problem as applied to the location of an imaging radar in Canada for imaging spaceborne objects. The classical mathematical formulation of the maximal coverage p-median problem is converted into network-flow with side constraint formulations that are developed using a scaled down version of the imaging radar location problem. Two types of network-flow with side constraint formulations are developed: a network using side constraints that simulates the gains in a generalized network; and a network resembling a multi-commodity flow problem that uses side constraints to force flow along identical arcs. These small formulations are expanded to encompass a case study using 12 candidate radar sites, and 48 satellites divided into three states. SAS/OR PROC NETFLOW was used to solve the network-flow with side constraint formulations. The case study show that potential for both formulations, although the simulated gains formulation encountered singular matrix computational difficulties as a result of the very organized nature of its side constraint matrix. The multi-commodity flow formulation, when combined with equi-distribution of flow constraints, provided solutions for various values of p, the number of facilities to be selected.

A neural network model of semantic memory linking feature-based object representation and words.

PubMed

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

NASA Astrophysics Data System (ADS)

De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

2016-05-01

This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

Identifying multiple influential spreaders based on generalized closeness centrality

NASA Astrophysics Data System (ADS)

Liu, Huan-Li; Ma, Chuang; Xiang, Bing-Bing; Tang, Ming; Zhang, Hai-Feng

2018-02-01

To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics-epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

Flight dynamics software in a distributed network environment

NASA Technical Reports Server (NTRS)

Jeletic, J.; Weidow, D.; Boland, D.

1995-01-01

As with all NASA facilities, the announcement of reduced budgets, reduced staffing, and the desire to implement smaller/quicker/cheaper missions has required the Agency's organizations to become more efficient in what they do. To accomplish these objectives, the FDD has initiated the development of the Flight Dynamics Distributed System (FDDS). The underlying philosophy of FDDS is to build an integrated system that breaks down the traditional barriers of attitude, mission planning, and navigation support software to provide a uniform approach to flight dynamics applications. Through the application of open systems concepts and state-of-the-art technologies, including object-oriented specification concepts, object-oriented software, and common user interface, communications, data management, and executive services, the FDD will reengineer most of its six million lines of code.

Distributed Computing Framework for Synthetic Radar Application

NASA Technical Reports Server (NTRS)

Gurrola, Eric M.; Rosen, Paul A.; Aivazis, Michael

2006-01-01

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python based software framework, that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

DAVE: A plug and play model for distributed multimedia application development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mines, R.F.; Friesen, J.A.; Yang, C.L.

1994-07-01

This paper presents a model being used for the development of distributed multimedia applications. The Distributed Audio Video Environment (DAVE) was designed to support the development of a wide range of distributed applications. The implementation of this model is described. DAVE is unique in that it combines a simple ``plug and play`` programming interface, supports both centralized and fully distributed applications, provides device and media extensibility, promotes object reuseability, and supports interoperability and network independence. This model enables application developers to easily develop distributed multimedia applications and create reusable multimedia toolkits. DAVE was designed for developing applications such as videomore » conferencing, media archival, remote process control, and distance learning.« less

Design of object-oriented distributed simulation classes

NASA Technical Reports Server (NTRS)

Schoeffler, James D. (Principal Investigator)

1995-01-01

Distributed simulation of aircraft engines as part of a computer aided design package is being developed by NASA Lewis Research Center for the aircraft industry. The project is called NPSS, an acronym for 'Numerical Propulsion Simulation System'. NPSS is a flexible object-oriented simulation of aircraft engines requiring high computing speed. It is desirable to run the simulation on a distributed computer system with multiple processors executing portions of the simulation in parallel. The purpose of this research was to investigate object-oriented structures such that individual objects could be distributed. The set of classes used in the simulation must be designed to facilitate parallel computation. Since the portions of the simulation carried out in parallel are not independent of one another, there is the need for communication among the parallel executing processors which in turn implies need for their synchronization. Communication and synchronization can lead to decreased throughput as parallel processors wait for data or synchronization signals from other processors. As a result of this research, the following have been accomplished. The design and implementation of a set of simulation classes which result in a distributed simulation control program have been completed. The design is based upon MIT 'Actor' model of a concurrent object and uses 'connectors' to structure dynamic connections between simulation components. Connectors may be dynamically created according to the distribution of objects among machines at execution time without any programming changes. Measurements of the basic performance have been carried out with the result that communication overhead of the distributed design is swamped by the computation time of modules unless modules have very short execution times per iteration or time step. An analytical performance model based upon queuing network theory has been designed and implemented. Its application to realistic configurations has not been carried out.

Design of Object-Oriented Distributed Simulation Classes

NASA Technical Reports Server (NTRS)

Schoeffler, James D.

1995-01-01

Distributed simulation of aircraft engines as part of a computer aided design package being developed by NASA Lewis Research Center for the aircraft industry. The project is called NPSS, an acronym for "Numerical Propulsion Simulation System". NPSS is a flexible object-oriented simulation of aircraft engines requiring high computing speed. It is desirable to run the simulation on a distributed computer system with multiple processors executing portions of the simulation in parallel. The purpose of this research was to investigate object-oriented structures such that individual objects could be distributed. The set of classes used in the simulation must be designed to facilitate parallel computation. Since the portions of the simulation carried out in parallel are not independent of one another, there is the need for communication among the parallel executing processors which in turn implies need for their synchronization. Communication and synchronization can lead to decreased throughput as parallel processors wait for data or synchronization signals from other processors. As a result of this research, the following have been accomplished. The design and implementation of a set of simulation classes which result in a distributed simulation control program have been completed. The design is based upon MIT "Actor" model of a concurrent object and uses "connectors" to structure dynamic connections between simulation components. Connectors may be dynamically created according to the distribution of objects among machines at execution time without any programming changes. Measurements of the basic performance have been carried out with the result that communication overhead of the distributed design is swamped by the computation time of modules unless modules have very short execution times per iteration or time step. An analytical performance model based upon queuing network theory has been designed and implemented. Its application to realistic configurations has not been carried out.

Monitoring industrial facilities using principles of integration of fiber classifier and local sensor networks

NASA Astrophysics Data System (ADS)

Korotaev, Valery V.; Denisov, Victor M.; Rodrigues, Joel J. P. C.; Serikova, Mariya G.; Timofeev, Andrey V.

2015-05-01

The paper deals with the creation of integrated monitoring systems. They combine fiber-optic classifiers and local sensor networks. These systems allow for the monitoring of complex industrial objects. Together with adjacent natural objects, they form the so-called geotechnical systems. An integrated monitoring system may include one or more spatially continuous fiber-optic classifiers based on optic fiber and one or more arrays of discrete measurement sensors, which are usually combined in sensor networks. Fiber-optic classifiers are already widely used for the control of hazardous extended objects (oil and gas pipelines, railways, high-rise buildings, etc.). To monitor local objects, discrete measurement sensors are generally used (temperature, pressure, inclinometers, strain gauges, accelerometers, sensors measuring the composition of impurities in the air, and many others). However, monitoring complex geotechnical systems require a simultaneous use of continuous spatially distributed sensors based on fiber-optic cable and connected local discrete sensors networks. In fact, we are talking about integration of the two monitoring methods. This combination provides an additional way to create intelligent monitoring systems. Modes of operation of intelligent systems can automatically adapt to changing environmental conditions. For this purpose, context data received from one sensor (e.g., optical channel) may be used to change modes of work of other sensors within the same monitoring system. This work also presents experimental results of the prototype of the integrated monitoring system.

Output power distributions of terminals in a 3G mobile communication network.

PubMed

Persson, Tomas; Törnevik, Christer; Larsson, Lars-Eric; Lovén, Jan

2012-05-01

The objective of this study was to examine the distribution of the output power of mobile phones and other terminals connected to a 3G network in Sweden. It is well known that 3G terminals can operate with very low output power, particularly for voice calls. Measurements of terminal output power were conducted in the Swedish TeliaSonera 3G network in November 2008 by recording network statistics. In the analysis, discrimination was made between rural, suburban, urban, and dedicated indoor networks. In addition, information about terminal output power was possible to collect separately for voice and data traffic. Information from six different Radio Network Controllers (RNCs) was collected during at least 1 week. In total, more than 800000 h of voice calls were collected and in addition to that a substantial amount of data traffic. The average terminal output power for 3G voice calls was below 1 mW for any environment including rural, urban, and dedicated indoor networks. This is <1% of the maximum available output power. For data applications the average output power was about 6-8 dB higher than for voice calls. For rural areas the output power was about 2 dB higher, on average, than in urban areas. Copyright © 2011 Wiley Periodicals, Inc.

Distributed least-squares estimation of a remote chemical source via convex combination in wireless sensor networks.

PubMed

Cao, Meng-Li; Meng, Qing-Hao; Zeng, Ming; Sun, Biao; Li, Wei; Ding, Cheng-Jun

2014-06-27

This paper investigates the problem of locating a continuous chemical source using the concentration measurements provided by a wireless sensor network (WSN). Such a problem exists in various applications: eliminating explosives or drugs, detecting the leakage of noxious chemicals, etc. The limited power and bandwidth of WSNs have motivated collaborative in-network processing which is the focus of this paper. We propose a novel distributed least-squares estimation (DLSE) method to solve the chemical source localization (CSL) problem using a WSN. The DLSE method is realized by iteratively conducting convex combination of the locally estimated chemical source locations in a distributed manner. Performance assessments of our method are conducted using both simulations and real experiments. In the experiments, we propose a fitting method to identify both the release rate and the eddy diffusivity. The results show that the proposed DLSE method can overcome the negative interference of local minima and saddle points of the objective function, which would hinder the convergence of local search methods, especially in the case of locating a remote chemical source.

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

PubMed

Gholami, Mohammad; Brennan, Robert W

2016-01-06

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks

PubMed Central

Gholami, Mohammad; Brennan, Robert W.

2016-01-01

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency. PMID:26751447

[Map of the family social support network for the promotion of child development].

PubMed

Alexandre, Ana Maria Cosvoski; Labronici, Liliana Maria; Maftum, Mariluci Alves; Mazza, Verônica de Azevedo

2012-04-01

This descriptive, qualitative study was performed from September to November 2009, at a Family Health Strategy unit in a city in the metropolitan region of Curitiba-PR. Participants were eight families, represented by mothers, fathers and grandmothers. The study objective was to identify the family social support network for the promotion of child development, from the family's perspective. Data were collected through focal groups and subjected to content analysis. The family social support network was classified as located, consisting of 16 members distributed between the informal and formal network, established by close relationships, with a smaller level of commitment, and occasional. It is considered that the health workers' understanding regarding the role and importance of this network favors the networking proposal between members that contribute to supporting families in the promotion of child development.

A Scalable Distributed Approach to Mobile Robot Vision

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin; Browning, Robert L.; Gribble, William S.

1997-01-01

This paper documents our progress during the first year of work on our original proposal entitled 'A Scalable Distributed Approach to Mobile Robot Vision'. We are pursuing a strategy for real-time visual identification and tracking of complex objects which does not rely on specialized image-processing hardware. In this system perceptual schemas represent objects as a graph of primitive features. Distributed software agents identify and track these features, using variable-geometry image subwindows of limited size. Active control of imaging parameters and selective processing makes simultaneous real-time tracking of many primitive features tractable. Perceptual schemas operate independently from the tracking of primitive features, so that real-time tracking of a set of image features is not hurt by latency in recognition of the object that those features make up. The architecture allows semantically significant features to be tracked with limited expenditure of computational resources, and allows the visual computation to be distributed across a network of processors. Early experiments are described which demonstrate the usefulness of this formulation, followed by a brief overview of our more recent progress (after the first year).

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

NASA Technical Reports Server (NTRS)

Crichton, Daniel; Srivastava, Sudhir; Johnsey, Donald

2003-01-01

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

Distributed nuclear medicine applications using World Wide Web and Java technology.

PubMed

Knoll, P; Höll, K; Mirzaei, S; Koriska, K; Köhn, H

2000-01-01

At present, medical applications applying World Wide Web (WWW) technology are mainly used to view static images and to retrieve some information. The Java platform is a relative new way of computing, especially designed for network computing and distributed applications which enables interactive connection between user and information via the WWW. The Java 2 Software Development Kit (SDK) including Java2D API, Java Remote Method Invocation (RMI) technology, Object Serialization and the Java Advanced Imaging (JAI) extension was used to achieve a robust, platform independent and network centric solution. Medical image processing software based on this technology is presented and adequate performance capability of Java is demonstrated by an iterative reconstruction algorithm for single photon emission computerized tomography (SPECT).

Optimization of pressure gauge locations for water distribution systems using entropy theory.

PubMed

Yoo, Do Guen; Chang, Dong Eil; Jun, Hwandon; Kim, Joong Hoon

2012-12-01

It is essential to select the optimal pressure gauge location for effective management and maintenance of water distribution systems. This study proposes an objective and quantified standard for selecting the optimal pressure gauge location by defining the pressure change at other nodes as a result of demand change at a specific node using entropy theory. Two cases are considered in terms of demand change: that in which demand at all nodes shows peak load by using a peak factor and that comprising the demand change of the normal distribution whose average is the base demand. The actual pressure change pattern is determined by using the emitter function of EPANET to reflect the pressure that changes practically at each node. The optimal pressure gauge location is determined by prioritizing the node that processes the largest amount of information it gives to (giving entropy) and receives from (receiving entropy) the whole system according to the entropy standard. The suggested model is applied to one virtual and one real pipe network, and the optimal pressure gauge location combination is calculated by implementing the sensitivity analysis based on the study results. These analysis results support the following two conclusions. Firstly, the installation priority of the pressure gauge in water distribution networks can be determined with a more objective standard through the entropy theory. Secondly, the model can be used as an efficient decision-making guide for gauge installation in water distribution systems.

Network Capacity Assessment of CHP-based Distributed Generation on Urban Energy Distribution Networks

NASA Astrophysics Data System (ADS)

Zhang, Xianjun

The combined heat and power (CHP)-based distributed generation (DG) or dis-tributed energy resources (DERs) are mature options available in the present energy market, considered to be an effective solution to promote energy efficiency. In the urban environment, the electricity, water and natural gas distribution networks are becoming increasingly interconnected with the growing penetration of the CHP-based DG. Subsequently, this emerging interdependence leads to new topics meriting serious consideration: how much of the CHP-based DG can be accommodated and where to locate these DERs, and given preexisting constraints, how to quantify the mutual impacts on operation performances between these urban energy distribution networks and the CHP-based DG. The early research work was conducted to investigate the feasibility and design methods for one residential microgrid system based on existing electricity, water and gas infrastructures of a residential community, mainly focusing on the economic planning. However, this proposed design method cannot determine the optimal DG sizing and siting for a larger test bed with the given information of energy infrastructures. In this context, a more systematic as well as generalized approach should be developed to solve these problems. In the later study, the model architecture that integrates urban electricity, water and gas distribution networks, and the CHP-based DG system was developed. The proposed approach addressed the challenge of identifying the optimal sizing and siting of the CHP-based DG on these urban energy networks and the mutual impacts on operation performances were also quantified. For this study, the overall objective is to maximize the electrical output and recovered thermal output of the CHP-based DG units. The electricity, gas, and water system models were developed individually and coupled by the developed CHP-based DG system model. The resultant integrated system model is used to constrain the DG's electrical output and recovered thermal output, which are affected by multiple factors and thus analyzed in different case studies. The results indicate that the designed typical gas system is capable of supplying sufficient natural gas for the DG normal operation, while the present water system cannot support the complete recovery of the exhaust heat from the DG units.

Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions

PubMed Central

Yip, Kevin Y.; Gerstein, Mark

2009-01-01

Motivation: An important problem in systems biology is reconstructing complete networks of interactions between biological objects by extrapolating from a few known interactions as examples. While there are many computational techniques proposed for this network reconstruction task, their accuracy is consistently limited by the small number of high-confidence examples, and the uneven distribution of these examples across the potential interaction space, with some objects having many known interactions and others few. Results: To address this issue, we propose two computational methods based on the concept of training set expansion. They work particularly effectively in conjunction with kernel approaches, which are a popular class of approaches for fusing together many disparate types of features. Both our methods are based on semi-supervised learning and involve augmenting the limited number of gold-standard training instances with carefully chosen and highly confident auxiliary examples. The first method, prediction propagation, propagates highly confident predictions of one local model to another as the auxiliary examples, thus learning from information-rich regions of the training network to help predict the information-poor regions. The second method, kernel initialization, takes the most similar and most dissimilar objects of each object in a global kernel as the auxiliary examples. Using several sets of experimentally verified protein–protein interactions from yeast, we show that training set expansion gives a measurable performance gain over a number of representative, state-of-the-art network reconstruction methods, and it can correctly identify some interactions that are ranked low by other methods due to the lack of training examples of the involved proteins. Contact: mark.gerstein@yale.edu Availability: The datasets and additional materials can be found at http://networks.gersteinlab.org/tse. PMID:19015141

Telescience - Optimizing aerospace science return through geographically distributed operations

NASA Technical Reports Server (NTRS)

Rasmussen, Daryl N.; Mian, Arshad M.

1990-01-01

The paper examines the objectives and requirements of teleoperations, defined as the means and process for scientists, NASA operations personnel, and astronauts to conduct payload operations as if these were colocated. This process is described in terms of Space Station era platforms. Some of the enabling technologies are discussed, including open architecture workstations, distributed computing, transaction management, expert systems, and high-speed networks. Recent testbedding experiments are surveyed to highlight some of the human factors requirements.

Does the Sound of a Barking Dog Activate its Corresponding Visual Form? An fMRI Investigation of Modality-Specific Semantic Access

PubMed Central

Reilly, Jamie; Garcia, Amanda; Binney, Richard J.

2016-01-01

Much remains to be learned about the neural architecture underlying word meaning. Fully distributed models of semantic memory predict that the sound of a barking dog will conjointly engage a network of distributed sensorimotor spokes. An alternative framework holds that modality-specific features additionally converge within transmodal hubs. Participants underwent functional MRI while covertly naming familiar objects versus newly learned novel objects from only one of their constituent semantic features (visual form, characteristic sound, or point-light motion representation). Relative to the novel object baseline, familiar concepts elicited greater activation within association regions specific to that presentation modality. Furthermore, visual form elicited activation within high-level auditory association cortex. Conversely, environmental sounds elicited activation in regions proximal to visual association cortex. Both conditions commonly engaged a putative hub region within lateral anterior temporal cortex. These results support hybrid semantic models in which local hubs and distributed spokes are dually engaged in service of semantic memory. PMID:27289210

Impact of orthophosphate addition on biofilm development in drinking water distribution systems.

PubMed

Gouider, Mbarka; Bouzid, Jalel; Sayadi, Sami; Montiel, Antoine

2009-08-15

The contamination of the water distribution network results from fixed bacteria multiplication (biofilm) on the water pipe walls, followed by their detachment and their transport in the circulating liquid. The presence of biofilms in distribution networks can result in numerous unwanted problems for the user such as microbial contamination of the distributed water and deterioration of the network (bio-corrosion). For old networks, lead-containing plumbings can be a serious cause of worry for the consumer owing to the release of lead ions in the circulating water. Among the solutions considered to reduce the presence of lead in drinking water, the addition of orthophosphates constitutes an interesting alternative. However, the added orthophosphate may cause--even at low doses--additional microbial growth. The main objective of this study was to evaluate the impact of the orthophosphate treatment on the biofilm development in the water supplied by the Joinville-le-Pont water treatment plant (Eau de Paris, France). For this purpose, a laboratory pilot plant was devised and connected to the considered water network. Two quantification methods for monitoring the biofilm formation were used: the enumeration on R2A agar and the determination of proteins. For the biofilm detachment operation, an optimization of the rinsing step was firstly conducted in order to distinguish between free and fixed biomass. The data obtained showed that there was a linear relation between both quantification methods. They also showed that, for the tested water, the bacterial densities were not affected by orthophosphate addition at a treatment rate of 1mg PO(4)(3-)/L.

Functional communication within a perceptual network processing letters and pseudoletters.

PubMed

Herdman, Anthony T

2011-10-01

Many studies have identified regions within human ventral visual stream to be important for object identification and categorization; however, knowledge of how perceptual information is communicated within the visual network is still limited. Current theories posit that if a high correspondence between incoming sensory information and internal representations exists, then the object is rapidly identified, and if there is not, then the object requires extra detailed processing. Event-related responses from the present magnetoencephalography study showed two main effects. The N1m peak latencies were approximately 15 milliseconds earlier to familiar letters than to unfamiliar pseudoletters, and the N2m was more negative to pseudoletters than to letters. Event-related beamforming analyses identified these effects to be within bilateral visual cortices with a right lateralization for the N2m effect. Furthermore, functional connectivity analyses revealed that gamma-band (50-80 Hz) oscillatory phase synchronizations among occipital regions were greater to letters than to pseudoletters (around 85 milliseconds). However, during a later time interval between 245 and 375 milliseconds, pseudoletters elicited greater gamma-band phase synchronizations among a more distributed occipital network than did letters. These findings indicate that familiar object processing begins by at least 85 milliseconds, which could represent an initial match to an internal template. In addition, unfamiliar object processing persisted longer than that for familiar objects, which could reflect greater attention to inexperienced objects to determine their identity and/or to consolidate a new template to aid in future identification.

The middleware architecture supports heterogeneous network systems for module-based personal robot system

NASA Astrophysics Data System (ADS)

Choo, Seongho; Li, Vitaly; Choi, Dong Hee; Jung, Gi Deck; Park, Hong Seong; Ryuh, Youngsun

2005-12-01

On developing the personal robot system presently, the internal architecture is every module those occupy separated functions are connected through heterogeneous network system. This module-based architecture supports specialization and division of labor at not only designing but also implementation, as an effect of this architecture, it can reduce developing times and costs for modules. Furthermore, because every module is connected among other modules through network systems, we can get easy integrations and synergy effect to apply advanced mutual functions by co-working some modules. In this architecture, one of the most important technologies is the network middleware that takes charge communications among each modules connected through heterogeneous networks systems. The network middleware acts as the human nerve system inside of personal robot system; it relays, transmits, and translates information appropriately between modules that are similar to human organizations. The network middleware supports various hardware platform, heterogeneous network systems (Ethernet, Wireless LAN, USB, IEEE 1394, CAN, CDMA-SMS, RS-232C). This paper discussed some mechanisms about our network middleware to intercommunication and routing among modules, methods for real-time data communication and fault-tolerant network service. There have designed and implemented a layered network middleware scheme, distributed routing management, network monitoring/notification technology on heterogeneous networks for these goals. The main theme is how to make routing information in our network middleware. Additionally, with this routing information table, we appended some features. Now we are designing, making a new version network middleware (we call 'OO M/W') that can support object-oriented operation, also are updating program sources itself for object-oriented architecture. It is lighter, faster, and can support more operation systems and heterogeneous network systems, but other general purposed middlewares like CORBA, UPnP, etc. can support only one network protocol or operating system.

Fuzzy probabilistic design of water distribution networks

NASA Astrophysics Data System (ADS)

Fu, Guangtao; Kapelan, Zoran

2011-05-01

The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two-objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster-Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost-effective but also have higher reliability to cope with severe future uncertainties.

A probabilistic dynamic energy model for ad-hoc wireless sensors network with varying topology

NASA Astrophysics Data System (ADS)

Al-Husseini, Amal

In this dissertation we investigate the behavior of Wireless Sensor Networks (WSNs) from the degree distribution and evolution perspective. In specific, we focus on implementation of a scale-free degree distribution topology for energy efficient WSNs. WSNs is an emerging technology that finds its applications in different areas such as environment monitoring, agricultural crop monitoring, forest fire monitoring, and hazardous chemical monitoring in war zones. This technology allows us to collect data without human presence or intervention. Energy conservation/efficiency is one of the major issues in prolonging the active life WSNs. Recently, many energy aware and fault tolerant topology control algorithms have been presented, but there is dearth of research focused on energy conservation/efficiency of WSNs. Therefore, we study energy efficiency and fault-tolerance in WSNs from the degree distribution and evolution perspective. Self-organization observed in natural and biological systems has been directly linked to their degree distribution. It is widely known that scale-free distribution bestows robustness, fault-tolerance, and access efficiency to system. Fascinated by these properties, we propose two complex network theoretic self-organizing models for adaptive WSNs. In particular, we focus on adopting the Barabasi and Albert scale-free model to fit into the constraints and limitations of WSNs. We developed simulation models to conduct numerical experiments and network analysis. The main objective of studying these models is to find ways to reducing energy usage of each node and balancing the overall network energy disrupted by faulty communication among nodes. The first model constructs the wireless sensor network relative to the degree (connectivity) and remaining energy of every individual node. We observed that it results in a scale-free network structure which has good fault tolerance properties in face of random node failures. The second model considers additional constraints on the maximum degree of each node as well as the energy consumption relative to degree changes. This gives more realistic results from a dynamical network perspective. It results in balanced network-wide energy consumption. The results show that networks constructed using the proposed approach have good properties for different centrality measures. The outcomes of the presented research are beneficial to building WSN control models with greater self-organization properties which leads to optimal energy consumption.

Visualizing Collagen Network Within Human and Rhesus Monkey Vocal Folds Using Polarized Light Microscopy

PubMed Central

Julias, Margaret; Riede, Tobias; Cook, Douglas

2014-01-01

Objectives Collagen fiber content and orientation affect the viscoelastic properties of the vocal folds, determining oscillation characteristics during speech and other vocalization. The investigation and reconstruction of the collagen network in vocal folds remains a challenge, because the collagen network requires at least micron-scale resolution. In this study, we used polarized light microscopy to investigate the distribution and alignment of collagen fibers within the vocal folds. Methods Data were collected in sections of human and rhesus monkey (Macaca mulatta) vocal folds cut at 3 different angles and stained with picrosirius red. Results Statistically significant differences were found between different section angles, implying that more than one section angle is required to capture the network’s complexity. In the human vocal folds, the collagen fiber distribution continuously varied across the lamina propria (medial to lateral). Distinct differences in birefringence distribution were observed between the species. For the human vocal folds, high birefringence was observed near the thyroarytenoid muscle and near the epithelium. However, in the rhesus monkey vocal folds, high birefringence was observed near the epithelium, and lower birefringence was seen near the thyroarytenoid muscle. Conclusions The differences between the collagen networks in human and rhesus monkey vocal folds provide a morphological basis for differences in viscoelastic properties between species. PMID:23534129

[Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].

PubMed

Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng

2015-03-01

Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.

Generation of Global Geodetic Networks for GGOS

NASA Astrophysics Data System (ADS)

MacMillan, Daniel; Pavlis, Erricos C.; Kuzmicz-Cieslak, Magda; Koenig, Daniel

2016-12-01

We simulated future networks of VLBI+SLR sites to assess their performance. The objective is to build a global network of geographically well distributed, co-located next-generation sites from each of the space geodetic techniques. The network is being designed to meet the GGOS terrestrial reference frame goals of 1 mm in accuracy and 0.1 mm/yr in stability. We simulated the next generation networks that should be available in five years and in ten years to assess the likelihood that these networks will meet the reference frame goals. Simulations were based on the expectation that 17 broadband VLBI stations will be available in five years and 27 stations in ten years. We also consider the improvement resulting from expanding the network by six additional VLBI sites to improve the global distribution of the network. In the simulations, the networks will operate continuously, but we account for station downtime for maintenance or because of bad weather. We ran SLR+VLBI combination TRF solutions, where site ties were used to connect the two networks in the same way as in combination solutions with observed data. The strengths of VLBI and SLR allows them to provide the necessary reference frame accuracy in scale, geocenter, and orientation. With the +10-year extended network operating for ten years, simulations indicate that scale, origin, and orientation accuracies will be at the level of 0.02 ppb, 0.2 mm, and 6 μas. Combining the +5-year and +10-year network realizations will provide better estimates of accuracy and estimates of stability.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

PubMed

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation

PubMed Central

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. PMID:26954783

Dynamics of bacterial communities before and after distribution in a full-scale drinking water network.

PubMed

El-Chakhtoura, Joline; Prest, Emmanuelle; Saikaly, Pascal; van Loosdrecht, Mark; Hammes, Frederik; Vrouwenvelder, Hans

2015-05-01

Understanding the biological stability of drinking water distribution systems is imperative in the framework of process control and risk management. The objective of this research was to examine the dynamics of the bacterial community during drinking water distribution at high temporal resolution. Water samples (156 in total) were collected over short time-scales (minutes/hours/days) from the outlet of a treatment plant and a location in its corresponding distribution network. The drinking water is treated by biofiltration and disinfectant residuals are absent during distribution. The community was analyzed by 16S rRNA gene pyrosequencing and flow cytometry as well as conventional, culture-based methods. Despite a random dramatic event (detected with pyrosequencing and flow cytometry but not with plate counts), the bacterial community profile at the two locations did not vary significantly over time. A diverse core microbiome was shared between the two locations (58-65% of the taxa and 86-91% of the sequences) and found to be dependent on the treatment strategy. The bacterial community structure changed during distribution, with greater richness detected in the network and phyla such as Acidobacteria and Gemmatimonadetes becoming abundant. The rare taxa displayed the highest dynamicity, causing the major change during water distribution. This change did not have hygienic implications and is contingent on the sensitivity of the applied methods. The concept of biological stability therefore needs to be revised. Biostability is generally desired in drinking water guidelines but may be difficult to achieve in large-scale complex distribution systems that are inherently dynamic. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of new type of distributed multimedia databases to networked electronic museum

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1999-01-01

Recently, various kinds of multimedia application systems have actively been developed based on the achievement of advanced high sped communication networks, computer processing technologies, and digital contents-handling technologies. Under this background, this paper proposed a new distributed multimedia database system which can effectively perform a new function of cooperative retrieval among distributed databases. The proposed system introduces a new concept of 'Retrieval manager' which functions as an intelligent controller so that the user can recognize a set of distributed databases as one logical database. The logical database dynamically generates and performs a preferred combination of retrieving parameters on the basis of both directory data and the system environment. Moreover, a concept of 'domain' is defined in the system as a managing unit of retrieval. The retrieval can effectively be performed by cooperation of processing among multiple domains. Communication language and protocols are also defined in the system. These are used in every action for communications in the system. A language interpreter in each machine translates a communication language into an internal language used in each machine. Using the language interpreter, internal processing, such internal modules as DBMS and user interface modules can freely be selected. A concept of 'content-set' is also introduced. A content-set is defined as a package of contents. Contents in the content-set are related to each other. The system handles a content-set as one object. The user terminal can effectively control the displaying of retrieved contents, referring to data indicating the relation of the contents in the content- set. In order to verify the function of the proposed system, a networked electronic museum was experimentally built. The results of this experiment indicate that the proposed system can effectively retrieve the objective contents under the control to a number of distributed domains. The result also indicate that the system can effectively work even if the system becomes large.

Implementation of Single Source Based Hospital Information System for the Catholic Medical Center Affiliated Hospitals

PubMed Central

Choi, Inyoung; Choi, Ran; Lee, Jonghyun

2010-01-01

Objectives The objective of this research is to introduce the unique approach of the Catholic Medical Center (CMC) integrate network hospitals with organizational and technical methodologies adopted for seamless implementation. Methods The Catholic Medical Center has developed a new hospital information system to connect network hospitals and adopted new information technology architecture which uses single source for multiple distributed hospital systems. Results The hospital information system of the CMC was developed to integrate network hospitals adopting new system development principles; one source, one route and one management. This information architecture has reduced the cost for system development and operation, and has enhanced the efficiency of the management process. Conclusions Integrating network hospital through information system was not simple; it was much more complicated than single organization implementation. We are still looking for more efficient communication channel and decision making process, and also believe that our new system architecture will be able to improve CMC health care system and provide much better quality of health care service to patients and customers. PMID:21818432

Supervised Detection of Anomalous Light Curves in Massive Astronomical Catalogs

NASA Astrophysics Data System (ADS)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

2014-09-01

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each ofmore » the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.« less

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

PubMed Central

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1–V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy. PMID:25157228

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation.

PubMed

Tschechne, Stephan; Neumann, Heiko

2014-01-01

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

PubMed

Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

2017-09-21

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

Connectivist Learning Objects and Learning Styles

ERIC Educational Resources Information Center

del Moral, M. Esther; Cernea, Ana; Villalustre, Lourdes

2013-01-01

The Web 2.0 brought in the use of social tools at a large scale in every area: a transformation which led to redefining the teaching-learning process. In this new context knowledge is distributed over network connections in an uncontrolled way - thus learning consists of recognizing relevant information patterns and constructing new connections.…

A Novel Trust Service Provider for Internet Based Commerce Applications.

ERIC Educational Resources Information Center

Siyal, M. Y.; Barkat, B.

2002-01-01

Presents a framework for enhancing trust in Internet commerce. Shows how trust can be provided through a network of Trust Service Providers (TSp). Identifies a set of services that should be offered by a TSp. Presents a distributed object-oriented implementation of trust services using CORBA, JAVA and XML. (Author/AEF)

An Unsupervised Deep Hyperspectral Anomaly Detector

PubMed Central

Ma, Ning; Peng, Yu; Wang, Shaojun

2018-01-01

Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD), local RX detector (LRXD) and the-state-of-the-art Collaborative Representation detector (CRD). PMID:29495410

Polarization-interference Jones-matrix mapping of biological crystal networks

NASA Astrophysics Data System (ADS)

Ushenko, O. G.; Dubolazov, O. V.; Pidkamin, L. Y.; Sidor, M. I.; Pavlyukovich, N.; Pavlyukovich, O.

2018-01-01

The paper consists of two parts. The first part presents short theoretical basics of the method of Jones-matrix mapping with the help of reference wave. It was provided experimentally measured coordinate distributions of modulus of Jones-matrix elements of polycrystalline film of bile. It was defined the values and ranges of changing of statistic moments, which characterize such distributions. The second part presents the data of statistic analysis of the distributions of matrix elements of polycrystalline film of urine of donors and patients with albuminuria. It was defined the objective criteria of differentiation of albuminuria.

Discrete-event simulation of a wide-area health care network.

PubMed Central

McDaniel, J G

1995-01-01

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired. PMID:7583646

Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

PubMed

Stevens, Hallam

2015-10-01

The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.

Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

NASA Astrophysics Data System (ADS)

Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

2010-08-01

This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

Content-based image exploitation for situational awareness

NASA Astrophysics Data System (ADS)

Gains, David

2008-04-01

Image exploitation is of increasing importance to the enterprise of building situational awareness from multi-source data. It involves image acquisition, identification of objects of interest in imagery, storage, search and retrieval of imagery, and the distribution of imagery over possibly bandwidth limited networks. This paper describes an image exploitation application that uses image content alone to detect objects of interest, and that automatically establishes and preserves spatial and temporal relationships between images, cameras and objects. The application features an intuitive user interface that exposes all images and information generated by the system to an operator thus facilitating the formation of situational awareness.

Optimization of European call options considering physical delivery network and reservoir operation rules

NASA Astrophysics Data System (ADS)

Cheng, Wei-Chen; Hsu, Nien-Sheng; Cheng, Wen-Ming; Yeh, William W.-G.

2011-10-01

This paper develops alternative strategies for European call options for water purchase under hydrological uncertainties that can be used by water resources managers for decision making. Each alternative strategy maximizes its own objective over a selected sequence of future hydrology that is characterized by exceedance probability. Water trade provides flexibility and enhances water distribution system reliability. However, water trade between two parties in a regional water distribution system involves many issues, such as delivery network, reservoir operation rules, storage space, demand, water availability, uncertainty, and any existing contracts. An option is a security giving the right to buy or sell an asset; in our case, the asset is water. We extend a flow path-based water distribution model to include reservoir operation rules. The model simultaneously considers both the physical distribution network as well as the relationships between water sellers and buyers. We first test the model extension. Then we apply the proposed optimization model for European call options to the Tainan water distribution system in southern Taiwan. The formulation lends itself to a mixed integer linear programming model. We use the weighing method to formulate a composite function for a multiobjective problem. The proposed methodology provides water resources managers with an overall picture of water trade strategies and the consequence of each strategy. The results from the case study indicate that the strategy associated with a streamflow exceedence probability of 50% or smaller should be adopted as the reference strategy for the Tainan water distribution system.

Generalized Fluid System Simulation Program (GFSSP) Version 6 - General Purpose Thermo-Fluid Network Analysis Software

NASA Technical Reports Server (NTRS)

Majumdar, Alok; Leclair, Andre; Moore, Ric; Schallhorn, Paul

2011-01-01

GFSSP stands for Generalized Fluid System Simulation Program. It is a general-purpose computer program to compute pressure, temperature and flow distribution in a flow network. GFSSP calculates pressure, temperature, and concentrations at nodes and calculates flow rates through branches. It was primarily developed to analyze Internal Flow Analysis of a Turbopump Transient Flow Analysis of a Propulsion System. GFSSP development started in 1994 with an objective to provide a generalized and easy to use flow analysis tool for thermo-fluid systems.

UGV navigation in wireless sensor and actuator network environments

NASA Astrophysics Data System (ADS)

Zhang, Guyu; Li, Jianfeng; Duncan, Christian A.; Kanno, Jinko; Selmic, Rastko R.

2012-06-01

We consider a navigation problem in a distributed, self-organized and coordinate-free Wireless Sensor and Ac- tuator Network (WSAN). We rst present navigation algorithms that are veried using simulation results. Con- sidering more than one destination and multiple mobile Unmanned Ground Vehicles (UGVs), we introduce a distributed solution to the Multi-UGV, Multi-Destination navigation problem. The objective of the solution to this problem is to eciently allocate UGVs to dierent destinations and carry out navigation in the network en- vironment that minimizes total travel distance. The main contribution of this paper is to develop a solution that does not attempt to localize either the UGVs or the sensor and actuator nodes. Other than some connectivity as- sumptions about the communication graph, we consider that no prior information about the WSAN is available. The solution presented here is distributed, and the UGV navigation is solely based on feedback from neigh- boring sensor and actuator nodes. One special case discussed in the paper, the Single-UGV, Multi-Destination navigation problem, is essentially equivalent to the well-known and dicult Traveling Salesman Problem (TSP). Simulation results are presented that illustrate the navigation distance traveled through the network. We also introduce an experimental testbed for the realization of coordinate-free and localization-free UGV navigation. We use the Cricket platform as the sensor and actuator network and a Pioneer 3-DX robot as the UGV. The experiments illustrate the UGV navigation in a coordinate-free WSAN environment where the UGV successfully arrives at the assigned destinations.

Active and Reactive Power Optimal Dispatch Associated with Load and DG Uncertainties in Active Distribution Network

NASA Astrophysics Data System (ADS)

Gao, F.; Song, X. H.; Zhang, Y.; Li, J. F.; Zhao, S. S.; Ma, W. Q.; Jia, Z. Y.

2017-05-01

In order to reduce the adverse effects of uncertainty on optimal dispatch in active distribution network, an optimal dispatch model based on chance-constrained programming is proposed in this paper. In this model, the active and reactive power of DG can be dispatched at the aim of reducing the operating cost. The effect of operation strategy on the cost can be reflected in the objective which contains the cost of network loss, DG curtailment, DG reactive power ancillary service, and power quality compensation. At the same time, the probabilistic constraints can reflect the operation risk degree. Then the optimal dispatch model is simplified as a series of single stage model which can avoid large variable dimension and improve the convergence speed. And the single stage model is solved using a combination of particle swarm optimization (PSO) and point estimate method (PEM). Finally, the proposed optimal dispatch model and method is verified by the IEEE33 test system.

Classification and global distribution of ocean precipitation types based on satellite passive microwave signatures

NASA Astrophysics Data System (ADS)

Gautam, Nitin

The main objectives of this thesis are to develop a robust statistical method for the classification of ocean precipitation based on physical properties to which the SSM/I is sensitive and to examine how these properties vary globally and seasonally. A two step approach is adopted for the classification of oceanic precipitation classes from multispectral SSM/I data: (1)we subjectively define precipitation classes using a priori information about the precipitating system and its possible distinct signature on SSM/I data such as scattering by ice particles aloft in the precipitating cloud, emission by liquid rain water below freezing level, the difference of polarization at 19 GHz-an indirect measure of optical depth, etc.; (2)we then develop an objective classification scheme which is found to reproduce the subjective classification with high accuracy. This hybrid strategy allows us to use the characteristics of the data to define and encode classes and helps retain the physical interpretation of classes. The classification methods based on k-nearest neighbor and neural network are developed to objectively classify six precipitation classes. It is found that the classification method based neural network yields high accuracy for all precipitation classes. An inversion method based on minimum variance approach was used to retrieve gross microphysical properties of these precipitation classes such as column integrated liquid water path, column integrated ice water path, and column integrated min water path. This classification method is then applied to 2 years (1991-92) of SSM/I data to examine and document the seasonal and global distribution of precipitation frequency corresponding to each of these objectively defined six classes. The characteristics of the distribution are found to be consistent with assumptions used in defining these six precipitation classes and also with well known climatological patterns of precipitation regions. The seasonal and global distribution of these six classes is also compared with the earlier results obtained from Comprehensive Ocean Atmosphere Data Sets (COADS). It is found that the gross pattern of the distributions obtained from SSM/I and COADS data match remarkably well with each other.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Modular sensor network node

DOEpatents

Davis, Jesse Harper Zehring [Berkeley, CA; Stark, Jr., Douglas Paul; Kershaw, Christopher Patrick [Hayward, CA; Kyker, Ronald Dean [Livermore, CA

2008-06-10

A distributed wireless sensor network node is disclosed. The wireless sensor network node includes a plurality of sensor modules coupled to a system bus and configured to sense a parameter. The parameter may be an object, an event or any other parameter. The node collects data representative of the parameter. The node also includes a communication module coupled to the system bus and configured to allow the node to communicate with other nodes. The node also includes a processing module coupled to the system bus and adapted to receive the data from the sensor module and operable to analyze the data. The node also includes a power module connected to the system bus and operable to generate a regulated voltage.

A reaction-diffusion-based coding rate control mechanism for camera sensor networks.

PubMed

Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki

2010-01-01

A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 controlmore » 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.« less

Method and Apparatus for Predicting Unsteady Pressure and Flow Rate Distribution in a Fluid Network

NASA Technical Reports Server (NTRS)

Majumdar, Alok K. (Inventor)

2009-01-01

A method and apparatus for analyzing steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics, external body forces such as gravity and centrifugal force and conjugate heat transfer. In some embodiments, a graphical user interface provides for the interactive development of a fluid network simulation having nodes and branches. In some embodiments, mass, energy, and specific conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. In some embodiments, contained herein are data objects for computing thermodynamic and thermophysical properties for fluids. In some embodiments, the systems of equations describing the fluid network are solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods.

System of and method for transparent management of data objects in containers across distributed heterogenous resources

DOEpatents

Moore, Reagan W.; Rajasekar, Arcot; Wan, Michael Y.

2004-01-13

A system of and method for maintaining data objects in containers across a network of distributed heterogeneous resources in a manner which is transparent to a client. A client request pertaining to containers is resolved by querying meta data for the container, processing the request through one or more copies of the container maintained on the system, updating the meta data for the container to reflect any changes made to the container as a result processing the request, and, if a copy of the container has changed, changing the status of the copy to indicate dirty status or synchronizing the copy to one or more other copies that may be present on the system.

System of and method for transparent management of data objects in containers across distributed heterogenous resources

DOEpatents

Moore, Reagan W [San Diego, CA; Rajasekar, Arcot [Del Mar, CA; Wan, Michael Y [San Diego, CA

2007-09-11

A system of and method for maintaining data objects in containers across a network of distributed heterogeneous resources in a manner which is transparent to a client. A client request pertaining to containers is resolved by querying meta data for the container, processing the request through one or more copies of the container maintained on the system, updating the meta data for the container to reflect any changes made to the container as a result processing the re quest, and, if a copy of the container has changed, changing the status of the copy to indicate dirty status or synchronizing the copy to one or more other copies that may be present on the system.

System of and method for transparent management of data objects in containers across distributed heterogenous resources

DOEpatents

Moore, Reagan W.; Rajasekar, Arcot; Wan, Michael Y.

2010-09-21

A system of and method for maintaining data objects in containers across a network of distributed heterogeneous resources in a manner which is transparent to a client. A client request pertaining to containers is resolved by querying meta data for the container, processing the request through one or more copies of the container maintained on the system, updating the meta data for the container to reflect any changes made to the container as a result processing the request, and, if a copy of the container has changed, changing the status of the copy to indicate dirty status or synchronizing the copy to one or more other copies that may be present on the system.

Acoustic Source Localization via Time Difference of Arrival Estimation for Distributed Sensor Networks Using Tera-Scale Optical Core Devices

DOE PAGES

Imam, Neena; Barhen, Jacob

2009-01-01

For real-time acoustic source localization applications, one of the primary challenges is the considerable growth in computational complexity associated with the emergence of ever larger, active or passive, distributed sensor networks. These sensors rely heavily on battery-operated system components to achieve highly functional automation in signal and information processing. In order to keep communication requirements minimal, it is desirable to perform as much processing on the receiver platforms as possible. However, the complexity of the calculations needed to achieve accurate source localization increases dramatically with the size of sensor arrays, resulting in substantial growth of computational requirements that cannot bemore » readily met with standard hardware. One option to meet this challenge builds upon the emergence of digital optical-core devices. The objective of this work was to explore the implementation of key building block algorithms used in underwater source localization on the optical-core digital processing platform recently introduced by Lenslet Inc. This demonstration of considerably faster signal processing capability should be of substantial significance to the design and innovation of future generations of distributed sensor networks.« less

Dissolution Front Instabilities in Reacting Porous Media

NASA Astrophysics Data System (ADS)

Raoof, Amir; Spiers, Chris; Hassanizadeh, Majid

2013-04-01

The main objective of this research is to gain a better understanding of the relation between regime of reaction and dissolution front instability, leading to formation of channels or wormholes. Potential applications are geological sequestration of CO2 and acid-gas injection during enhanced oil recovery. The microscopic pore space is modeled using a multi-directional pore network, allowing for a distribution of pore coordination number, together with distribution of pore sizes. In order to simulate transport of multi-component chemical species, mass balance equations are solved within each element of the network (i.e., pore body and pore throat). We have considered advective and diffusive transport processes within the pore spaces together with multi-component chemical reactions, including both equilibrium and kinetic reactions. Using dimensionless scaling groups (such as Damköhler number and Péclet-Damköhler number) we characterized the dissolution front behavior, and by averaging over the network domain we calculated the evolution of porosity and permeability as well as flux-averaged concentration breakthrough curves. We obtain constitutive relations linking porosity and permeability, under conditions relevant to geological storage of CO2. Effect of distribution of reactive minerals is also evaluated and regime of reaction is shown to play a key role.

Evaluation of exposure scenarios on intentional microbiological contamination in a drinking water distribution network.

PubMed

Schijven, Jack; Forêt, Jean Marie; Chardon, Jurgen; Teunis, Peter; Bouwknegt, Martijn; Tangena, Ben

2016-06-01

Drinking water distribution networks are vulnerable to accidental or intentional contamination events. The objective of this study was to investigate the effects of seeding duration and concentration, exposure pathway (ingestion via drinking of water and tooth brushing and inhalation by taking a shower) and pathogen infectivity on exposure and infection risk in the case of an intentional pathogenic contamination in a drinking water distribution network. Seeding of a pathogen for 10 min and 120 min, and subsequent spreading through a drinking water distribution network were simulated. For exposure via drinking, actual data on drinking events and volumes were used. Ingestion of a small volume of water by tooth brushing twice a day by every person in the network was assumed. Inhalation of contaminated aerosol droplets took place when taking a shower. Infection risks were estimated for pathogens with low (r = 0.0001) and high (r = 0.1) infectivity. In the served population (48 000 persons) and within 24 h, about 1400 persons were exposed to the pathogen by ingestion of water in the 10-min seeding scenario and about 3400 persons in the 120-min scenario. The numbers of exposed persons via tooth brushing were about the same as via drinking of water. Showering caused (inhalation) exposure in about 450 persons in the 10-min scenario and about 1500 in the 120-min scenario. Regardless of pathogen infectivity, if the seeding concentration is 10(6) pathogens per litre or more, infection risks are close to one. Exposure by taking a shower is of relevance if the pathogen is highly infectious via inhalation. A longer duration of the seeding of a pathogen increases the probability of exposure. Copyright © 2016 Elsevier Ltd. All rights reserved.

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty.

PubMed

Fathollah Bayati, Mohsen; Sadjadi, Seyed Jafar

2017-01-01

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty

PubMed Central

Sadjadi, Seyed Jafar

2017-01-01

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model. PMID:28953900

A knowledge base architecture for distributed knowledge agents

NASA Technical Reports Server (NTRS)

Riedesel, Joel; Walls, Bryan

1990-01-01

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

PubMed

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.

Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy

PubMed Central

Khambhati, Ankit N.; Davis, Kathryn A.; Oommen, Brian S.; Chen, Stephanie H.; Lucas, Timothy H.; Litt, Brian; Bassett, Danielle S.

2015-01-01

The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices. PMID:26680762

Mobile and replicated alignment of arrays in data-parallel programs

NASA Technical Reports Server (NTRS)

Chatterjee, Siddhartha; Gilbert, John R.; Schreiber, Robert

1993-01-01

When a data-parallel language like FORTRAN 90 is compiled for a distributed-memory machine, aggregate data objects (such as arrays) are distributed across the processor memories. The mapping determines the amount of residual communication needed to bring operands of parallel operations into alignment with each other. A common approach is to break the mapping into two stages: first, an alignment that maps all the objects to an abstract template, and then a distribution that maps the template to the processors. We solve two facets of the problem of finding alignments that reduce residual communication: we determine alignments that vary in loops, and objects that should have replicated alignments. We show that loop-dependent mobile alignment is sometimes necessary for optimum performance, and we provide algorithms with which a compiler can determine good mobile alignments for objects within do loops. We also identify situations in which replicated alignment is either required by the program itself (via spread operations) or can be used to improve performance. We propose an algorithm based on network flow that determines which objects to replicate so as to minimize the total amount of broadcast communication in replication. This work on mobile and replicated alignment extends our earlier work on determining static alignment.

Space Domain Awareness

DTIC Science & Technology

2012-09-01

the Space Surveillance Network has been tracking orbital objects and maintaining a catalog that allows space operators to safely operate satellites ...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources...Distribution Unlimited) backward) in time , but the accuracy degrades as the amount of propagation time increases. Thus, the need to maintain a

Distribution System Reliability Analysis for Smart Grid Applications

NASA Astrophysics Data System (ADS)

Aljohani, Tawfiq Masad

Reliability of power systems is a key aspect in modern power system planning, design, and operation. The ascendance of the smart grid concept has provided high hopes of developing an intelligent network that is capable of being a self-healing grid, offering the ability to overcome the interruption problems that face the utility and cost it tens of millions in repair and loss. To address its reliability concerns, the power utilities and interested parties have spent extensive amount of time and effort to analyze and study the reliability of the generation and transmission sectors of the power grid. Only recently has attention shifted to be focused on improving the reliability of the distribution network, the connection joint between the power providers and the consumers where most of the electricity problems occur. In this work, we will examine the effect of the smart grid applications in improving the reliability of the power distribution networks. The test system used in conducting this thesis is the IEEE 34 node test feeder, released in 2003 by the Distribution System Analysis Subcommittee of the IEEE Power Engineering Society. The objective is to analyze the feeder for the optimal placement of the automatic switching devices and quantify their proper installation based on the performance of the distribution system. The measures will be the changes in the reliability system indices including SAIDI, SAIFI, and EUE. The goal is to design and simulate the effect of the installation of the Distributed Generators (DGs) on the utility's distribution system and measure the potential improvement of its reliability. The software used in this work is DISREL, which is intelligent power distribution software that is developed by General Reliability Co.

Predicting the particle size distribution of eroded sediment using artificial neural networks.

PubMed

Lagos-Avid, María Paz; Bonilla, Carlos A

2017-03-01

Water erosion causes soil degradation and nonpoint pollution. Pollutants are primarily transported on the surfaces of fine soil and sediment particles. Several soil loss models and empirical equations have been developed for the size distribution estimation of the sediment leaving the field, including the physically-based models and empirical equations. Usually, physically-based models require a large amount of data, sometimes exceeding the amount of available data in the modeled area. Conversely, empirical equations do not always predict the sediment composition associated with individual events and may require data that are not always available. Therefore, the objective of this study was to develop a model to predict the particle size distribution (PSD) of eroded soil. A total of 41 erosion events from 21 soils were used. These data were compiled from previous studies. Correlation and multiple regression analyses were used to identify the main variables controlling sediment PSD. These variables were the particle size distribution in the soil matrix, the antecedent soil moisture condition, soil erodibility, and hillslope geometry. With these variables, an artificial neural network was calibrated using data from 29 events (r 2 =0.98, 0.97, and 0.86; for sand, silt, and clay in the sediment, respectively) and then validated and tested on 12 events (r 2 =0.74, 0.85, and 0.75; for sand, silt, and clay in the sediment, respectively). The artificial neural network was compared with three empirical models. The network presented better performance in predicting sediment PSD and differentiating rain-runoff events in the same soil. In addition to the quality of the particle distribution estimates, this model requires a small number of easily obtained variables, providing a convenient routine for predicting PSD in eroded sediment in other pollutant transport models. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization based on benefit of regional energy suppliers of distributed generation in active distribution network

NASA Astrophysics Data System (ADS)

Huo, Xianxu; Li, Guodong; Jiang, Ling; Wang, Xudong

2017-08-01

With the development of electricity market, distributed generation (DG) technology and related policies, regional energy suppliers are encouraged to build DG. Under this background, the concept of active distribution network (ADN) is put forward. In this paper, a bi-level model of intermittent DG considering benefit of regional energy suppliers is proposed. The objective of the upper level is the maximization of benefit of regional energy suppliers. On this basis, the lower level is optimized for each scene. The uncertainties of DG output and load of users, as well as four active management measures, which include demand-side management, curtailing the output power of DG, regulating reactive power compensation capacity and regulating the on-load tap changer, are considered. Harmony search algorithm and particle swarm optimization are combined as a hybrid strategy to solve the model. This model and strategy are tested with IEEE-33 node system, and results of case study indicate that the model and strategy successfully increase the capacity of DG and benefit of regional energy suppliers.

Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks.

PubMed

Shah, Shashi; Kittipiyakul, Somsak; Lim, Yuto; Tan, Yasuo

2018-05-10

The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.

[Innovative teleradiology network: concept and experience report].

PubMed

Kämmerer, M; Bethge, O T; Antoch, G

2014-04-01

(DICOM E-MAIL provides a standardized way for exchanging DICOM objects (Digital Imaging and Communications in Medicine) and further relevant patient data for the treatment context reliably and securely via encrypted e-mails. The current version of the DICOM E-MAIL standard recommendations of the"Deutsche Röntgengesellschaft" (DRG, German Röntgen Society) defines for the first time options for setting up a special directory service for the provision and distribution of communication data of all participants in a network. By using such"telephone books", networks of any size can be operated independent of the provider. Compared to a Cross-Enterprise Document Sharing (XDS) scenario, the required infrastructure is considerably less complex and quicker to realize. Critical success factors are, in addition to the technology and an effective support, that the participants themselves contribute to the further development of the network and in this way, the network approach can be practiced.

Graph Design via Convex Optimization: Online and Distributed Perspectives

NASA Astrophysics Data System (ADS)

Meng, De

Network and graph have long been natural abstraction of relations in a variety of applications, e.g. transportation, power system, social network, communication, electrical circuit, etc. As a large number of computation and optimization problems are naturally defined on graphs, graph structures not only enable important properties of these problems, but also leads to highly efficient distributed and online algorithms. For example, graph separability enables the parallelism for computation and operation as well as limits the size of local problems. More interestingly, graphs can be defined and constructed in order to take best advantage of those problem properties. This dissertation focuses on graph structure and design in newly proposed optimization problems, which establish a bridge between graph properties and optimization problem properties. We first study a new optimization problem called Geodesic Distance Maximization Problem (GDMP). Given a graph with fixed edge weights, finding the shortest path, also known as the geodesic, between two nodes is a well-studied network flow problem. We introduce the Geodesic Distance Maximization Problem (GDMP): the problem of finding the edge weights that maximize the length of the geodesic subject to convex constraints on the weights. We show that GDMP is a convex optimization problem for a wide class of flow costs, and provide a physical interpretation using the dual. We present applications of the GDMP in various fields, including optical lens design, network interdiction, and resource allocation in the control of forest fires. We develop an Alternating Direction Method of Multipliers (ADMM) by exploiting specific problem structures to solve large-scale GDMP, and demonstrate its effectiveness in numerical examples. We then turn our attention to distributed optimization on graph with only local communication. Distributed optimization arises in a variety of applications, e.g. distributed tracking and localization, estimation problems in sensor networks, multi-agent coordination. Distributed optimization aims to optimize a global objective function formed by summation of coupled local functions over a graph via only local communication and computation. We developed a weighted proximal ADMM for distributed optimization using graph structure. This fully distributed, single-loop algorithm allows simultaneous updates and can be viewed as a generalization of existing algorithms. More importantly, we achieve faster convergence by jointly designing graph weights and algorithm parameters. Finally, we propose a new problem on networks called Online Network Formation Problem: starting with a base graph and a set of candidate edges, at each round of the game, player one first chooses a candidate edge and reveals it to player two, then player two decides whether to accept it; player two can only accept limited number of edges and make online decisions with the goal to achieve the best properties of the synthesized network. The network properties considered include the number of spanning trees, algebraic connectivity and total effective resistance. These network formation games arise in a variety of cooperative multiagent systems. We propose a primal-dual algorithm framework for the general online network formation game, and analyze the algorithm performance by the competitive ratio and regret.

Self-determined mechanisms in complex networks

NASA Astrophysics Data System (ADS)

Liu, Yang; Yuan, Jian; Shan, Xiuming; Ren, Yong; Ma, Zhengxin

2008-03-01

Self-organized networks are pervasive in communication systems such as the Internet, overlay networks, peer-to-peer networks, and cluster-based services. These networks evolve into complex topologies, under specific driving forces, i.e. user demands, technological innovations, design objectives and so on. Our study focuses on the driving forces behind individual evolutions of network components, and their stimulation and domination to the self-organized networks which are defined as self-determined mechanisms in this paper. Understanding forces underlying the evolution of networks should enable informed design decisions and help to avoid unwanted surprises, such as congestion collapse. A case study on the macroscopic evolution of the Internet topology of autonomous systems under a specific driving force is then presented. Using computer simulations, it is found that the power-law degree distribution can originate from a connection preference to larger numbers of users, and that the small-world property can be caused by rapid growth in the number of users. Our results provide a new feasible perspective to understand intrinsic fundamentals in the topological evolution of complex networks.

The neural network classification of false killer whale (Pseudorca crassidens) vocalizations.

PubMed

Murray, S O; Mercado, E; Roitblat, H L

1998-12-01

This study reports the use of unsupervised, self-organizing neural network to categorize the repertoire of false killer whale vocalizations. Self-organizing networks are capable of detecting patterns in their input and partitioning those patterns into categories without requiring that the number or types of categories be predefined. The inputs for the neural networks were two-dimensional characterization of false killer whale vocalization, where each vocalization was characterized by a sequence of short-time measurements of duty cycle and peak frequency. The first neural network used competitive learning, where units in a competitive layer distributed themselves to recognize frequently presented input vectors. This network resulted in classes representing typical patterns in the vocalizations. The second network was a Kohonen feature map which organized the outputs topologically, providing a graphical organization of pattern relationships. The networks performed well as measured by (1) the average correlation between the input vectors and the weight vectors for each category, and (2) the ability of the networks to classify novel vocalizations. The techniques used in this study could easily be applied to other species and facilitate the development of objective, comprehensive repertoire models.

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

PubMed

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

KeyWare: an open wireless distributed computing environment

NASA Astrophysics Data System (ADS)

Shpantzer, Isaac; Schoenfeld, Larry; Grindahl, Merv; Kelman, Vladimir

1995-12-01

Deployment of distributed applications in the wireless domain lack equivalent tools, methodologies, architectures, and network management that exist in LAN based applications. A wireless distributed computing environment (KeyWareTM) based on intelligent agents within a multiple client multiple server scheme was developed to resolve this problem. KeyWare renders concurrent application services to wireline and wireless client nodes encapsulated in multiple paradigms such as message delivery, database access, e-mail, and file transfer. These services and paradigms are optimized to cope with temporal and spatial radio coverage, high latency, limited throughput and transmission costs. A unified network management paradigm for both wireless and wireline facilitates seamless extensions of LAN- based management tools to include wireless nodes. A set of object oriented tools and methodologies enables direct asynchronous invocation of agent-based services supplemented by tool-sets matched to supported KeyWare paradigms. The open architecture embodiment of KeyWare enables a wide selection of client node computing platforms, operating systems, transport protocols, radio modems and infrastructures while maintaining application portability.

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

NASA Astrophysics Data System (ADS)

Fazayeli, Saeed; Eydi, Alireza; Kamalabadi, Isa Nakhai

2017-07-01

Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.

A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

NASA Astrophysics Data System (ADS)

Fazayeli, Saeed; Eydi, Alireza; Kamalabadi, Isa Nakhai

2018-07-01

Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.

Fiber-Optic Network Architectures for Onboard Avionics Applications Investigated

NASA Technical Reports Server (NTRS)

Nguyen, Hung D.; Ngo, Duc H.

2003-01-01

This project is part of a study within the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Center. The main focus of the program is the improvement of air transportation, with particular emphasis on air transportation safety. Current and future advances in digital data communications between an aircraft and the outside world will require high-bandwidth onboard communication networks. Radiofrequency (RF) systems, with their interconnection network based on coaxial cables and waveguides, increase the complexity of communication systems onboard modern civil and military aircraft with respect to weight, power consumption, and safety. In addition, safety and reliability concerns from electromagnetic interference between the RF components embedded in these communication systems exist. A simple, reliable, and lightweight network that is free from the effects of electromagnetic interference and capable of supporting the broadband communications needs of future onboard digital avionics systems cannot be easily implemented using existing coaxial cable-based systems. Fiber-optical communication systems can meet all these challenges of modern avionics applications in an efficient, cost-effective manner. The objective of this project is to present a number of optical network architectures for onboard RF signal distribution. Because of the emergence of a number of digital avionics devices requiring high-bandwidth connectivity, fiber-optic RF networks onboard modern aircraft will play a vital role in ensuring a low-noise, highly reliable RF communication system. Two approaches are being used for network architectures for aircraft onboard fiber-optic distribution systems: a hybrid RF-optical network and an all-optical wavelength division multiplexing (WDM) network.

Method for stitching microbial images using a neural network

NASA Astrophysics Data System (ADS)

Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.; Tolstova, I. V.

2017-05-01

Currently an analog microscope has a wide distribution in the following fields: medicine, animal husbandry, monitoring technological objects, oceanography, agriculture and others. Automatic method is preferred because it will greatly reduce the work involved. Stepper motors are used to move the microscope slide and allow to adjust the focus in semi-automatic or automatic mode view with transfer images of microbiological objects from the eyepiece of the microscope to the computer screen. Scene analysis allows to locate regions with pronounced abnormalities for focusing specialist attention. This paper considers the method for stitching microbial images, obtained of semi-automatic microscope. The method allows to keep the boundaries of objects located in the area of capturing optical systems. Objects searching are based on the analysis of the data located in the area of the camera view. We propose to use a neural network for the boundaries searching. The stitching image boundary is held of the analysis borders of the objects. To auto focus, we use the criterion of the minimum thickness of the line boundaries of object. Analysis produced the object located in the focal axis of the camera. We use method of recovery of objects borders and projective transform for the boundary of objects which are based on shifted relative to the focal axis. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

Objective Video Quality Assessment Based on Machine Learning for Underwater Scientific Applications

PubMed Central

Moreno-Roldán, José-Miguel; Luque-Nieto, Miguel-Ángel; Poncela, Javier; Otero, Pablo

2017-01-01

Video services are meant to be a fundamental tool in the development of oceanic research. The current technology for underwater networks (UWNs) imposes strong constraints in the transmission capacity since only a severely limited bitrate is available. However, previous studies have shown that the quality of experience (QoE) is enough for ocean scientists to consider the service useful, although the perceived quality can change significantly for small ranges of variation of video parameters. In this context, objective video quality assessment (VQA) methods become essential in network planning and real time quality adaptation fields. This paper presents two specialized models for objective VQA, designed to match the special requirements of UWNs. The models are built upon machine learning techniques and trained with actual user data gathered from subjective tests. Our performance analysis shows how both of them can successfully estimate quality as a mean opinion score (MOS) value and, for the second model, even compute a distribution function for user scores. PMID:28333123

Distributed Data Networks That Support Public Health Information Needs.

PubMed

Tabano, David C; Cole, Elizabeth; Holve, Erin; Davidson, Arthur J

Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

Distributed sensor coordination for advanced energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumer, Kagan

Motivation: The ability to collect key system level information is critical to the safe, efficient and reliable operation of advanced power systems. Recent advances in sensor technology have enabled some level of decision making directly at the sensor level. However, coordinating large numbers of sensors, particularly heterogeneous sensors, to achieve system level objectives such as predicting plant efficiency, reducing downtime or predicting outages requires sophisticated coordination algorithms. Indeed, a critical issue in such systems is how to ensure the interaction of a large number of heterogenous system components do not interfere with one another and lead to undesirable behavior. Objectivesmore » and Contributions: The long-term objective of this work is to provide sensor deployment, coordination and networking algorithms for large numbers of sensors to ensure the safe, reliable, and robust operation of advanced energy systems. Our two specific objectives are to: 1. Derive sensor performance metrics for heterogeneous sensor networks. 2. Demonstrate effectiveness, scalability and reconfigurability of heterogeneous sensor network in advanced power systems. The key technical contribution of this work is to push the coordination step to the design of the objective functions of the sensors, allowing networks of heterogeneous sensors to be controlled. By ensuring that the control and coordination is not specific to particular sensor hardware, this approach enables the design and operation of large heterogeneous sensor networks. In addition to the coordination coordination mechanism, this approach allows the system to be reconfigured in response to changing needs (e.g., sudden external events requiring new responses) or changing sensor network characteristics (e.g., sudden changes to plant condition). Impact: The impact of this work extends to a large class of problems relevant to the National Energy Technology Laboratory including sensor placement, heterogeneous sensor coordination, and sensor network control in advanced power systems. Each application has specific needs, but they all share the one crucial underlying problem: how to ensure that the interactions of a large number of heterogenous agents lead to coordinated system behavior. This proposal describes a new paradigm that addresses that very issue in a systematic way. Key Results and Findings: All milestones have been completed. Our results demonstrate that by properly shaping agent objective functions, we can develop large (up to 10,000 devices) heterogeneous sensor networks with key desirable properties. The first milestone shows that properly choosing agent-specific objective functions increases system performance by up to 99.9% compared to global evaluations. The second milestone shows evolutionary algorithms learn excellent sensor network coordination policies prior to network deployment, and these policies can be refined online once the network is deployed. The third milestone shows the resulting sensor networks networks are extremely robust to sensor noise, where networks with up to 25% sensor noise are capable of providing measurements with errors on the order of 10⁻³. The fourth milestone shows the resulting sensor networks are extremely robust to sensor failure, with 25% of the sensors in the system failing resulting in no significant performance losses after system reconfiguration.« less

Advanced systems engineering and network planning support

NASA Technical Reports Server (NTRS)

Walters, David H.; Barrett, Larry K.; Boyd, Ronald; Bazaj, Suresh; Mitchell, Lionel; Brosi, Fred

1990-01-01

The objective of this task was to take a fresh look at the NASA Space Network Control (SNC) element for the Advanced Tracking and Data Relay Satellite System (ATDRSS) such that it can be made more efficient and responsive to the user by introducing new concepts and technologies appropriate for the 1997 timeframe. In particular, it was desired to investigate the technologies and concepts employed in similar systems that may be applicable to the SNC. The recommendations resulting from this study include resource partitioning, on-line access to subsets of the SN schedule, fluid scheduling, increased use of demand access on the MA service, automating Inter-System Control functions using monitor by exception, increase automation for distributed data management and distributed work management, viewing SN operational control in terms of the OSI Management framework, and the introduction of automated interface management.

Implementing Access to Data Distributed on Many Processors

NASA Technical Reports Server (NTRS)

James, Mark

2006-01-01

A reference architecture is defined for an object-oriented implementation of domains, arrays, and distributions written in the programming language Chapel. This technology primarily addresses domains that contain arrays that have regular index sets with the low-level implementation details being beyond the scope of this discussion. What is defined is a complete set of object-oriented operators that allows one to perform data distributions for domain arrays involving regular arithmetic index sets. What is unique is that these operators allow for the arbitrary regions of the arrays to be fragmented and distributed across multiple processors with a single point of access giving the programmer the illusion that all the elements are collocated on a single processor. Today's massively parallel High Productivity Computing Systems (HPCS) are characterized by a modular structure, with a large number of processing and memory units connected by a high-speed network. Locality of access as well as load balancing are primary concerns in these systems that are typically used for high-performance scientific computation. Data distributions address these issues by providing a range of methods for spreading large data sets across the components of a system. Over the past two decades, many languages, systems, tools, and libraries have been developed for the support of distributions. Since the performance of data parallel applications is directly influenced by the distribution strategy, users often resort to low-level programming models that allow fine-tuning of the distribution aspects affecting performance, but, at the same time, are tedious and error-prone. This technology presents a reusable design of a data-distribution framework for data parallel high-performance applications. Distributions are a means to express locality in systems composed of large numbers of processor and memory components connected by a network. Since distributions have a great effect on the performance of applications, it is important that the distribution strategy is flexible, so its behavior can change depending on the needs of the application. At the same time, high productivity concerns require that the user be shielded from error-prone, tedious details such as communication and synchronization.

Cavity method for force transmission in jammed disordered packings of hard particles.

PubMed

Bo, Lin; Mari, Romain; Song, Chaoming; Makse, Hernán A

2014-10-07

The force distribution of jammed disordered packings has always been considered a central object in the physics of granular materials. However, many of its features are poorly understood. In particular, analytic relations to other key macroscopic properties of jammed matter, such as the contact network and its coordination number, are still lacking. Here we develop a mean-field theory for this problem, based on the consideration of the contact network as a random graph where the force transmission becomes a constraint satisfaction problem. We can thus use the cavity method developed in the past few decades within the statistical physics of spin glasses and hard computer science problems. This method allows us to compute the force distribution P(f) for random packings of hard particles of any shape, with or without friction. We find a new signature of jamming in the small force behavior P(f) ∼ f(θ), whose exponent has attracted recent active interest: we find a finite value for P(f = 0), along with θ = 0. Furthermore, we relate the force distribution to a lower bound of the average coordination number z[combining macron](μ) of jammed packings of frictional spheres with coefficient μ. This bridges the gap between the two known isostatic limits z[combining macron]c (μ = 0) = 2D (in dimension D) and z[combining macron]c(μ → ∞) = D + 1 by extending the naive Maxwell's counting argument to frictional spheres. The theoretical framework describes different types of systems, such as non-spherical objects in arbitrary dimensions, providing a common mean-field scenario to investigate force transmission, contact networks and coordination numbers of jammed disordered packings.

The LCOGT near-Earth-object follow-up network

NASA Astrophysics Data System (ADS)

Lister, T.

2014-07-01

Las Cumbres Observatory Global Telescope (LCOGT) network is a planned homogeneous network that will eventually consist of over 35 telescopes at 6 locations in the northern and southern hemispheres [1]. This network is versatile and designed to respond rapidly to target of opportunity events and also to do long term monitoring of slowly changing astronomical phenomena. The global coverage of the network and the apertures of telescope available make the LCOGT network ideal for follow-up and characterization of a wide range of solar-system objects (e.g. asteroids, Kuiper-belt objects, comets) and in particular near-Earth objects (NEOs). There are 3 classes to the telescope resources: 2-meter aperture, 1-meter aperture and 0.4-meter aperture. We have been operating our two 2-meter telescopes since 2005 and began a specific program of NEO follow-up for the Pan-STARRS survey in October 2010. The combination of all-sky access, large aperture, rapid response, robotic operation and good site conditions allows us to provide time-critical follow-up astrometry and photometry on newly discovered objects and faint objects as they recede from the Earth, allowing the orbital arc to be extended and preventing loss of objects. These telescope resources have greatly increased as LCOGT has completed the first phase of the deployment, designated as ''Version 1.0'', with the installation, commissioning and ongoing operation of nine 1-meter telescopes. These are distributed among four sites with one 1-meter at McDonald Observatory (Texas), three telescopes at Cerro Tololo (Chile), three telescopes at SAAO (South Africa) and the final two telescope at Siding Spring Observatory (Australia). In addition to the 1-meter network, the scheduling and control system for the two 2-meter telescopes have been upgraded and unified with that of the 1-meter network to provide a coherent robotic telescopic network. The telescope network is now operating and observations are being executed remotely and robotically. I am using the LCOGT network to confirm newly detected NEO candidates produced by the major sky surveys such as Catalina Sky Survey (CSS) and Pan-STARRS (PS1) with additional targets coming from the NEOWISE satellite and the Palomar Transient Factory (PTF). Robotic observations of NEOs and other solar-system objects have been routinely carried out for several years on the 2-m and 1-m telescopes, with over 20,000 positional and magnitude measurements reported to the Minor Planet Center (MPC) in the last two years. We have developed software to automatically fetch candidates from Pan-STARRS and the MPC Confirmation Page, compute orbits and ephemerides, plan and schedule observations on the telescopes and retrieve the processed data [2]. The program is being expanded which will allow us to greatly increase the amount of survey discoveries that are followed-up, obtain accurate astrometry and provide important characterization data in the form of colors, lightcurves, rotation rates and spectra for NEOs. An increasing amount of time is being spent to obtain follow-up astrometry and photometry for radar-targeted objects in order to improve the orbits and determine the rotation periods. Priority for follow-up is now given to the fainter and most southern targets on the Confirmation Page, objects that are scheduled for Goldstone/Arecibo radar targeting and those objects which could become potential mission destinations for spacecraft. This will be extended to obtain more light curves of other NEOs which could be Near-Earth Object Human Space Flight Accessible Targets Study (NHATS) or Asteroid Retrieval Mission (ARM) targets. With the increase in time available from the LCOGT 1-meter network and commissioning of low-resolution spectrographs on the 2-meter telescopes for moving objects, this will produce a large advance in capabilities for NEO follow-up and characterization. This will produce an unprecedented network for NEO follow-up, particularly in the Southern Hemisphere where there is currently a shortage of suitable facilities. We will continue to develop our software to take advantage of the increased resources and capabilities of the LCOGT Network.

Development of a system dynamics model for financially sustainable management of municipal watermain networks.

PubMed

Rehan, R; Knight, M A; Unger, A J A; Haas, C T

2013-12-15

This paper develops causal loop diagrams and a system dynamics model for financially sustainable management of urban water distribution networks. The developed causal loop diagrams are a novel contribution in that it illustrates the unique characteristics and feedback loops for financially self-sustaining water distribution networks. The system dynamics model is a mathematical realization of the developed interactions among system variables over time and is comprised of three sectors namely watermains network, consumer, and finance. This is the first known development of a water distribution network system dynamics model. The watermains network sector accounts for the unique characteristics of watermain pipes such as service life, deterioration progression, pipe breaks, and water leakage. The finance sector allows for cash reserving by the utility in addition to the pay-as-you-go and borrowing strategies. The consumer sector includes controls to model water fee growth as a function of service performance and a household's financial burden due to water fees. A series of policy levers are provided that allow the impact of various financing strategies to be evaluated in terms of financial sustainability and household affordability. The model also allows for examination of the impact of different management strategies on the water fee in terms of consistency and stability over time. The paper concludes with a discussion on how the developed system dynamics water model can be used by water utilities to achieve a variety of utility short and long-term objectives and to establish realistic and defensible water utility policies. It also discusses how the model can be used by regulatory bodies, government agencies, the financial industry, and researchers. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Operational Data Quality Assessment of the Combined PBO, TLALOCNet and COCONet Real-Time GNSS Networks

NASA Astrophysics Data System (ADS)

Hodgkinson, K. M.; Mencin, D.; Fox, O.; Walls, C. P.; Mann, D.; Blume, F.; Berglund, H. T.; Phillips, D.; Meertens, C. M.; Mattioli, G. S.

2015-12-01

The GAGE facility, managed by UNAVCO, currently operates a network of ~460, real-time, high-rate GNSS stations (RT-GNSS). The majority of these RT stations are part of the Earthscope PBO network, which spans the western US Pacific North-American plate boundary. Approximately 50 are distributed throughout the Mexico and Caribbean region funded by the TLALOCNet and COCONet projects. The entire network is processed in real-time at UNAVCO using Precise Point Positioning (PPP). The real-time streams are freely available to all and user demand has grown almost exponentially since 2010. Data usage is multidisciplinary, including tectonic and volcanic deformation studies, meteorological applications, atmospheric science research in addition to use by national, state and commercial entities. 21 RT-GNSS sites in California now include 200-sps accelerometers for the development of Earthquake Early Warning systems. All categories of users of real-time streams have similar requirements, reliable, low-latency, high-rate, and complete data sets. To meet these requirements, UNAVCO tracks the latency and completeness of the incoming raw observations and also is developing tools to monitor the quality of the processed data streams. UNAVCO is currently assessing the precision, accuracy and latency of solutions from various PPP software packages. Also under review are the data formats UNAVCO distributes; for example, the PPP solutions are currently distributed in NMEA format, but other formats such as SEED or GeoJSON may be preferred by different user groups to achieve specific mission objectives. In this presentation we will share our experiences of the challenges involved in the data operations of a continental-scale, multi-project, real-time GNSS network, summarize the network's performance in terms of latency and completeness, and present the comparisons of PPP solutions using different PPP processing techniques.

Jones matrix polarization-correlation mapping of biological crystals networks

NASA Astrophysics Data System (ADS)

Ushenko, O. G.; Ushenko, Yu. O.; Pidkamin, L. Y.; Sidor, M. I.; Vanchuliak, O.; Motrich, A. V.; Gorsky, M. P.; Meglinskiy, I.; Marchuk, Yu. F.

2017-08-01

It has been proposed the optical model of Jones-matrix description of mechanisms of optical anisotropy of polycrystalline films of human bile, namely optical activity and birefringence. The algorithm of reconstruction of distributions of parameters - optical rotation angles and phase shifts of the indicated anisotropy types has been elaborated. The objective criteria of differentiation of bile films taken from healthy donors and patients with cholelithiasis by means of statistic analysis of such distributions have been determined. The operational characteristics (sensitivity, specificity and accuracy) of Jones-matrix reconstruction method of optical anisotropy parameters were defined.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Parallel Computing for Probabilistic Response Analysis of High Temperature Composites

NASA Technical Reports Server (NTRS)

Sues, R. H.; Lua, Y. J.; Smith, M. D.

1994-01-01

The objective of this Phase I research was to establish the required software and hardware strategies to achieve large scale parallelism in solving PCM problems. To meet this objective, several investigations were conducted. First, we identified the multiple levels of parallelism in PCM and the computational strategies to exploit these parallelisms. Next, several software and hardware efficiency investigations were conducted. These involved the use of three different parallel programming paradigms and solution of two example problems on both a shared-memory multiprocessor and a distributed-memory network of workstations.

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems.

PubMed

Munera, Eduardo; Poza-Lujan, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, José-Enrique; Noguera, Juan Fco Blanes

2015-07-24

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.

Social power, conflict policing, and the role of subordination signals in rhesus macaque society

PubMed Central

Beisner, Brianne A.; Hannibal, Darcy L.; Finn, Kelly R.; Fushing, Hsieh; McCowan, Brenda

2017-01-01

Objectives Policing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others’ fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques. Materials and Methods For each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways). Results Networks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing. Conclusions Our results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others’ signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing. PMID:26801956

Advanced Distribution Network Modelling with Distributed Energy Resources

NASA Astrophysics Data System (ADS)

O'Connell, Alison

The addition of new distributed energy resources, such as electric vehicles, photovoltaics, and storage, to low voltage distribution networks means that these networks will undergo major changes in the future. Traditionally, distribution systems would have been a passive part of the wider power system, delivering electricity to the customer and not needing much control or management. However, the introduction of these new technologies may cause unforeseen issues for distribution networks, due to the fact that they were not considered when the networks were originally designed. This thesis examines different types of technologies that may begin to emerge on distribution systems, as well as the resulting challenges that they may impose. Three-phase models of distribution networks are developed and subsequently utilised as test cases. Various management strategies are devised for the purposes of controlling distributed resources from a distribution network perspective. The aim of the management strategies is to mitigate those issues that distributed resources may cause, while also keeping customers' preferences in mind. A rolling optimisation formulation is proposed as an operational tool which can manage distributed resources, while also accounting for the uncertainties that these resources may present. Network sensitivities for a particular feeder are extracted from a three-phase load flow methodology and incorporated into an optimisation. Electric vehicles are the focus of the work, although the method could be applied to other types of resources. The aim is to minimise the cost of electric vehicle charging over a 24-hour time horizon by controlling the charge rates and timings of the vehicles. The results demonstrate the advantage that controlled EV charging can have over an uncontrolled case, as well as the benefits provided by the rolling formulation and updated inputs in terms of cost and energy delivered to customers. Building upon the rolling optimisation, a three-phase optimal power flow method is developed. The formulation has the capability to provide optimal solutions for distribution system control variables, for a chosen objective function, subject to required constraints. It can, therefore, be utilised for numerous technologies and applications. The three-phase optimal power flow is employed to manage various distributed resources, such as photovoltaics and storage, as well as distribution equipment, including tap changers and switches. The flexibility of the methodology allows it to be applied in both an operational and a planning capacity. The three-phase optimal power flow is employed in an operational planning capacity to determine volt-var curves for distributed photovoltaic inverters. The formulation finds optimal reactive power settings for a number of load and solar scenarios and uses these reactive power points to create volt-var curves. Volt-var curves are determined for 10 PV systems on a test feeder. A universal curve is also determined which is applicable to all inverters. The curves are validated by testing them in a power flow setting over a 24-hour test period. The curves are shown to provide advantages to the feeder in terms of reduction of voltage deviations and unbalance, with the individual curves proving to be more effective. It is also shown that adding a new PV system to the feeder only requires analysis for that system. In order to represent the uncertainties that inherently occur on distribution systems, an information gap decision theory method is also proposed and integrated into the three-phase optimal power flow formulation. This allows for robust network decisions to be made using only an initial prediction for what the uncertain parameter will be. The work determines tap and switch settings for a test network with demand being treated as uncertain. The aim is to keep losses below a predefined acceptable value. The results provide the decision maker with the maximum possible variation in demand for a given acceptable variation in the losses. A validation is performed with the resulting tap and switch settings being implemented, and shows that the control decisions provided by the formulation keep losses below the acceptable value while adhering to the limits imposed by the network.

Evaluation and design of a rain gauge network using a statistical optimization method in a severe hydro-geological hazard prone area

NASA Astrophysics Data System (ADS)

Fattoruso, Grazia; Longobardi, Antonia; Pizzuti, Alfredo; Molinara, Mario; Marocco, Claudio; De Vito, Saverio; Tortorella, Francesco; Di Francia, Girolamo

2017-06-01

Rainfall data collection gathered in continuous by a distributed rain gauge network is instrumental to more effective hydro-geological risk forecasting and management services though the input estimated rainfall fields suffer from prediction uncertainty. Optimal rain gauge networks can generate accurate estimated rainfall fields. In this research work, a methodology has been investigated for evaluating an optimal rain gauges network aimed at robust hydrogeological hazard investigations. The rain gauges of the Sarno River basin (Southern Italy) has been evaluated by optimizing a two-objective function that maximizes the estimated accuracy and minimizes the total metering cost through the variance reduction algorithm along with the climatological variogram (time-invariant). This problem has been solved by using an enumerative search algorithm, evaluating the exact Pareto-front by an efficient computational time.

Toward an Autonomous Telescope Network: the TBT Scheduler

NASA Astrophysics Data System (ADS)

Racero, E.; Ibarra, A.; Ocaña, F.; de Lis, S. B.; Ponz, J. D.; Castillo, M.; Sánchez-Portal, M.

2015-09-01

Within the ESA SSA program, it is foreseen to deploy several robotic telescopes to provide surveillance and tracking services for hazardous objects. The TBT project will procure a validation platform for an autonomous optical observing system in a realistic scenario, consisting of two telescopes located in Spain and Australia, to collect representative test data for precursor SSA services. In this context, the planning and scheduling of the night consists of two software modules, the TBT Scheduler, that will allow the manual and autonomous planning of the night, and the control of the real-time response of the system, done by the RTS2 internal scheduler. The TBT Scheduler allocates tasks for both telescopes without human intervention. Every night it takes all the inputs needed and prepares the schedule following some predefined rules. The main purpose of the scheduler is the distribution of the time for follow-up of recently discovered targets and surveys. The TBT Scheduler considers the overall performance of the system, and combine follow-up with a priori survey strategies for both kind of objects. The strategy is defined according to the expected combined performance for both systems the upcoming night (weather, sky brightness, object accessibility and priority). Therefore, TBT Scheduler defines the global approach for the network and relies on the RTS2 internal scheduler for the final detailed distribution of tasks at each sensor.

Fast Reliability Assessing Method for Distribution Network with Distributed Renewable Energy Generation

NASA Astrophysics Data System (ADS)

Chen, Fan; Huang, Shaoxiong; Ding, Jinjin; Ding, Jinjin; Gao, Bo; Xie, Yuguang; Wang, Xiaoming

2018-01-01

This paper proposes a fast reliability assessing method for distribution grid with distributed renewable energy generation. First, the Weibull distribution and the Beta distribution are used to describe the probability distribution characteristics of wind speed and solar irradiance respectively, and the models of wind farm, solar park and local load are built for reliability assessment. Then based on power system production cost simulation probability discretization and linearization power flow, a optimal power flow objected with minimum cost of conventional power generation is to be resolved. Thus a reliability assessment for distribution grid is implemented fast and accurately. The Loss Of Load Probability (LOLP) and Expected Energy Not Supplied (EENS) are selected as the reliability index, a simulation for IEEE RBTS BUS6 system in MATLAB indicates that the fast reliability assessing method calculates the reliability index much faster with the accuracy ensured when compared with Monte Carlo method.

Evaluating a Novel Cellular Automata-Based Distributed Power Management Approach for Mobile Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Adabi, Sepideh; Adabi, Sahar; Rezaee, Ali

According to the traditional definition of Wireless Sensor Networks (WSNs), static sensors have limited the feasibility of WSNs in some kind of approaches, so the mobility was introduced in WSN. Mobile nodes in a WSN come equipped with battery and from the point of deployment, this battery reserve becomes a valuable resource since it cannot be replenished. Hence, maximizing the network lifetime by minimizing the energy is an important challenge in Mobile WSN. Energy conservation can be accomplished by different approaches. In this paper, we presented an energy conservation solution based on Cellular Automata. The main objective of this solution is based on dynamically adjusting the transmission range and switching between operational states of the sensor nodes.

Distributed Sensing and Processing for Multi-Camera Networks

NASA Astrophysics Data System (ADS)

Sankaranarayanan, Aswin C.; Chellappa, Rama; Baraniuk, Richard G.

Sensor networks with large numbers of cameras are becoming increasingly prevalent in a wide range of applications, including video conferencing, motion capture, surveillance, and clinical diagnostics. In this chapter, we identify some of the fundamental challenges in designing such systems: robust statistical inference, computationally efficiency, and opportunistic and parsimonious sensing. We show that the geometric constraints induced by the imaging process are extremely useful for identifying and designing optimal estimators for object detection and tracking tasks. We also derive pipelined and parallelized implementations of popular tools used for statistical inference in non-linear systems, of which multi-camera systems are examples. Finally, we highlight the use of the emerging theory of compressive sensing in reducing the amount of data sensed and communicated by a camera network.

Emerging Technologies for Software-Reliant Systems of Systems

DTIC Science & Technology

2010-09-01

conditions, such as temperature, sound, vibration, light intensity , motion, or proximity to objects [Raghavendra 2006]. Cognitive Network A cognitive...systems evolutionary development emergent behavior geographic distribution Maier also defines four types of SoS based on their management...by multinational teams. Many organizations use offshoring as a way to reduce costs of software development. Large web- based systems often use

Modeling a hierarchical structure of factors influencing exploitation policy for water distribution systems using ISM approach

NASA Astrophysics Data System (ADS)

Jasiulewicz-Kaczmarek, Małgorzata; Wyczółkowski, Ryszard; Gładysiak, Violetta

2017-12-01

Water distribution systems are one of the basic elements of contemporary technical infrastructure of urban and rural areas. It is a complex engineering system composed of transmission networks and auxiliary equipment (e.g. controllers, checkouts etc.), scattered territorially over a large area. From the water distribution system operation point of view, its basic features are: functional variability, resulting from the need to adjust the system to temporary fluctuations in demand for water and territorial dispersion. The main research questions are: What external factors should be taken into account when developing an effective water distribution policy? Does the size and nature of the water distribution system significantly affect the exploitation policy implemented? These questions have shaped the objectives of research and the method of research implementation.

3D Mueller-matrix mapping of biological optically anisotropic networks

NASA Astrophysics Data System (ADS)

Ushenko, O. G.; Ushenko, V. O.; Bodnar, G. B.; Zhytaryuk, V. G.; Prydiy, O. G.; Koval, G.; Lukashevich, I.; Vanchuliak, O.

2018-01-01

The paper consists of two parts. The first part presents short theoretical basics of the method of azimuthally-invariant Mueller-matrix description of optical anisotropy of biological tissues. It was provided experimentally measured coordinate distributions of Mueller-matrix invariants (MMI) of linear and circular birefringences of skeletal muscle tissue. It was defined the values of statistic moments, which characterize the distributions of amplitudes of wavelet coefficients of MMI at different scales of scanning. The second part presents the data of statistic analysis of the distributions of amplitude of wavelet coefficients of the distributions of linear birefringence of myocardium tissue died after the infarction and ischemic heart disease. It was defined the objective criteria of differentiation of the cause of death.

Multiscale polarization diagnostics of birefringent networks in problems of necrotic changes diagnostics

NASA Astrophysics Data System (ADS)

Sakhnovskiy, M. Yu.; Ushenko, Yu. O.; Ushenko, V. O.; Besaha, R. N.; Pavlyukovich, N.; Pavlyukovich, O.

2018-01-01

The paper consists of two parts. The first part presents short theoretical basics of the method of azimuthally-invariant Mueller-matrix description of optical anisotropy of biological tissues. It was provided experimentally measured coordinate distributions of Mueller-matrix invariants (MMI) of linear and circular birefringences of skeletal muscle tissue. It was defined the values of statistic moments, which characterize the distributions of amplitudes of wavelet coefficients of MMI at different scales of scanning. The second part presents the data of statistic analysis of the distributions of amplitude of wavelet coefficients of the distributions of linear birefringence of myocardium tissue died after the infarction and ischemic heart disease. It was defined the objective criteria of differentiation of the cause of death.

Comprehensive evaluation index system of total supply capability in distribution network

NASA Astrophysics Data System (ADS)

Zhang, Linyao; Wu, Guilian; Yang, Jingyuan; Jia, Shuangrui; Zhang, Wei; Sun, Weiqing

2018-01-01

Aiming at the lack of a comprehensive evaluation of the distribution network, based on the existing distribution network evaluation index system, combined with the basic principles of constructing the evaluation index, put forward a new evaluation index system of distribution network capacity. This paper is mainly based on the total supply capability of the distribution network, combining single index and various factors, into a multi-evaluation index of the distribution network, thus forming a reasonable index system, and various indicators of rational quantification make the evaluation results more intuitive. In order to have a comprehensive judgment of distribution network, this paper uses weights to analyse the importance of each index, verify the rationality of the index system through the example, it is proved that the rationality of the index system, so as to guide the direction of distribution network planning.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

A convergent model for distributed processing of Big Sensor Data in urban engineering networks

NASA Astrophysics Data System (ADS)

Parygin, D. S.; Finogeev, A. G.; Kamaev, V. A.; Finogeev, A. A.; Gnedkova, E. P.; Tyukov, A. P.

2017-01-01

The problems of development and research of a convergent model of the grid, cloud, fog and mobile computing for analytical Big Sensor Data processing are reviewed. The model is meant to create monitoring systems of spatially distributed objects of urban engineering networks and processes. The proposed approach is the convergence model of the distributed data processing organization. The fog computing model is used for the processing and aggregation of sensor data at the network nodes and/or industrial controllers. The program agents are loaded to perform computing tasks for the primary processing and data aggregation. The grid and the cloud computing models are used for integral indicators mining and accumulating. A computing cluster has a three-tier architecture, which includes the main server at the first level, a cluster of SCADA system servers at the second level, a lot of GPU video cards with the support for the Compute Unified Device Architecture at the third level. The mobile computing model is applied to visualize the results of intellectual analysis with the elements of augmented reality and geo-information technologies. The integrated indicators are transferred to the data center for accumulation in a multidimensional storage for the purpose of data mining and knowledge gaining.

NASA Earth Observing System Data and Information System (EOSDIS): A U.S. Network of Data Centers Serving Earth Science Data: A Network Member of ICSU WDS

NASA Technical Reports Server (NTRS)

Behnke, Jeanne; Ramapriyan, H. K. " Rama"

2016-01-01

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, and serving a diverse user community around the world with Earth science data from satellites, aircraft, field campaigns and research investigations. The ESDIS Project, responsible for EOSDIS is a Network Member of the International Council for Sciences (ICSU) World Data System (WDS). Nine of the 12 Distributed Active Archive Centers (DAACs), which are part of EOSDIS, are Regular Members of the ICSUWDS. This poster presents the EOSDIS mission objectives, key characteristics of the DAACs that make them world class Earth science data centers, successes, challenges and best practices of EOSDIS focusing on the years 2014-2016, and illustrates some highlights of accomplishments of EOSDIS. The highlights include: high customer satisfaction, growing archive and distribution volumes, exponential growth in number of products distributed to users around the world, unified metadata model and common metadata repository, flexibility provided to uses by supporting data transformations to suit their applications, near-real-time capabilities to support various operational and research applications, and full resolution image browse capabilities to help users select data of interest. The poster also illustrates how the ESDIS Project is actively involved in several US and international data system organizations.

Coupling root architecture and pore network modeling - an attempt towards better understanding root-soil interactions

NASA Astrophysics Data System (ADS)

Leitner, Daniel; Bodner, Gernot; Raoof, Amir

2013-04-01

Understanding root-soil interactions is of high importance for environmental and agricultural management. Root uptake is an essential component in water and solute transport modeling. The amount of groundwater recharge and solute leaching significantly depends on the demand based plant extraction via its root system. Plant uptake however not only responds to the potential demand, but in most situations is limited by supply form the soil. The ability of the plant to access water and solutes in the soil is governed mainly by root distribution. Particularly under conditions of heterogeneous distribution of water and solutes in the soil, it is essential to capture the interaction between soil and roots. Root architecture models allow studying plant uptake from soil by describing growth and branching of root axes in the soil. Currently root architecture models are able to respond dynamically to water and nutrient distribution in the soil by directed growth (tropism), modified branching and enhanced exudation. The porous soil medium as rooting environment in these models is generally described by classical macroscopic water retention and sorption models, average over the pore scale. In our opinion this simplified description of the root growth medium implies several shortcomings for better understanding root-soil interactions: (i) It is well known that roots grow preferentially in preexisting pores, particularly in more rigid/dry soil. Thus the pore network contributes to the architectural form of the root system; (ii) roots themselves can influence the pore network by creating preferential flow paths (biopores) which are an essential element of structural porosity with strong impact on transport processes; (iii) plant uptake depend on both the spatial location of water/solutes in the pore network as well as the spatial distribution of roots. We therefore consider that for advancing our understanding in root-soil interactions, we need not only to extend our root models, but also improve the description of the rooting environment. Until now there have been no attempts to couple root architecture and pore network models. In our work we present a first attempt to join both types of models using the root architecture model of Leitner et al., (2010) and a pore network model presented by Raoof et al. (2010). The two main objectives of coupling both models are: (i) Representing the effect of root induced biopores on flow and transport processes: For this purpose a fixed root architecture created by the root model is superimposed as a secondary root induced pore network to the primary soil network, thus influencing the final pore topology in the network generation. (ii) Representing the influence of pre-existing pores on root branching: Using a given network of (rigid) pores, the root architecture model allocates its root axes into these preexisting pores as preferential growth paths with thereby shape the final root architecture. The main objective of our study is to reveal the potential of using a pore scale description of the plant growth medium for an improved representation of interaction processes at the interface of root and soil. References Raoof, A., Hassanizadeh, S.M. 2010. A New Method for Generating Pore-Network Models. Transp. Porous Med. 81, 391-407. Leitner, D, Klepsch, S., Bodner, G., Schnepf, S. 2010. A dynamic root system growth model based on L-Systems. Tropisms and coupling to nutrient uptake from soil. Plant Soil 332, 177-192.

Electrical localization of weakly electric fish using neural networks

NASA Astrophysics Data System (ADS)

Kiar, Greg; Mamatjan, Yasin; Jun, James; Maler, Len; Adler, Andy

2013-04-01

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.

Predicting high-risk preterm birth using artificial neural networks.

PubMed

Catley, Christina; Frize, Monique; Walker, C Robin; Petriu, Dorina C

2006-07-01

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

The finite body triangulation: algorithms, subgraphs, homogeneity estimation and application.

PubMed

Carson, Cantwell G; Levine, Jonathan S

2016-09-01

The concept of a finite body Dirichlet tessellation has been extended to that of a finite body Delaunay 'triangulation' to provide a more meaningful description of the spatial distribution of nonspherical secondary phase bodies in 2- and 3-dimensional images. A finite body triangulation (FBT) consists of a network of minimum edge-to-edge distances between adjacent objects in a microstructure. From this is also obtained the characteristic object chords formed by the intersection of the object boundary with the finite body tessellation. These two sets of distances form the basis of a parsimonious homogeneity estimation. The characteristics of the spatial distribution are then evaluated with respect to the distances between objects and the distances within them. Quantitative analysis shows that more physically representative distributions can be obtained by selecting subgraphs, such as the relative neighbourhood graph and the minimum spanning tree, from the finite body tessellation. To demonstrate their potential, we apply these methods to 3-dimensional X-ray computed tomographic images of foamed cement and their 2-dimensional cross sections. The Python computer code used to estimate the FBT is made available. Other applications for the algorithm - such as porous media transport and crack-tip propagation - are also discussed. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Incorporating location, routing, and inventory decisions in a bi-objective supply chain design problem with risk-pooling

NASA Astrophysics Data System (ADS)

Tavakkoli-Moghaddam, Reza; Forouzanfar, Fateme; Ebrahimnejad, Sadoullah

2013-07-01

This paper considers a single-sourcing network design problem for a three-level supply chain. For the first time, a novel mathematical model is presented considering risk-pooling, the inventory existence at distribution centers (DCs) under demand uncertainty, the existence of several alternatives to transport the product between facilities, and routing of vehicles from distribution centers to customer in a stochastic supply chain system, simultaneously. This problem is formulated as a bi-objective stochastic mixed-integer nonlinear programming model. The aim of this model is to determine the number of located distribution centers, their locations, and capacity levels, and allocating customers to distribution centers and distribution centers to suppliers. It also determines the inventory control decisions on the amount of ordered products and the amount of safety stocks at each opened DC, selecting a type of vehicle for transportation. Moreover, it determines routing decisions, such as determination of vehicles' routes starting from an opened distribution center to serve its allocated customers and returning to that distribution center. All are done in a way that the total system cost and the total transportation time are minimized. The Lingo software is used to solve the presented model. The computational results are illustrated in this paper.

Effects of lesions to the left lateral prefrontal cortex on task-specific top-down biases and response strategies in blocked-cyclic naming.

PubMed

Belke, Eva

Anders, Riès, van Maanen and Alario put forward evidence accumulation modelling of object naming times as an alternative to neural network models of lexical retrieval. The authors exemplify their approach using data from the blocked-cyclic naming paradigm, requiring speakers to repeatedly name small sets of related or unrelated objects. The effects observed with this paradigm are understood reasonably well within the tradition of neural network modelling. However, implemented neural network models do not specify interfaces for task-specific top-down influences and response strategies that are likely to play a role in the blocked-cyclic naming paradigm, distinguishing it from continuous, non-cyclic manipulations of the naming context. I argue that the evidence accumulation approach falls short on this account as well, as it does not specify the potential contribution of task-specific top-down processes and strategic facilitation effects to the response time distributions. Future endeavours to model or fit data from blocked-cyclic naming experiments should strive to do so by simultaneously considering data from continuous context manipulations.

Named Data Networking in Climate Research and HEP Applications

NASA Astrophysics Data System (ADS)

Shannigrahi, Susmit; Papadopoulos, Christos; Yeh, Edmund; Newman, Harvey; Jerzy Barczyk, Artur; Liu, Ran; Sim, Alex; Mughal, Azher; Monga, Inder; Vlimant, Jean-Roch; Wu, John

2015-12-01

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

A heuristic method for consumable resource allocation in multi-class dynamic PERT networks

NASA Astrophysics Data System (ADS)

Yaghoubi, Saeed; Noori, Siamak; Mazdeh, Mohammad Mahdavi

2013-06-01

This investigation presents a heuristic method for consumable resource allocation problem in multi-class dynamic Project Evaluation and Review Technique (PERT) networks, where new projects from different classes (types) arrive to system according to independent Poisson processes with different arrival rates. Each activity of any project is operated at a devoted service station located in a node of the network with exponential distribution according to its class. Indeed, each project arrives to the first service station and continues its routing according to precedence network of its class. Such system can be represented as a queuing network, while the discipline of queues is first come, first served. On the basis of presented method, a multi-class system is decomposed into several single-class dynamic PERT networks, whereas each class is considered separately as a minisystem. In modeling of single-class dynamic PERT network, we use Markov process and a multi-objective model investigated by Azaron and Tavakkoli-Moghaddam in 2007. Then, after obtaining the resources allocated to service stations in every minisystem, the final resources allocated to activities are calculated by the proposed method.

Consistent Steering System using SCTP for Bluetooth Scatternet Sensor Network

NASA Astrophysics Data System (ADS)

Dhaya, R.; Sadasivam, V.; Kanthavel, R.

2012-12-01

Wireless communication is the best way to convey information from source to destination with flexibility and mobility and Bluetooth is the wireless technology suitable for short distance. On the other hand a wireless sensor network (WSN) consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Using Bluetooth piconet wireless technique in sensor nodes creates limitation in network depth and placement. The introduction of Scatternet solves the network restrictions with lack of reliability in data transmission. When the depth of the network increases, it results in more difficulties in routing. No authors so far focused on the reliability factors of Scatternet sensor network's routing. This paper illustrates the proposed system architecture and routing mechanism to increase the reliability. The another objective is to use reliable transport protocol that uses the multi-homing concept and supports multiple streams to prevent head-of-line blocking. The results show that the Scatternet sensor network has lower packet loss even in the congestive environment than the existing system suitable for all surveillance applications.

Internal services simulation control in 220/110kV power transformer station Mintia

NASA Astrophysics Data System (ADS)

Ciulica, D.; Rob, R.

2018-01-01

The main objectives in developing the electric transport and distribution networks infrastructure are satisfying the electric energy demand, ensuring the continuity of supply to customers, minimizing electricity losses in the transmission and distribution networks of public interest. This paper presents simulations in functioning of the internal services system 400/230 V ac in the 220/110 kV power transformer station Mintia. Using simulations in Visual Basic, the following premises are taken into consideration. All the ac consumers of the 220/110 kV power transformer station Mintia will be supplied by three 400/230 V transformers for internal services which can mutual reserve. In case of damaging at one transformer, the others are able to assume the entire consumption using automatic release of reserves. The simulation program studies three variants in which the continuity of supply to customers are ensured. As well, by simulations, all the functioning situations are analyzed in detail.

T-LECS: The Control Software System for MOIRCS

NASA Astrophysics Data System (ADS)

Yoshikawa, T.; Omata, K.; Konishi, M.; Ichikawa, T.; Suzuki, R.; Tokoku, C.; Katsuno, Y.; Nishimura, T.

2006-07-01

MOIRCS (Multi-Object Infrared Camera and Spectrograph) is a new instrument for the Subaru Telescope. We present the system design of the control software system for MOIRCS, named T-LECS (Tohoku University - Layered Electronic Control System). T-LECS is a PC-Linux based network distributed system. Two PCs equipped with the focal plane array system operate two HAWAII2 detectors, respectively, and another PC is used for user interfaces and a database server. Moreover, these PCs control various devices for observations distributed on a TCP/IP network. T-LECS has three interfaces; interfaces to the devices and two user interfaces. One of the user interfaces is to the integrated observation control system (Subaru Observation Software System) for observers, and another one provides the system developers the direct access to the devices of MOIRCS. In order to help the communication between these interfaces, we employ an SQL database system.

An approach for access differentiation design in medical distributed applications built on databases.

PubMed

Shoukourian, S K; Vasilyan, A M; Avagyan, A A; Shukurian, A K

1999-01-01

A formalized "top to bottom" design approach was described in [1] for distributed applications built on databases, which were considered as a medium between virtual and real user environments for a specific medical application. Merging different components within a unified distributed application posits new essential problems for software. Particularly protection tools, which are sufficient separately, become deficient during the integration due to specific additional links and relationships not considered formerly. E.g., it is impossible to protect a shared object in the virtual operating room using only DBMS protection tools, if the object is stored as a record in DB tables. The solution of the problem should be found only within the more general application framework. Appropriate tools are absent or unavailable. The present paper suggests a detailed outline of a design and testing toolset for access differentiation systems (ADS) in distributed medical applications which use databases. The appropriate formal model as well as tools for its mapping to a DMBS are suggested. Remote users connected via global networks are considered too.

Study of an Audio Playback Machine Storage, Distribution, and Repair System. Options for Machine Operation. Study II, Part 1, Phase 2, Final Report.

ERIC Educational Resources Information Center

ManTech Technical Services Corp., Fairfax, VA.

This report presents the results of a management study of audio playback equipment operations conducted by the National Library Service, Library of Congress, its associated network of state and local machine lending agencies (MLA), and other parties that play a role in current operations. The objectives were to document current operations,…

Next-Generation Undersea Warfare and Undersea Distributed Networked Systems

DTIC Science & Technology

2007-01-31

Probability of false alarm R5 Redeployment, refueling, repositioning, replacement, and recovery ROE Rules of engagement RSTA Reconnaissance, surveillance...and decision aids) at a given point, considering mission, tasks, rules of engagement (ROE), objectives, and other appropriate factors. " Manning within...trajectories are important and must occur concurrently; they must, however, be governed by different rule sets.21 II Mission Capability Centric .•UDNS

Multi-Objective Optimization for Trustworthy Tactical Networks: A Survey and Insights

DTIC Science & Technology

2013-06-01

existing data sources, gathering and maintaining the data needed , and completing and reviewing the collection of information. Send comments regarding...problems: using repeated cooperative games [12], hedonic games [25], and nontransferable utility cooperative games [27]. It should be noted that trust...examined an optimal task allocation problem in a distributed computing system where program modules need to be allocated to different processors to

An Autonomous Distributed Fault-Tolerant Local Positioning System

NASA Technical Reports Server (NTRS)

Malekpour, Mahyar R.

2017-01-01

We describe a fault-tolerant, GPS-independent (Global Positioning System) distributed autonomous positioning system for static/mobile objects and present solutions for providing highly-accurate geo-location data for the static/mobile objects in dynamic environments. The reliability and accuracy of a positioning system fundamentally depends on two factors; its timeliness in broadcasting signals and the knowledge of its geometry, i.e., locations and distances of the beacons. Existing distributed positioning systems either synchronize to a common external source like GPS or establish their own time synchrony using a scheme similar to a master-slave by designating a particular beacon as the master and other beacons synchronize to it, resulting in a single point of failure. Another drawback of existing positioning systems is their lack of addressing various fault manifestations, in particular, communication link failures, which, as in wireless networks, are increasingly dominating the process failures and are typically transient and mobile, in the sense that they typically affect different messages to/from different processes over time.

Distributed reconfigurable control strategies for switching topology networked multi-agent systems.

PubMed

Gallehdari, Z; Meskin, N; Khorasani, K

2017-11-01

In this paper, distributed control reconfiguration strategies for directed switching topology networked multi-agent systems are developed and investigated. The proposed control strategies are invoked when the agents are subject to actuator faults and while the available fault detection and isolation (FDI) modules provide inaccurate and unreliable information on the estimation of faults severities. Our proposed strategies will ensure that the agents reach a consensus while an upper bound on the team performance index is ensured and satisfied. Three types of actuator faults are considered, namely: the loss of effectiveness fault, the outage fault, and the stuck fault. By utilizing quadratic and convex hull (composite) Lyapunov functions, two cooperative and distributed recovery strategies are designed and provided to select the gains of the proposed control laws such that the team objectives are guaranteed. Our proposed reconfigurable control laws are applied to a team of autonomous underwater vehicles (AUVs) under directed switching topologies and subject to simultaneous actuator faults. Simulation results demonstrate the effectiveness of our proposed distributed reconfiguration control laws in compensating for the effects of sudden actuator faults and subject to fault diagnosis module uncertainties and unreliabilities. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A resilient and secure software platform and architecture for distributed spacecraft

NASA Astrophysics Data System (ADS)

Otte, William R.; Dubey, Abhishek; Karsai, Gabor

2014-06-01

A distributed spacecraft is a cluster of independent satellite modules flying in formation that communicate via ad-hoc wireless networks. This system in space is a cloud platform that facilitates sharing sensors and other computing and communication resources across multiple applications, potentially developed and maintained by different organizations. Effectively, such architecture can realize the functions of monolithic satellites at a reduced cost and with improved adaptivity and robustness. Openness of these architectures pose special challenges because the distributed software platform has to support applications from different security domains and organizations, and where information flows have to be carefully managed and compartmentalized. If the platform is used as a robust shared resource its management, configuration, and resilience becomes a challenge in itself. We have designed and prototyped a distributed software platform for such architectures. The core element of the platform is a new operating system whose services were designed to restrict access to the network and the file system, and to enforce resource management constraints for all non-privileged processes Mixed-criticality applications operating at different security labels are deployed and controlled by a privileged management process that is also pre-configuring all information flows. This paper describes the design and objective of this layer.

Upscaling permeability for three-dimensional fractured porous rocks with the multiple boundary method

NASA Astrophysics Data System (ADS)

Chen, Tao; Clauser, Christoph; Marquart, Gabriele; Willbrand, Karen; Hiller, Thomas

2018-02-01

Upscaling permeability of grid blocks is crucial for groundwater models. A novel upscaling method for three-dimensional fractured porous rocks is presented. The objective of the study was to compare this method with the commonly used Oda upscaling method and the volume averaging method. First, the multiple boundary method and its computational framework were defined for three-dimensional stochastic fracture networks. Then, the different upscaling methods were compared for a set of rotated fractures, for tortuous fractures, and for two discrete fracture networks. The results computed by the multiple boundary method are comparable with those of the other two methods and fit best the analytical solution for a set of rotated fractures. The errors in flow rate of the equivalent fracture model decrease when using the multiple boundary method. Furthermore, the errors of the equivalent fracture models increase from well-connected fracture networks to poorly connected ones. Finally, the diagonal components of the equivalent permeability tensors tend to follow a normal or log-normal distribution for the well-connected fracture network model with infinite fracture size. By contrast, they exhibit a power-law distribution for the poorly connected fracture network with multiple scale fractures. The study demonstrates the accuracy and the flexibility of the multiple boundary upscaling concept. This makes it attractive for being incorporated into any existing flow-based upscaling procedures, which helps in reducing the uncertainty of groundwater models.

Networked Microgrids for Self-healing Power Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhaoyu; Chen, Bokan; Wang, Jianhui

This paper proposes a transformative architecture for the normal operation and self-healing of networked microgrids (MGs). MGs can support and interchange electricity with each other in the proposed infrastructure. The networked MGs are connected by a physical common bus and a designed two-layer cyber communication network. The lower layer is within each MG where the energy management system (EMS) schedules the MG operation; the upper layer links a number of EMSs for global optimization and communication. In the normal operation mode, the objective is to schedule dispatchable distributed generators (DGs), energy storage systems (ESs) and controllable loads to minimize themore » operation costs and maximize the supply adequacy of each MG. When a generation deficiency or fault happens in a MG, the model switches to the self-healing mode and the local generation capacities of other MGs can be used to support the on-emergency portion of the system. A consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way. The allocated portion corresponds to each MG’s local power exchange target which is used by its EMS to perform the optimal schedule. The resultant aggregated power output of networked MGs will be used to provide the requested power support. Test cases demonstrate the effectiveness of the proposed methodology.« less

Using Bayesian neural networks to classify forest scenes

NASA Astrophysics Data System (ADS)

Vehtari, Aki; Heikkonen, Jukka; Lampinen, Jouko; Juujarvi, Jouni

1998-10-01

We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.

The Situation Awareness Weighted Network (SAWN) model and method: Theory and application.

PubMed

Kalloniatis, Alexander; Ali, Irena; Neville, Timothy; La, Phuong; Macleod, Iain; Zuparic, Mathew; Kohn, Elizabeth

2017-05-01

We introduce a novel model and associated data collection method to examine how a distributed organisation of military staff who feed a Common Operating Picture (COP) generates Situation Awareness (SA), a critical component in organisational performance. The proposed empirically derived Situation Awareness Weighted Network (SAWN) model draws on two scientific models of SA, by Endsley involving perception, comprehension and projection, and by Stanton et al. positing that SA exists across a social and semantic network of people and information objects in activities connected across a set of tasks. The output of SAWN is a representation as a weighted semi-bipartite network of the interaction between people ('human nodes') and information artefacts such as documents and system displays ('product nodes'); link weights represent the Endsley levels of SA that individuals acquire from or provide to information objects and other individuals. The SAWN method is illustrated with aggregated empirical data from a case study of Australian military staff undertaking their work during two very different scenarios, during steady-state operations and in a crisis threat context. A key outcome of analysis of the weighted networks is that we are able to quantify flow of SA through an organisation as staff seek to "value-add" in the conduct of their work. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Prior knowledge guided active modules identification: an integrated multi-objective approach.

PubMed

Chen, Weiqi; Liu, Jing; He, Shan

2017-03-14

Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

Analysis of critical operating conditions for LV distribution networks with microgrids

NASA Astrophysics Data System (ADS)

Zehir, M. A.; Batman, A.; Sonmez, M. A.; Font, A.; Tsiamitros, D.; Stimoniaris, D.; Kollatou, T.; Bagriyanik, M.; Ozdemir, A.; Dialynas, E.

2016-11-01

Increase in the penetration of Distributed Generation (DG) in distribution networks, raises the risk of voltage limit violations while contributing to line losses. Especially in low voltage (LV) distribution networks (secondary distribution networks), impacts of active power flows on the bus voltages and on the network losses are more dominant. As network operators must meet regulatory limitations, they have to take into account the most critical operating conditions in their systems. In this study, it is aimed to present the impact of the worst operation cases of LV distribution networks comprising microgrids. Simulation studies are performed on a field data-based virtual test-bed. The simulations are repeated for several cases consisting different microgrid points of connection with different network loading and microgrid supply/demand conditions.

Design of the primary pre-TRMM and TRMM ground truth site

NASA Technical Reports Server (NTRS)

Garstang, Michael

1988-01-01

The primary objective of the Tropical Rain Measuring Mission (TRMM) were to: integrate the rain gage measurements with radar measurements of rainfall using the KSFC/Patrick digitized radar and associated rainfall network; delineate the major rain bearing systems over Florida using the Weather Service reported radar/rainfall distributions; combine the integrated measurements with the delineated rain bearing systems; use the results of the combined measurements and delineated rain bearing systems to represent patterns of rainfall which actually exist and contribute significantly to the rainfall to test sampling strategies and based on the results of these analyses decide upon the ground truth network; and complete the design begun in Phase 1 of a multi-scale (space and time) surface observing precipitation network centered upon KSFC. Work accomplished and in progress is discussed.

M Dwarf Exoplanet Survey by the Falcon Telescope Network

NASA Astrophysics Data System (ADS)

Carlson, Randall E.

2016-10-01

The Falcon Telescope Network (FTN) consists of twelve automated 20-inch telescopes located around the globe. We control it at the US Air Force Academy in Colorado Springs, Colorado from the Cadet Space Operations Center. We have installed 10 of the 12 sites and anticipate full operational capability by the beginning of 2017. The network's worldwide geographic distribution provides advantages. The primary mission of the FTN is Space Situational Awareness and studying Near Earth Objects. However, we are employing the FTN with its 11' x 11' field-of-view for a five-year, M dwarf exoplanet survey. Specifically, we are searching for Earth-radius exoplanets. We describe the FTN, design considerations going into the FTN's M dwarf exoplanet survey including automated operations, and initial results of the survey.

Stochastic user equilibrium model with a tradable credit scheme and application in maximizing network reserve capacity

NASA Astrophysics Data System (ADS)

Han, Fei; Cheng, Lin

2017-04-01

The tradable credit scheme (TCS) outperforms congestion pricing in terms of social equity and revenue neutrality, apart from the same perfect performance on congestion mitigation. This article investigates the effectiveness and efficiency of TCS on enhancing transportation network capacity in a stochastic user equilibrium (SUE) modelling framework. First, the SUE and credit market equilibrium conditions are presented; then an equivalent general SUE model with TCS is established by virtue of two constructed functions, which can be further simplified under a specific probability distribution. To enhance the network capacity by utilizing TCS, a bi-level mathematical programming model is established for the optimal TCS design problem, with the upper level optimization objective maximizing network reserve capacity and lower level being the proposed SUE model. The heuristic sensitivity analysis-based algorithm is developed to solve the bi-level model. Three numerical examples are provided to illustrate the improvement effect of TCS on the network in different scenarios.

ARACHNID: A prototype object-oriented database tool for distributed systems

NASA Technical Reports Server (NTRS)

Younger, Herbert; Oreilly, John; Frogner, Bjorn

1994-01-01

This paper discusses the results of a Phase 2 SBIR project sponsored by NASA and performed by MIMD Systems, Inc. A major objective of this project was to develop specific concepts for improved performance in accessing large databases. An object-oriented and distributed approach was used for the general design, while a geographical decomposition was used as a specific solution. The resulting software framework is called ARACHNID. The Faint Source Catalog developed by NASA was the initial database testbed. This is a database of many giga-bytes, where an order of magnitude improvement in query speed is being sought. This database contains faint infrared point sources obtained from telescope measurements of the sky. A geographical decomposition of this database is an attractive approach to dividing it into pieces. Each piece can then be searched on individual processors with only a weak data linkage between the processors being required. As a further demonstration of the concepts implemented in ARACHNID, a tourist information system is discussed. This version of ARACHNID is the commercial result of the project. It is a distributed, networked, database application where speed, maintenance, and reliability are important considerations. This paper focuses on the design concepts and technologies that form the basis for ARACHNID.

Convergence and Applications of a Gossip-Based Gauss-Newton Algorithm

NASA Astrophysics Data System (ADS)

Li, Xiao; Scaglione, Anna

2013-11-01

The Gauss-Newton algorithm is a popular and efficient centralized method for solving non-linear least squares problems. In this paper, we propose a multi-agent distributed version of this algorithm, named Gossip-based Gauss-Newton (GGN) algorithm, which can be applied in general problems with non-convex objectives. Furthermore, we analyze and present sufficient conditions for its convergence and show numerically that the GGN algorithm achieves performance comparable to the centralized algorithm, with graceful degradation in case of network failures. More importantly, the GGN algorithm provides significant performance gains compared to other distributed first order methods.

Representing connectivity: quantifying effective habitat availability based on area and connectivity for conservation status assessment and recovery.

PubMed

Neel, Maile; Tumas, Hayley R; Marsden, Brittany W

2014-01-01

We apply a comprehensive suite of graph theoretic metrics to illustrate how landscape connectivity can be effectively incorporated into conservation status assessments and in setting conservation objectives. These metrics allow conservation practitioners to evaluate and quantify connectivity in terms of representation, resiliency, and redundancy and the approach can be applied in spite of incomplete knowledge of species-specific biology and dispersal processes. We demonstrate utility of the graph metrics by evaluating changes in distribution and connectivity that would result from implementing two conservation plans for three endangered plant species (Erigeron parishii, Acanthoscyphus parishii var. goodmaniana, and Eriogonum ovalifolium var. vineum) relative to connectivity under current conditions. Although distributions of the species differ from one another in terms of extent and specific location of occupied patches within the study landscape, the spatial scale of potential connectivity in existing networks were strikingly similar for Erigeron and Eriogonum, but differed for Acanthoscyphus. Specifically, patches of the first two species were more regularly distributed whereas subsets of patches of Acanthoscyphus were clustered into more isolated components. Reserves based on US Fish and Wildlife Service critical habitat designation would not greatly contribute to maintain connectivity; they include 83-91% of the extant occurrences and >92% of the aerial extent of each species. Effective connectivity remains within 10% of that in the whole network for all species. A Forest Service habitat management strategy excluded up to 40% of the occupied habitat of each species resulting in both range reductions and loss of occurrences from the central portions of each species' distribution. Overall effective network connectivity was reduced to 62-74% of the full networks. The distance at which each CHMS network first became fully connected was reduced relative to the full network in Erigeron and Acanthoscyphus due to exclusion of peripheral patches, but was slightly increased for Eriogonum. Distances at which networks were sensitive to loss of connectivity due to presence non-redundant connections were affected mostly for Acanthoscyphos. Of most concern was that the range of distances at which lack of redundancy yielded high risk was much greater than in the full network. Through this in-depth example evaluating connectivity using a comprehensive suite of developed graph theoretic metrics, we establish an approach as well as provide sample interpretations of subtle variations in connectivity that conservation managers can incorporate into planning.

Representing connectivity: quantifying effective habitat availability based on area and connectivity for conservation status assessment and recovery

PubMed Central

Tumas, Hayley R.; Marsden, Brittany W.

2014-01-01

We apply a comprehensive suite of graph theoretic metrics to illustrate how landscape connectivity can be effectively incorporated into conservation status assessments and in setting conservation objectives. These metrics allow conservation practitioners to evaluate and quantify connectivity in terms of representation, resiliency, and redundancy and the approach can be applied in spite of incomplete knowledge of species-specific biology and dispersal processes. We demonstrate utility of the graph metrics by evaluating changes in distribution and connectivity that would result from implementing two conservation plans for three endangered plant species (Erigeron parishii, Acanthoscyphus parishii var. goodmaniana, and Eriogonum ovalifolium var. vineum) relative to connectivity under current conditions. Although distributions of the species differ from one another in terms of extent and specific location of occupied patches within the study landscape, the spatial scale of potential connectivity in existing networks were strikingly similar for Erigeron and Eriogonum, but differed for Acanthoscyphus. Specifically, patches of the first two species were more regularly distributed whereas subsets of patches of Acanthoscyphus were clustered into more isolated components. Reserves based on US Fish and Wildlife Service critical habitat designation would not greatly contribute to maintain connectivity; they include 83–91% of the extant occurrences and >92% of the aerial extent of each species. Effective connectivity remains within 10% of that in the whole network for all species. A Forest Service habitat management strategy excluded up to 40% of the occupied habitat of each species resulting in both range reductions and loss of occurrences from the central portions of each species’ distribution. Overall effective network connectivity was reduced to 62–74% of the full networks. The distance at which each CHMS network first became fully connected was reduced relative to the full network in Erigeron and Acanthoscyphus due to exclusion of peripheral patches, but was slightly increased for Eriogonum. Distances at which networks were sensitive to loss of connectivity due to presence non-redundant connections were affected mostly for Acanthoscyphos. Of most concern was that the range of distances at which lack of redundancy yielded high risk was much greater than in the full network. Through this in-depth example evaluating connectivity using a comprehensive suite of developed graph theoretic metrics, we establish an approach as well as provide sample interpretations of subtle variations in connectivity that conservation managers can incorporate into planning. PMID:25320685

Feedback-Based Projected-Gradient Method for Real-Time Optimization of Aggregations of Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Bernstein, Andrey; Simonetto, Andrea

This paper develops an online optimization method to maximize operational objectives of distribution-level distributed energy resources (DERs), while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power flows, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claimsmore » are established in terms of tracking of the solution of a well-posed time-varying convex optimization problem.« less

Methods and means of 3D diffuse Mueller-matrix tomography of depolarizing optically anisotropic biological layers

NASA Astrophysics Data System (ADS)

Dubolazov, O. V.; Ushenko, V. O.; Trifoniuk, L.; Ushenko, Yu. O.; Zhytaryuk, V. G.; Prydiy, O. G.; Grytsyuk, M.; Kushnerik, L.; Meglinskiy, I.

2017-09-01

A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular birefringence of prostate histological sections are found. The interconnections between such distributions and parameters of linear and circular birefringence of prostate tissue histological sections are defined. The comparative investigations of coordinate distributions of phase anisotropy parameters formed by fibrillar networks of prostate tissues of different pathological states (adenoma and carcinoma) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular birefringence are defined. The objective criteria of cause of Benign and malignant conditions differentiation are determined.

Feedback-Based Projected-Gradient Method For Real-Time Optimization of Aggregations of Energy Resources: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Bernstein, Andrey; Simonetto, Andrea

This paper develops an online optimization method to maximize the operational objectives of distribution-level distributed energy resources (DERs) while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claimsmore » are established in terms of tracking of the solution of a well-posed time-varying optimization problem.« less

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

PubMed

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Using the IMS infrasound network for the identification of mountain-associated waves and gravity waves hotspots

NASA Astrophysics Data System (ADS)

Hupe, Patrick; Ceranna, Lars; Pilger, Christoph; Le Pichon, Alexis

2017-04-01

The infrasound network of the International Monitoring System (IMS) has been established for monitoring the atmosphere to detect violations of the Comprehensive nuclear-Test-Ban Treaty (CTBT). The IMS comprises 49 certified infrasound stations which are globally distributed. Each station provides data for up to 16 years. Due to the uniform distribution of the stations, the IMS infrasound network can be used to derive global information on atmospheric dynamics' features. This study focuses on mountain-associated waves (MAWs), i.e. acoustic waves in the frequency range between approximately 0.01 Hz and 0.05 Hz. MAWs can be detected in infrasound data by applying the Progressive Multi-Channel Correlation (PMCC) algorithm. As a result of triangulation, global hotspots of MAWs can be identified. Previous studies on gravity waves indicate that global hotspots of gravity waves are similar to those found for MAWs by using the PMCC algorithm. The objective of our study is an enhanced understanding of the excitation sources and of possible interactions between MAWs and gravity waves. Therefore, spatial and temporal correlation analyses will be performed. As a preceding step, we will present (seasonal) hotspots of MAWs as well as hotspots of gravity waves derived by the IMS infrasound network.

Voltage regulation in distribution networks with distributed generation

NASA Astrophysics Data System (ADS)

Blažič, B.; Uljanić, B.; Papič, I.

2012-11-01

The paper deals with the topic of voltage regulation in distribution networks with relatively high distributed energy resources (DER) penetration. The problem of voltage rise is described and different options for voltage regulation are given. The influence of DER on voltage profile and the effectiveness of the investigated solutions are evaluated by means of simulation in DIgSILENT. The simulated network is an actual distribution network in Slovenia with a relatively high penetration of distributed generation. Recommendations for voltage control in networks with DER penetration are given at the end.

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

PubMed Central

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

The Determination of Production and Distribution Policy in Push-Pull Production Chain with Supply Hub as the Junction Point

NASA Astrophysics Data System (ADS)

Sinaga, A. T.; Wangsaputra, R.

2018-03-01

The development of technology causes the needs of products and services become increasingly complex, diverse, and fluctuating. This causes the level of inter-company dependencies within a production chains increased. To be able to compete, efficiency improvements need to be done collaboratively in the production chain network. One of the efforts to increase efficiency is to harmonize production and distribution activities in the production chain network. This paper describes the harmonization of production and distribution activities by applying the use of push-pull system and supply hub in the production chain between two companies. The research methodology begins with conducting empirical and literature studies, formulating research questions, developing mathematical models, conducting trials and analyses, and taking conclusions. The relationship between the two companies is described in the MINLP mathematical model with the total cost of production chain as the objective function. Decisions generated by the mathematical models are the size of production lot, size of delivery lot, number of kanban, frequency of delivery, and the number of understock and overstock lot.

Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory

NASA Astrophysics Data System (ADS)

Scutari, Gesualdo; Facchinei, Francisco; Lampariello, Lorenzo

2017-04-01

In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex (smooth) objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the application of the framework to some resource allocation problems in communication networks. In particular, we consider two non-trivial case-study applications, namely: (generalizations of) i) the rate profile maximization in MIMO interference broadcast networks; and the ii) the max-min fair multicast multigroup beamforming problem in a multi-cell environment. We develop a new class of algorithms enjoying the following distinctive features: i) they are \\emph{distributed} across the base stations (with limited signaling) and lead to subproblems whose solutions are computable in closed form; and ii) differently from current relaxation-based schemes (e.g., semidefinite relaxation), they are proved to always converge to d-stationary solutions of the aforementioned class of nonconvex problems. Numerical results show that the proposed (distributed) schemes achieve larger worst-case rates (resp. signal-to-noise interference ratios) than state-of-the-art centralized ones while having comparable computational complexity.

PrismTech Data Distribution Service Java API Evaluation

NASA Technical Reports Server (NTRS)

Riggs, Cortney

2008-01-01

My internship duties with Launch Control Systems required me to start performance testing of an Object Management Group's (OMG) Data Distribution Service (DDS) specification implementation by PrismTech Limited through the Java programming language application programming interface (API). DDS is a networking middleware for Real-Time Data Distribution. The performance testing involves latency, redundant publishers, extended duration, redundant failover, and read performance. Time constraints allowed only for a data throughput test. I have designed the testing applications to perform all performance tests when time is allowed. Performance evaluation data such as megabits per second and central processing unit (CPU) time consumption were not easily attainable through the Java programming language; they required new methods and classes created in the test applications. Evaluation of this product showed the rate that data can be sent across the network. Performance rates are better on Linux platforms than AIX and Sun platforms. Compared to previous C++ programming language API, the performance evaluation also shows the language differences for the implementation. The Java API of the DDS has a lower throughput performance than the C++ API.

Distribution of shortest cycle lengths in random networks

NASA Astrophysics Data System (ADS)

Bonneau, Haggai; Hassid, Aviv; Biham, Ofer; Kühn, Reimer; Katzav, Eytan

2017-12-01

We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdős-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.

Optical Properties of Aerosols from Long Term Ground-Based Aeronet Measurements

NASA Technical Reports Server (NTRS)

Holben, B. N.; Tanre, D.; Smirnov, A.; Eck, T. F.; Slutsker, I.; Dubovik, O.; Lavenu, F.; Abuhassen, N.; Chatenet, B.

1999-01-01

AERONET is an optical ground-based aerosol monitoring network and data archive supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions including AEROCAN (AERONET CANada) and PHOTON (PHOtometrie pour le Traiteinent Operatonnel de Normalisation Satellitaire). The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities purchased for their own monitoring and research objectives. Data are transmitted hourly through the data collection system (DCS) on board the geostationary meteorological satellites GMS, GOES and METEOSAT and received in a common archive for daily processing utilizing a peer reviewed series of algorithms thus imposing a standardization and quality control of the product data base. Data from this collaboration provides globally distributed near real time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. Access to the AERONET data base has shifted from the interactive program 'demonstrat' (reserved for PI's) to the AERONET homepage allowing faster access and greater development for GIS object oriented retrievals and analysis with companion geocoded data sets from satellites, LIDAR and solar flux measurements for example. We feel that a significant yet under utilized component of the AERONET data base are inversion products made from hourly principal plane and almucanter measurements. The current inversions have been shown to retrieve aerosol volume size distributions. A significant enhancement to the inversion code has been developed and is presented in these proceedings.

Collaborative Clustering for Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Learning spatially coherent properties of the visual world in connectionist networks

NASA Astrophysics Data System (ADS)

Becker, Suzanna; Hinton, Geoffrey E.

1991-10-01

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

Active distribution network planning considering linearized system loss

NASA Astrophysics Data System (ADS)

Li, Xiao; Wang, Mingqiang; Xu, Hao

2018-02-01

In this paper, various distribution network planning techniques with DGs are reviewed, and a new distribution network planning method is proposed. It assumes that the location of DGs and the topology of the network are fixed. The proposed model optimizes the capacities of DG and the optimal distribution line capacity simultaneously by a cost/benefit analysis and the benefit is quantified by the reduction of the expected interruption cost. Besides, the network loss is explicitly analyzed in the paper. For simplicity, the network loss is appropriately simplified as a quadratic function of difference of voltage phase angle. Then it is further piecewise linearized. In this paper, a piecewise linearization technique with different segment lengths is proposed. To validate its effectiveness and superiority, the proposed distribution network planning model with elaborate linearization technique is tested on the IEEE 33-bus distribution network system.

Rapid detection and identification of pedestrian impacts using a distributed sensor network

NASA Astrophysics Data System (ADS)

Kim, Andrew C.; Chang, Fu-Kuo

2005-05-01

Pedestrian fatalities from automobile accidents often occur as a result of head injuries suffered from impacts with an automobile front end. Active pedestrian protection systems with proper pedestrian recognition algorithms can protect pedestrians from such head trauma. An investigation was conducted to assess the feasibility of using a network of piezoelectric sensors mounted on the front bumper beam of an automobile to discriminate between impacts with "pedestrian" and "non-pedestrian" objects. This information would be used to activate a safety device (e.g., external airbag or pop-up hood) to provide protection for the vulnerable pedestrian. An analytical foundation for the object-bumper impact problem will be presented, as well as the classical beam impact theory. The mechanical waves that propagate in the structure from an external impact contain a wealth of information about the specifics of a particular impact -- object mass, size, impact speed, etc. -- but most notably the object stiffness, which identifies the impacted object. Using the frequency content of the sensor signals, it can be shown that impacts with a "pedestrian" object of varying size, weight, and speed can be easily differentiated from impacts with other "non-pedestrian" objects. Simulation results will illustrate this phenomenon, and experimental tests will verify the results. A comprehensive series of impact tests were performed for validation, using both a stationary front bumper with a drop-pendulum impactor and a moving car with stationary impact objects. Results from both tests will be presented.

Optical design of transmitter lens for asymmetric distributed free space optical networks

NASA Astrophysics Data System (ADS)

Wojtanowski, Jacek; Traczyk, Maciej

2018-05-01

We present a method of transmitter lens design dedicated for light distribution shaping on a curved and asymmetric target. In this context, target is understood as a surface determined by hypothetical optical detectors locations. In the proposed method, ribbon-like surfaces of arbitrary shape are considered. The designed lens has the task to transform collimated and generally non-uniform input beam into desired irradiance distribution on such irregular targets. Desired irradiance is associated with space-dependant efficiency of power flow between the source and receivers distributed on the target surface. This unconventional nonimaging task is different from most illumination or beam shaping objectives, where constant or prescribed irradiance has to be produced on a flat target screen. The discussed optical challenge comes from the applications where single transmitter cooperates with multitude of receivers located in various positions in space and oriented in various directions. The proposed approach is not limited to optical networks, but can be applied in a variety of other applications where nonconventional irradiance distribution has to be engineered. The described method of lens design is based on geometrical optics, radiometry and ray mapping philosophy. Rays are processed as a vector field, each of them carrying a certain amount of power. Having the target surface shape and orientation of receivers distribution, the rays-surface crossings map is calculated. It corresponds to the output rays vector field, which is referred to the calculated input rays spatial distribution on the designed optical surface. The application of Snell's law in a vector form allows one to obtain surface local normal vector and calculate lens profile. In the paper, we also present the case study dealing with exemplary optical network. The designed freeform lens is implemented in commercially available optical design software and irradiance three-dimensional spatial distribution is examined, showing perfect agreement with expectations.

A Minimax Network Flow Model for Characterizing the Impact of Slot Restrictions

NASA Technical Reports Server (NTRS)

Lee, Douglas W.; Patek, Stephen D.; Alexandrov, Natalia; Bass, Ellen J.; Kincaid, Rex K.

2010-01-01

This paper proposes a model for evaluating long-term measures to reduce congestion at airports in the National Airspace System (NAS). This model is constructed with the goal of assessing the global impacts of congestion management strategies, specifically slot restrictions. We develop the Minimax Node Throughput Problem (MINNTHRU), a multicommodity network flow model that provides insight into air traffic patterns when one minimizes the worst-case operation across all airports in a given network. MINNTHRU is thus formulated as a model where congestion arises from network topology. It reflects not market-driven airline objectives, but those of a regulatory authority seeking a distribution of air traffic beneficial to all airports, in response to congestion management measures. After discussing an algorithm for solving MINNTHRU for moderate-sized (30 nodes) and larger networks, we use this model to study the impacts of slot restrictions on the operation of an entire hub-spoke airport network. For both a small example network and a medium-sized network based on 30 airports in the NAS, we use MINNTHRU to demonstrate that increasing the severity of slot restrictions increases the traffic around unconstrained hub airports as well as the worst-case level of operation over all airports.

Network Interventions on Physical Activity in an Afterschool Program: An Agent-Based Social Network Study

PubMed Central

Zhang, Jun; Shoham, David A.; Tesdahl, Eric

2015-01-01

Objectives. We studied simulated interventions that leveraged social networks to increase physical activity in children. Methods. We studied a real-world social network of 81 children (average age = 7.96 years) who lived in low socioeconomic status neighborhoods, and attended public schools and 1 of 2 structured afterschool programs. The sample was ethnically diverse, and 44% were overweight or obese. We used social network analysis and agent-based modeling simulations to test whether implementing a network intervention would increase children’s physical activity. We tested 3 intervention strategies. Results. The intervention that targeted opinion leaders was effective in increasing the average level of physical activity across the entire network. However, the intervention that targeted the most sedentary children was the best at increasing their physical activity levels. Conclusions. Which network intervention to implement depends on whether the goal is to shift the entire distribution of physical activity or to influence those most adversely affected by low physical activity. Agent-based modeling could be an important complement to traditional project planning tools, analogous to sample size and power analyses, to help researchers design more effective interventions for increasing children’s physical activity. PMID:25689202

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

Evaluation model of distribution network development based on ANP and grey correlation analysis

NASA Astrophysics Data System (ADS)

Ma, Kaiqiang; Zhan, Zhihong; Zhou, Ming; Wu, Qiang; Yan, Jun; Chen, Genyong

2018-06-01

The existing distribution network evaluation system cannot scientifically and comprehensively reflect the distribution network development status. Furthermore, the evaluation model is monotonous and it is not suitable for horizontal analysis of many regional power grids. For these reason, this paper constructs a set of universal adaptability evaluation index system and model of distribution network development. Firstly, distribution network evaluation system is set up by power supply capability, power grid structure, technical equipment, intelligent level, efficiency of the power grid and development benefit of power grid. Then the comprehensive weight of indices is calculated by combining the AHP with the grey correlation analysis. Finally, the index scoring function can be obtained by fitting the index evaluation criterion to the curve, and then using the multiply plus operator to get the result of sample evaluation. The example analysis shows that the model can reflect the development of distribution network and find out the advantages and disadvantages of distribution network development. Besides, the model provides suggestions for the development and construction of distribution network.

Fast, Distributed Algorithms in Deep Networks

DTIC Science & Technology

2016-05-11

may not have realized how vital she was in making this project a reality is Professor Crainiceanu. Without knowing who you were, you invited me into...objective function. Training is complete when (2) converges, or stated alternatively , when the difference between t and φL can no longer be...the state-of-the art approaches simply rely on random initialization. We propose an alternative 10 (a) Features in 1-dimensional space (b) Features

Decentralized Optimal Dispatch of Photovoltaic Inverters in Residential Distribution Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Anese, Emiliano; Dhople, Sairaj V.; Johnson, Brian B.

Summary form only given. Decentralized methods for computing optimal real and reactive power setpoints for residential photovoltaic (PV) inverters are developed in this paper. It is known that conventional PV inverter controllers, which are designed to extract maximum power at unity power factor, cannot address secondary performance objectives such as voltage regulation and network loss minimization. Optimal power flow techniques can be utilized to select which inverters will provide ancillary services, and to compute their optimal real and reactive power setpoints according to well-defined performance criteria and economic objectives. Leveraging advances in sparsity-promoting regularization techniques and semidefinite relaxation, this papermore » shows how such problems can be solved with reduced computational burden and optimality guarantees. To enable large-scale implementation, a novel algorithmic framework is introduced - based on the so-called alternating direction method of multipliers - by which optimal power flow-type problems in this setting can be systematically decomposed into sub-problems that can be solved in a decentralized fashion by the utility and customer-owned PV systems with limited exchanges of information. Since the computational burden is shared among multiple devices and the requirement of all-to-all communication can be circumvented, the proposed optimization approach scales favorably to large distribution networks.« less

Radiological tele-immersion for next generation networks.

PubMed

Ai, Z; Dech, F; Rasmussen, M; Silverstein, J C

2000-01-01

Since the acquisition of high-resolution three-dimensional patient images has become widespread, medical volumetric datasets (CT or MR) larger than 100 MB and encompassing more than 250 slices are common. It is important to make this patient-specific data quickly available and usable to many specialists at different geographical sites. Web-based systems have been developed to provide volume or surface rendering of medical data over networks with low fidelity, but these cannot adequately handle stereoscopic visualization or huge datasets. State-of-the-art virtual reality techniques and high speed networks have made it possible to create an environment for clinicians geographically distributed to immersively share these massive datasets in real-time. An object-oriented method for instantaneously importing medical volumetric data into Tele-Immersive environments has been developed at the Virtual Reality in Medicine Laboratory (VRMedLab) at the University of Illinois at Chicago (UIC). This networked-VR setup is based on LIMBO, an application framework or template that provides the basic capabilities of Tele-Immersion. We have developed a modular general purpose Tele-Immersion program that automatically combines 3D medical data with the methods for handling the data. For this purpose a DICOM loader for IRIS Performer has been developed. The loader was designed for SGI machines as a shared object, which is executed at LIMBO's runtime. The loader loads not only the selected DICOM dataset, but also methods for rendering, handling, and interacting with the data, bringing networked, real-time, stereoscopic interaction with radiological data to reality. Collaborative, interactive methods currently implemented in the loader include cutting planes and windowing. The Tele-Immersive environment has been tested on the UIC campus over an ATM network. We tested the environment with 3 nodes; one ImmersaDesk at the VRMedLab, one CAVE at the Electronic Visualization Laboratory (EVL) on east campus, and a CT scan machine in UIC Hospital. CT data was pulled directly from the scan machine to the Tele-Immersion server in our Laboratory, and then the data was synchronously distributed by our Onyx2 Rack server to all the VR setups. Instead of permitting medical volume visualization at one VR device, by combining teleconferencing, tele-presence, and virtual reality, the Tele-Immersive environment will enable geographically distributed clinicians to intuitively interact with the same medical volumetric models, point, gesture, converse, and see each other. This environment will bring together clinicians at different geographic locations to participate in Tele-Immersive consultation and collaboration.

Packets Distributing Evolutionary Algorithm Based on PSO for Ad Hoc Network

NASA Astrophysics Data System (ADS)

Xu, Xiao-Feng

2018-03-01

Wireless communication network has such features as limited bandwidth, changeful channel and dynamic topology, etc. Ad hoc network has lots of difficulties in accessing control, bandwidth distribution, resource assign and congestion control. Therefore, a wireless packets distributing Evolutionary algorithm based on PSO (DPSO)for Ad Hoc Network is proposed. Firstly, parameters impact on performance of network are analyzed and researched to obtain network performance effective function. Secondly, the improved PSO Evolutionary Algorithm is used to solve the optimization problem from local to global in the process of network packets distributing. The simulation results show that the algorithm can ensure fairness and timeliness of network transmission, as well as improve ad hoc network resource integrated utilization efficiency.

Analysis on Voltage Profile of Distribution Network with Distributed Generation

NASA Astrophysics Data System (ADS)

Shao, Hua; Shi, Yujie; Yuan, Jianpu; An, Jiakun; Yang, Jianhua

2018-02-01

Penetration of distributed generation has some impacts on a distribution network in load flow, voltage profile, reliability, power loss and so on. After the impacts and the typical structures of the grid-connected distributed generation are analyzed, the back/forward sweep method of the load flow calculation of the distribution network is modelled including distributed generation. The voltage profiles of the distribution network affected by the installation location and the capacity of distributed generation are thoroughly investigated and simulated. The impacts on the voltage profiles are summarized and some suggestions to the installation location and the capacity of distributed generation are given correspondingly.

Michigan Orbital DEbris Survey Telescope Observations of the Geosynchronous Orbital Debris Environment. Observing Years: 2007-2009

NASA Technical Reports Server (NTRS)

Abercromby, K. J.; Seitzer, P.; Cowardin, H. M.; Barker, E. S.; Matney, M. J.

2011-01-01

NASA uses the Michigan Orbital DEbris Survey Telescope (MODEST), the University of Michigan's 0.61-m aperture Curtis-Schmidt telescope at the Cerro Tololo Inter-American Observatory in Chile, to help characterize the debris environment in geosynchronous orbit; this began in February 2001 and continues to the present day. Detected objects that are found to be on the U.S. Space Surveillance Network cataloged objects list are termed correlated targets (CTs), while those not found on the list are called uncorrelated targets (UCTs). This Johnson Space Center report provides details of observational and data-reduction processes for the entire MODEST dataset acquired in calendar years (CYs) 2007, 2008, and 2009. Specifically, this report describes the collection and analysis of 36 nights of data collected in CY 2007, 43 nights of data collected in CY 2008, and 43 nights of data collected in CY 2009. MODEST is equipped with a 2048 x 2048-pixel charged coupled device camera with a 1.3 by 1.3 deg field of view. This system is capable of detecting objects fainter than 18th magnitude (R filter) using a 5-s integration. This corresponds to a 20-cm diameter, 0.175-albedo object at 36,000 km altitude assuming a diffuse Lambertian phase function. The average number of detections each night over all 3 years was 26. The percentage of this number that represented the UCT population ranged from 34% to 18%, depending on the observing strategy and the field center location. Due to the short orbital arc over which observations are made, the eccentricity of the object s orbit is extremely difficult to measure accurately. Therefore, a circular orbit was assumed when calculating the orbital elements. A comparison of the measured inclination (INC), right ascension of ascending node (RAAN), and mean motion to the quantities for CTs from the U.S. Space Surveillance Network shows acceptable errors. This analysis lends credibility to the determination of the UCT orbital distributions. Figure 1 shows the size distribution of 3,143 objects detected in the data processed for CYs 2007, 2008, and 2009. The actual peak of the absolute magnitude distribution for the functional correlated targets is 10th magnitude, whereas the peak was 11th magnitude in 2002 2003 and 10th magnitude for 2004-2006. An absolute magnitude of 10.5 corresponds to objects with average diameters of 6.3 m, assuming an albedo of 0.175 and a diffuse Lambertian phase function. This result generally agrees with the known sizes of intact satellites. The absolute magnitude distribution for the UCTs is broad, but starts to roll off near 25 cm diameter or 17.5 magnitude. This roll off in the distribution reflects the detection capability of MODEST, not the true nature of the population. The true population is believed to continue at the same slope through fainter magnitudes based on comparisons with the LEO break-up law.

Word and face processing engage overlapping distributed networks: Evidence from RSVP and EEG investigations.

PubMed

Robinson, Amanda K; Plaut, David C; Behrmann, Marlene

2017-07-01

Words and faces have vastly different visual properties, but increasing evidence suggests that word and face processing engage overlapping distributed networks. For instance, fMRI studies have shown overlapping activity for face and word processing in the fusiform gyrus despite well-characterized lateralization of these objects to the left and right hemispheres, respectively. To investigate whether face and word perception influences perception of the other stimulus class and elucidate the mechanisms underlying such interactions, we presented images using rapid serial visual presentations. Across 3 experiments, participants discriminated 2 face, word, and glasses targets (T1 and T2) embedded in a stream of images. As expected, T2 discrimination was impaired when it followed T1 by 200 to 300 ms relative to longer intertarget lags, the so-called attentional blink. Interestingly, T2 discrimination accuracy was significantly reduced at short intertarget lags when a face was followed by a word (face-word) compared with glasses-word and word-word combinations, indicating that face processing interfered with word perception. The reverse effect was not observed; that is, word-face performance was no different than the other object combinations. EEG results indicated the left N170 to T1 was correlated with the word decrement for face-word trials, but not for other object combinations. Taken together, the results suggest face processing interferes with word processing, providing evidence for overlapping neural mechanisms of these 2 object types. Furthermore, asymmetrical face-word interference points to greater overlap of face and word representations in the left than the right hemisphere. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Impacts on the Voltage Profile of DC Distribution Network with DG Access

NASA Astrophysics Data System (ADS)

Tu, J. J.; Yin, Z. D.

2017-07-01

With the development of electronic, more and more distributed generations (DGs) access into grid and cause the research fever of direct current (DC) distribution network. Considering distributed generation (DG) location and capacity have great impacts on voltage profile, so use IEEE9 and IEEE33 typical circuit as examples, with DGs access in centralized and decentralized mode, to compare voltage profile in alternating and direct current (AC/DC) distribution network. Introducing the voltage change ratio as an evaluation index, so gets the general results on voltage profile of DC distributed network with DG access. Simulation shows that, in the premise of reasonable location and capacity, DC distribution network is more suitable for DG access.

Intelligent Distribution Voltage Control with Distributed Generation =

NASA Astrophysics Data System (ADS)

Castro Mendieta, Jose

In this thesis, three methods for the optimal participation of the reactive power of distributed generations (DGs) in unbalanced distributed network have been proposed, developed, and tested. These new methods were developed with the objectives of maintain voltage within permissible limits and reduce losses. The first method proposes an optimal participation of reactive power of all devices available in the network. The propose approach is validated by comparing the results with other methods reported in the literature. The proposed method was implemented using Simulink of Matlab and OpenDSS. Optimization techniques and the presentation of results are from Matlab. The co-simulation of Electric Power Research Institute's (EPRI) OpenDSS program solves a three-phase optimal power flow problem in the unbalanced IEEE 13 and 34-node test feeders. The results from this work showed a better loss reduction compared to the Coordinated Voltage Control (CVC) method. The second method aims to minimize the voltage variation on the pilot bus on distribution network using DGs. It uses Pareto and Fuzzy-PID logic to reduce the voltage variation. Results indicate that the proposed method reduces the voltage variation more than the other methods. Simulink of Matlab and OpenDSS is used in the development of the proposed approach. The performance of the method is evaluated on IEEE 13-node test feeder with one and three DGs. Variables and unbalanced loads are used, based on real consumption data, over a time window of 48 hours. The third method aims to minimize the reactive losses using DGs on distribution networks. This method analyzes the problem using the IEEE 13-node test feeder with three different loads and the IEEE 123-node test feeder with four DGs. The DGs can be fixed or variables. Results indicate that integration of DGs to optimize the reactive power of the network helps to maintain the voltage within the allowed limits and to reduce the reactive power losses. The thesis is presented in the form of the three articles. The first article is published in the journal Electrical Power and Energy System, the second is published in the international journal Energies and the third was submitted to the journal Electrical Power and Energy System. Two other articles have been published in conferences with reviewing committee. This work is based on six chapters, which are detailed in the various sections of the thesis.

Research on key technology of planning and design for AC/DC hybrid distribution network

NASA Astrophysics Data System (ADS)

Shen, Yu; Wu, Guilian; Zheng, Huan; Deng, Junpeng; Shi, Pengjia

2018-04-01

With the increasing demand of DC generation and DC load, the development of DC technology, AC and DC distribution network integrating will become an important form of future distribution network. In this paper, the key technology of planning and design for AC/DC hybrid distribution network is proposed, including the selection of AC and DC voltage series, the design of typical grid structure and the comprehensive evaluation method of planning scheme. The research results provide some ideas and directions for the future development of AC/DC hybrid distribution network.

Design and implementation of a biomedical image database (BDIM).

PubMed

Aubry, F; Badaoui, S; Kaplan, H; Di Paola, R

1988-01-01

We developed a biomedical image database (BDIM) which proposes a standardized representation of value arrays such as images and curves, and of their associated parameters, independently of their acquisition mode to make their transmission and processing easier. It includes three kinds of interactions, oriented to the users. The network concept was kept as a constraint to incorporate the BDIM in a distributed structure and we maintained compatibility with the ACR/NEMA communication protocol. The management of arrays and their associated parameters includes two distinct bases of objects, linked together via a gateway. The first one manages arrays according to their storage mode: long term storage on optionally on-line mass storage devices, and, for consultations, partial copies of long term stored arrays on hard disk. The second one manages the associated parameters and the gateway by means of the relational DBMS ORACLE. Parameters are grouped into relations. Some of them are in agreement with groups defined by the ACR/NEMA. The other relations describe objects resulting from processed initial objects. These new objects are not described by the ACR/NEMA but they can be inserted as shadow groups of ACR/NEMA description. The relations describing the storage and their pathname constitute the gateway. ORACLE distributed tools and the two-level storage technique will allow the integration of the BDIM into a distributed structure, Queries and array (alone or in sequences) retrieval module has access to the relations via a level in which a dictionary managed by ORACLE is included. This dictionary translates ACR/NEMA objects into objects that can be handled by the DBMS.(ABSTRACT TRUNCATED AT 250 WORDS)

Implementation of a Web-Based Collaborative Process Planning System

NASA Astrophysics Data System (ADS)

Wang, Huifen; Liu, Tingting; Qiao, Li; Huang, Shuangxi

Under the networked manufacturing environment, all phases of product manufacturing involving design, process planning, machining and assembling may be accomplished collaboratively by different enterprises, even different manufacturing stages of the same part may be finished collaboratively by different enterprises. Based on the self-developed networked manufacturing platform eCWS(e-Cooperative Work System), a multi-agent-based system framework for collaborative process planning is proposed. In accordance with requirements of collaborative process planning, share resources provided by cooperative enterprises in the course of collaboration are classified into seven classes. Then a reconfigurable and extendable resource object model is built. Decision-making strategy is also studied in this paper. Finally a collaborative process planning system e-CAPP is developed and applied. It provides strong support for distributed designers to collaboratively plan and optimize product process though network.

Diamond Eye: a distributed architecture for image data mining

NASA Astrophysics Data System (ADS)

Burl, Michael C.; Fowlkes, Charless; Roden, Joe; Stechert, Andre; Mukhtar, Saleem

1999-02-01

Diamond Eye is a distributed software architecture, which enables users (scientists) to analyze large image collections by interacting with one or more custom data mining servers via a Java applet interface. Each server is coupled with an object-oriented database and a computational engine, such as a network of high-performance workstations. The database provides persistent storage and supports querying of the 'mined' information. The computational engine provides parallel execution of expensive image processing, object recognition, and query-by-content operations. Key benefits of the Diamond Eye architecture are: (1) the design promotes trial evaluation of advanced data mining and machine learning techniques by potential new users (all that is required is to point a web browser to the appropriate URL), (2) software infrastructure that is common across a range of science mining applications is factored out and reused, and (3) the system facilitates closer collaborations between algorithm developers and domain experts.

Realizations of highly heterogeneous collagen networks via stochastic reconstruction for micromechanical analysis of tumor cell invasion

NASA Astrophysics Data System (ADS)

Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang

2018-03-01

Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group of cells are analyzed by considering a nonlinear fiber model incorporating strain hardening upon large stretching and buckling upon compression. Our analysis shows that oriented fibers can significantly enhance long-range force transmission in the network. Moreover, in the oriented-collagen-Matrigel system, the forces generated by a polarized cell in collagen can penetrate deeply into the Matrigel region. The stressed Matrigel fibers could provide contact guidance for the migrating cell cells, and thus enhance their penetration into Matrigel. This suggests a possible mechanism for the observed enhanced intravasation by oriented collagen.

On mobile wireless ad hoc IP video transports

NASA Astrophysics Data System (ADS)

Kazantzidis, Matheos

2006-05-01

Multimedia transports in wireless, ad-hoc, multi-hop or mobile networks must be capable of obtaining information about the network and adaptively tune sending and encoding parameters to the network response. Obtaining meaningful metrics to guide a stable congestion control mechanism in the transport (i.e. passive, simple, end-to-end and network technology independent) is a complex problem. Equally difficult is obtaining a reliable QoS metrics that agrees with user perception in a client/server or distributed environment. Existing metrics, objective or subjective, are commonly used after or before to test or report on a transmission and require access to both original and transmitted frames. In this paper, we propose that an efficient and successful video delivery and the optimization of overall network QoS requires innovation in a) a direct measurement of available and bottleneck capacity for its congestion control and b) a meaningful subjective QoS metric that is dynamically reported to video sender. Once these are in place, a binomial -stable, fair and TCP friendly- algorithm can be used to determine the sending rate and other packet video parameters. An adaptive mpeg codec can then continually test and fit its parameters and temporal-spatial data-error control balance using the perceived QoS dynamic feedback. We suggest a new measurement based on a packet dispersion technique that is independent of underlying network mechanisms. We then present a binomial control based on direct measurements. We implement a QoS metric that is known to agree with user perception (MPQM) in a client/server, distributed environment by using predetermined table lookups and characterization of video content.

CHARACTERIZATION OF THE COMPLETE FIBER NETWORK TOPOLOGY OF PLANAR FIBROUS TISSUES AND SCAFFOLDS

PubMed Central

D'Amore, Antonio; Stella, John A.; Wagner, William R.; Sacks, Michael S.

2010-01-01

Understanding how engineered tissue scaffold architecture affects cell morphology, metabolism, phenotypic expression, as well as predicting material mechanical behavior have recently received increased attention. In the present study, an image-based analysis approach that provides an automated tool to characterize engineered tissue fiber network topology is presented. Micro-architectural features that fully defined fiber network topology were detected and quantified, which include fiber orientation, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using scanning electron microscopy (SEM) images of electrospun poly(ester urethane)urea (ES-PEUU) scaffolds. SEM images of rabbit mesenchymal stem cell (MSC) seeded collagen gel scaffolds and decellularized rat carotid arteries were also analyzed to further evaluate the ability of the algorithm to capture fiber network morphology regardless of scaffold type and the evaluated size scale. The image analysis procedure was validated qualitatively and quantitatively, comparing fiber network topology manually detected by human operators (n=5) with that automatically detected by the algorithm. Correlation values between manual detected and algorithm detected results for the fiber angle distribution and for the fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersections and fiber diameter values were comparable (within the mean ± standard deviation) with those detected by human operators. This automated approach identifies and quantifies fiber network morphology as demonstrated for three relevant scaffold types and provides a means to: (1) guarantee objectivity, (2) significantly reduce analysis time, and (3) potentiate broader analysis of scaffold architecture effects on cell behavior and tissue development both in vitro and in vivo. PMID:20398930

Entropy-based heavy tailed distribution transformation and visual analytics for monitoring massive network traffic

NASA Astrophysics Data System (ADS)

Han, Keesook J.; Hodge, Matthew; Ross, Virginia W.

2011-06-01

For monitoring network traffic, there is an enormous cost in collecting, storing, and analyzing network traffic datasets. Data mining based network traffic analysis has a growing interest in the cyber security community, but is computationally expensive for finding correlations between attributes in massive network traffic datasets. To lower the cost and reduce computational complexity, it is desirable to perform feasible statistical processing on effective reduced datasets instead of on the original full datasets. Because of the dynamic behavior of network traffic, traffic traces exhibit mixtures of heavy tailed statistical distributions or overdispersion. Heavy tailed network traffic characterization and visualization are important and essential tasks to measure network performance for the Quality of Services. However, heavy tailed distributions are limited in their ability to characterize real-time network traffic due to the difficulty of parameter estimation. The Entropy-Based Heavy Tailed Distribution Transformation (EHTDT) was developed to convert the heavy tailed distribution into a transformed distribution to find the linear approximation. The EHTDT linearization has the advantage of being amenable to characterize and aggregate overdispersion of network traffic in realtime. Results of applying the EHTDT for innovative visual analytics to real network traffic data are presented.

Monitoring air quality in mountains: Designing an effective network

USGS Publications Warehouse

Peterson, D.L.

2000-01-01

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.

Estimation of Blood Flow Rates in Large Microvascular Networks

PubMed Central

Fry, Brendan C.; Lee, Jack; Smith, Nicolas P.; Secomb, Timothy W.

2012-01-01

Objective Recent methods for imaging microvascular structures provide geometrical data on networks containing thousands of segments. Prediction of functional properties, such as solute transport, requires information on blood flow rates also, but experimental measurement of many individual flows is difficult. Here, a method is presented for estimating flow rates in a microvascular network based on incomplete information on the flows in the boundary segments that feed and drain the network. Methods With incomplete boundary data, the equations governing blood flow form an underdetermined linear system. An algorithm was developed that uses independent information about the distribution of wall shear stresses and pressures in microvessels to resolve this indeterminacy, by minimizing the deviation of pressures and wall shear stresses from target values. Results The algorithm was tested using previously obtained experimental flow data from four microvascular networks in the rat mesentery. With two or three prescribed boundary conditions, predicted flows showed relatively small errors in most segments and fewer than 10% incorrect flow directions on average. Conclusions The proposed method can be used to estimate flow rates in microvascular networks, based on incomplete boundary data and provides a basis for deducing functional properties of microvessel networks. PMID:22506980

Brain connectivity dynamics during social interaction reflect social network structure

PubMed Central

Schmälzle, Ralf; Brook O’Donnell, Matthew; Garcia, Javier O.; Cascio, Christopher N.; Bayer, Joseph; Vettel, Jean M.

2017-01-01

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics. PMID:28465434

Creatiing a Collaborative Research Network for Scientists

NASA Astrophysics Data System (ADS)

Gunn, W.

2012-12-01

This abstract proposes a discussion of how professional science communication and scientific cooperation can become more efficient through the use of modern social network technology, using the example of Mendeley. Mendeley is a research workflow and collaboration tool which crowdsources real-time research trend information and semantic annotations of research papers in a central data store, thereby creating a "social research network" that is emergent from the research data added to the platform. We describe how Mendeley's model can overcome barriers for collaboration by turning research papers into social objects, making academic data publicly available via an open API, and promoting more efficient collaboration. Central to the success of Mendeley has been the creation of a tool that works for the researcher without the requirement of being part of an explicit social network. Mendeley automatically extracts metadata from research papers, and allows a researcher to annotate, tag and organize their research collection. The tool integrates with the paper writing workflow and provides advanced collaboration options, thus significantly improving researchers' productivity. By anonymously aggregating usage data, Mendeley enables the emergence of social metrics and real-time usage stats on top of the articles' abstract metadata. In this way a social network of collaborators, and people genuinely interested in content, emerges. By building this research network around the article as the social object, a social layer of direct relevance to academia emerges. As science, particularly Earth sciences with their large shared resources, become more and more global, the management and coordination of research is more and more dependent on technology to support these distributed collaborations.

Statistical Neurodynamics.

NASA Astrophysics Data System (ADS)

Paine, Gregory Harold

1982-03-01

The primary objective of the thesis is to explore the dynamical properties of small nerve networks by means of the methods of statistical mechanics. To this end, a general formalism is developed and applied to elementary groupings of model neurons which are driven by either constant (steady state) or nonconstant (nonsteady state) forces. Neuronal models described by a system of coupled, nonlinear, first-order, ordinary differential equations are considered. A linearized form of the neuronal equations is studied in detail. A Lagrange function corresponding to the linear neural network is constructed which, through a Legendre transformation, provides a constant of motion. By invoking the Maximum-Entropy Principle with the single integral of motion as a constraint, a probability distribution function for the network in a steady state can be obtained. The formalism is implemented for some simple networks driven by a constant force; accordingly, the analysis focuses on a study of fluctuations about the steady state. In particular, a network composed of N noninteracting neurons, termed Free Thinkers, is considered in detail, with a view to interpretation and numerical estimation of the Lagrange multiplier corresponding to the constant of motion. As an archetypical example of a net of interacting neurons, the classical neural oscillator, consisting of two mutually inhibitory neurons, is investigated. It is further shown that in the case of a network driven by a nonconstant force, the Maximum-Entropy Principle can be applied to determine a probability distribution functional describing the network in a nonsteady state. The above examples are reconsidered with nonconstant driving forces which produce small deviations from the steady state. Numerical studies are performed on simplified models of two physical systems: the starfish central nervous system and the mammalian olfactory bulb. Discussions are given as to how statistical neurodynamics can be used to gain a better understanding of the behavior of these systems.

Optimization of Actuating Origami Networks

NASA Astrophysics Data System (ADS)

Buskohl, Philip; Fuchi, Kazuko; Bazzan, Giorgio; Joo, James; Gregory, Reich; Vaia, Richard

2015-03-01

Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form, function and mobility of the structure. By leveraging design concepts from action origami, a subset of origami art focused on kinematic mechanisms, reversible folding patterns for applications such as solar array packaging, tunable antennae, and deployable sensing platforms may be designed. However, the enormity of the design space and the need to identify the requisite actuation forces within the structure places a severe limitation on design strategies based on intuition and geometry alone. The present work proposes a topology optimization method, using truss and frame element analysis, to distribute foldline mechanical properties within a reference crease pattern. Known actuating patterns are placed within a reference grid and the optimizer adjusts the fold stiffness of the network to optimally connect them. Design objectives may include a target motion, stress level, or mechanical energy distribution. Results include the validation of known action origami structures and their optimal connectivity within a larger network. This design suite offers an important step toward systematic incorporation of origami design concepts into new, novel and reconfigurable engineering devices. This research is supported under the Air Force Office of Scientific Research (AFOSR) funding, LRIR 13RQ02COR.

Quantum teleportation between remote atomic-ensemble quantum memories.

PubMed

Bao, Xiao-Hui; Xu, Xiao-Fan; Li, Che-Ming; Yuan, Zhen-Sheng; Lu, Chao-Yang; Pan, Jian-Wei

2012-12-11

Quantum teleportation and quantum memory are two crucial elements for large-scale quantum networks. With the help of prior distributed entanglement as a "quantum channel," quantum teleportation provides an intriguing means to faithfully transfer quantum states among distant locations without actual transmission of the physical carriers [Bennett CH, et al. (1993) Phys Rev Lett 70(13):1895-1899]. Quantum memory enables controlled storage and retrieval of fast-flying photonic quantum bits with stationary matter systems, which is essential to achieve the scalability required for large-scale quantum networks. Combining these two capabilities, here we realize quantum teleportation between two remote atomic-ensemble quantum memory nodes, each composed of ∼10(8) rubidium atoms and connected by a 150-m optical fiber. The spin wave state of one atomic ensemble is mapped to a propagating photon and subjected to Bell state measurements with another single photon that is entangled with the spin wave state of the other ensemble. Two-photon detection events herald the success of teleportation with an average fidelity of 88(7)%. Besides its fundamental interest as a teleportation between two remote macroscopic objects, our technique may be useful for quantum information transfer between different nodes in quantum networks and distributed quantum computing.

Remote quantum entanglement between two micromechanical oscillators.

PubMed

Riedinger, Ralf; Wallucks, Andreas; Marinković, Igor; Löschnauer, Clemens; Aspelmeyer, Markus; Hong, Sungkun; Gröblacher, Simon

2018-04-01

Entanglement, an essential feature of quantum theory that allows for inseparable quantum correlations to be shared between distant parties, is a crucial resource for quantum networks 1 . Of particular importance is the ability to distribute entanglement between remote objects that can also serve as quantum memories. This has been previously realized using systems such as warm 2,3 and cold atomic vapours 4,5 , individual atoms 6 and ions 7,8 , and defects in solid-state systems 9-11 . Practical communication applications require a combination of several advantageous features, such as a particular operating wavelength, high bandwidth and long memory lifetimes. Here we introduce a purely micromachined solid-state platform in the form of chip-based optomechanical resonators made of nanostructured silicon beams. We create and demonstrate entanglement between two micromechanical oscillators across two chips that are separated by 20 centimetres . The entangled quantum state is distributed by an optical field at a designed wavelength near 1,550 nanometres. Therefore, our system can be directly incorporated in a realistic fibre-optic quantum network operating in the conventional optical telecommunication band. Our results are an important step towards the development of large-area quantum networks based on silicon photonics.

Spatial properties of snow cover in the Upper Merced River Basin: implications for a distributed snow measurement network

NASA Astrophysics Data System (ADS)

Bouffon, T.; Rice, R.; Bales, R.

2006-12-01

The spatial distributions of snow water equivalent (SWE) and snow depth within a 1, 4, and 16 km2 grid element around two automated snow pillows in a forested and open- forested region of the Upper Merced River Basin (2,800 km2) of Yosemite National Park were characterized using field observations and analyzed using binary regression trees. Snow surveys occurred at the forested site during the accumulation and ablation seasons, while at the open-forest site a survey was performed only during the accumulation season. An average of 130 snow depth and 7 snow density measurements were made on each survey, within the 4 km2 grid. Snow depth was distributed using binary regression trees and geostatistical methods using the physiographic parameters (e.g. elevation, slope, vegetation, aspect). Results in the forest region indicate that the snow pillow overestimated average SWE within the 1, 4, and 16 km2 areas by 34 percent during ablation, but during accumulation the snow pillow provides a good estimate of the modeled mean SWE grid value, however it is suspected that the snow pillow was underestimating SWE. However, at the open forest site, during accumulation, the snow pillow was 28 percent greater than the mean modeled grid element. In addition, the binary regression trees indicate that the independent variables of vegetation, slope, and aspect are the most influential parameters of snow depth distribution. The binary regression tree and multivariate linear regression models explain about 60 percent of the initial variance for snow depth and 80 percent for density, respectively. This short-term study provides motivation and direction for the installation of a distributed snow measurement network to fill the information gap in basin-wide SWE and snow depth measurements. Guided by these results, a distributed snow measurement network was installed in the Fall 2006 at Gin Flat in the Upper Merced River Basin with the specific objective of measuring accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs.

Organization of the secure distributed computing based on multi-agent system

NASA Astrophysics Data System (ADS)

Khovanskov, Sergey; Rumyantsev, Konstantin; Khovanskova, Vera

2018-04-01

Nowadays developing methods for distributed computing is received much attention. One of the methods of distributed computing is using of multi-agent systems. The organization of distributed computing based on the conventional network computers can experience security threats performed by computational processes. Authors have developed the unified agent algorithm of control system of computing network nodes operation. Network PCs is used as computing nodes. The proposed multi-agent control system for the implementation of distributed computing allows in a short time to organize using of the processing power of computers any existing network to solve large-task by creating a distributed computing. Agents based on a computer network can: configure a distributed computing system; to distribute the computational load among computers operated agents; perform optimization distributed computing system according to the computing power of computers on the network. The number of computers connected to the network can be increased by connecting computers to the new computer system, which leads to an increase in overall processing power. Adding multi-agent system in the central agent increases the security of distributed computing. This organization of the distributed computing system reduces the problem solving time and increase fault tolerance (vitality) of computing processes in a changing computing environment (dynamic change of the number of computers on the network). Developed a multi-agent system detects cases of falsification of the results of a distributed system, which may lead to wrong decisions. In addition, the system checks and corrects wrong results.

Network-Cognizant Design of Decentralized Volt/VAR Controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri A; Bernstein, Andrey; Zhao, Changhong

This paper considers the problem of designing decentralized Volt/VAR controllers for distributed energy resources (DERs). The voltage-reactive power characteristics of individual DERs are obtained by solving a convex optimization problem, where given performance objectives (e.g., minimization of the voltage deviations from a given profile) are specified and stability constraints are enforced. The resultant Volt/VAR characteristics are network-cognizant, in the sense that they embed information on the location of the DERs and, consequently, on the effect of reactive-power adjustments on the voltages throughout the feeder. Bounds on the maximum voltage deviation incurred by the controllers are analytically established. Numerical results aremore » reported to corroborate the technical findings.« less

Design of a national distributed health data network.

PubMed

Maro, Judith C; Platt, Richard; Holmes, John H; Strom, Brian L; Hennessy, Sean; Lazarus, Ross; Brown, Jeffrey S

2009-09-01

A distributed health data network is a system that allows secure remote analysis of separate data sets, each comprising a different medical organization's or health plan's records. Distributed health data networks are currently being planned that could cover millions of people, permitting studies of comparative clinical effectiveness, best practices, diffusion of medical technologies, and quality of care. These networks could also support assessment of medical product safety and other public health needs. Distributed network technologies allow data holders to control all uses of their data, which overcomes many practical obstacles related to confidentiality, regulation, and proprietary interests. Some of the challenges and potential methods of operation of a multipurpose, multi-institutional distributed health data network are described.

Application of SQL database to the control system of MOIRCS

NASA Astrophysics Data System (ADS)

Yoshikawa, Tomohiro; Omata, Koji; Konishi, Masahiro; Ichikawa, Takashi; Suzuki, Ryuji; Tokoku, Chihiro; Uchimoto, Yuka Katsuno; Nishimura, Tetsuo

2006-06-01

MOIRCS (Multi-Object Infrared Camera and Spectrograph) is a new instrument for the Subaru telescope. In order to perform observations of near-infrared imaging and spectroscopy with cold slit mask, MOIRCS contains many device components, which are distributed on an Ethernet LAN. Two PCs wired to the focal plane array electronics operate two HAWAII2 detectors, respectively, and other two PCs are used for integrated control and quick data reduction, respectively. Though most of the devices (e.g., filter and grism turrets, slit exchange mechanism for spectroscopy) are controlled via RS232C interface, they are accessible from TCP/IP connection using TCP/IP to RS232C converters. Moreover, other devices are also connected to the Ethernet LAN. This network distributed structure provides flexibility of hardware configuration. We have constructed an integrated control system for such network distributed hardwares, named T-LECS (Tohoku University - Layered Electronic Control System). T-LECS has also network distributed software design, applying TCP/IP socket communication to interprocess communication. In order to help the communication between the device interfaces and the user interfaces, we defined three layers in T-LECS; an external layer for user interface applications, an internal layer for device interface applications, and a communication layer, which connects two layers above. In the communication layer, we store the data of the system to an SQL database server; they are status data, FITS header data, and also meta data such as device configuration data and FITS configuration data. We present our software system design and the database schema to manage observations of MOIRCS with Subaru.

Breaks and maintenance work in the water distribution systems and gastrointestinal illness: a cohort study.

PubMed

Nygård, Karin; Wahl, Erik; Krogh, Truls; Tveit, Odd Atle; Bøhleng, Erik; Tverdal, Aage; Aavitsland, Preben

2007-08-01

During maintenance work or breaks on the water distribution system, water pressure occasionally will be reduced. This may lead to intrusion of polluted water-either at the place of repair or through cracks or leaks elsewhere in the distribution system. The objective of this study was to assess whether breaks or maintenance work in the water distribution system with presumed loss of water pressure was associated with an increased risk of gastrointestinal illness among recipients. We conducted a cohort study among recipients of water from seven waterworks in Norway during 2003-04. One week after an episode of mains breaks or maintenance work on the water distribution system, the exposed and unexposed households were interviewed about gastrointestinal illness in the week following the episode. During the 1-week period after the episode, 12.7% of the exposed households reported gastrointestinal illness in the household, compared with 8.0% in the unexposed households [risk ratio (RR) 1.58, 95% confidence interval (CI): 1.1, 2.3]. The risk was highest in households with higher average water consumption. The attributable fraction among the exposed households was 37% in the week following exposure. Our results show that breaks and maintenance work in the water distribution systems caused an increased risk of gastrointestinal illness among water recipients. Better data on the occurrence of low-pressure episodes and improved registration of mains breaks and maintenance work on the water distribution network are needed in order to assess the public health burden of contamination of drinking water within the distribution network.

mHealthMon: toward energy-efficient and distributed mobile health monitoring using parallel offloading.

PubMed

Ahnn, Jong Hoon; Potkonjak, Miodrag

2013-10-01

Although mobile health monitoring where mobile sensors continuously gather, process, and update sensor readings (e.g. vital signals) from patient's sensors is emerging, little effort has been investigated in an energy-efficient management of sensor information gathering and processing. Mobile health monitoring with the focus of energy consumption may instead be holistically analyzed and systematically designed as a global solution to optimization subproblems. This paper presents an attempt to decompose the very complex mobile health monitoring system whose layer in the system corresponds to decomposed subproblems, and interfaces between them are quantified as functions of the optimization variables in order to orchestrate the subproblems. We propose a distributed and energy-saving mobile health platform, called mHealthMon where mobile users publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the mobile health monitoring application's quality of service requirements by modeling each subsystem: mobile clients with medical sensors, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 10.1 times more energy-efficient and 20.2 times faster compared to a standalone mobile health monitoring application, in various mobile health monitoring scenarios applying a realistic mobility model.

Verification of mesoscale objective analyses of VAS and rawinsonde data using the March 1982 AVE/VAS special network data

NASA Technical Reports Server (NTRS)

Doyle, James D.; Warner, Thomas T.

1987-01-01

Various combinations of VAS (Visible and Infrared Spin Scan Radiometer Atmospheric Sounder) data, conventional rawinsonde data, and gridded data from the National Weather Service's (NWS) global analysis, were used in successive-correction and variational objective-analysis procedures. Analyses are produced for 0000 GMT 7 March 1982, when the VAS sounding distribution was not greatly limited by the existence of cloud cover. The successive-correction (SC) procedure was used with VAS data alone, rawinsonde data alone, and both VAS and rawinsonde data. Variational techniques were applied in three ways. Each of these techniques was discussed.

The Value of Information in Distributed Decision Networks

DTIC Science & Technology

2016-03-04

formulation, and then we describe the various results at- tained. 1 Mathematical description of Distributed Decision Network un- der Information...Constraints We now define a mathematical framework for networks. Let G = (V,E) be an undirected random network (graph) drawn from a known distribution pG, 1

Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

NASA Astrophysics Data System (ADS)

Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.

2013-06-01

Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m × 60 m grid block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Web-based modelling of energy, water and matter fluxes to support decision making in mesoscale catchments??the integrative perspective of GLOWA-Danube

NASA Astrophysics Data System (ADS)

Ludwig, R.; Mauser, W.; Niemeyer, S.; Colgan, A.; Stolz, R.; Escher-Vetter, H.; Kuhn, M.; Reichstein, M.; Tenhunen, J.; Kraus, A.; Ludwig, M.; Barth, M.; Hennicker, R.

The GLOWA-initiative (Global Change of the water cycle), funded by the German Ministry of Research and Education (BMBF), has been established to address the manifold consequences of Global Change on regional water resources in a variety of catchment areas with different natural and cultural characteristics. Within this framework, the GLOWA-Danube project is dealing with the Upper Danube watershed as a representative mesoscale test site (∼75.000 km 2) for mountain-foreland regions in the temperate mid-latitudes. The principle objective is to identify, examine and develop new techniques of coupled distributed modelling for the integration of natural and socio-economic sciences. The transdisciplinary research in GLOWA-Danube develops an integrated decision support system, called DANUBIA, to investigate the sustainability of future water use. GLOWA-Danube, which is scheduled for a total run-time of eight years to operationally implement and establish DANUBIA, comprises a university-based network of experts with water-related competence in the fields of engineering, natural and social sciences. Co-operation with a network of stakeholders in water resources management of the Upper Danube catchment ensures that practical issues and future problems in the water sector of the region can be addressed. In order to synthesize a common understanding between the project partners, a standardized notation of parameters and functions and a platform-independent structure of computational methods and interfaces has been established, by making use of the unified modelling language, an industry standard for the structuring and co-ordination of large projects in software development [Booch et al., The Unified Modelling Language User Guide, Addison-Wesley, Reading, 1999]. DANUBIA is object-oriented, spatially distributed and raster-based at its core. It applies the concept of “proxels” (process pixels) as its basic objects, which have different dimensions depending on the viewing scale and connect to their environment through fluxes. The presented paper excerpts the hydrological view point of GLOWA-Danube, its approach of model coupling and network-based communication, and object-oriented techniques to simulate physical processes and interactions at the land surface. The mechanisms and technologies applied to communicate data and model parameters across the typical discipline borders are demonstrated from the perspective of the Landsurface object. It comprises the capabilities of interdependent expert models for energy exchange at various surface types, snowmelt, soil water movement, runoff formation and plant growth in a distributed Java-based modelling environment using the remote method invocation [Pitt et al., Java.rmi: The Remote Method Invocation Guide, Addison Wesley Professional, Reading, 2001, p. 320]. The presented text summarizes the GLOWA-Danube concept and shows the state of an implemented DANUBIA prototype after completion of the first project-year (2001).

Hiding the Source Based on Limited Flooding for Sensor Networks.

PubMed

Chen, Juan; Lin, Zhengkui; Hu, Ying; Wang, Bailing

2015-11-17

Wireless sensor networks are widely used to monitor valuable objects such as rare animals or armies. Once an object is detected, the source, i.e., the sensor nearest to the object, generates and periodically sends a packet about the object to the base station. Since attackers can capture the object by localizing the source, many protocols have been proposed to protect source location. Instead of transmitting the packet to the base station directly, typical source location protection protocols first transmit packets randomly for a few hops to a phantom location, and then forward the packets to the base station. The problem with these protocols is that the generated phantom locations are usually not only near the true source but also close to each other. As a result, attackers can easily trace a route back to the source from the phantom locations. To address the above problem, we propose a new protocol for source location protection based on limited flooding, named SLP. Compared with existing protocols, SLP can generate phantom locations that are not only far away from the source, but also widely distributed. It improves source location security significantly with low communication cost. We further propose a protocol, namely SLP-E, to protect source location against more powerful attackers with wider fields of vision. The performance of our SLP and SLP-E are validated by both theoretical analysis and simulation results.

Real Time Assessment of Potable Water Quality in Distribution Network based on Low Cost Multi-Sensor Array

NASA Astrophysics Data System (ADS)

Bhardwaj, Jyotirmoy; Gupta, Karunesh K.; Khatri, Punit

2018-03-01

New concepts and techniques are replacing traditional methods of water quality parameters measurement systems. This paper proposed a new way of potable water quality assessment in distribution network using Multi Sensor Array (MSA). Extensive research suggests that following parameters i.e. pH, Dissolved Oxygen (D.O.), Conductivity, Oxygen Reduction Potential (ORP), Temperature and Salinity are most suitable to detect overall quality of potable water. Commonly MSA is not an integrated sensor array on some substrate, but rather comprises a set of individual sensors measuring simultaneously different water parameters all together. Based on research, a MSA has been developed followed by signal conditioning unit and finally, an algorithm for easy user interfacing. A dedicated part of this paper also discusses the platform design and significant results. The Objective of this proposed research is to provide simple, efficient, cost effective and socially acceptable means to detect and analyse water bodies regularly and automatically.

Multi-period response management to contaminated water distribution networks: dynamic programming versus genetic algorithms

NASA Astrophysics Data System (ADS)

Bashi-Azghadi, Seyyed Nasser; Afshar, Abbas; Afshar, Mohammad Hadi

2018-03-01

Previous studies on consequence management assume that the selected response action including valve closure and/or hydrant opening remains unchanged during the entire management period. This study presents a new embedded simulation-optimization methodology for deriving time-varying operational response actions in which the network topology may change from one stage to another. Dynamic programming (DP) and genetic algorithm (GA) are used in order to minimize selected objective functions. Two networks of small and large sizes are used in order to illustrate the performance of the proposed modelling schemes if a time-dependent consequence management strategy is to be implemented. The results show that for a small number of decision variables even in large-scale networks, DP is superior in terms of accuracy and computer runtime. However, as the number of potential actions grows, DP loses its merit over the GA approach. This study clearly proves the priority of the proposed dynamic operation strategy over the commonly used static strategy.

Reactive power optimization strategy considering analytical impedance ratio

NASA Astrophysics Data System (ADS)

Wu, Zhongchao; Shen, Weibing; Liu, Jinming; Guo, Maoran; Zhang, Shoulin; Xu, Keqiang; Wang, Wanjun; Sui, Jinlong

2017-05-01

In this paper, considering the traditional reactive power optimization cannot realize the continuous voltage adjustment and voltage stability, a dynamic reactive power optimization strategy is proposed in order to achieve both the minimization of network loss and high voltage stability with wind power. Due to the fact that wind power generation is fluctuant and uncertain, electrical equipments such as transformers and shunt capacitors may be operated frequently in order to achieve minimization of network loss, which affect the lives of these devices. In order to solve this problem, this paper introduces the derivation process of analytical impedance ratio based on Thevenin equivalent. Thus, the multiple objective function is proposed to minimize the network loss and analytical impedance ratio. Finally, taking the improved IEEE 33-bus distribution system as example, the result shows that the movement of voltage control equipment has been reduced and network loss increment is controlled at the same time, which proves the applicable value of this strategy.

Information Weighted Consensus for Distributed Estimation in Vision Networks

ERIC Educational Resources Information Center

Kamal, Ahmed Tashrif

2013-01-01

Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms…

Magnetoacoustic microscopic imaging of conductive objects and nanoparticles distribution

NASA Astrophysics Data System (ADS)

Liu, Siyu; Zhang, Ruochong; Luo, Yunqi; Zheng, Yuanjin

2017-09-01

Magnetoacoustic tomography has been demonstrated as a powerful and low-cost multi-wave imaging modality. However, due to limited spatial resolution and detection efficiency of magnetoacoustic signal, full potential of the magnetoacoustic imaging remains to be tapped. Here we report a high-resolution magnetoacoustic microscopy method, where magnetic stimulation is provided by a compact solenoid resonance coil connected with a matching network, and acoustic reception is realized by using a high-frequency focused ultrasound transducer. Scanning the magnetoacoustic microscopy system perpendicularly to the acoustic axis of the focused transducer would generate a two-dimensional microscopic image with acoustically determined lateral resolution. It is analyzed theoretically and demonstrated experimentally that magnetoacoustic generation in this microscopic system depends on the conductivity profile of conductive objects and localized distribution of superparamagnetic iron magnetic nanoparticles, based on two different but related implementations. The lateral resolution is characterized. Directional nature of magnetoacoustic vibration and imaging sensitivity for mapping magnetic nanoparticles are also discussed. The proposed microscopy system offers a high-resolution method that could potentially map intrinsic conductivity distribution in biological tissue and extraneous magnetic nanoparticles.

Research and Design of the Three-tier Distributed Network Management System Based on COM / COM + and DNA

NASA Astrophysics Data System (ADS)

Liang, Likai; Bi, Yushen

Considered on the distributed network management system's demand of high distributives, extensibility and reusability, a framework model of Three-tier distributed network management system based on COM/COM+ and DNA is proposed, which adopts software component technology and N-tier application software framework design idea. We also give the concrete design plan of each layer of this model. Finally, we discuss the internal running process of each layer in the distributed network management system's framework model.

A Distributed Prognostic Health Management Architecture

NASA Technical Reports Server (NTRS)

Bhaskar, Saha; Saha, Sankalita; Goebel, Kai

2009-01-01

This paper introduces a generic distributed prognostic health management (PHM) architecture with specific application to the electrical power systems domain. Current state-of-the-art PHM systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to loss of functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become unsuitable for successful deployment, and efficient distributed architectures are required. A distributed architecture though, is not effective unless there is an algorithmic framework to take advantage of its unique abilities. The health management paradigm envisaged here incorporates a heterogeneous set of system components monitored by a varied suite of sensors and a particle filtering (PF) framework that has the power and the flexibility to adapt to the different diagnostic and prognostic needs. Both the diagnostic and prognostic tasks are formulated as a particle filtering problem in order to explicitly represent and manage uncertainties; however, typically the complexity of the prognostic routine is higher than the computational power of one computational element ( CE). Individual CEs run diagnostic routines until the system variable being monitored crosses beyond a nominal threshold, upon which it coordinates with other networked CEs to run the prognostic routine in a distributed fashion. Implementation results from a network of distributed embedded devices monitoring a prototypical aircraft electrical power system are presented, where the CEs are Sun Microsystems Small Programmable Object Technology (SPOT) devices.

Characteristic and intermingled neocortical circuits encode different visual object discriminations.

PubMed

Zhang, Guo-Rong; Zhao, Hua; Cook, Nathan; Svestka, Michael; Choi, Eui M; Jan, Mary; Cook, Robert G; Geller, Alfred I

2017-07-28

Synaptic plasticity and neural network theories hypothesize that the essential information for advanced cognitive tasks is encoded in specific circuits and neurons within distributed neocortical networks. However, these circuits are incompletely characterized, and we do not know if a specific discrimination is encoded in characteristic circuits among multiple animals. Here, we determined the spatial distribution of active neurons for a circuit that encodes some of the essential information for a cognitive task. We genetically activated protein kinase C pathways in several hundred spatially-grouped glutamatergic and GABAergic neurons in rat postrhinal cortex, a multimodal associative area that is part of a distributed circuit that encodes visual object discriminations. We previously established that this intervention enhances accuracy for specific discriminations. Moreover, the genetically-modified, local circuit in POR cortex encodes some of the essential information, and this local circuit is preferentially activated during performance, as shown by activity-dependent gene imaging. Here, we mapped the positions of the active neurons, which revealed that two image sets are encoded in characteristic and different circuits. While characteristic circuits are known to process sensory information, in sensory areas, this is the first demonstration that characteristic circuits encode specific discriminations, in a multimodal associative area. Further, the circuits encoding the two image sets are intermingled, and likely overlapping, enabling efficient encoding. Consistent with reconsolidation theories, intermingled and overlapping encoding could facilitate formation of associations between related discriminations, including visually similar discriminations or discriminations learned at the same time or place. Copyright © 2017 Elsevier B.V. All rights reserved.

A Mars environmental survey (MESUR) - Feasibility of a low cost global approach

NASA Technical Reports Server (NTRS)

Hubbard, G. S.; Wercinski, Paul F.; Sarver, George L.; Hanel, Robert P.; Ramos, Ruben

1991-01-01

In situ measurements of Mars' surface and atmosphere are the objectives of a novel network mission concept called the Mars Environmental SURvey (MESUR). As envisioned, the MESUR mission will emplace a pole-to-pole global distribution of 16 landers on the Martian surface over three launch opportunites using medium-lift (Delta-class) launch vehicles. The basic concept is to deploy small free-flying probes which would directly enter the Martian atmosphere, measure the upper atmospheric structure, image the local terrain before landing, and survive landing to perform meteorology, seismology, surface imaging, and soil chemistry measurements. Data will be returned via dedicated relay orbiter or direct-to-earth transmission. The mission philosophy is to: (1) 'grow' a network over a period of years using a series of launch opportunities; (2) develop a level-of-effort which is flexible and responsive to a broad set of objectives; (3) focus on Mars science while providing a solid basis for future human presence; and (4) minimize overall project cost and complexity wherever possible.

A new paradigm for the molecular basis of rubber elasticity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanson, David E.; Barber, John L.

The molecular basis for rubber elasticity is arguably the oldest and one of the most important questions in the field of polymer physics. The theoretical investigation of rubber elasticity began in earnest almost a century ago with the development of analytic thermodynamic models, based on simple, highly-symmetric configurations of so-called Gaussian chains, i.e. polymer chains that obey Markov statistics. Numerous theories have been proposed over the past 90 years based on the ansatz that the elastic force for individual network chains arises from the entropy change associated with the distribution of end-to-end distances of a free polymer chain. There aremore » serious philosophical objections to this assumption and others, such as the assumption that all network nodes undergo affine motion and that all of the network chains have the same length. Recently, a new paradigm for elasticity in rubber networks has been proposed that is based on mechanisms that originate at the molecular level. Using conventional statistical mechanics analyses, quantum chemistry, and molecular dynamics simulations, the fundamental entropic and enthalpic chain extension forces for polyisoprene (natural rubber) have been determined, along with estimates for the basic force constants. Concurrently, the complex morphology of natural rubber networks (the joint probability density distributions that relate the chain end-to-end distance to its contour length) has also been captured in a numerical model. When molecular chain forces are merged with the network structure in this model, it is possible to study the mechanical response to tensile and compressive strains of a representative volume element of a polymer network. As strain is imposed on a network, pathways of connected taut chains, that completely span the network along strain axis, emerge. Although these chains represent only a few percent of the total, they account for nearly all of the elastic stress at high strain. Here we provide a brief review of previous elasticity theories and their deficiencies, and present a new paradigm with an emphasis on experimental comparisons.« less

A new paradigm for the molecular basis of rubber elasticity

DOE PAGES

Hanson, David E.; Barber, John L.

2015-02-19

The molecular basis for rubber elasticity is arguably the oldest and one of the most important questions in the field of polymer physics. The theoretical investigation of rubber elasticity began in earnest almost a century ago with the development of analytic thermodynamic models, based on simple, highly-symmetric configurations of so-called Gaussian chains, i.e. polymer chains that obey Markov statistics. Numerous theories have been proposed over the past 90 years based on the ansatz that the elastic force for individual network chains arises from the entropy change associated with the distribution of end-to-end distances of a free polymer chain. There aremore » serious philosophical objections to this assumption and others, such as the assumption that all network nodes undergo affine motion and that all of the network chains have the same length. Recently, a new paradigm for elasticity in rubber networks has been proposed that is based on mechanisms that originate at the molecular level. Using conventional statistical mechanics analyses, quantum chemistry, and molecular dynamics simulations, the fundamental entropic and enthalpic chain extension forces for polyisoprene (natural rubber) have been determined, along with estimates for the basic force constants. Concurrently, the complex morphology of natural rubber networks (the joint probability density distributions that relate the chain end-to-end distance to its contour length) has also been captured in a numerical model. When molecular chain forces are merged with the network structure in this model, it is possible to study the mechanical response to tensile and compressive strains of a representative volume element of a polymer network. As strain is imposed on a network, pathways of connected taut chains, that completely span the network along strain axis, emerge. Although these chains represent only a few percent of the total, they account for nearly all of the elastic stress at high strain. Here we provide a brief review of previous elasticity theories and their deficiencies, and present a new paradigm with an emphasis on experimental comparisons.« less

Power allocation for target detection in radar networks based on low probability of intercept: A cooperative game theoretical strategy

NASA Astrophysics Data System (ADS)

Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang

2017-08-01

Distributed radar network systems have been shown to have many unique features. Due to their advantage of signal and spatial diversities, radar networks are attractive for target detection. In practice, the netted radars in radar networks are supposed to maximize their transmit power to achieve better detection performance, which may be in contradiction with low probability of intercept (LPI). Therefore, this paper investigates the problem of adaptive power allocation for radar networks in a cooperative game-theoretic framework such that the LPI performance can be improved. Taking into consideration both the transmit power constraints and the minimum signal to interference plus noise ratio (SINR) requirement of each radar, a cooperative Nash bargaining power allocation game based on LPI is formulated, whose objective is to minimize the total transmit power by optimizing the power allocation in radar networks. First, a novel SINR-based network utility function is defined and utilized as a metric to evaluate power allocation. Then, with the well-designed network utility function, the existence and uniqueness of the Nash bargaining solution are proved analytically. Finally, an iterative Nash bargaining algorithm is developed that converges quickly to a Pareto optimal equilibrium for the cooperative game. Numerical simulations and theoretic analysis are provided to evaluate the effectiveness of the proposed algorithm.

Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

NASA Astrophysics Data System (ADS)

van der Linden, Joost H.; Narsilio, Guillermo A.; Tordesillas, Antoinette

2016-08-01

We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

SONG China project - participating in the global network

NASA Astrophysics Data System (ADS)

Deng, Licai; Xin, Yu; Zhang, Xiaobin; Li, Yan; Jiang, Xiaojun; Wang, Guomin; Wang, Kun; Zhou, Jilin; Yan, Zhengzhou; Luo, Zhiquan

2013-01-01

SONG (Stellar Observations Network Goup) is a low-cost ground based international collaboration aimed at two cutting edge problems in contemporary astrophysics in the time-domain: 1) Direct diagnostics of the internal structure of stars and 2) looking for and studying extra solar planets, possibly in the habitable zone. The general plan is to set up a network of 1m telescopes uniformly distributed in geographic latitude (in both hemispheres). China jointed the collaboration (initiated by Danish astronomers) at the very beginning. In addition to SONG's original plan (http://song.phys.au.dk), the Chinese team proposed a parallel photometry subnet work in the northern hemisphere, namely 50BiN (50cm Binocular Network, previously known as mini-SONG), to enable a large field photometric capability for the network, therefore maximising the potential of the network platform. The network will be able to produce nearly continuous time series observations of a number of selected objects with high resolution spectroscopy (SONG) and accurate photometry (50BiN), and to produce ultra-high accuracy photometry in dense field to look for micro-lensing events caused by planetary systems. This project has great synergy with Chinese Astronomical activities in Antarctica (Dome A), and other similar networks (e.g. LCOGT). The plan and current status of the project are overviewed in this poster.

Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies

PubMed Central

Mirzakhalili, Ehsan; Gourgou, Eleni; Booth, Victoria; Epureanu, Bogdan

2017-01-01

Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons. PMID:28659765

Mental health and social networks in early adolescence: a dynamic study of objectively-measured social interaction behaviors.

PubMed

Pachucki, Mark C; Ozer, Emily J; Barrat, Alain; Cattuto, Ciro

2015-01-01

How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment--such as physical activity and food choice--may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early adolescence may be less subject to mechanisms of social influence than network research in even slightly older adolescents currently indicates. Copyright © 2014. Published by Elsevier Ltd.

Structural diversity effects of multilayer networks on the threshold of interacting epidemics

NASA Astrophysics Data System (ADS)

Wang, Weihong; Chen, MingMing; Min, Yong; Jin, Xiaogang

2016-02-01

Foodborne diseases always spread through multiple vectors (e.g. fresh vegetables and fruits) and reveal that multilayer network could spread fatal pathogen with complex interactions. In this paper, first, we use a "top-down analysis framework that depends on only two distributions to describe a random multilayer network with any number of layers. These two distributions are the overlaid degree distribution and the edge-type distribution of the multilayer network. Second, based on the two distributions, we adopt three indicators of multilayer network diversity to measure the correlation between network layers, including network richness, likeness, and evenness. The network richness is the number of layers forming the multilayer network. The network likeness is the degree of different layers sharing the same edge. The network evenness is the variance of the number of edges in every layer. Third, based on a simple epidemic model, we analyze the influence of network diversity on the threshold of interacting epidemics with the coexistence of collaboration and competition. Our work extends the "top-down" analysis framework to deal with the more complex epidemic situation and more diversity indicators and quantifies the trade-off between thresholds of inter-layer collaboration and intra-layer transmission.

Distributed Detection with Collisions in a Random, Single-Hop Wireless Sensor Network

DTIC Science & Technology

2013-05-26

public release; distribution is unlimited. Distributed detection with collisions in a random, single-hop wireless sensor network The views, opinions...1274 2 ABSTRACT Distributed detection with collisions in a random, single-hop wireless sensor network Report Title We consider the problem of... WIRELESS SENSOR NETWORK Gene T. Whipps?† Emre Ertin† Randolph L. Moses† ?U.S. Army Research Laboratory, Adelphi, MD 20783 †The Ohio State University

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

The evolution of cooperation on geographical networks

NASA Astrophysics Data System (ADS)

Li, Yixiao; Wang, Yi; Sheng, Jichuan

2017-11-01

We study evolutionary public goods game on geographical networks, i.e., complex networks which are located on a geographical plane. The geographical feature effects in two ways: In one way, the geographically-induced network structure influences the overall evolutionary dynamics, and, in the other way, the geographical length of an edge influences the cost when the two players at the two ends interact. For the latter effect, we design a new cost function of cooperators, which simply assumes that the longer the distance between two players, the higher cost the cooperator(s) of them have to pay. In this study, network substrates are generated by a previous spatial network model with a cost-benefit parameter controlling the network topology. Our simulations show that the greatest promotion of cooperation is achieved in the intermediate regime of the parameter, in which empirical estimates of various railway networks fall. Further, we investigate how the distribution of edges' geographical costs influences the evolutionary dynamics and consider three patterns of the distribution: an approximately-equal distribution, a diverse distribution, and a polarized distribution. For normal geographical networks which are generated using intermediate values of the cost-benefit parameter, a diverse distribution hinders the evolution of cooperation, whereas a polarized distribution lowers the threshold value of the amplification factor for cooperation in public goods game. These results are helpful for understanding the evolution of cooperation on real-world geographical networks.

Software-Enabled Distributed Network Governance: The PopMedNet Experience.

PubMed

Davies, Melanie; Erickson, Kyle; Wyner, Zachary; Malenfant, Jessica; Rosen, Rob; Brown, Jeffrey

2016-01-01

The expanded availability of electronic health information has led to increased interest in distributed health data research networks. The distributed research network model leaves data with and under the control of the data holder. Data holders, network coordinating centers, and researchers have distinct needs and challenges within this model. The concerns of network stakeholders are addressed in the design and governance models of the PopMedNet software platform. PopMedNet features include distributed querying, customizable workflows, and auditing and search capabilities. Its flexible role-based access control system enables the enforcement of varying governance policies. Four case studies describe how PopMedNet is used to enforce network governance models. Trust is an essential component of a distributed research network and must be built before data partners may be willing to participate further. The complexity of the PopMedNet system must be managed as networks grow and new data, analytic methods, and querying approaches are developed. The PopMedNet software platform supports a variety of network structures, governance models, and research activities through customizable features designed to meet the needs of network stakeholders.

75 FR 9343 - Nomenclature Change Relating to the Network Distribution Center Transition

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-02

... POSTAL SERVICE 39 CFR Parts 111 and 121 Nomenclature Change Relating to the Network Distribution... (BMC) to network distribution centers (NDC), by replacing all text references to ``BMC'' with ``NDC...: Background: The BMC network was established in the 1970s to process Parcel Post[supreg], Bound Printed Matter...

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Optimal Regulation of Virtual Power Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall Anese, Emiliano; Guggilam, Swaroop S.; Simonetto, Andrea

This paper develops a real-time algorithmic framework for aggregations of distributed energy resources (DERs) in distribution networks to provide regulation services in response to transmission-level requests. Leveraging online primal-dual-type methods for time-varying optimization problems and suitable linearizations of the nonlinear AC power-flow equations, we believe this work establishes the system-theoretic foundation to realize the vision of distribution-level virtual power plants. The optimization framework controls the output powers of dispatchable DERs such that, in aggregate, they respond to automatic-generation-control and/or regulation-services commands. This is achieved while concurrently regulating voltages within the feeder and maximizing customers' and utility's performance objectives. Convergence andmore » tracking capabilities are analytically established under suitable modeling assumptions. Simulations are provided to validate the proposed approach.« less

Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

PubMed

Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

2016-08-18

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.

PubMed

Ding, Rui; Ujang, Norsidah; Hamid, Hussain Bin; Wu, Jianjun

2015-01-01

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Multiple-Ring Digital Communication Network

NASA Technical Reports Server (NTRS)

Kirkham, Harold

1992-01-01

Optical-fiber digital communication network to support data-acquisition and control functions of electric-power-distribution networks. Optical-fiber links of communication network follow power-distribution routes. Since fiber crosses open power switches, communication network includes multiple interconnected loops with occasional spurs. At each intersection node is needed. Nodes of communication network include power-distribution substations and power-controlling units. In addition to serving data acquisition and control functions, each node acts as repeater, passing on messages to next node(s). Multiple-ring communication network operates on new AbNET protocol and features fiber-optic communication.

Cooperative water network system to reduce carbon footprint.

PubMed

Lim, Seong-Rin; Park, Jong Moon

2008-08-15

Much effort has been made in reducing the carbon footprint to mitigate climate change. However, water network synthesis has been focused on reducing the consumption and cost of freshwater within each industrial plant. The objective of this study is to illustrate the necessity of the cooperation of industrial plants to reduce the total carbon footprint of their water supply systems. A mathematical optimization model to minimize global warming potentials is developed to synthesize (1) a cooperative water network system (WNS) integrated over two plants and (2) an individual WNS consisting of two WNSs separated for each plant. The cooperative WNS is compared to the individual WNS. The cooperation reduces their carbon footprint and is economically feasible and profitable. A strategy for implementing the cooperation is suggested for the fair distribution of costs and benefits. As a consequence, industrial plants should cooperate with their neighbor plants to further reduce the carbon footprint.

Computer vision in roadway transportation systems: a survey

NASA Astrophysics Data System (ADS)

Loce, Robert P.; Bernal, Edgar A.; Wu, Wencheng; Bala, Raja

2013-10-01

There is a worldwide effort to apply 21st century intelligence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emission reduction. Computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art.

Reliable Adaptive Video Streaming Driven by Perceptual Semantics for Situational Awareness

PubMed Central

Pimentel-Niño, M. A.; Saxena, Paresh; Vazquez-Castro, M. A.

2015-01-01

A novel cross-layer optimized video adaptation driven by perceptual semantics is presented. The design target is streamed live video to enhance situational awareness in challenging communications conditions. Conventional solutions for recreational applications are inadequate and novel quality of experience (QoE) framework is proposed which allows fully controlled adaptation and enables perceptual semantic feedback. The framework relies on temporal/spatial abstraction for video applications serving beyond recreational purposes. An underlying cross-layer optimization technique takes into account feedback on network congestion (time) and erasures (space) to best distribute available (scarce) bandwidth. Systematic random linear network coding (SRNC) adds reliability while preserving perceptual semantics. Objective metrics of the perceptual features in QoE show homogeneous high performance when using the proposed scheme. Finally, the proposed scheme is in line with content-aware trends, by complying with information-centric-networking philosophy and architecture. PMID:26247057

An energy-aware routing protocol for query-based applications in wireless sensor networks.

PubMed

Ahvar, Ehsan; Ahvar, Shohreh; Lee, Gyu Myoung; Crespi, Noel

2014-01-01

Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption.

Advanced information processing system: Authentication protocols for network communication

NASA Technical Reports Server (NTRS)

Harper, Richard E.; Adams, Stuart J.; Babikyan, Carol A.; Butler, Bryan P.; Clark, Anne L.; Lala, Jaynarayan H.

1994-01-01

In safety critical I/O and intercomputer communication networks, reliable message transmission is an important concern. Difficulties of communication and fault identification in networks arise primarily because the sender of a transmission cannot be identified with certainty, an intermediate node can corrupt a message without certainty of detection, and a babbling node cannot be identified and silenced without lengthy diagnosis and reconfiguration . Authentication protocols use digital signature techniques to verify the authenticity of messages with high probability. Such protocols appear to provide an efficient solution to many of these problems. The objective of this program is to develop, demonstrate, and evaluate intercomputer communication architectures which employ authentication. As a context for the evaluation, the authentication protocol-based communication concept was demonstrated under this program by hosting a real-time flight critical guidance, navigation and control algorithm on a distributed, heterogeneous, mixed redundancy system of workstations and embedded fault-tolerant computers.

An Energy-Aware Routing Protocol for Query-Based Applications in Wireless Sensor Networks

PubMed Central

Crespi, Noel

2014-01-01

Wireless sensor network (WSN) typically has energy consumption restriction. Designing energy-aware routing protocol can significantly reduce energy consumption in WSNs. Energy-aware routing protocols can be classified into two categories, energy savers and energy balancers. Energy saving protocols are used to minimize the overall energy consumed by a WSN, while energy balancing protocols attempt to efficiently distribute the consumption of energy throughout the network. In general terms, energy saving protocols are not necessarily good at balancing energy consumption and energy balancing protocols are not always good at reducing energy consumption. In this paper, we propose an energy-aware routing protocol (ERP) for query-based applications in WSNs, which offers a good trade-off between traditional energy balancing and energy saving objectives and supports a soft real time packet delivery. This is achieved by means of fuzzy sets and learning automata techniques along with zonal broadcasting to decrease total energy consumption. PMID:24696640

A growth model for directed complex networks with power-law shape in the out-degree distribution

PubMed Central

Esquivel-Gómez, J.; Stevens-Navarro, E.; Pineda-Rico, U.; Acosta-Elias, J.

2015-01-01

Many growth models have been published to model the behavior of real complex networks. These models are able to reproduce several of the topological properties of such networks. However, in most of these growth models, the number of outgoing links (i.e., out-degree) of nodes added to the network is constant, that is all nodes in the network are born with the same number of outgoing links. In other models, the resultant out-degree distribution decays as a poisson or an exponential distribution. However, it has been found that in real complex networks, the out-degree distribution decays as a power-law. In order to obtain out-degree distribution with power-law behavior some models have been proposed. This work introduces a new model that allows to obtain out-degree distributions that decay as a power-law with an exponent in the range from 0 to 1. PMID:25567141

Site selection for the future stations of the french permanent broadband network

NASA Astrophysics Data System (ADS)

Vergne, Jérôme; Charade, Olivier

2013-04-01

RESIF (REseau SIsmologique et géodésique Français) is a new French research infrastructure dedicated to the observation of earth deformation based on seismic and geodetic instruments mainly located in France. One of its major component, called RESIF-CLB (Construction Large Bande), is devoted to the evolution of the permanent seismic broadband network in metropolitan France with the objective to complement the 45 existing stations with ~155 new stations within the next eight years. This network will be used for various scientific objectives including deep structures imaging and national seismicity monitoring. The chosen network topology consists in a backbone of homogeneously distributed stations (long wavelength array) completed by additional stations in seismically active regions. Management of the RESIF-CLB project is carried out by the technical division of INSU (Institut National des Sciences de l'Univers) who will rely on eight regional observatories and the CEA-LDG for the construction and operation of the stations. To optimize the performance of the network, we put a strong emphasis on the standardization of the stations in term of vault types, scientific and technical instrumentation and operation procedures. We also set up a procedure for site selection requiring that every potential site has to be tested for at least 3 weeks with a minimalist installation. Analysis of the continuous ambient noise records is then included in a standardized report submitted to all committed partners for acceptance. During the last two years, about 60 potential new sites have been tested, spanning various places and environments. We present a review of the seismic noise measurements at these sites and discuss the influence of different types of noise sources depending on the frequency band of interest. For example, we show that regional population distribution can be used as a proxy to infer the noise level at frequencies higher than 1 Hz. Based on similar noise analyses at existing permanent sites, we also discuss the fair benefit of our site testing procedure for the estimation of the long period noise level once the station is settled.

The Role of Graphlets in Viral Processes on Networks

NASA Astrophysics Data System (ADS)

Khorshidi, Samira; Al Hasan, Mohammad; Mohler, George; Short, Martin B.

2018-05-01

Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks and recent attempts have been made to use assortativity to address this shortcoming. In this paper, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results not only highlight the importance of graphlets but also identify a small collection of graphlets which may have the highest influence over the viral processes on a network.

Comprehensive evaluation of impacts of distributed generation integration in distribution network

NASA Astrophysics Data System (ADS)

Peng, Sujiang; Zhou, Erbiao; Ji, Fengkun; Cao, Xinhui; Liu, Lingshuang; Liu, Zifa; Wang, Xuyang; Cai, Xiaoyu

2018-04-01

All Distributed generation (DG) as the supplement to renewable energy centralized utilization, is becoming the focus of development direction of renewable energy utilization. With the increasing proportion of DG in distribution network, the network power structure, power flow distribution, operation plans and protection are affected to some extent. According to the main impacts of DG, a comprehensive evaluation model of distributed network with DG is proposed in this paper. A comprehensive evaluation index system including 7 aspects, along with their corresponding index calculation method is established for quantitative analysis. The indices under different access capacity of DG in distribution network are calculated based on the IEEE RBTS-Bus 6 system and the evaluation result is calculated by analytic hierarchy process (AHP). The proposed model and method are verified effective and validity through case study.

Research of the self-healing technologies in the optical communication network of distribution automation

NASA Astrophysics Data System (ADS)

Wang, Hao; Zhong, Guoxin

2018-03-01

Optical communication network is the mainstream technique of the communication networks for distribution automation, and self-healing technologies can improve the in reliability of the optical communication networks significantly. This paper discussed the technical characteristics and application scenarios of several network self-healing technologies in the access layer, the backbone layer and the core layer of the optical communication networks for distribution automation. On the base of the contrastive analysis, this paper gives an application suggestion of these self-healing technologies.

Strategy on energy saving reconstruction of distribution networks based on life cycle cost

NASA Astrophysics Data System (ADS)

Chen, Xiaofei; Qiu, Zejing; Xu, Zhaoyang; Xiao, Chupeng

2017-08-01

Because the actual distribution network reconstruction project funds are often limited, the cost-benefit model and the decision-making method are crucial for distribution network energy saving reconstruction project. From the perspective of life cycle cost (LCC), firstly the research life cycle is determined for the energy saving reconstruction of distribution networks with multi-devices. Then, a new life cycle cost-benefit model for energy-saving reconstruction of distribution network is developed, in which the modification schemes include distribution transformers replacement, lines replacement and reactive power compensation. In the operation loss cost and maintenance cost area, the operation cost model considering the influence of load season characteristics and the maintenance cost segmental model of transformers are proposed. Finally, aiming at the highest energy saving profit per LCC, a decision-making method is developed while considering financial and technical constraints as well. The model and method are applied to a real distribution network reconstruction, and the results prove that the model and method are effective.

A bipartite fitness model for online music streaming services

NASA Astrophysics Data System (ADS)

Pongnumkul, Suchit; Motohashi, Kazuyuki

2018-01-01

This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

Neural-Network Object-Recognition Program

NASA Technical Reports Server (NTRS)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

Topology Counts: Force Distributions in Circular Spring Networks.

PubMed

Heidemann, Knut M; Sageman-Furnas, Andrew O; Sharma, Abhinav; Rehfeldt, Florian; Schmidt, Christoph F; Wardetzky, Max

2018-02-09

Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle. We show that characteristic quantities, such as the mean and variance of the force supported by individual springs, can be derived explicitly in terms of only two parameters: (i) average connectivity and (ii) number of nodes. Our analysis shows that a classical mean-field approach fails to capture these characteristic quantities correctly. In contrast, we demonstrate that network topology is a crucial determinant of force distributions in an elastic spring network. Our results for 1D linear spring networks readily generalize to arbitrary dimensions.

Epidemic extinction paths in complex networks

NASA Astrophysics Data System (ADS)

Hindes, Jason; Schwartz, Ira B.

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Epidemic extinction paths in complex networks.

PubMed

Hindes, Jason; Schwartz, Ira B

2017-05-01

We study the extinction of long-lived epidemics on finite complex networks induced by intrinsic noise. Applying analytical techniques to the stochastic susceptible-infected-susceptible model, we predict the distribution of large fluctuations, the most probable or optimal path through a network that leads to a disease-free state from an endemic state, and the average extinction time in general configurations. Our predictions agree with Monte Carlo simulations on several networks, including synthetic weighted and degree-distributed networks with degree correlations, and an empirical high school contact network. In addition, our approach quantifies characteristic scaling patterns for the optimal path and distribution of large fluctuations, both near and away from the epidemic threshold, in networks with heterogeneous eigenvector centrality and degree distributions.

Research on social communication network evolution based on topology potential distribution

NASA Astrophysics Data System (ADS)

Zhao, Dongjie; Jiang, Jian; Li, Deyi; Zhang, Haisu; Chen, Guisheng

2011-12-01

Aiming at the problem of social communication network evolution, first, topology potential is introduced to measure the local influence among nodes in networks. Second, from the perspective of topology potential distribution the method of network evolution description based on topology potential distribution is presented, which takes the artificial intelligence with uncertainty as basic theory and local influence among nodes as essentiality. Then, a social communication network is constructed by enron email dataset, the method presented is used to analyze the characteristic of the social communication network evolution and some useful conclusions are got, implying that the method is effective, which shows that topology potential distribution can effectively describe the characteristic of sociology and detect the local changes in social communication network.

Properties of four real world collaboration--competition networks

NASA Astrophysics Data System (ADS)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.

Azimuthally invariant Mueller-matrix mapping of biological optically anisotropic network

NASA Astrophysics Data System (ADS)

Ushenko, Yu. O.; Vanchuliak, O.; Bodnar, G. B.; Ushenko, V. O.; Grytsyuk, M.; Pavlyukovich, N.; Pavlyukovich, O. V.; Antonyuk, O.

2017-09-01

A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular dichroism of histological sections liver tissue of mice with different degrees of severity of diabetes are found. The interconnections between such distributions and parameters of linear and circular dichroism of liver of mice tissue histological sections are defined. The comparative investigations of coordinate distributions of parameters of amplitude anisotropy formed by Liver tissue with varying severity of diabetes (10 days and 24 days) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular dichroism are defined. The objective criteria of cause of the degree of severity of the diabetes differentiation are determined.

EOS: A project to investigate the design and construction of real-time distributed embedded operating systems

NASA Technical Reports Server (NTRS)

Campbell, R. H.; Essick, R. B.; Grass, J.; Johnston, G.; Kenny, K.; Russo, V.

1986-01-01

The EOS project is investigating the design and construction of a family of real-time distributed embedded operating systems for reliable, distributed aerospace applications. Using the real-time programming techniques developed in co-operation with NASA in earlier research, the project staff is building a kernel for a multiple processor networked system. The first six months of the grant included a study of scheduling in an object-oriented system, the design philosophy of the kernel, and the architectural overview of the operating system. In this report, the operating system and kernel concepts are described. An environment for the experiments has been built and several of the key concepts of the system have been prototyped. The kernel and operating system is intended to support future experimental studies in multiprocessing, load-balancing, routing, software fault-tolerance, distributed data base design, and real-time processing.

Semantic Indexing of Medical Learning Objects: Medical Students' Usage of a Semantic Network

PubMed Central

Gießler, Paul; Ohnesorge-Radtke, Ursula; Spreckelsen, Cord

2015-01-01

Background The Semantically Annotated Media (SAM) project aims to provide a flexible platform for searching, browsing, and indexing medical learning objects (MLOs) based on a semantic network derived from established classification systems. Primarily, SAM supports the Aachen emedia skills lab, but SAM is ready for indexing distributed content and the Simple Knowledge Organizing System standard provides a means for easily upgrading or even exchanging SAM’s semantic network. There is a lack of research addressing the usability of MLO indexes or search portals like SAM and the user behavior with such platforms. Objective The purpose of this study was to assess the usability of SAM by investigating characteristic user behavior of medical students accessing MLOs via SAM. Methods In this study, we chose a mixed-methods approach. Lean usability testing was combined with usability inspection by having the participants complete four typical usage scenarios before filling out a questionnaire. The questionnaire was based on the IsoMetrics usability inventory. Direct user interaction with SAM (mouse clicks and pages accessed) was logged. Results The study analyzed the typical usage patterns and habits of students using a semantic network for accessing MLOs. Four scenarios capturing characteristics of typical tasks to be solved by using SAM yielded high ratings of usability items and showed good results concerning the consistency of indexing by different users. Long-tail phenomena emerge as they are typical for a collaborative Web 2.0 platform. Suitable but nonetheless rarely used keywords were assigned to MLOs by some users. Conclusions It is possible to develop a Web-based tool with high usability and acceptance for indexing and retrieval of MLOs. SAM can be applied to indexing multicentered repositories of MLOs collaboratively. PMID:27731860

Robust quantum network architectures and topologies for entanglement distribution

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Khatri, Sumeet; Dowling, Jonathan P.

2018-01-01

Entanglement distribution is a prerequisite for several important quantum information processing and computing tasks, such as quantum teleportation, quantum key distribution, and distributed quantum computing. In this work, we focus on two-dimensional quantum networks based on optical quantum technologies using dual-rail photonic qubits for the building of a fail-safe quantum internet. We lay out a quantum network architecture for entanglement distribution between distant parties using a Bravais lattice topology, with the technological constraint that quantum repeaters equipped with quantum memories are not easily accessible. We provide a robust protocol for simultaneous entanglement distribution between two distant groups of parties on this network. We also discuss a memory-based quantum network architecture that can be implemented on networks with an arbitrary topology. We examine networks with bow-tie lattice and Archimedean lattice topologies and use percolation theory to quantify the robustness of the networks. In particular, we provide figures of merit on the loss parameter of the optical medium that depend only on the topology of the network and quantify the robustness of the network against intermittent photon loss and intermittent failure of nodes. These figures of merit can be used to compare the robustness of different network topologies in order to determine the best topology in a given real-world scenario, which is critical in the realization of the quantum internet.

A New Approach to Computing Information in Measurements of Non-Resolved Space Objects by the Falcon Telescope Network

DTIC Science & Technology

2014-09-01

Analysis Simulation for Advanced Tracking (TASAT) satellite modeling tool [8,9]. The method uses the bi-reflectance distribution functions ( BRDF ...directional Reflectance Model Validation and Utilization, Air Force Avionics Laboratory Technical Report, AFAL-TR-73-303, October 1973. [10] Hall, D...failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE SEP 2014 2. REPORT

EPICS as a MARTe Configuration Environment

NASA Astrophysics Data System (ADS)

Valcarcel, Daniel F.; Barbalace, Antonio; Neto, André; Duarte, André S.; Alves, Diogo; Carvalho, Bernardo B.; Carvalho, Pedro J.; Sousa, Jorge; Fernandes, Horácio; Goncalves, Bruno; Sartori, Filippo; Manduchi, Gabriele

2011-08-01

The Multithreaded Application Real-Time executor (MARTe) software provides an environment for the hard real-time execution of codes while leveraging a standardized algorithm development process. The Experimental Physics and Industrial Control System (EPICS) software allows the deployment and remote monitoring of networked control systems. Channel Access (CA) is the protocol that enables the communication between EPICS distributed components. It allows to set and monitor process variables across the network belonging to different systems. The COntrol and Data Acquisition and Communication (CODAC) system for the ITER Tokamak will be EPICS based and will be used to monitor and live configure the plant controllers. The reconfiguration capability in a hard real-time system requires strict latencies from the request to the actuation and it is a key element in the design of the distributed control algorithm. Presently, MARTe and its objects are configured using a well-defined structured language. After each configuration, all objects are destroyed and the system rebuilt, following the strong hard real-time rule that a real-time system in online mode must behave in a strictly deterministic fashion. This paper presents the design and considerations to use MARTe as a plant controller and enable it to be EPICS monitorable and configurable without disturbing the execution at any time, in particular during a plasma discharge. The solutions designed for this will be presented and discussed.

A decentralized mechanism for improving the functional robustness of distribution networks.

PubMed

Shi, Benyun; Liu, Jiming

2012-10-01

Most real-world distribution systems can be modeled as distribution networks, where a commodity can flow from source nodes to sink nodes through junction nodes. One of the fundamental characteristics of distribution networks is the functional robustness, which reflects the ability of maintaining its function in the face of internal or external disruptions. In view of the fact that most distribution networks do not have any centralized control mechanisms, we consider the problem of how to improve the functional robustness in a decentralized way. To achieve this goal, we study two important problems: 1) how to formally measure the functional robustness, and 2) how to improve the functional robustness of a network based on the local interaction of its nodes. First, we derive a utility function in terms of network entropy to characterize the functional robustness of a distribution network. Second, we propose a decentralized network pricing mechanism, where each node need only communicate with its distribution neighbors by sending a "price" signal to its upstream neighbors and receiving "price" signals from its downstream neighbors. By doing so, each node can determine its outflows by maximizing its own payoff function. Our mathematical analysis shows that the decentralized pricing mechanism can produce results equivalent to those of an ideal centralized maximization with complete information. Finally, to demonstrate the properties of our mechanism, we carry out a case study on the U.S. natural gas distribution network. The results validate the convergence and effectiveness of our mechanism when comparing it with an existing algorithm.

Distributing File-Based Data to Remote Sites Within the BABAR Collaboration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gowdy, Stephen J.

BABAR [1] uses two formats for its data: Objectivity database and root [2] files. This poster concerns the distribution of the latter--for Objectivity data see [3]. The BABAR analysis data is stored in root files--one per physics run and analysis selection channel--maintained in a large directory tree. Currently BABAR has more than 4.5 TBytes in 200,000 root files. This data is (mostly) produced at SLAC, but is required for analysis at universities and research centers throughout the us and Europe. Two basic problems confront us when we seek to import bulk data from slac to an institute's local storage viamore » the network. We must determine which files must be imported (depending on the local site requirements and which files have already been imported), and we must make the optimum use of the network when transferring the data. Basic ftp-like tools (ftp, scp, etc) do not attempt to solve the first problem. More sophisticated tools like rsync [4], the widely-used mirror/synchronization program, compare local and remote file systems, checking for changes (based on file date, size and, if desired, an elaborate checksum) in order to only copy new or modified files. However rsync allows for only limited file selection. Also when, as in BABAR, an extremely large directory structure must be scanned, rsync can take several hours just to determine which files need to be copied. Although rsync (and scp) provides on-the-fly compression, it does not allow us to optimize the network transfer by using multiple streams, adjusting the tcp window size, or separating encrypted authentication from unencrypted data channels.« less

Establishing a Network of faint DA white dwarfs as Spectrophotometric Standards

NASA Astrophysics Data System (ADS)

Saha, Abhijit; Narayan, Gautham; Holberg, Jay; Matheson, Thomas; Olszewski, Edward; Stubbs, Christopher; Bohlin, Ralph; Sabbi, Elena; Deustua, Susana; Rest, Armin; Axelrod, Tim; MacKenty, John W.; Camarota, Larry; Gilliland, Ron

2015-08-01

Systematic uncertainties in photometric calibration are the dominant source of error in current type Ia supernova dark energy studies, as well as other forefront cosmology efforts, e.g. photo-redshift determinations for weak lensing mass tomography. Current and next-generation ground-based all-sky surveys require a network of calibration stars with 1) known SEDs (to properly and unambiguously take into account filter differences), and 2) that are on a common photometric zeropoint scale across the sky to sub-percent accuracy. We are using a combination of HST panchromatic photometry and ground based spectroscopy to establish such an essential network of faint primary photometric standards, exploiting the well-understood spectral energy distributions of DA white dwarf stars that are free from the complications of observing through the Earth's time-variable atmosphere. The Balmer features in the spectra are used to deduce the two parameters (temperature and log(g)) from which we model the spectral energy distribution (SED) from these stars which have pure hydrogen atmospheres. By comparing against panchromatic broadband HST photometry, and allowing for an achromatic zero-point adjustment and mild scaling of the interstellar reddening, we find that model prediction and observation agree to a few milli-mag. By combining the zero-point and reddening adjustments with the modeled SED, for each star we obtain the incident SED above the terrestrial atmosphere, thus establishing these objects as spectrophotometric standards. We are pursuing 23 objects between 16 and 19 mag spread over the sky uniformly around the equator and northern mid-latitudes, with plans to extend this to southern latitudes. This precision photometric heritage from HST will benefit essentially all existing and upcoming survey projects, and in prticular, directly addresses one of the current barriers to understanding the nature of dark energy.

An improved AVC strategy applied in distributed wind power system

NASA Astrophysics Data System (ADS)

Zhao, Y. N.; Liu, Q. H.; Song, S. Y.; Mao, W.

2016-08-01

Traditional AVC strategy is mainly used in wind farm and only concerns about grid connection point, which is not suitable for distributed wind power system. Therefore, this paper comes up with an improved AVC strategy applied in distributed wind power system. The strategy takes all nodes of distribution network into consideration and chooses the node having the most serious voltage deviation as control point to calculate the reactive power reference. In addition, distribution principles can be divided into two conditions: when wind generators access to network on single node, the reactive power reference is distributed according to reactive power capacity; when wind generators access to network on multi-node, the reference is distributed according to sensitivity. Simulation results show the correctness and reliability of the strategy. Compared with traditional control strategy, the strategy described in this paper can make full use of generators reactive power output ability according to the distribution network voltage condition and improve the distribution network voltage level effectively.

Final Report - Cloud-Based Management Platform for Distributed, Multi-Domain Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chowdhury, Pulak; Mukherjee, Biswanath

2017-11-03

In this Department of Energy (DOE) Small Business Innovation Research (SBIR) Phase II project final report, Ennetix presents the development of a solution for end-to-end monitoring, analysis, and visualization of network performance for distributed networks. This solution benefits enterprises of all sizes, operators of distributed and federated networks, and service providers.

Neural networks and MIMD-multiprocessors

NASA Technical Reports Server (NTRS)

Vanhala, Jukka; Kaski, Kimmo

1990-01-01

Two artificial neural network models are compared. They are the Hopfield Neural Network Model and the Sparse Distributed Memory model. Distributed algorithms for both of them are designed and implemented. The run time characteristics of the algorithms are analyzed theoretically and tested in practice. The storage capacities of the networks are compared. Implementations are done using a distributed multiprocessor system.

MASM: a market architecture for sensor management in distributed sensor networks

NASA Astrophysics Data System (ADS)

Viswanath, Avasarala; Mullen, Tracy; Hall, David; Garga, Amulya

2005-03-01

Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.

Structured pedigree information for distributed fusion systems

NASA Astrophysics Data System (ADS)

Arambel, Pablo O.

2008-04-01

One of the most critical challenges in distributed data fusion is the avoidance of information double counting (also called "data incest" or "rumor propagation"). This occurs when a node in a network incorporates information into an estimate - e.g. the position of an object - and the estimate is injected into the network. Other nodes fuse this estimate with their own estimates, and continue to propagate estimates through the network. When the first node receives a fused estimate from the network, it does not know if it already contains its own contributions or not. Since the correlation between its own estimate and the estimate received from the network is not known, the node can not fuse the estimates in an optimal way. If it assumes that both estimates are independent from each other, it unknowingly double counts the information that has already being used to obtain the two estimates. This leads to overoptimistic error covariance matrices. If the double-counting is not kept under control, it may lead to serious performance degradation. Double counting can be avoided by propagating uniquely tagged raw measurements; however, that forces each node to process all the measurements and precludes the propagation of derived information. Another approach is to fuse the information using the Covariance Intersection (CI) equations, which maintain consistent estimates irrespective of the cross-correlation among estimates. However, CI does not exploit pedigree information of any kind. In this paper we present an approach that propagates multiple covariance matrices, one for each uncorrelated source in the network. This is a way to compress the pedigree information and avoids the need to propagate raw measurements. The approach uses a generalized version of the Split CI to fuse different estimates with appropriate weights to guarantee the consistency of the estimates.

"Geo-statistics methods and neural networks in geophysical applications: A case study"

NASA Astrophysics Data System (ADS)

Rodriguez Sandoval, R.; Urrutia Fucugauchi, J.; Ramirez Cruz, L. C.

2008-12-01

The study is focus in the Ebano-Panuco basin of northeastern Mexico, which is being explored for hydrocarbon reservoirs. These reservoirs are in limestones and there is interest in determining porosity and permeability in the carbonate sequences. The porosity maps presented in this study are estimated from application of multiattribute and neural networks techniques, which combine geophysics logs and 3-D seismic data by means of statistical relationships. The multiattribute analysis is a process to predict a volume of any underground petrophysical measurement from well-log and seismic data. The data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs are neutron porosity logs. From the 3-D seismic volume a series of sample attributes is calculated. The objective of this study is to derive a set of attributes and the target log values. The selected set is determined by a process of forward stepwise regression. The analysis can be linear or nonlinear. In the linear mode the method consists of a series of weights derived by least-square minimization. In the nonlinear mode, a neural network is trained using the select attributes as inputs. In this case we used a probabilistic neural network PNN. The method is applied to a real data set from PEMEX. For better reservoir characterization the porosity distribution was estimated using both techniques. The case shown a continues improvement in the prediction of the porosity from the multiattribute to the neural network analysis. The improvement is in the training and the validation, which are important indicators of the reliability of the results. The neural network showed an improvement in resolution over the multiattribute analysis. The final maps provide more realistic results of the porosity distribution.

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

Method for distributed object communications based on dynamically acquired and assembled software components

NASA Technical Reports Server (NTRS)

Sundermier, Amy (Inventor)

2002-01-01

A method for acquiring and assembling software components at execution time into a client program, where the components may be acquired from remote networked servers is disclosed. The acquired components are assembled according to knowledge represented within one or more acquired mediating components. A mediating component implements knowledge of an object model. A mediating component uses its implemented object model knowledge, acquired component class information and polymorphism to assemble components into an interacting program at execution time. The interactions or abstract relationships between components in the object model may be implemented by the mediating component as direct invocations or indirect events or software bus exchanges. The acquired components may establish communications with remote servers. The acquired components may also present a user interface representing data to be exchanged with the remote servers. The mediating components may be assembled into layers, allowing arbitrarily complex programs to be constructed at execution time.

Wireless sensor networks for heritage object deformation detection and tracking algorithm.

PubMed

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-10-31

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

PubMed Central

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-01-01

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection. PMID:25365458

Disentangling visual imagery and perception of real-world objects

PubMed Central

Lee, Sue-Hyun; Kravitz, Dwight J.; Baker, Chris I.

2011-01-01

During mental imagery, visual representations can be evoked in the absence of “bottom-up” sensory input. Prior studies have reported similar neural substrates for imagery and perception, but studies of brain-damaged patients have revealed a double dissociation with some patients showing preserved imagery in spite of impaired perception and others vice versa. Here, we used fMRI and multi-voxel pattern analysis to investigate the specificity, distribution, and similarity of information for individual seen and imagined objects to try and resolve this apparent contradiction. In an event-related design, participants either viewed or imagined individual named object images on which they had been trained prior to the scan. We found that the identity of both seen and imagined objects could be decoded from the pattern of activity throughout the ventral visual processing stream. Further, there was enough correspondence between imagery and perception to allow discrimination of individual imagined objects based on the response during perception. However, the distribution of object information across visual areas was strikingly different during imagery and perception. While there was an obvious posterior-anterior gradient along the ventral visual stream for seen objects, there was an opposite gradient for imagined objects. Moreover, the structure of representations (i.e. the pattern of similarity between responses to all objects) was more similar during imagery than perception in all regions along the visual stream. These results suggest that while imagery and perception have similar neural substrates, they involve different network dynamics, resolving the tension between previous imaging and neuropsychological studies. PMID:22040738

Distributed controller clustering in software defined networks.

PubMed

Abdelaziz, Ahmed; Fong, Ang Tan; Gani, Abdullah; Garba, Usman; Khan, Suleman; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

2017-01-01

Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

Surrogate-based optimization of hydraulic fracturing in pre-existing fracture networks

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Sun, Yunwei; Fu, Pengcheng; Carrigan, Charles R.; Lu, Zhiming; Tong, Charles H.; Buscheck, Thomas A.

2013-08-01

Hydraulic fracturing has been used widely to stimulate production of oil, natural gas, and geothermal energy in formations with low natural permeability. Numerical optimization of fracture stimulation often requires a large number of evaluations of objective functions and constraints from forward hydraulic fracturing models, which are computationally expensive and even prohibitive in some situations. Moreover, there are a variety of uncertainties associated with the pre-existing fracture distributions and rock mechanical properties, which affect the optimized decisions for hydraulic fracturing. In this study, a surrogate-based approach is developed for efficient optimization of hydraulic fracturing well design in the presence of natural-system uncertainties. The fractal dimension is derived from the simulated fracturing network as the objective for maximizing energy recovery sweep efficiency. The surrogate model, which is constructed using training data from high-fidelity fracturing models for mapping the relationship between uncertain input parameters and the fractal dimension, provides fast approximation of the objective functions and constraints. A suite of surrogate models constructed using different fitting methods is evaluated and validated for fast predictions. Global sensitivity analysis is conducted to gain insights into the impact of the input variables on the output of interest, and further used for parameter screening. The high efficiency of the surrogate-based approach is demonstrated for three optimization scenarios with different and uncertain ambient conditions. Our results suggest the critical importance of considering uncertain pre-existing fracture networks in optimization studies of hydraulic fracturing.

Ukrainian network of Optical Stations for man-made space objects observation

NASA Astrophysics Data System (ADS)

Sybiryakova, Yevgeniya

2016-07-01

The Ukrainian Network of Optical Stations (UNOS) for man-made objects research was founded in 2012 as an association of professional astronomers. The main goals of network are: positional and photometric observations of man-made space objects, calculation of orbital elements, research of shape and period of rotation. The network consists of 8 stations: Kiev, Nikolaev, Odesa, Uzhgorod, Lviv, Yevpatoriya, Alchevsk. UNOS has 12 telescopes for observation of man-made space objects. The new original methods of positional observation were developed for optical observation of geosynchronous and low earth orbit satellites. The observational campaigns of LEO satellites held in the network every year. The numerical model of space object motion, developed in UNOS, is using for orbit calculation. The results of orbital elements calculation are represented on the UNOS web-site http://umos.mao.kiev.ua/eng/. The photometric observation of selected objects is also carried out in network.

Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

PubMed

Menezes, Mozart B C; Kim, Seokjin; Huang, Rongbing

2017-01-01

Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

Wealth distribution on complex networks

NASA Astrophysics Data System (ADS)

Ichinomiya, Takashi

2012-12-01

We study the wealth distribution of the Bouchaud-Mézard model on complex networks. It is known from numerical simulations that this distribution depends on the topology of the network; however, no one has succeeded in explaining it. Using “adiabatic” and “independent” assumptions along with the central-limit theorem, we derive equations that determine the probability distribution function. The results are compared to those of simulations for various networks. We find good agreement between our theory and the simulations, except for the case of Watts-Strogatz networks with a low rewiring rate due to the breakdown of independent assumption.

Assessment of the National Park network of mainland Spain by the Insecurity Index of vertebrate species.

PubMed

Estrada, Alba; Real, Raimundo

2018-01-01

The evaluation of protected area networks on their capacity to preserve species distributions is a key topic in conservation biology. There are different types of protected areas, with National Parks those with highest level of protection. National Parks can be declared attending to many ecological features that include the presence of certain animal species. Here, we selected 37 vertebrate species that were highlighted as having relevant natural value for at least one of the 10 National Parks of mainland Spain. We modelled species distributions with the favourability function, and applied the Insecurity Index to detect the degree of protection of favourable areas for each species. Two metrics of Insecurity Index were defined for each species: the Insecurity Index in each of the cells, and the Overall Insecurity Index of a species. The former allows the identification of insecure areas for each species that can be used to establish spatial conservation priorities. The latter gives a value of Insecurity for each species, which we used to calculate the Representativeness of favourable areas for the species in the network. As expected, due to the limited extension of the National Park network, all species have high values of Insecurity; i.e., just a narrow proportion of their favourable areas are covered by a National Park. However, the majority of species favourable areas are well represented in the network, i.e., the percentage of favourable areas covered by the National Park network is higher than the percentage of mainland Spain covered by the network (result also supported by a randomization approach). Even if a reserve network only covers a low percentage of a country, the Overall Insecurity Index allows an objective assessment of its capacity to represent species. Beyond the results presented here, the Insecurity Index has the potential to be extrapolated to other areas and to cover a wide range of species.

Conservation priorities for mammals in megadiverse Mexico: the efficiency of reserve networks.

PubMed

Ceballos, Gerardo

2007-03-01

A major goal of conservation biologists is to identify critical areas for the conservation of biological diversity and then strategically include them in an efficient system of reserves. In general, however, reserve networks have been selected for different objectives, and most countries lack an evaluation of their reserves' ability to represent a percentage of the national diversity. This paper evaluates the effectiveness of a network of reserves to represent the species of mammals in Mexico. The focus of the analyses is on species and site level, evaluating the representation of all terrestrial mammals in the 30 most important reserves. The representation of all species, endemic species, endangered species, and species with restricted distributions in the reserves was assessed and compared. Endemic or endangered species with restricted distributions were expected to be less represented in reserves than were widespread species. The most important reserves for the conservation of mammals were determined with the use of complementarity analyses. Priority sites for the representation of all the species currently absent from the reserve network were then selected. The results have broad applications for conservation. First, 82% of the mammal species from Mexico were represented in the reserve network, which covers a small portion (3.8%) of the country. Second, this percentage is certainly larger as several reserves were not evaluated due to a lack of data. A priority for a national conservation strategy could be to conduct biological surveys in those reserves lacking inventories to evaluate their contribution to conservation. Third, in spite of its demonstrated value, Mexico's reserve network can be improved by designating complementary areas. Additional priority sites, where reserves are required to represent most gap species in the network, were identified. Finally, it is clear that this reserve network has limitations for maintaining biodiversity and ecosystem services at regional scales. A comprehensive conservation strategy has, therefore, to incorporate mechanisms that enhance the value of human-dominated landscapes for the maintenance of biodiversity.

Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

NASA Astrophysics Data System (ADS)

Maiti, Saumen; Tiwari, Ram Krishna

2010-10-01

A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho-section of the KTB. The comparisons of maximum a posteriori geological sections constructed here, based on the maximum a posteriori probability distribution, with the available geological information and the existing geophysical findings suggest that the BNN results reveal some additional finer details in the KTB borehole data at certain depths, which appears to be of some geological significance. We also demonstrate that the proposed BNN approach is superior to the conventional artificial neural network in terms of both avoiding "over-fitting" and aiding uncertainty estimation, which are vital for meaningful interpretation of geophysical records. Our analyses demonstrate that the BNN-based approach renders a robust means for the classification of complex changes in the litho-facies successions and thus could provide a useful guide for understanding the crustal inhomogeneity and the structural discontinuity in many other tectonically complex regions.

A Method of Dynamic Extended Reactive Power Optimization in Distribution Network Containing Photovoltaic-Storage System

NASA Astrophysics Data System (ADS)

Wang, Wu; Huang, Wei; Zhang, Yongjun

2018-03-01

The grid-integration of Photovoltaic-Storage System brings some undefined factors to the network. In order to make full use of the adjusting ability of Photovoltaic-Storage System (PSS), this paper puts forward a reactive power optimization model, which are used to construct the objective function based on power loss and the device adjusting cost, including energy storage adjusting cost. By using Cataclysmic Genetic Algorithm to solve this optimization problem, and comparing with other optimization method, the result proved that: the method of dynamic extended reactive power optimization this article puts forward, can enhance the effect of reactive power optimization, including reducing power loss and device adjusting cost, meanwhile, it gives consideration to the safety of voltage.

Portable programming on parallel/networked computers using the Application Portable Parallel Library (APPL)

NASA Technical Reports Server (NTRS)

Quealy, Angela; Cole, Gary L.; Blech, Richard A.

1993-01-01

The Application Portable Parallel Library (APPL) is a subroutine-based library of communication primitives that is callable from applications written in FORTRAN or C. APPL provides a consistent programmer interface to a variety of distributed and shared-memory multiprocessor MIMD machines. The objective of APPL is to minimize the effort required to move parallel applications from one machine to another, or to a network of homogeneous machines. APPL encompasses many of the message-passing primitives that are currently available on commercial multiprocessor systems. This paper describes APPL (version 2.3.1) and its usage, reports the status of the APPL project, and indicates possible directions for the future. Several applications using APPL are discussed, as well as performance and overhead results.

The embedded operating system project

NASA Technical Reports Server (NTRS)

Campbell, R. H.

1984-01-01

This progress report describes research towards the design and construction of embedded operating systems for real-time advanced aerospace applications. The applications concerned require reliable operating system support that must accommodate networks of computers. The report addresses the problems of constructing such operating systems, the communications media, reconfiguration, consistency and recovery in a distributed system, and the issues of realtime processing. A discussion is included on suitable theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based systems. In particular, this report addresses: atomic actions, fault tolerance, operating system structure, program development, reliability and availability, and networking issues. This document reports the status of various experiments designed and conducted to investigate embedded operating system design issues.

Space station data system analysis/architecture study. Task 3: Trade studies, DR-5, volume 1

NASA Technical Reports Server (NTRS)

1985-01-01

The primary objective of Task 3 is to provide additional analysis and insight necessary to support key design/programmatic decision for options quantification and selection for system definition. This includes: (1) the identification of key trade study topics; (2) the definition of a trade study procedure for each topic (issues to be resolved, key inputs, criteria/weighting, methodology); (3) conduct tradeoff and sensitivity analysis; and (4) the review/verification of results within the context of evolving system design and definition. The trade study topics addressed in this volume include space autonomy and function automation, software transportability, system network topology, communications standardization, onboard local area networking, distributed operating system, software configuration management, and the software development environment facility.

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

Minimizing communication cost among distributed controllers in software defined networks

NASA Astrophysics Data System (ADS)

Arlimatti, Shivaleela; Elbreiki, Walid; Hassan, Suhaidi; Habbal, Adib; Elshaikh, Mohamed

2016-08-01

Software Defined Networking (SDN) is a new paradigm to increase the flexibility of today's network by promising for a programmable network. The fundamental idea behind this new architecture is to simplify network complexity by decoupling control plane and data plane of the network devices, and by making the control plane centralized. Recently controllers have distributed to solve the problem of single point of failure, and to increase scalability and flexibility during workload distribution. Even though, controllers are flexible and scalable to accommodate more number of network switches, yet the problem of intercommunication cost between distributed controllers is still challenging issue in the Software Defined Network environment. This paper, aims to fill the gap by proposing a new mechanism, which minimizes intercommunication cost with graph partitioning algorithm, an NP hard problem. The methodology proposed in this paper is, swapping of network elements between controller domains to minimize communication cost by calculating communication gain. The swapping of elements minimizes inter and intra communication cost among network domains. We validate our work with the OMNeT++ simulation environment tool. Simulation results show that the proposed mechanism minimizes the inter domain communication cost among controllers compared to traditional distributed controllers.

Empirical Reference Distributions for Networks of Different Size

PubMed Central

Smith, Anna; Calder, Catherine A.; Browning, Christopher R.

2016-01-01

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data. PMID:27721556

A distributed lumped active all-pass network configuration.

NASA Technical Reports Server (NTRS)

Huelsman, L. P.; Raghunath, S.

1972-01-01

In this correspondence a new and interesting distributed lumped active network configuration that realizes an all-pass network function is described. A design chart for determining the values of the network elements is included.

Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and Navigation Support

DTIC Science & Technology

2014-09-30

underwater acoustic communication technologies for autonomous distributed underwater networks , through innovative signal processing, coding, and...4. TITLE AND SUBTITLE Advancing Underwater Acoustic Communication for Autonomous Distributed Networks via Sparse Channel Sensing, Coding, and...coding: 3) OFDM modulated dynamic coded cooperation in underwater acoustic channels; 3 Localization, Networking , and Testbed: 4) On-demand

System of multifunctional laser polarimetry of phase and amplitude anisotropy in the diagnosis of endometriosis

NASA Astrophysics Data System (ADS)

Ushenko, Yu. O.; Dubolazov, O. V.; Olar, O. V.

2015-11-01

The theoretical background of azimuthally stable method Jones matrix mapping of histological sections of biopsy of uterine neck on the basis of spatial-frequency selection of the mechanisms of linear and circular birefringence is presented. The comparative results of measuring the coordinate distributions of complex degree of mutual anisotropy formed by polycristalline networks of blood plasma layers of donors (group 1) and patients with endometriosis (group 2). The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of complex degree of mutual anisotropy coordinate distributions are studied. The objective criteria of diagnostics of the pathology and differentiation of its severity degree are determined.

Multifunctional polarization tomography of optical anisotropy of biological layers in diagnosis of endometriosis

NASA Astrophysics Data System (ADS)

Ushenko, O. G.; Koval, L. D.; Dubolazov, O. V.; Ushenko, Yu. O.; Savich, V. O.; Sidor, M. I.; Marchuk, Yu. F.

2015-09-01

The theoretical background of azimuthally stable method Jones matrix mapping of histological sections of biopsy of uterine neck on the basis of spatial-frequency selection of the mechanisms of linear and circular birefringence is presented. The comparative results of measuring the coordinate distributions of complex degree of mutual anisotropy formed by polycristalline networks of blood plasma layers of donors (group 1) and patients with endometriosis (group 2). The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of complex degree of mutual anisotropy coordinate distributions are studied. The objective criteria of diagnostics of the pathology and differentiation of its severity degree are determined.

Azimuth-invariant mueller-matrix differentiation of the optical anisotropy of biological tissues

NASA Astrophysics Data System (ADS)

Ushenko, V. A.; Sidor, M. I.; Marchuk, Yu. F.; Pashkovskaya, N. V.; Andreichuk, D. R.

2014-07-01

A Mueller-matrix model is proposed for analysis of the optical anisotropy of protein networks of optically thin nondepolarizing layers of biological tissues with allowance for birefringence and dichroism. The model is used to construct algorithms for reconstruction of coordinate distributions of phase shifts and coefficient of linear dichroism. Objective criteria for differentiation of benign and malignant tissues of female genitals are formulated in the framework of the statistical analysis of such distributions. Approaches of evidence-based medicine are used to determine the working characteristics (sensitivity, specificity, and accuracy) of the Mueller-matrix method for the reconstruction of the parameters of optical anisotropy and show its efficiency in the differentiation of benign and malignant tumors.

The embedded operating system project

NASA Technical Reports Server (NTRS)

Campbell, R. H.

1985-01-01

The design and construction of embedded operating systems for real-time advanced aerospace applications was investigated. The applications require reliable operating system support that must accommodate computer networks. Problems that arise in the construction of such operating systems, reconfiguration, consistency and recovery in a distributed system, and the issues of real-time processing are reported. A thesis that provides theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based system is included. The following items are addressed: (1) atomic actions and fault-tolerance issues; (2) operating system structure; (3) program development; (4) a reliable compiler for path Pascal; and (5) mediators, a mechanism for scheduling distributed system processes.

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

PubMed Central

Garagnani, Max; Lucchese, Guglielmo; Tomasello, Rosario; Wennekers, Thomas; Pulvermüller, Friedemann

2017-01-01

Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned “word” forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful “word” and novel, senseless “pseudoword” patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience. PMID:28149276

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Verification of mesoscale objective analyses of VAS and rawinsode data using the March 1982 AVE/VAS special network data. [Atmospheric Variability Experiment/Visible-infrared spin-scan radiometer Atmospheric Sounder

NASA Technical Reports Server (NTRS)

Doyle, James D.; Warner, Thomas T.

1988-01-01

Various combinations of VAS (Visible and Infrared Spin Scan Radiometer Atmospheric Sounder) data, conventional rawinsonde data, and gridded data from the National Weather Service's (NWS) global analysis, were used in successive-correction and variational objective-analysis procedures. Analyses are produced for 0000 GMT 7 March 1982, when the VAS sounding distribution was not greatly limited by the existence of cloud cover. The successive-correction (SC) Procedure was used with VAS data alone, rawinsonde data alone, and both VAS and rawinsonde data. Variational techniques were applied in three ways. Each of these techniques was discussed.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

PubMed Central

Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic. PMID:28245222

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

PubMed

Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

Characterisation of Network Objects in Natural and Anthropic Environments

NASA Astrophysics Data System (ADS)

Harris, B.; McDougall, K.; Barry, M.

2014-11-01

Networks are structures that organise component objects, and they are extensive and recognisable across a range of environments. Estimating lengths of networks objects and their relationships to areas contiguous to them could assist provide owners with additional knowledge of their assets. There is currently some understanding of the way in which networks (such as waterways) relate and respond to their natural and anthropogenic environments. Despite this knowledge, there is no straight forward formula, method or model that can be applied to assess these relationships to a sufficient level of detail. Whilst waterway networks and their structures are well understood from the work of Horton and Strahler, relatively little attention has been paid to how (or if) these properties and behaviours can inform the understanding of other, unrelated, networks. Analysis of existing natural and built network objects exhibited how relationships derived from waterway networks can be applied in new areas of interest. We create a predictive approach to associate dissimilar objects such as pipe networks to assess if using the model established for waterway networks and their relationships can be functional in other areas. Using diversity of inputs we create data to assist with the creation of a predictive model. This work provides a clean theoretical connection between a formula applied to evaluate waterways and their environments, and other natural and anthropogenic network objects. It fills a key knowledge gap in the assessment and application of approaches used to measure natural and built networks.

Distributed Processing System for Restoration of Electric Power Distribution Network Using Two-Layered Contract Net Protocol

NASA Astrophysics Data System (ADS)

Kodama, Yu; Hamagami, Tomoki

Distributed processing system for restoration of electric power distribution network using two-layered CNP is proposed. The goal of this study is to develop the restoration system which adjusts to the future power network with distributed generators. The state of the art of this study is that the two-layered CNP is applied for the distributed computing environment in practical use. The two-layered CNP has two classes of agents, named field agent and operating agent in the network. In order to avoid conflicts of tasks, operating agent controls privilege for managers to send the task announcement messages in CNP. This technique realizes the coordination between agents which work asynchronously in parallel with others. Moreover, this study implements the distributed processing system using a de-fact standard multi-agent framework, JADE(Java Agent DEvelopment framework). This study conducts the simulation experiments of power distribution network restoration and compares the proposed system with the previous system. We confirmed the results show effectiveness of the proposed system.

High Voltage Distribution System (HVDS) as a better system compared to Low Voltage Distribution System (LVDS) applied at Medan city power network

NASA Astrophysics Data System (ADS)

Dinzi, R.; Hamonangan, TS; Fahmi, F.

2018-02-01

In the current distribution system, a large-capacity distribution transformer supplies loads to remote locations. The use of 220/380 V network is nowadays less common compared to 20 kV network. This results in losses due to the non-optimal distribution transformer, which neglected the load location, poor consumer profile, and large power losses along the carrier. This paper discusses how high voltage distribution systems (HVDS) can be a better system used in distribution networks than the currently used distribution system (Low Voltage Distribution System, LVDS). The proposed change of the system into the new configuration is done by replacing a large-capacity distribution transformer with some smaller-capacity distribution transformers and installed them in positions that closest to the load. The use of high voltage distribution systems will result in better voltage profiles and fewer power losses. From the non-technical side, the annual savings and payback periods on high voltage distribution systems will also be the advantage.

Empirical research on complex networks modeling of combat SoS based on data from real war-game, Part I: Statistical characteristics

NASA Astrophysics Data System (ADS)

Chen, Lei; Kou, Yingxin; Li, Zhanwu; Xu, An; Wu, Cheng

2018-01-01

We build a complex networks model of combat System-of-Systems (SoS) based on empirical data from a real war-game, this model is a combination of command & control (C2) subnetwork, sensors subnetwork, influencers subnetwork and logistical support subnetwork, each subnetwork has idiographic components and statistical characteristics. The C2 subnetwork is the core of whole combat SoS, it has a hierarchical structure with no modularity, of which robustness is strong enough to maintain normal operation after any two nodes is destroyed; the sensors subnetwork and influencers subnetwork are like sense organ and limbs of whole combat SoS, they are both flat modular networks of which degree distribution obey GEV distribution and power-law distribution respectively. The communication network is the combination of all subnetworks, it is an assortative Small-World network with core-periphery structure, the Intelligence & Communication Stations/Command Center integrated with C2 nodes in the first three level act as the hub nodes in communication network, and all the fourth-level C2 nodes, sensors, influencers and logistical support nodes have communication capability, they act as the periphery nodes in communication network, its degree distribution obeys exponential distribution in the beginning, Gaussian distribution in the middle, and power-law distribution in the end, and its path length obeys GEV distribution. The betweenness centrality distribution, closeness centrality distribution and eigenvector centrality are also been analyzed to measure the vulnerability of nodes.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

PubMed

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.

Research on the framework and key technologies of panoramic visualization for smart distribution network

NASA Astrophysics Data System (ADS)

Du, Jian; Sheng, Wanxing; Lin, Tao; Lv, Guangxian

2018-05-01

Nowadays, the smart distribution network has made tremendous progress, and the business visualization becomes even more significant and indispensable. Based on the summarization of traditional visualization technologies and demands of smart distribution network, a panoramic visualization application is proposed in this paper. The overall architecture, integrated architecture and service architecture of panoramic visualization application is firstly presented. Then, the architecture design and main functions of panoramic visualization system are elaborated in depth. In addition, the key technologies related to the application is discussed briefly. At last, two typical visualization scenarios in smart distribution network, which are risk warning and fault self-healing, proves that the panoramic visualization application is valuable for the operation and maintenance of the distribution network.

Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care.

PubMed

Brown, Jeffrey S; Holmes, John H; Shah, Kiran; Hall, Ken; Lazarus, Ross; Platt, Richard

2010-06-01

Comparative effectiveness research, medical product safety evaluation, and quality measurement will require the ability to use electronic health data held by multiple organizations. There is no consensus about whether to create regional or national combined (eg, "all payer") databases for these purposes, or distributed data networks that leave most Protected Health Information and proprietary data in the possession of the original data holders. Demonstrate functions of a distributed research network that supports research needs and also address data holders concerns about participation. Key design functions included strong local control of data uses and a centralized web-based querying interface. We implemented a pilot distributed research network and evaluated the design considerations, utility for research, and the acceptability to data holders of methods for menu-driven querying. We developed and tested a central, web-based interface with supporting network software. Specific functions assessed include query formation and distribution, query execution and review, and aggregation of results. This pilot successfully evaluated temporal trends in medication use and diagnoses at 5 separate sites, demonstrating some of the possibilities of using a distributed research network. The pilot demonstrated the potential utility of the design, which addressed the major concerns of both users and data holders. No serious obstacles were identified that would prevent development of a fully functional, scalable network. Distributed networks are capable of addressing nearly all anticipated uses of routinely collected electronic healthcare data. Distributed networks would obviate the need for centralized databases, thus avoiding numerous obstacles.

Quantum teleportation between remote atomic-ensemble quantum memories

PubMed Central

Bao, Xiao-Hui; Xu, Xiao-Fan; Li, Che-Ming; Yuan, Zhen-Sheng; Lu, Chao-Yang; Pan, Jian-Wei

2012-01-01

Quantum teleportation and quantum memory are two crucial elements for large-scale quantum networks. With the help of prior distributed entanglement as a “quantum channel,” quantum teleportation provides an intriguing means to faithfully transfer quantum states among distant locations without actual transmission of the physical carriers [Bennett CH, et al. (1993) Phys Rev Lett 70(13):1895–1899]. Quantum memory enables controlled storage and retrieval of fast-flying photonic quantum bits with stationary matter systems, which is essential to achieve the scalability required for large-scale quantum networks. Combining these two capabilities, here we realize quantum teleportation between two remote atomic-ensemble quantum memory nodes, each composed of ∼108 rubidium atoms and connected by a 150-m optical fiber. The spin wave state of one atomic ensemble is mapped to a propagating photon and subjected to Bell state measurements with another single photon that is entangled with the spin wave state of the other ensemble. Two-photon detection events herald the success of teleportation with an average fidelity of 88(7)%. Besides its fundamental interest as a teleportation between two remote macroscopic objects, our technique may be useful for quantum information transfer between different nodes in quantum networks and distributed quantum computing. PMID:23144222

Research on key technologies for data-interoperability-based metadata, data compression and encryption, and their application

NASA Astrophysics Data System (ADS)

Yu, Xu; Shao, Quanqin; Zhu, Yunhai; Deng, Yuejin; Yang, Haijun

2006-10-01

With the development of informationization and the separation between data management departments and application departments, spatial data sharing becomes one of the most important objectives for the spatial information infrastructure construction, and spatial metadata management system, data transmission security and data compression are the key technologies to realize spatial data sharing. This paper discusses the key technologies for metadata based on data interoperability, deeply researches the data compression algorithms such as adaptive Huffman algorithm, LZ77 and LZ78 algorithm, studies to apply digital signature technique to encrypt spatial data, which can not only identify the transmitter of spatial data, but also find timely whether the spatial data are sophisticated during the course of network transmission, and based on the analysis of symmetric encryption algorithms including 3DES,AES and asymmetric encryption algorithm - RAS, combining with HASH algorithm, presents a improved mix encryption method for spatial data. Digital signature technology and digital watermarking technology are also discussed. Then, a new solution of spatial data network distribution is put forward, which adopts three-layer architecture. Based on the framework, we give a spatial data network distribution system, which is efficient and safe, and also prove the feasibility and validity of the proposed solution.

Use of artificial neural network for spatial rainfall analysis

NASA Astrophysics Data System (ADS)

Paraskevas, Tsangaratos; Dimitrios, Rozos; Andreas, Benardos

2014-04-01

In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature

PubMed Central

Holmes, John H; Elliott, Thomas E; Brown, Jeffrey S; Raebel, Marsha A; Davidson, Arthur; Nelson, Andrew F; Chung, Annie; La Chance, Pierre; Steiner, John F

2014-01-01

Objective To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). Materials and methods Medline, PubMed, EMBASE, CINAHL, and INSPEC were searched for relevant documents published through July 31, 2013 using a systematic approach. Only documents relating to DRNs in the USA were included. Documents were analyzed using a classification framework consisting of 10 facets to identify themes. Results 6641 documents were retrieved. After screening for duplicates and relevance, 38 were included in the final review. A peer-reviewed literature on data warehouse governance is emerging, but is still sparse. Peer-reviewed publications on UK research network governance were more prevalent, although not reviewed for this analysis. All 10 classification facets were used, with some documents falling into two or more classifications. No document addressed costs associated with governance. Discussion Even though DRNs are emerging as vehicles for research and public health surveillance, understanding of DRN data governance policies and procedures is limited. This is expected to change as more DRN projects disseminate their governance approaches as publicly available toolkits and peer-reviewed publications. Conclusions While peer-reviewed, US-based DRN data warehouse governance publications have increased, DRN developers and administrators are encouraged to publish information about these programs. PMID:24682495

On Prolonging Network Lifetime through Load-Similar Node Deployment in Wireless Sensor Networks

PubMed Central

Li, Qiao-Qin; Gong, Haigang; Liu, Ming; Yang, Mei; Zheng, Jun

2011-01-01

This paper is focused on the study of the energy hole problem in the Progressive Multi-hop Rotational Clustered (PMRC)-structure, a highly scalable wireless sensor network (WSN) architecture. Based on an analysis on the traffic load distribution in PMRC-based WSNs, we propose a novel load-similar node distribution strategy combined with the Minimum Overlapping Layers (MOL) scheme to address the energy hole problem in PMRC-based WSNs. In this strategy, sensor nodes are deployed in the network area according to the load distribution. That is, more nodes shall be deployed in the range where the average load is higher, and then the loads among different areas in the sensor network tend to be balanced. Simulation results demonstrate that the load-similar node distribution strategy prolongs network lifetime and reduces the average packet latency in comparison with existing nonuniform node distribution and uniform node distribution strategies. Note that, besides the PMRC structure, the analysis model and the proposed load-similar node distribution strategy are also applicable to other multi-hop WSN structures. PMID:22163809

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, Tao; Li, Cheng; Huang, Can

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

A Hierarchical Modeling for Reactive Power Optimization With Joint Transmission and Distribution Networks by Curve Fitting

DOE PAGES

Ding, Tao; Li, Cheng; Huang, Can; ...

2017-01-09

Here, in order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost function of the slave model for the master model, which reflects the impacts of each slave model. Second,more » the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global optimality. Finally, numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods.« less

Calculating a checksum with inactive networking components in a computing system

DOEpatents

Aho, Michael E; Chen, Dong; Eisley, Noel A; Gooding, Thomas M; Heidelberger, Philip; Tauferner, Andrew T

2014-12-16

Calculating a checksum utilizing inactive networking components in a computing system, including: identifying, by a checksum distribution manager, an inactive networking component, wherein the inactive networking component includes a checksum calculation engine for computing a checksum; sending, to the inactive networking component by the checksum distribution manager, metadata describing a block of data to be transmitted by an active networking component; calculating, by the inactive networking component, a checksum for the block of data; transmitting, to the checksum distribution manager from the inactive networking component, the checksum for the block of data; and sending, by the active networking component, a data communications message that includes the block of data and the checksum for the block of data.

Calculating a checksum with inactive networking components in a computing system

DOEpatents

Aho, Michael E; Chen, Dong; Eisley, Noel A; Gooding, Thomas M; Heidelberger, Philip; Tauferner, Andrew T

2015-01-27

Calculating a checksum utilizing inactive networking components in a computing system, including: identifying, by a checksum distribution manager, an inactive networking component, wherein the inactive networking component includes a checksum calculation engine for computing a checksum; sending, to the inactive networking component by the checksum distribution manager, metadata describing a block of data to be transmitted by an active networking component; calculating, by the inactive networking component, a checksum for the block of data; transmitting, to the checksum distribution manager from the inactive networking component, the checksum for the block of data; and sending, by the active networking component, a data communications message that includes the block of data and the checksum for the block of data.

Complex Network Simulation of Forest Network Spatial Pattern in Pearl River Delta

NASA Astrophysics Data System (ADS)

Zeng, Y.

2017-09-01

Forest network-construction uses for the method and model with the scale-free features of complex network theory based on random graph theory and dynamic network nodes which show a power-law distribution phenomenon. The model is suitable for ecological disturbance by larger ecological landscape Pearl River Delta consistent recovery. Remote sensing and GIS spatial data are available through the latest forest patches. A standard scale-free network node distribution model calculates the area of forest network's power-law distribution parameter value size; The recent existing forest polygons which are defined as nodes can compute the network nodes decaying index value of the network's degree distribution. The parameters of forest network are picked up then make a spatial transition to GIS real world models. Hence the connection is automatically generated by minimizing the ecological corridor by the least cost rule between the near nodes. Based on scale-free network node distribution requirements, select the number compared with less, a huge point of aggregation as a future forest planning network's main node, and put them with the existing node sequence comparison. By this theory, the forest ecological projects in the past avoid being fragmented, scattered disorderly phenomena. The previous regular forest networks can be reduced the required forest planting costs by this method. For ecological restoration of tropical and subtropical in south China areas, it will provide an effective method for the forest entering city project guidance and demonstration with other ecological networks (water, climate network, etc.) for networking a standard and base datum.

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.