Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design.

PubMed

Hu, Chelsea Y; Takahashi, Melissa K; Zhang, Yan; Lucks, Julius B

2018-05-22

RNA regulators are powerful components of the synthetic biology toolbox. Here, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). NAR network motifs are core motifs of natural genetic networks, and are known for reducing network response time and steady state signal. Here we use cell-free transcription-translation (TX-TL) reactions and a computational model to design and prototype sRNA NAR constructs. Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict NAR function in TX-TL. We transfer successful network designs into Escherichia coli and show that our sRNA transcriptional network reduces both network response time and steady-state gene expression. This work broadens our ability to construct increasingly sophisticated RNA genetic networks with predictable function.

Multidisciplinary Design Optimization for Aeropropulsion Engines and Solid Modeling/Animation via the Integrated Forced Methods

NASA Technical Reports Server (NTRS)

2004-01-01

The grant closure report is organized in the following four chapters: Chapter describes the two research areas Design optimization and Solid mechanics. Ten journal publications are listed in the second chapter. Five highlights is the subject matter of chapter three. CHAPTER 1. The Design Optimization Test Bed CometBoards. CHAPTER 2. Solid Mechanics: Integrated Force Method of Analysis. CHAPTER 3. Five Highlights: Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft. Neural Network and Regression Soft Model Extended for PX-300 Aircraft Engine. Engine with Regression and Neural Network Approximators Designed. Cascade Optimization Strategy with Neural network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design. Neural Network and Regression Approximations Used in Aircraft Design.

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

Dissipative rendering and neural network control system design

NASA Technical Reports Server (NTRS)

Gonzalez, Oscar R.

1995-01-01

Model-based control system designs are limited by the accuracy of the models of the plant, plant uncertainty, and exogenous signals. Although better models can be obtained with system identification, the models and control designs still have limitations. One approach to reduce the dependency on particular models is to design a set of compensators that will guarantee robust stability to a set of plants. Optimization over the compensator parameters can then be used to get the desired performance. Conservativeness of this approach can be reduced by integrating fundamental properties of the plant models. This is the approach of dissipative control design. Dissipative control designs are based on several variations of the Passivity Theorem, which have been proven for nonlinear/linear and continuous-time/discrete-time systems. These theorems depend not on a specific model of a plant, but on its general dissipative properties. Dissipative control design has found wide applicability in flexible space structures and robotic systems that can be configured to be dissipative. Currently, there is ongoing research to improve the performance of dissipative control designs. For aircraft systems that are not dissipative active control may be used to make them dissipative and then a dissipative control design technique can be used. It is also possible that rendering a system dissipative and dissipative control design may be combined into one step. Furthermore, the transformation of a non-dissipative system to dissipative can be done robustly. One sequential design procedure for finite dimensional linear time-invariant systems has been developed. For nonlinear plants that cannot be controlled adequately with a single linear controller, model-based techniques have additional problems. Nonlinear system identification is still a research topic. Lacking analytical models for model-based design, artificial neural network algorithms have recently received considerable attention. Using their universal approximation property, neural networks have been introduced into nonlinear control designs in several ways. Unfortunately, little work has appeared that analyzes neural network control systems and establishes margins for stability and performance. One approach for this analysis is to set up neural network control systems in the framework presented above. For example, one neural network could be used to render a system to be dissipative, a second strictly dissipative neural network controller could be used to guarantee robust stability.

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres

PubMed Central

Gronau, Greta; Jacobsen, Matthew M.; Huang, Wenwen; Rizzo, Daniel J.; Li, David; Staii, Cristian; Pugno, Nicola M.; Wong, Joyce Y.; Kaplan, David L.; Buehler, Markus J.

2016-01-01

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified. PMID:26017575

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres.

PubMed

Lin, Shangchao; Ryu, Seunghwa; Tokareva, Olena; Gronau, Greta; Jacobsen, Matthew M; Huang, Wenwen; Rizzo, Daniel J; Li, David; Staii, Cristian; Pugno, Nicola M; Wong, Joyce Y; Kaplan, David L; Buehler, Markus J

2015-05-28

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.

Integrated forward and reverse supply chain: A tire case study.

PubMed

Pedram, Ali; Yusoff, Nukman Bin; Udoncy, Olugu Ezutah; Mahat, Abu Bakar; Pedram, Payam; Babalola, Ayo

2017-02-01

This paper attempts to integrate both a forward and reverse supply chain to design a closed-loop supply chain network (CLSC). The problem in the design of a CLSC network is uncertainty in demand, return products and the quality of return products. Scenario analyses are generated to overcome this uncertainty. In contrast to the existing supply chain network design models, a new application of a CLSC network was studied in this paper to reduce waste. A multi-product, multi-tier mixed integer linear model is developed for a CLSC network design. The main objective is to maximize profit and provide waste management decision support in order to minimize pollution. The result shows applicability of the model in the tire industry. The model determines the number and the locations of facilities and the material flows between these facilities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

NASA Astrophysics Data System (ADS)

Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

2018-06-01

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

Toward automatic time-series forecasting using neural networks.

PubMed

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Visual social network analysis: effective approach to model complex human social, behaviour & culture.

PubMed

Ahram, Tareq Z; Karwowski, Waldemar

2012-01-01

The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

Hybrid Network Defense Model Based on Fuzzy Evaluation

PubMed Central

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture. PMID:24574870

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

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

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Research in Network Management Techniques for Tactical Data Communications Network.

DTIC Science & Technology

1982-09-01

the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques...contro!lers are designed to perform their limited tasks optimally. For the dynamic routing problem considered here, the local controllers are node...feedback to finding in optimum stead-o-state routing (static strategies) under non - control which can be easily implemented in real time. congested

A fault-tolerant small world topology control model in ad hoc networks for search and rescue

NASA Astrophysics Data System (ADS)

Tan, Mian; Fang, Ling; Wu, Yue; Zhang, Bo; Chang, Bowen; Holme, Petter; Zhao, Jing

2018-02-01

Due to their self-organized, multi-hop and distributed characteristics, ad hoc networks are useful in search and rescue. Topology control models need to be designed for energy-efficient, robust and fast communication in ad hoc networks. This paper proposes a topology control model which specializes for search and rescue-Compensation Small World-Repeated Game (CSWRG)-which integrates mobility models, constructing small world networks and a game-theoretic approach to the allocation of resources. Simulation results show that our mobility models can enhance the communication performance of the constructed small-world networks. Our strategy, based on repeated game, can suppress selfish behavior and compensate agents that encounter selfish or faulty neighbors. This model could be useful for the design of ad hoc communication networks.

Campus network security model study

NASA Astrophysics Data System (ADS)

Zhang, Yong-ku; Song, Li-ren

2011-12-01

Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

The use of network theory to model disparate ship design information

NASA Astrophysics Data System (ADS)

Rigterink, Douglas; Piks, Rebecca; Singer, David J.

2014-06-01

This paper introduces the use of network theory to model and analyze disparate ship design information. This work will focus on a ship's distributed systems and their intra- and intersystem structures and interactions. The three system to be analyzed are: a passageway system, an electrical system, and a fire fighting system. These systems will be analyzed individually using common network metrics to glean information regarding their structures and attributes. The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions. Network theory will be shown to be useful in the early design stage due to its simplicity and ability to model any shipboard system.

Enterprise virtual private network (VPN) with dense wavelength division multiplexing (DWDM) design

NASA Astrophysics Data System (ADS)

Carranza, Aparicio

An innovative computer simulation and modeling tool for metropolitan area optical data communication networks is presented. These models address the unique requirements of Virtual Private Networks for enterprise data centers, which may comprise a mixture of protocols including ESCON, FICON, Fibre Channel, Sysplex protocols (ETR, CLO, ISC); and other links interconnected over dark fiber using Dense Wavelength Division Multiplexing (DWDM). Our models have the capability of designing a network with minimal inputs; to compute optical link budgets; suggest alternative configurations; and also optimize the design based on user-defined performance metrics. The models make use of Time Division Multiplexing (TDM) wherever possible for lower data rate traffics. Simulation results for several configurations are presented and they have been validated by means of experiments conducted on the IBM enterprise network testbed in Poughkeepsie, N.Y.

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Redundant Design in Interdependent Networks

PubMed Central

2016-01-01

Modern infrastructure networks are often coupled together and thus could be modeled as interdependent networks. Overload and interdependent effect make interdependent networks more fragile when suffering from attacks. Existing research has primarily concentrated on the cascading failure process of interdependent networks without load, or the robustness of isolated network with load. Only limited research has been done on the cascading failure process caused by overload in interdependent networks. Redundant design is a primary approach to enhance the reliability and robustness of the system. In this paper, we propose two redundant methods, node back-up and dependency redundancy, and the experiment results indicate that two measures are effective and costless. Two detailed models about redundant design are introduced based on the non-linear load-capacity model. Based on the attributes and historical failure distribution of nodes, we introduce three static selecting strategies-Random-based, Degree-based, Initial load-based and a dynamic strategy-HFD (historical failure distribution) to identify which nodes could have a back-up with priority. In addition, we consider the cost and efficiency of different redundant proportions to determine the best proportion with maximal enhancement and minimal cost. Experiments on interdependent networks demonstrate that the combination of HFD and dependency redundancy is an effective and preferred measure to implement redundant design on interdependent networks. The results suggest that the redundant design proposed in this paper can permit construction of highly robust interactive networked systems. PMID:27764174

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

NASA Astrophysics Data System (ADS)

Vahdani, Behnam; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz; Baboli, Arman

2013-06-01

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

Closed loop supply chain network design with fuzzy tactical decisions

NASA Astrophysics Data System (ADS)

Sherafati, Mahtab; Bashiri, Mahdi

2016-09-01

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved.

Design Science Research toward Designing/Prototyping a Repeatable Model for Testing Location Management (LM) Algorithms for Wireless Networking

ERIC Educational Resources Information Center

Peacock, Christopher

2012-01-01

The purpose of this research effort was to develop a model that provides repeatable Location Management (LM) testing using a network simulation tool, QualNet version 5.1 (2011). The model will provide current and future protocol developers a framework to simulate stable protocol environments for development. This study used the Design Science…

A method of network topology optimization design considering application process characteristic

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

2018-03-01

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

Design of a Model-Based Online Management Information System for Interlibrary Loan Networks.

ERIC Educational Resources Information Center

Rouse, Sandra H.; Rouse, William B.

1979-01-01

Discusses the design of a model-based management information system in terms of mathematical/statistical, information processing, and human factors issues and presents a prototype system for interlibrary loan networks. (Author/CWM)

A network control theory approach to modeling and optimal control of zoonoses: case study of brucellosis transmission in sub-Saharan Africa.

PubMed

Roy, Sandip; McElwain, Terry F; Wan, Yan

2011-10-01

Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases.

A Network Control Theory Approach to Modeling and Optimal Control of Zoonoses: Case Study of Brucellosis Transmission in Sub-Saharan Africa

PubMed Central

Roy, Sandip; McElwain, Terry F.; Wan, Yan

2011-01-01

Background Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. Methodology/Principal Findings A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. Conclusions/Significance The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases. PMID:22022621

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

NASA Astrophysics Data System (ADS)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

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.

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Modeling Diagnostic Assessments with Bayesian Networks

ERIC Educational Resources Information Center

Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

2007-01-01

This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

Optimization of an electromagnetic linear actuator using a network and a finite element model

NASA Astrophysics Data System (ADS)

Neubert, Holger; Kamusella, Alfred; Lienig, Jens

2011-03-01

Model based design optimization leads to robust solutions only if the statistical deviations of design, load and ambient parameters from nominal values are considered. We describe an optimization methodology that involves these deviations as stochastic variables for an exemplary electromagnetic actuator used to drive a Braille printer. A combined model simulates the dynamic behavior of the actuator and its non-linear load. It consists of a dynamic network model and a stationary magnetic finite element (FE) model. The network model utilizes lookup tables of the magnetic force and the flux linkage computed by the FE model. After a sensitivity analysis using design of experiment (DoE) methods and a nominal optimization based on gradient methods, a robust design optimization is performed. Selected design variables are involved in form of their density functions. In order to reduce the computational effort we use response surfaces instead of the combined system model obtained in all stochastic analysis steps. Thus, Monte-Carlo simulations can be applied. As a result we found an optimum system design meeting our requirements with regard to function and reliability.

Neural network application to aircraft control system design

NASA Technical Reports Server (NTRS)

Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

1991-01-01

The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

Neural network application to aircraft control system design

NASA Technical Reports Server (NTRS)

Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

1991-01-01

The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

Simulator design for advanced ISDN satellite design and experiments

NASA Technical Reports Server (NTRS)

Pepin, Gerald R.

1992-01-01

This simulation design task completion report documents the simulation techniques associated with the network models of both the Interim Service ISDN (integrated services digital network) Satellite (ISIS) and the Full Service ISDN Satellite (FSIS) architectures. The ISIS network model design represents satellite systems like the Advanced Communication Technology Satellite (ACTS) orbiting switch. The FSIS architecture, the ultimate aim of this element of the Satellite Communications Applications Research (SCAR) program, moves all control and switching functions on-board the next generation ISDN communication satellite. The technical and operational parameters for the advanced ISDN communications satellite design will be obtained from the simulation of ISIS and FSIS engineering software models for their major subsystems. Discrete events simulation experiments will be performed with these models using various traffic scenarios, design parameters and operational procedures. The data from these simulations will be used to determine the engineering parameters for the advanced ISDN communications satellite.

Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

PubMed

Zhang, Yanjun; Tao, Gang; Chen, Mou

2016-09-01

This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

Modeling Renewable Penertration Using a Network Economic Model

NASA Astrophysics Data System (ADS)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Tensor Basis Neural Network v. 1.0 (beta)

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

Ling, Julia; Templeton, Jeremy

This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

NASA Technical Reports Server (NTRS)

Fusaro, Robert L.

1998-01-01

Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

USGS Publications Warehouse

Fienen, Michael N.; Doherty, John E.; Hunt, Randall J.; Reeves, Howard W.

2010-01-01

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Model Of Neural Network With Creative Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail; Barhen, Jacob

1993-01-01

Paper presents analysis of mathematical model of one-neuron/one-synapse neural network featuring coupled activation and learning dynamics and parametrical periodic excitation. Demonstrates self-programming, partly random behavior of suitable designed neural network; believed to be related to spontaneity and creativity of biological neural networks.

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.

Optimization of municipal pressure pumping station layout and sewage pipe network design

NASA Astrophysics Data System (ADS)

Tian, Jiandong; Cheng, Jilin; Gong, Yi

2018-03-01

Accelerated urbanization places extraordinary demands on sewer networks; thus optimization research to improve the design of these systems has practical significance. In this article, a subsystem nonlinear programming model is developed to optimize pumping station layout and sewage pipe network design. The subsystem model is expanded into a large-scale complex nonlinear programming system model to find the minimum total annual cost of the pumping station and network of all pipe segments. A comparative analysis is conducted using the sewage network in Taizhou City, China, as an example. The proposed method demonstrated that significant cost savings could have been realized if the studied system had been optimized using the techniques described in this article. Therefore, the method has practical value for optimizing urban sewage projects and provides a reference for theoretical research on optimization of urban drainage pumping station layouts.

Deployment of e-health services - a business model engineering strategy.

PubMed

Kijl, Björn; Nieuwenhuis, Lambert J M; Huis in 't Veld, Rianne M H A; Hermens, Hermie J; Vollenbroek-Hutten, Miriam M R

2010-01-01

We designed a business model for deploying a myofeedback-based teletreatment service. An iterative and combined qualitative and quantitative action design approach was used for developing the business model and the related value network. Insights from surveys, desk research, expert interviews, workshops and quantitative modelling were combined to produce the first business model and then to refine it in three design cycles. The business model engineering strategy provided important insights which led to an improved, more viable and feasible business model and related value network design. Based on this experience, we conclude that the process of early stage business model engineering reduces risk and produces substantial savings in costs and resources related to service deployment.

Computer-aided design of biological circuits using TinkerCell

PubMed Central

Bergmann, Frank T; Sauro, Herbert M

2010-01-01

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. PMID:21327060

Modeling, Evaluation and Detection of Jamming Attacks in Time-Critical Wireless Applications

DTIC Science & Technology

2014-08-01

computing, modeling and analysis of wireless networks , network topol- ogy, and architecture design. Dr. Wang has been a Member of the Association for...important, yet open research question is how to model and detect jamming attacks in such wireless networks , where communication traffic is more time...against time-critical wireless networks with applications to the smart grid. In contrast to communication networks where packets-oriented metrics

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model

PubMed Central

2013-01-01

Background High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. Methods/Design A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants’ online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Discussion Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living. Trial registration ClinicalTrials.gov NCT01142804 PMID:23945138

Model-Based Design of Tree WSNs for Decentralized Detection.

PubMed

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-08-20

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.

Modeling and performance analysis using extended fuzzy-timing Petri nets for networked virtual environments.

PubMed

Zhou, Y; Murata, T; Defanti, T A

2000-01-01

Despite their attractive properties, networked virtual environments (net-VEs) are notoriously difficult to design, implement, and test due to the concurrency, real-time and networking features in these systems. Net-VEs demand high quality-of-service (QoS) requirements on the network to maintain natural and real-time interactions among users. The current practice for net-VE design is basically trial and error, empirical, and totally lacks formal methods. This paper proposes to apply a Petri net formal modeling technique to a net-VE-NICE (narrative immersive constructionist/collaborative environment), predict the net-VE performance based on simulation, and improve the net-VE performance. NICE is essentially a network of collaborative virtual reality systems called the CAVE-(CAVE automatic virtual environment). First, we introduce extended fuzzy-timing Petri net (EFTN) modeling and analysis techniques. Then, we present EFTN models of the CAVE, NICE, and transport layer protocol used in NICE: transmission control protocol (TCP). We show the possibility analysis based on the EFTN model for the CAVE. Then, by using these models and design/CPN as the simulation tool, we conducted various simulations to study real-time behavior, network effects and performance (latencies and jitters) of NICE. Our simulation results are consistent with experimental data.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Designing optimal greenhouse gas monitoring networks for Australia

NASA Astrophysics Data System (ADS)

Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.

2016-01-01

Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

Formal specification and design techniques for wireless sensor and actuator networks.

PubMed

Martínez, Diego; González, Apolinar; Blanes, Francisco; Aquino, Raúl; Simo, José; Crespo, Alfons

2011-01-01

A current trend in the development and implementation of industrial applications is to use wireless networks to communicate the system nodes, mainly to increase application flexibility, reliability and portability, as well as to reduce the implementation cost. However, the nondeterministic and concurrent behavior of distributed systems makes their analysis and design complex, often resulting in less than satisfactory performance in simulation and test bed scenarios, which is caused by using imprecise models to analyze, validate and design these systems. Moreover, there are some simulation platforms that do not support these models. This paper presents a design and validation method for Wireless Sensor and Actuator Networks (WSAN) which is supported on a minimal set of wireless components represented in Colored Petri Nets (CPN). In summary, the model presented allows users to verify the design properties and structural behavior of the system.

Local-Area-Network Simulator

NASA Technical Reports Server (NTRS)

Gibson, Jim; Jordan, Joe; Grant, Terry

1990-01-01

Local Area Network Extensible Simulator (LANES) computer program provides method for simulating performance of high-speed local-area-network (LAN) technology. Developed as design and analysis software tool for networking computers on board proposed Space Station. Load, network, link, and physical layers of layered network architecture all modeled. Mathematically models according to different lower-layer protocols: Fiber Distributed Data Interface (FDDI) and Star*Bus. Written in FORTRAN 77.

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks.

PubMed

Jenness, Samuel M; Goodreau, Steven M; Morris, Martina

2018-04-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel , designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel , designed to facilitate the exploration of novel research questions for advanced modelers.

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

PubMed Central

Jenness, Samuel M.; Goodreau, Steven M.; Morris, Martina

2018-01-01

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers. PMID:29731699

A Systems Approach to Scalable Transportation Network Modeling

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

Perumalla, Kalyan S

2006-01-01

Emerging needs in transportation network modeling and simulation are raising new challenges with respect to scal-ability of network size and vehicular traffic intensity, speed of simulation for simulation-based optimization, and fidel-ity of vehicular behavior for accurate capture of event phe-nomena. Parallel execution is warranted to sustain the re-quired detail, size and speed. However, few parallel simulators exist for such applications, partly due to the challenges underlying their development. Moreover, many simulators are based on time-stepped models, which can be computationally inefficient for the purposes of modeling evacuation traffic. Here an approach is presented to de-signing a simulator with memory andmore » speed efficiency as the goals from the outset, and, specifically, scalability via parallel execution. The design makes use of discrete event modeling techniques as well as parallel simulation meth-ods. Our simulator, called SCATTER, is being developed, incorporating such design considerations. Preliminary per-formance results are presented on benchmark road net-works, showing scalability to one million vehicles simu-lated on one processor.« less

Internet Tomography in Support of Internet and Network Simulation and Emulation Modelling

NASA Astrophysics Data System (ADS)

Moloisane, A.; Ganchev, I.; O'Droma, M.

Internet performance measurement data extracted through Internet Tomography techniques and metrics and how it may be used to enhance the capacity of network simulation and emulation modelling is addressed in this paper. The advantages of network simulation and emulation as a means to aid design and develop the component networks, which make up the Internet and are fundamental to its ongoing evolution, are highlighted. The Internet's rapid growth has spurred development of new protocols and algorithms to meet changing operational requirements such as security, multicast delivery, mobile networking, policy management, and quality of service (QoS) support. Both the development and evaluation of these operational tools requires the answering of many design and operational questions. Creating the technical support required by network engineers and managers in their efforts to seek answers to these questions is in itself a major challenge. Within the Internet the number and range of services supported continues to grow exponentially, from legacy and client/server applications to VoIP, multimedia streaming services and interactive multimedia services. Services have their own distinctive requirements and idiosyncrasies. They respond differently to bandwidth limitations, latency and jitter problems. They generate different types of “conversations” between end-user terminals, back-end resources and middle-tier servers. To add to the complexity, each new or enhanced service introduced onto the network contends for available bandwidth with every other service. In an effort to ensure networking products and resources being designed and developed handling diverse conditions encountered in real Internet environments, network simulation and emulation modelling is a valuable tool, and becoming a critical element, in networking product and application design and development. The better these laboratory tools reflect real-world environment and conditions the more helpful to designers they will be.

Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

NASA Technical Reports Server (NTRS)

Rai, Man Mohan; Madavan, Nateri

1997-01-01

Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

A Unified Framework for Analyzing and Designing for Stationary Arterial Networks

DOT National Transportation Integrated Search

2017-05-17

This research aims to develop a unified theoretical and simulation framework for analyzing and designing signals for stationary arterial networks. Existing traffic flow models used in design and analysis of signal control strategies are either too si...

Metabolic design of macroscopic bioreaction models: application to Chinese hamster ovary cells.

PubMed

Provost, A; Bastin, G; Agathos, S N; Schneider, Y-J

2006-12-01

The aim of this paper is to present a systematic methodology to design macroscopic bioreaction models for cell cultures based upon metabolic networks. The cell culture is seen as a succession of phases. During each phase, a metabolic network represents the set of reactions occurring in the cell. Then, through the use of the elementary flux modes, these metabolic networks are used to derive macroscopic bioreactions linking the extracellular substrates and products. On this basis, as many separate models are obtained as there are phases. Then, a complete model is obtained by smoothly switching from model to model. This is illustrated with batch cultures of Chinese hamster ovary cells.

Model-Based Design of Tree WSNs for Decentralized Detection †

PubMed Central

Tantawy, Ashraf; Koutsoukos, Xenofon; Biswas, Gautam

2015-01-01

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches. PMID:26307989

Topology design and performance analysis of an integrated communication network

NASA Technical Reports Server (NTRS)

Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.

1985-01-01

A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.

Optimal cost design of water distribution networks using a decomposition approach

NASA Astrophysics Data System (ADS)

Lee, Ho Min; Yoo, Do Guen; Sadollah, Ali; Kim, Joong Hoon

2016-12-01

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Interim Service ISDN Satellite (ISIS) simulator development for advanced satellite designs and experiments

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.

1992-01-01

The simulation development associated with the network models of both the Interim Service Integrated Services Digital Network (ISDN) Satellite (ISIS) and the Full Service ISDN Satellite (FSIS) architectures is documented. The ISIS Network Model design represents satellite systems like the Advanced Communications Technology Satellite (ACTS) orbiting switch. The FSIS architecture, the ultimate aim of this element of the Satellite Communications Applications Research (SCAR) Program, moves all control and switching functions on-board the next generation ISDN communications satellite. The technical and operational parameters for the advanced ISDN communications satellite design will be obtained from the simulation of ISIS and FSIS engineering software models for their major subsystems. Discrete event simulation experiments will be performed with these models using various traffic scenarios, design parameters, and operational procedures. The data from these simulations will be used to determine the engineering parameters for the advanced ISDN communications satellite.

Broadband electrical impedance matching for piezoelectric ultrasound transducers.

PubMed

Huang, Haiying; Paramo, Daniel

2011-12-01

This paper presents a systematic method for designing broadband electrical impedance matching networks for piezoelectric ultrasound transducers. The design process involves three steps: 1) determine the equivalent circuit of the unmatched piezoelectric transducer based on its measured admittance; 2) design a set of impedance matching networks using a computerized Smith chart; and 3) establish the simulation model of the matched transducer to evaluate the gain and bandwidth of the impedance matching networks. The effectiveness of the presented approach is demonstrated through the design, implementation, and characterization of impedance matching networks for a broadband acoustic emission sensor. The impedance matching network improved the power of the acquired signal by 9 times.

75 FR 2433 - Special Conditions: Boeing Model 747-8/-8F Airplanes, Systems and Data Networks Security...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-15

... design features associated with the architecture and connectivity capabilities of the airplane's computer... novel or unusual design features: digital systems architecture composed of several connected networks. The architecture and network configuration may be used for, or interfaced with, a diverse set of...

Engineering online and in-person social networks to sustain physical activity: application of a conceptual model.

PubMed

Rovniak, Liza S; Sallis, James F; Kraschnewski, Jennifer L; Sciamanna, Christopher N; Kiser, Elizabeth J; Ray, Chester A; Chinchilli, Vernon M; Ding, Ding; Matthews, Stephen A; Bopp, Melissa; George, Daniel R; Hovell, Melbourne F

2013-08-14

High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA. A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants' online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met. Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living. ClinicalTrials.gov NCT01142804.

Optimizing Experimental Design for Comparing Models of Brain Function

PubMed Central

Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas

2011-01-01

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485

Construction of a pulse-coupled dipole network capable of fear-like and relief-like responses

NASA Astrophysics Data System (ADS)

Lungsi Sharma, B.

2016-07-01

The challenge for neuroscience as an interdisciplinary programme is the integration of ideas among the disciplines to achieve a common goal. This paper deals with the problem of deriving a pulse-coupled neural network that is capable of demonstrating behavioural responses (fear-like and relief-like). Current pulse-coupled neural networks are designed mostly for engineering applications, particularly image processing. The discovered neural network was constructed using the method of minimal anatomies approach. The behavioural response of a level-coded activity-based model was used as a reference. Although the spiking-based model and the activity-based model are of different scales, the use of model-reference principle means that the characteristics that is referenced is its functional properties. It is demonstrated that this strategy of dissection and systematic construction is effective in the functional design of pulse-coupled neural network system with nonlinear signalling. The differential equations for the elastic weights in the reference model are replicated in the pulse-coupled network geometrically. The network reflects a possible solution to the problem of punishment and avoidance. The network developed in this work is a new network topology for pulse-coupled neural networks. Therefore, the model-reference principle is a powerful tool in connecting neuroscience disciplines. The continuity of concepts and phenomena is further maintained by systematic construction using methods like the method of minimal anatomies.

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

Formal Specification and Design Techniques for Wireless Sensor and Actuator Networks

PubMed Central

Martínez, Diego; González, Apolinar; Blanes, Francisco; Aquino, Raúl; Simo, José; Crespo, Alfons

2011-01-01

A current trend in the development and implementation of industrial applications is to use wireless networks to communicate the system nodes, mainly to increase application flexibility, reliability and portability, as well as to reduce the implementation cost. However, the nondeterministic and concurrent behavior of distributed systems makes their analysis and design complex, often resulting in less than satisfactory performance in simulation and test bed scenarios, which is caused by using imprecise models to analyze, validate and design these systems. Moreover, there are some simulation platforms that do not support these models. This paper presents a design and validation method for Wireless Sensor and Actuator Networks (WSAN) which is supported on a minimal set of wireless components represented in Colored Petri Nets (CPN). In summary, the model presented allows users to verify the design properties and structural behavior of the system. PMID:22344203

Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

ERIC Educational Resources Information Center

Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

2016-01-01

Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

Real-Time Communication Support for Underwater Acoustic Sensor Networks †.

PubMed

Santos, Rodrigo; Orozco, Javier; Micheletto, Matias; Ochoa, Sergio F; Meseguer, Roc; Millan, Pere; Molina, And Carlos

2017-07-14

Underwater sensor networks represent an important and promising field of research due to the large diversity of underwater ubiquitous applications that can be supported by these networks, e.g., systems that deliver tsunami and oil spill warnings, or monitor submarine ecosystems. Most of these monitoring and warning systems require real-time communication in wide area networks that have a low density of nodes. The underwater communication medium involved in these networks is very harsh and imposes strong restrictions to the communication process. In this scenario, the real-time transmission of information is done mainly using acoustic signals, since the network nodes are not physically close. The features of the communication scenario and the requirements of the communication process represent major challenges for designers of both, communication protocols and monitoring and warning systems. The lack of models to represent these networks is the main stumbling block for the proliferation of underwater ubiquitous systems. This paper presents a real-time communication model for underwater acoustic sensor networks (UW-ASN) that are designed to cover wide areas with a low density of nodes, using any-to-any communication. This model is analytic, considers two solution approaches for scheduling the real-time messages, and provides a time-constraint analysis for the network performance. Using this model, the designers of protocols and underwater ubiquitous systems can quickly prototype and evaluate their solutions in an evolving way, in order to determine the best solution to the problem being addressed. The suitability of the proposal is illustrated with a case study that shows the performance of a UW-ASN under several initial conditions. This is the first analytic model for representing real-time communication in this type of network, and therefore, it opens the door for the development of underwater ubiquitous systems for several application scenarios.

Real-Time Communication Support for Underwater Acoustic Sensor Networks †

PubMed Central

Santos, Rodrigo; Orozco, Javier; Micheletto, Matias

2017-01-01

Underwater sensor networks represent an important and promising field of research due to the large diversity of underwater ubiquitous applications that can be supported by these networks, e.g., systems that deliver tsunami and oil spill warnings, or monitor submarine ecosystems. Most of these monitoring and warning systems require real-time communication in wide area networks that have a low density of nodes. The underwater communication medium involved in these networks is very harsh and imposes strong restrictions to the communication process. In this scenario, the real-time transmission of information is done mainly using acoustic signals, since the network nodes are not physically close. The features of the communication scenario and the requirements of the communication process represent major challenges for designers of both, communication protocols and monitoring and warning systems. The lack of models to represent these networks is the main stumbling block for the proliferation of underwater ubiquitous systems. This paper presents a real-time communication model for underwater acoustic sensor networks (UW-ASN) that are designed to cover wide areas with a low density of nodes, using any-to-any communication. This model is analytic, considers two solution approaches for scheduling the real-time messages, and provides a time-constraint analysis for the network performance. Using this model, the designers of protocols and underwater ubiquitous systems can quickly prototype and evaluate their solutions in an evolving way, in order to determine the best solution to the problem being addressed. The suitability of the proposal is illustrated with a case study that shows the performance of a UW-ASN under several initial conditions. This is the first analytic model for representing real-time communication in this type of network, and therefore, it opens the door for the development of underwater ubiquitous systems for several application scenarios. PMID:28708093

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks

PubMed Central

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-01

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability. PMID:28937632

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks.

PubMed

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-09-22

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability.

Computer-aided design of biological circuits using TinkerCell.

PubMed

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2010-01-01

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze, and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. © 2010 Landes Bioscience

Accounting for system dynamics in reserve design.

PubMed

Leroux, Shawn J; Schmiegelow, Fiona K A; Cumming, Steve G; Lessard, Robert B; Nagy, John

2007-10-01

Systematic conservation plans have only recently considered the dynamic nature of ecosystems. Methods have been developed to incorporate climate change, population dynamics, and uncertainty in reserve design, but few studies have examined how to account for natural disturbance. Considering natural disturbance in reserve design may be especially important for the world's remaining intact areas, which still experience active natural disturbance regimes. We developed a spatially explicit, dynamic simulation model, CONSERV, which simulates patch dynamics and fire, and used it to evaluate the efficacy of hypothetical reserve networks in northern Canada. We designed six networks based on conventional reserve design methods, with different conservation targets for woodland caribou habitat, high-quality wetlands, vegetation, water bodies, and relative connectedness. We input the six reserve networks into CONSERV and tracked the ability of each to maintain initial conservation targets through time under an active natural disturbance regime. None of the reserve networks maintained all initial targets, and some over-represented certain features, suggesting that both effectiveness and efficiency of reserve design could be improved through use of spatially explicit dynamic simulation during the planning process. Spatial simulation models of landscape dynamics are commonly used in natural resource management, but we provide the first illustration of their potential use for reserve design. Spatial simulation models could be used iteratively to evaluate competing reserve designs and select targets that have a higher likelihood of being maintained through time. Such models could be combined with dynamic planning techniques to develop a general theory for reserve design in an uncertain world.

The Science DMZ: A Network Design Pattern for Data-Intensive Science

DOE PAGES

Dart, Eli; Rotman, Lauren; Tierney, Brian; ...

2014-01-01

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers andmore » research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.« less

Freight Transportation Energy Use : Appendix. Transportation Network Model Output.

DOT National Transportation Integrated Search

1978-07-01

The overall design of the TSC Freight Energy Model is presented. A hierarchical modeling strategy is used, in which detailed modal simulators estimate the performance characteristics of transportation network elements, and the estimates are input to ...

Model-based design of RNA hybridization networks implemented in living cells

PubMed Central

Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter

2017-01-01

Abstract Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. PMID:28934501

A study of the spreading scheme for viral marketing based on a complex network model

NASA Astrophysics Data System (ADS)

Yang, Jianmei; Yao, Canzhong; Ma, Weicheng; Chen, Guanrong

2010-02-01

Buzzword-based viral marketing, known also as digital word-of-mouth marketing, is a marketing mode attached to some carriers on the Internet, which can rapidly copy marketing information at a low cost. Viral marketing actually uses a pre-existing social network where, however, the scale of the pre-existing network is believed to be so large and so random, so that its theoretical analysis is intractable and unmanageable. There are very few reports in the literature on how to design a spreading scheme for viral marketing on real social networks according to the traditional marketing theory or the relatively new network marketing theory. Complex network theory provides a new model for the study of large-scale complex systems, using the latest developments of graph theory and computing techniques. From this perspective, the present paper extends the complex network theory and modeling into the research of general viral marketing and develops a specific spreading scheme for viral marking and an approach to design the scheme based on a real complex network on the QQ instant messaging system. This approach is shown to be rather universal and can be further extended to the design of various spreading schemes for viral marketing based on different instant messaging systems.

Robust Weak Chimeras in Oscillator Networks with Delayed Linear and Quadratic Interactions

NASA Astrophysics Data System (ADS)

Bick, Christian; Sebek, Michael; Kiss, István Z.

2017-10-01

We present an approach to generate chimera dynamics (localized frequency synchrony) in oscillator networks with two populations of (at least) two elements using a general method based on a delayed interaction with linear and quadratic terms. The coupling design yields robust chimeras through a phase-model-based design of the delay and the ratio of linear and quadratic components of the interactions. We demonstrate the method in the Brusselator model and experiments with electrochemical oscillators. The technique opens the way to directly bridge chimera dynamics in phase models and real-world oscillator networks.

Development of an Integrated Team Training Design and Assessment Architecture to Support Adaptability in Healthcare Teams

DTIC Science & Technology

2016-10-01

and implementation of embedded, adaptive feedback and performance assessment. The investigators also initiated work designing a Bayesian Belief ...training; Teamwork; Adaptive performance; Leadership; Simulation; Modeling; Bayesian belief networks (BBN) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Trauma teams Team training Teamwork Adaptability Adaptive performance Leadership Simulation Modeling Bayesian belief networks (BBN) 6

Developing a Framework for Effective Network Capacity Planning

NASA Technical Reports Server (NTRS)

Yaprak, Ece

2005-01-01

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Neural Network and Regression Methods Demonstrated in the Design Optimization of a Subsonic Aircraft

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Lavelle, Thomas M.; Patnaik, Surya

2003-01-01

The neural network and regression methods of NASA Glenn Research Center s COMETBOARDS design optimization testbed were used to generate approximate analysis and design models for a subsonic aircraft operating at Mach 0.85 cruise speed. The analytical model is defined by nine design variables: wing aspect ratio, engine thrust, wing area, sweep angle, chord-thickness ratio, turbine temperature, pressure ratio, bypass ratio, fan pressure; and eight response parameters: weight, landing velocity, takeoff and landing field lengths, approach thrust, overall efficiency, and compressor pressure and temperature. The variables were adjusted to optimally balance the engines to the airframe. The solution strategy included a sensitivity model and the soft analysis model. Researchers generated the sensitivity model by training the approximators to predict an optimum design. The trained neural network predicted all response variables, within 5-percent error. This was reduced to 1 percent by the regression method. The soft analysis model was developed to replace aircraft analysis as the reanalyzer in design optimization. Soft models have been generated for a neural network method, a regression method, and a hybrid method obtained by combining the approximators. The performance of the models is graphed for aircraft weight versus thrust as well as for wing area and turbine temperature. The regression method followed the analytical solution with little error. The neural network exhibited 5-percent maximum error over all parameters. Performance of the hybrid method was intermediate in comparison to the individual approximators. Error in the response variable is smaller than that shown in the figure because of a distortion scale factor. The overall performance of the approximators was considered to be satisfactory because aircraft analysis with NASA Langley Research Center s FLOPS (Flight Optimization System) code is a synthesis of diverse disciplines: weight estimation, aerodynamic analysis, engine cycle analysis, propulsion data interpolation, mission performance, airfield length for landing and takeoff, noise footprint, and others.

Study of mobile satellite network based on GEO/LEO satellite constellation

NASA Astrophysics Data System (ADS)

Hu, Xiulin; Zeng, Yujiang; Wang, Ying; Wang, Xianhui

2005-11-01

Mobile satellite network with Inter Satellite Links (ISLs), which consists of non-geostationary satellites, has the characteristic of network topology's variability. This is a great challenge to the design and management of mobile satellite network. This paper analyzes the characteristics of mobile satellite network, takes multimedia Quality of Service (QoS) as the chief object and presents a reference model based on Geostationary Earth Orbit (GEO)/ Low Earth Orbit (LEO) satellite constellation which adapts to the design and management of mobile satellite network. In the reference model, LEO satellites constitute service subnet with responsibility for the access, transmission and switch of the multimedia services for mobile users, while GEO satellites constitute management subnet taking on the centralized management to service subnet. Additionally ground control centre realizes the whole monitoring and control via management subnet. Comparing with terrestrial network, the above reference model physically separates management subnet from service subnet, which not only enhances the advantage of centralized management but also overcomes the shortcoming of low reliability in terrestrial network. Routing of mobile satellite network based on GEO/LEO satellite constellation is also discussed in this paper.

Sign: large-scale gene network estimation environment for high performance computing.

PubMed

Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

Analysis and Design of Complex Network Environments

DTIC Science & Technology

2012-03-01

and J. Lowe, “The myths and facts behind cyber security risks for industrial control systems ,” in the Proceedings of the VDE Kongress, VDE Congress...questions about 1) how to model them, 2) the design of experiments necessary to discover their structure (and thus adapt system inputs to optimize the...theoretical work that clarifies fundamental limitations of complex networks with network engineering and systems biology to implement specific designs and

Reliable Communication Models in Interdependent Critical Infrastructure Networks

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

Lee, Sangkeun; Chinthavali, Supriya; Shankar, Mallikarjun

Modern critical infrastructure networks are becoming increasingly interdependent where the failures in one network may cascade to other dependent networks, causing severe widespread national-scale failures. A number of previous efforts have been made to analyze the resiliency and robustness of interdependent networks based on different models. However, communication network, which plays an important role in today's infrastructures to detect and handle failures, has attracted little attention in the interdependency studies, and no previous models have captured enough practical features in the critical infrastructure networks. In this paper, we study the interdependencies between communication network and other kinds of critical infrastructuremore » networks with an aim to identify vulnerable components and design resilient communication networks. We propose several interdependency models that systematically capture various features and dynamics of failures spreading in critical infrastructure networks. We also discuss several research challenges in building reliable communication solutions to handle failures in these models.« less

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: a simulation study.

PubMed

Padhi, Radhakant; Bhardhwaj, Jayender R

2009-06-01

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An LMI approach to design H(infinity) controllers for discrete-time nonlinear systems based on unified models.

PubMed

Liu, Meiqin; Zhang, Senlin

2008-10-01

A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L(2) gain (i.e. robust H(infinity) performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be robust H(infinity) performance analyzed or robust H(infinity) controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

An ultra-low-voltage electronic implementation of inertial neuron model with nonmonotonous Liao's activation function.

PubMed

Kant, Nasir Ali; Dar, Mohamad Rafiq; Khanday, Farooq Ahmad

2015-01-01

The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN. While attempting to design the AF in hardware, the designs should be compatible with the modern Very Large Scale Integration (VLSI) design techniques. In this regard, the implemented designs should: only be in Metal Oxide Semiconductor (MOS) technology in order to be compatible with the digital designs, provide electronic tunability feature, and be able to operate at ultra-low voltage. Companding is one of the promising circuit design techniques for achieving these goals. In this paper, 0.5 V design of Liao's AF using sinh-domain technique is introduced. Furthermore, the function is tested by implementing inertial neuron model. The performance of the AF and inertial neuron model have been evaluated through simulation results, using the PSPICE software with the MOS transistor models provided by the 0.18-μm Taiwan Semiconductor Manufacturer Complementary Metal Oxide Semiconductor (TSM CMOS) process.

Supply Chain Engineering and the Use of a Supporting Knowledge Management Application

NASA Astrophysics Data System (ADS)

Laakmann, Frank

The future competition in markets will happen between logistics networks and no longer between enterprises. A new approach for supporting the engineering of logistics networks is developed by this research as a part of the Collaborative Research Centre (SFB) 559: "Modeling of Large Networks in Logistics" at the University of Dortmund together with the Fraunhofer-Institute of Material Flow and Logistics founded by Deutsche Forschungsgemeinschaft (DFG). Based on a reference model for logistics processes, the process chain model, a guideline for logistics engineers is developed to manage the different types of design tasks of logistics networks. The technical background of this solution is a collaborative knowledge management application. This paper will introduce how new Internet-based technologies support supply chain design projects.

Neural Network Development Tool (NETS)

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1990-01-01

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

Optimizing digital elevation models (DEMs) accuracy for planning and design of mobile communication networks

NASA Astrophysics Data System (ADS)

Hassan, Mahmoud A.

2004-02-01

Digital elevation models (DEMs) are important tools in the planning, design and maintenance of mobile communication networks. This research paper proposes a method for generating high accuracy DEMs based on SPOT satellite 1A stereo pair images, ground control points (GCP) and Erdas OrthoBASE Pro image processing software. DEMs with 0.2911 m mean error were achieved for the hilly and heavily populated city of Amman. The generated DEM was used to design a mobile communication network resulted in a minimum number of radio base transceiver stations, maximum number of covered regions and less than 2% of dead zones.

Incorporating environmental justice measures into equilibrium-based transportation network design models

DOT National Transportation Integrated Search

2007-08-01

This research outlines three major challenges of incorporating Environmental Justice (EJ) into metropolitan transportation planning and proposes a new variation of the user equilibrium discrete network design problem (UEDNDP) for achieving EJ amongst...

REGIONAL-SCALE (1000 KM) MODEL OF PHOTOCHEMICAL AIR POLLUTION. PART 2. INPUT PROCESSOR NETWORK DESIGN

EPA Science Inventory

Detailed specifications are given for a network of data processors and submodels that can generate the parameter fields required by the regional oxidant model formulated in Part 1 of this report. Operations performed by the processor network include simulation of the motion and d...

Behavior-based network management: a unique model-based approach to implementing cyber superiority

NASA Astrophysics Data System (ADS)

Seng, Jocelyn M.

2016-05-01

Behavior-Based Network Management (BBNM) is a technological and strategic approach to mastering the identification and assessment of network behavior, whether human-driven or machine-generated. Recognizing that all five U.S. Air Force (USAF) mission areas rely on the cyber domain to support, enhance and execute their tasks, BBNM is designed to elevate awareness and improve the ability to better understand the degree of reliance placed upon a digital capability and the operational risk.2 Thus, the objective of BBNM is to provide a holistic view of the digital battle space to better assess the effects of security, monitoring, provisioning, utilization management, allocation to support mission sustainment and change control. Leveraging advances in conceptual modeling made possible by a novel advancement in software design and implementation known as Vector Relational Data Modeling (VRDM™), the BBNM approach entails creating a network simulation in which meaning can be inferred and used to manage network behavior according to policy, such as quickly detecting and countering malicious behavior. Initial research configurations have yielded executable BBNM models as combinations of conceptualized behavior within a network management simulation that includes only concepts of threats and definitions of "good" behavior. A proof of concept assessment called "Lab Rat," was designed to demonstrate the simplicity of network modeling and the ability to perform adaptation. The model was tested on real world threat data and demonstrated adaptive and inferential learning behavior. Preliminary results indicate this is a viable approach towards achieving cyber superiority in today's volatile, uncertain, complex and ambiguous (VUCA) environment.

Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2005-01-01

A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.

Information Dynamics in Networks: Models and Algorithms

DTIC Science & Technology

2016-09-13

Twitter ; we investigated how to detect spam accounts on Facebook and other social networks by graph analytics; and finally we investigated how to design...networks. We investigated the appropriateness of existing mathematical models for explaining the structure of retweet cascades on Twitter ; we investigated...Received Paper 1.00 2.00 3.00 . A Note on Modeling Retweet Cascades on Twitter , Workshop on Algorithms and Models for the Web Graph. 09-DEC-15

Designing Industrial Networks Using Ecological Food Web Metrics.

PubMed

Layton, Astrid; Bras, Bert; Weissburg, Marc

2016-10-18

Biologically Inspired Design (biomimicry) and Industrial Ecology both look to natural systems to enhance the sustainability and performance of engineered products, systems and industries. Bioinspired design (BID) traditionally has focused on a unit operation and single product level. In contrast, this paper describes how principles of network organization derived from analysis of ecosystem properties can be applied to industrial system networks. Specifically, this paper examines the applicability of particular food web matrix properties as design rules for economically and biologically sustainable industrial networks, using an optimization model developed for a carpet recycling network. Carpet recycling network designs based on traditional cost and emissions based optimization are compared to designs obtained using optimizations based solely on ecological food web metrics. The analysis suggests that networks optimized using food web metrics also were superior from a traditional cost and emissions perspective; correlations between optimization using ecological metrics and traditional optimization ranged generally from 0.70 to 0.96, with flow-based metrics being superior to structural parameters. Four structural food parameters provided correlations nearly the same as that obtained using all structural parameters, but individual structural parameters provided much less satisfactory correlations. The analysis indicates that bioinspired design principles from ecosystems can lead to both environmentally and economically sustainable industrial resource networks, and represent guidelines for designing sustainable industry networks.

Design and implementation of a random neural network routing engine.

PubMed

Kocak, T; Seeber, J; Terzioglu, H

2003-01-01

Random neural network (RNN) is an analytically tractable spiked neural network model that has been implemented in software for a wide range of applications for over a decade. This paper presents the hardware implementation of the RNN model. Recently, cognitive packet networks (CPN) is proposed as an alternative packet network architecture where there is no routing table, instead the RNN based reinforcement learning is used to route packets. Particularly, we describe implementation details for the RNN based routing engine of a CPN network processor chip: the smart packet processor (SPP). The SPP is a dual port device that stores, modifies, and interprets the defining characteristics of multiple RNN models. In addition to hardware design improvements over the software implementation such as the dual access memory, output calculation step, and reduced output calculation module, this paper introduces a major modification to the reinforcement learning algorithm used in the original CPN specification such that the number of weight terms are reduced from 2n/sup 2/ to 2n. This not only yields significant memory savings, but it also simplifies the calculations for the steady state probabilities (neuron outputs in RNN). Simulations have been conducted to confirm the proper functionality for the isolated SPP design as well as for the multiple SPP's in a networked environment.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization.

PubMed

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

PubMed Central

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. PMID:27127499

SIMULATING SUB-DECADAL CHANNEL MORPHOLOGIC CHANGE IN EPHEMERAL STREAM NETWORKS

EPA Science Inventory

A distributed watershed model was modified to simulate cumulative channel morphologic
change from multiple runoff events in ephemeral stream networks. The model incorporates the general design of the event-based Kinematic Runoff and" Erosion Model (KINEROS), which describes t...

Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.

PubMed

Nochomovitz, Yigal D; Li, Hao

2006-03-14

Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three- and four-node network ensembles, we observe a large number of instances of the phenomenon of "mutational buffering," whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.

Electrooptical adaptive switching network for the hypercube computer

NASA Technical Reports Server (NTRS)

Chow, E.; Peterson, J.

1988-01-01

An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.

Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

NASA Technical Reports Server (NTRS)

Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

2010-01-01

Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

Model-based design of RNA hybridization networks implemented in living cells.

PubMed

Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter; Daròs, José-Antonio; Jaramillo, Alfonso

2017-09-19

Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Topology Design for Directional Range Extension Networks with Antenna Blockage

DTIC Science & Technology

2017-03-19

introduced by pod-based antenna blockages. Using certain modeling approximations, the paper presents a quantitative analysis showing design trade-offs...parameters. Sec- tion IV develops quantitative relationships among key design elements and performance metrics. Section V considers some implications of the...Topology Design for Directional Range Extension Networks with Antenna Blockage Thomas Shake MIT Lincoln Laboratory shake@ll.mit.edu Abstract

Explaining Inference on a Population of Independent Agents Using Bayesian Networks

ERIC Educational Resources Information Center

Sutovsky, Peter

2013-01-01

The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak…

Process and data fragmentation-oriented enterprise network integration with collaboration modelling and collaboration agents

NASA Astrophysics Data System (ADS)

Li, Qing; Wang, Ze-yuan; Cao, Zhi-chao; Du, Rui-yang; Luo, Hao

2015-08-01

With the process of globalisation and the development of management models and information technology, enterprise cooperation and collaboration has developed from intra-enterprise integration, outsourcing and inter-enterprise integration, and supply chain management, to virtual enterprises and enterprise networks. Some midfielder enterprises begin to serve for different supply chains. Therefore, they combine related supply chains into a complex enterprise network. The main challenges for enterprise network's integration and collaboration are business process and data fragmentation beyond organisational boundaries. This paper reviews the requirements of enterprise network's integration and collaboration, as well as the development of new information technologies. Based on service-oriented architecture (SOA), collaboration modelling and collaboration agents are introduced to solve problems of collaborative management for service convergence under the condition of process and data fragmentation. A model-driven methodology is developed to design and deploy the integrating framework. An industrial experiment is designed and implemented to illustrate the usage of developed technologies in this paper.

A multimodal logistics service network design with time windows and environmental concerns

PubMed Central

Zhang, Dezhi; He, Runzhong; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained. PMID:28934272

A multimodal logistics service network design with time windows and environmental concerns.

PubMed

Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

2017-01-01

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Modeling and optimization of Quality of Service routing in Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Rafsanjani, Marjan Kuchaki; Fatemidokht, Hamideh; Balas, Valentina Emilia

2016-01-01

Mobile ad hoc networks (MANETs) are a group of mobile nodes that are connected without using a fixed infrastructure. In these networks, nodes communicate with each other by forming a single-hop or multi-hop network. To design effective mobile ad hoc networks, it is important to evaluate the performance of multi-hop paths. In this paper, we present a mathematical model for a routing protocol under energy consumption and packet delivery ratio of multi-hop paths. In this model, we use geometric random graphs rather than random graphs. Our proposed model finds effective paths that minimize the energy consumption and maximizes the packet delivery ratio of the network. Validation of the mathematical model is performed through simulation.

Mixed Transportation Network Design under a Sustainable Development Perspective

PubMed Central

Qin, Jin; Ni, Ling-lin; Shi, Feng

2013-01-01

A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%. PMID:23476142

Mixed transportation network design under a sustainable development perspective.

PubMed

Qin, Jin; Ni, Ling-lin; Shi, Feng

2013-01-01

A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

PubMed Central

Weeding, Emma; Houle, Jason

2010-01-01

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications. PMID:20639523

Optimal observation network design for conceptual model discrimination and uncertainty reduction

NASA Astrophysics Data System (ADS)

Pham, Hai V.; Tsai, Frank T.-C.

2016-02-01

This study expands the Box-Hill discrimination function to design an optimal observation network to discriminate conceptual models and, in turn, identify a most favored model. The Box-Hill discrimination function measures the expected decrease in Shannon entropy (for model identification) before and after the optimal design for one additional observation. This study modifies the discrimination function to account for multiple future observations that are assumed spatiotemporally independent and Gaussian-distributed. Bayesian model averaging (BMA) is used to incorporate existing observation data and quantify future observation uncertainty arising from conceptual and parametric uncertainties in the discrimination function. In addition, the BMA method is adopted to predict future observation data in a statistical sense. The design goal is to find optimal locations and least data via maximizing the Box-Hill discrimination function value subject to a posterior model probability threshold. The optimal observation network design is illustrated using a groundwater study in Baton Rouge, Louisiana, to collect additional groundwater heads from USGS wells. The sources of uncertainty creating multiple groundwater models are geological architecture, boundary condition, and fault permeability architecture. Impacts of considering homoscedastic and heteroscedastic future observation data and the sources of uncertainties on potential observation areas are analyzed. Results show that heteroscedasticity should be considered in the design procedure to account for various sources of future observation uncertainty. After the optimal design is obtained and the corresponding data are collected for model updating, total variances of head predictions can be significantly reduced by identifying a model with a superior posterior model probability.

Information spread in networks: Games, optimal control, and stabilization

NASA Astrophysics Data System (ADS)

Khanafer, Ali

This thesis focuses on designing efficient mechanisms for controlling information spread in networks. We consider two models for information spread. The first one is the well-known distributed averaging dynamics. The second model is a nonlinear one that describes virus spread in computer and biological networks. We seek to design optimal, robust, and stabilizing controllers under practical constraints. For distributed averaging networks, we study the interaction between a network designer and an adversary. We consider two types of attacks on the network. In Attack-I, the adversary strategically disconnects a set of links to prevent the nodes from reaching consensus. Meanwhile, the network designer assists the nodes in reaching consensus by changing the weights of a limited number of links in the network. We formulate two problems to describe this competition where the order in which the players act is reversed in the two problems. Although the canonical equations provided by the Pontryagin's Maximum Principle (MP) seem to be intractable, we provide an alternative characterization for the optimal strategies that makes connection to potential theory. Further, we provide a sufficient condition for the existence of a saddle-point equilibrium (SPE) for the underlying zero-sum game. In Attack-II, the designer and the adversary are both capable of altering the measurements of all nodes in the network by injecting global signals. We impose two constraints on both players: a power constraint and an energy constraint. We assume that the available energy to each player is not sufficient to operate at maximum power throughout the horizon of the game. We show the existence of an SPE and derive the optimal strategies in closed form for this attack scenario. As an alternative to the "network designer vs. adversary" framework, we investigate the possibility of stabilizing unknown network diffusion processes using a distributed mechanism, where the uncertainty is due to an attack on the network. To this end, we propose a distributed version of the classical logic-based supervisory control scheme. Given a network of agents whose dynamics contain unknown parameters, the distributed supervisory control scheme is used to assist the agents to converge to a certain set-point without requiring them to have explicit knowledge of that set-point. Unlike the classical supervisory control scheme where a centralized supervisor makes switching decisions among the candidate controllers, in our scheme, each agent is equipped with a local supervisor that switches among the available controllers. The switching decisions made at a certain agent depend only on the information from its neighboring agents. We provide sufficient conditions for stabilization and apply our framework to the distributed averaging problem in the presence of large modeling uncertainty. For infected networks, we study the stability properties of a susceptible-infected-susceptible (SIS) diffusion model, so-called the n-intertwined Markov model, over arbitrary network topologies. Similar to the majority of infection spread dynamics, this model exhibits a threshold phenomenon. When the curing rates in the network are high, the all-healthy state is the unique equilibrium over the network. Otherwise, an endemic equilibrium state emerges, where some infection remains within the network. Using notions from positive systems theory, we provide conditions for the global asymptotic stability of the equilibrium points in both cases over strongly and weakly connected directed networks based on the value of the basic reproduction number, a fundamental quantity in the study of epidemics. Furthermore, we demonstrate that the n-intertwined Markov model can be viewed as a best-response dynamical system of a concave game among the nodes. This characterization allows us to cast new infection spread dynamics; additionally, we provide a sufficient condition, for the global convergence to the all-healthy state, that can be checked in a distributed fashion. Moreover, we investigate the problem of stabilizing the network when the curing rates of a limited number of nodes can be controlled. In particular, we characterize the number of controllers required for a class of undirected graphs. We also design optimal controllers capable of minimizing the total infection in the network at minimum cost. Finally, we outline a set of open problems in the area of information spread control.

Design of fuzzy systems using neurofuzzy networks.

PubMed

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Analytical transport network theory to guide the design of 3-D microstructural networks in energy materials: Part 1. Flow without reactions

NASA Astrophysics Data System (ADS)

Cocco, Alex P.; Nakajo, Arata; Chiu, Wilson K. S.

2017-12-01

We present a fully analytical, heuristic model - the "Analytical Transport Network Model" - for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5-1.5 and 5-6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

PubMed Central

Wang, Changjian; Liu, Xiaohui; Jin, Shiyao

2018-01-01

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment. PMID:29955227

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.

PubMed

Ziebarth, Jesse D; Cui, Yan

2017-01-01

The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

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

Kumar, Rajeeva; Kumar, Aditya; Dai, Dan

2012-12-31

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developedmore » will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.« less

Neural-Network Simulator

NASA Technical Reports Server (NTRS)

Mitchell, Paul H.

1991-01-01

F77NNS (FORTRAN 77 Neural Network Simulator) computer program simulates popular back-error-propagation neural network. Designed to take advantage of vectorization when used on computers having this capability, also used on any computer equipped with ANSI-77 FORTRAN Compiler. Problems involving matching of patterns or mathematical modeling of systems fit class of problems F77NNS designed to solve. Program has restart capability so neural network solved in stages suitable to user's resources and desires. Enables user to customize patterns of connections between layers of network. Size of neural network F77NNS applied to limited only by amount of random-access memory available to user.

Energy modelling in sensor networks

NASA Astrophysics Data System (ADS)

Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.

2007-06-01

Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.

TinkerCell: modular CAD tool for synthetic biology.

PubMed

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-10-29

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at http://www.tinkercell.com. An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology.

TinkerCell: modular CAD tool for synthetic biology

PubMed Central

Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

2009-01-01

Background Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks. Results An application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at . Conclusion An ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology. PMID:19874625

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

PubMed

Hui, David Shui Wing; Chen, Yi-Chao; Zhang, Gong; Wu, Weijie; Chen, Guanrong; Lui, John C S; Li, Yingtao

2017-06-16

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

Network model for thermal conductivities of unidirectional fiber-reinforced composites

NASA Astrophysics Data System (ADS)

Wang, Yang; Peng, Chaoyi; Zhang, Weihua

2014-12-01

An empirical network model has been developed to predict the in-plane thermal conductivities along arbitrary directions for unidirectional fiber-reinforced composites lamina. Measurements of thermal conductivities along different orientations were carried out. Good agreement was observed between values predicted by the network model and the experimental data; compared with the established analytical models, the newly proposed network model could give values with higher precision. Therefore, this network model is helpful to get a wider and more comprehensive understanding of heat transmission characteristics of fiber-reinforced composites and can be utilized as guidance to design and fabricate laminated composites with specific directional or specific locational thermal conductivities for structures that simultaneously perform mechanical and thermal functions, i.e. multifunctional structures (MFS).

Standard cell-based implementation of a digital optoelectronic neural-network hardware.

PubMed

Maier, K D; Beckstein, C; Blickhan, R; Erhard, W

2001-03-10

A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.

Global thermal analysis of air-air cooled motor based on thermal network

NASA Astrophysics Data System (ADS)

Hu, Tian; Leng, Xue; Shen, Li; Liu, Haidong

2018-02-01

The air-air cooled motors with high efficiency, large starting torque, strong overload capacity, low noise, small vibration and other characteristics, are widely used in different department of national industry, but its cooling structure is complex, it requires the motor thermal management technology should be high. The thermal network method is a common method to calculate the temperature field of the motor, it has the advantages of small computation time and short time consuming, it can save a lot of time in the initial design phase of the motor. The domain analysis of air-air cooled motor and its cooler was based on thermal network method, the combined thermal network model was based, the main components of motor internal and external cooler temperature were calculated and analyzed, and the temperature rise test results were compared to verify the correctness of the combined thermal network model, the calculation method can satisfy the need of engineering design, and provide a reference for the initial and optimum design of the motor.

Soft computing methods in design of superalloys

NASA Technical Reports Server (NTRS)

Cios, K. J.; Berke, L.; Vary, A.; Sharma, S.

1995-01-01

Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modeled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values.

Soft Computing Methods in Design of Superalloys

NASA Technical Reports Server (NTRS)

Cios, K. J.; Berke, L.; Vary, A.; Sharma, S.

1996-01-01

Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modelled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values.

Capacity of Heterogeneous Mobile Wireless Networks with D-Delay Transmission Strategy.

PubMed

Wu, Feng; Zhu, Jiang; Xi, Zhipeng; Gao, Kai

2016-03-25

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks.

A Control Simulation Method of High-Speed Trains on Railway Network with Irregular Influence

NASA Astrophysics Data System (ADS)

Yang, Li-Xing; Li, Xiang; Li, Ke-Ping

2011-09-01

Based on the discrete time method, an effective movement control model is designed for a group of highspeed trains on a rail network. The purpose of the model is to investigate the specific traffic characteristics of high-speed trains under the interruption of stochastic irregular events. In the model, the high-speed rail traffic system is supposed to be equipped with the moving-block signalling system to guarantee maximum traversing capacity of the railway. To keep the safety of trains' movements, some operational strategies are proposed to control the movements of trains in the model, including traction operation, braking operation, and entering-station operation. The numerical simulations show that the designed model can well describe the movements of high-speed trains on the rail network. The research results can provide the useful information not only for investigating the propagation features of relevant delays under the irregular disturbance but also for rerouting and rescheduling trains on the rail network.

Impact of malicious servers over trust and reputation models in wireless sensor networks

NASA Astrophysics Data System (ADS)

Verma, Vinod Kumar; Singh, Surinder; Pathak, N. P.

2016-03-01

This article deals with the impact of malicious servers over different trust and reputation models in wireless sensor networks. First, we analysed the five trust and reputation models, namely BTRM-WSN, Eigen trust, peer trust, power trust, linguistic fuzzy trust model. Further, we proposed wireless sensor network design for optimisation of these models. Finally, influence of malicious servers on the behaviour of above mentioned trust and reputation models is discussed. Statistical analysis has been carried out to prove the validity of our proposal.

A unified design space of synthetic stripe-forming networks

PubMed Central

Schaerli, Yolanda; Munteanu, Andreea; Gili, Magüi; Cotterell, James; Sharpe, James; Isalan, Mark

2014-01-01

Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space. PMID:25247316

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

PubMed

Lin, Chuan-Kai; Wang, Sheng-De

2004-11-01

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Chaotic simulated annealing by a neural network with a variable delay: design and application.

PubMed

Chen, Shyan-Shiou

2011-10-01

In this paper, we have three goals: the first is to delineate the advantages of a variably delayed system, the second is to find a more intuitive Lyapunov function for a delayed neural network, and the third is to design a delayed neural network for a quadratic cost function. For delayed neural networks, most researchers construct a Lyapunov function based on the linear matrix inequality (LMI) approach. However, that approach is not intuitive. We provide a alternative candidate Lyapunov function for a delayed neural network. On the other hand, if we are first given a quadratic cost function, we can construct a delayed neural network by suitably dividing the second-order term into two parts: a self-feedback connection weight and a delayed connection weight. To demonstrate the advantage of a variably delayed neural network, we propose a transiently chaotic neural network with variable delay and show numerically that the model should possess a better searching ability than Chen-Aihara's model, Wang's model, and Zhao's model. We discuss both the chaotic and the convergent phases. During the chaotic phase, we simply present bifurcation diagrams for a single neuron with a constant delay and with a variable delay. We show that the variably delayed model possesses the stochastic property and chaotic wandering. During the convergent phase, we not only provide a novel Lyapunov function for neural networks with a delay (the Lyapunov function is independent of the LMI approach) but also establish a correlation between the Lyapunov function for a delayed neural network and an objective function for the traveling salesman problem. © 2011 IEEE

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

PubMed

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Traffic modeling of transit oriented development : evaluation of transit friendly strategies and innovative intersection designs in West Valley City, UT.

DOT National Transportation Integrated Search

2014-07-01

Street networks designed to support Transit Oriented Development (TOD) increase accessibility for non-motorized traffic. However, the implications of TOD supportive networks for still dominant vehicular : traffic are rarely addressed. Due to this lac...

Early detection network design and search strategy issues

EPA Science Inventory

We conducted a series of field and related modeling studies (2005-2012) to evaluate search strategies for Great Lakes coastal ecosystems that are at risk of invasion by non-native aquatic species. In developing a network, we should design to achieve an acceptable limit of detect...

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

PubMed

Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

2018-05-08

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

A new traffic control design method for large networks with signalized intersections

NASA Technical Reports Server (NTRS)

Leininger, G. G.; Colony, D. C.; Seldner, K.

1979-01-01

The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

Investigating System Dependability Modeling Using AADL

NASA Technical Reports Server (NTRS)

Hall, Brendan; Driscoll, Kevin R.; Madl, Gabor

2013-01-01

This report describes Architecture Analysis & Design Language (AADL) models for a diverse set of fault-tolerant, embedded data networks and describes the methods and tools used to created these models. It also includes error models per the AADL Error Annex. Some networks were modeled using Error Detection Isolation Containment Types (EDICT). This report gives a brief description for each of the networks, a description of its modeling, the model itself, and evaluations of the tools used for creating the models. The methodology includes a naming convention that supports a systematic way to enumerate all of the potential failure modes.

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE PAGES

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.; ...

2017-10-03

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

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

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2002-01-01

In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.

Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).

PubMed

Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

2014-12-01

In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.

On Improved Least Squares Regression and Artificial Neural Network Meta-Models for Simulation via Control Variates

DTIC Science & Technology

2016-09-15

18] under the context of robust parameter design for simulation. Bellucci’s technique is used in this research, primarily because the interior -point...Fundamentals of Radial Basis Neural Network (RBNN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.2.2.2 Design of Experiments...with Neural Nets . . . . . . . . . . . . . 31 1.2.2.3 Factorial Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 1.2.2.4

Physical explosion analysis in heat exchanger network design

NASA Astrophysics Data System (ADS)

Pasha, M.; Zaini, D.; Shariff, A. M.

2016-06-01

The failure of shell and tube heat exchangers is being extensively experienced by the chemical process industries. This failure can create a loss of production for long time duration. Moreover, loss of containment through heat exchanger could potentially lead to a credible event such as fire, explosion and toxic release. There is a need to analyse the possible worst case effect originated from the loss of containment of the heat exchanger at the early design stage. Physical explosion analysis during the heat exchanger network design is presented in this work. Baker and Prugh explosion models are deployed for assessing the explosion effect. Microsoft Excel integrated with process design simulator through object linking and embedded (OLE) automation for this analysis. Aspen HYSYS V (8.0) used as a simulation platform in this work. A typical heat exchanger network of steam reforming and shift conversion process was presented as a case study. It is investigated from this analysis that overpressure generated from the physical explosion of each heat exchanger can be estimated in a more precise manner by using Prugh model. The present work could potentially assist the design engineer to identify the critical heat exchanger in the network at the preliminary design stage.

Communications network design and costing model technical manual

NASA Technical Reports Server (NTRS)

Logan, K. P.; Somes, S. S.; Clark, C. A.

1983-01-01

This computer model provides the capability for analyzing long-haul trunking networks comprising a set of user-defined cities, traffic conditions, and tariff rates. Networks may consist of all terrestrial connectivity, all satellite connectivity, or a combination of terrestrial and satellite connectivity. Network solutions provide the least-cost routes between all cities, the least-cost network routing configuration, and terrestrial and satellite service cost totals. The CNDC model allows analyses involving three specific FCC-approved tariffs, which are uniquely structured and representative of most existing service connectivity and pricing philosophies. User-defined tariffs that can be variations of these three tariffs are accepted as input to the model and allow considerable flexibility in network problem specification. The resulting model extends the domain of network analysis from traditional fixed link cost (distance-sensitive) problems to more complex problems involving combinations of distance and traffic-sensitive tariffs.

Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment

PubMed Central

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric—the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment. PMID:22163678

Automated construction of node software using attributes in a ubiquitous sensor network environment.

PubMed

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric-the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment.

Modelling fuel cell performance using artificial intelligence

NASA Astrophysics Data System (ADS)

Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

An investigation of networking techniques for the ASRM facility

NASA Technical Reports Server (NTRS)

Moorhead, Robert J., II; Smith, Wayne D.; Thompson, Dale R.

1992-01-01

This report is based on the early design concepts for a communications network for the Advanced Solid Rocket Motor (ASRM) facility being built at Yellow Creek near Iuka, MS. The investigators have participated in the early design concepts and in the evaluation of the initial concepts. The continuing system design effort and any modification of the plan will require a careful evaluation of the required bandwidth of the network, the capabilities of the protocol, and the requirements of the controllers and computers on the network. The overall network, which is heterogeneous in protocol and bandwidth, is being modeled, analyzed, simulated, and tested to obtain some degree of confidence in its performance capabilities and in its performance under nominal and heavy loads. The results of the proposed work should have an impact on the design and operation of the ASRM facility.

Planning and cost analysis of digital radiography services for a network of hospitals (the Veterans Integrated Service Network).

PubMed

Duerinckx, A J; Kenagy, J J; Grant, E G

1998-01-01

This study analysed the design and cost of a picture archiving and communications system (PACS), computerized radiography (CR) and a wide-area network for teleradiology. The Desert Pacific Healthcare Network comprises 10 facilities, including four tertiary medical centres and one small hospital. Data were collected on radiologists' workloads, and patient and image flow within and between these medical centres. These were used to estimate the size and cash flows associated with a system-wide implementation of PACS, CR and teleradiology services. A cost analysis model was used to estimate the potential cost savings in a filmless radiology environment. ATM technology was selected as the communications medium between the medical centres. A strategic plan and business plan were successfully developed. The cost model predicted the cost-effectiveness of the proposed PACS/CR configuration within four to six years, if the base costs were kept low. The experience gained in design and cost analysis of a PACS/teleradiology network will serve as a model for similar projects.

De novo design of protein homo-oligomers with modular hydrogen bond network-mediated specificity

PubMed Central

Boyken, Scott E.; Chen, Zibo; Groves, Benjamin; Langan, Robert A.; Oberdorfer, Gustav; Ford, Alex; Gilmore, Jason; Xu, Chunfu; DiMaio, Frank; Pereira, Jose Henrique; Sankaran, Banumathi; Seelig, Georg; Zwart, Peter H.; Baker, David

2017-01-01

In nature, structural specificity in DNA and proteins is encoded quite differently: in DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen bond networks with atomic accuracy is a milestone for protein design and enables the programming of protein interaction specificity for a broad range of synthetic biology applications. PMID:27151862

76 FR 36863 - Special Conditions: Gulfstream Model GVI Airplane; Electronic Systems Security Protection From...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-23

... airplane. This airplane will have novel or unusual design features associated with the architecture and... incorporate the following novel or unusual design features: Digital systems architecture composed of several connected networks. The proposed architecture and network configuration may be used for, or interfaced with...

A Neutral Network based Early Eathquake Warning model in California region

NASA Astrophysics Data System (ADS)

Xiao, H.; MacAyeal, D. R.

2016-12-01

Early Earthquake Warning systems could reduce loss of lives and other economic impact resulted from natural disaster or man-made calamity. Current systems could be further enhanced by neutral network method. A 3 layer neural network model combined with onsite method was deployed in this paper to improve the recognition time and detection time for large scale earthquakes.The 3 layer neutral network early earthquake warning model adopted the vector feature design for sample events happened within 150 km radius of the epicenters. Dataset used in this paper contained both destructive events and small scale events. All the data was extracted from IRIS database to properly train the model. In the training process, backpropagation algorithm was used to adjust the weight matrices and bias matrices during each iteration. The information in all three channels of the seismometers served as the source in this model. Through designed tests, it was indicated that this model could identify approximately 90 percent of the events' scale correctly. And the early detection could provide informative evidence for public authorities to make further decisions. This indicated that neutral network model could have the potential to strengthen current early warning system, since the onsite method may greatly reduce the responding time and save more lives in such disasters.

Effective contaminant detection networks in uncertain groundwater flow fields.

PubMed

Hudak, P F

2001-01-01

A mass transport simulation model tested seven contaminant detection-monitoring networks under a 40 degrees range of groundwater flow directions. Each monitoring network contained five wells located 40 m from a rectangular landfill. The 40-m distance (lag) was measured in different directions, depending upon the strategy used to design a particular monitoring network. Lagging the wells parallel to the central flow path was more effective than alternative design strategies. Other strategies allowed higher percentages of leaks to migrate between monitoring wells. Results of this study suggest that centrally lagged groundwater monitoring networks perform most effectively in uncertain groundwater-flow fields.

How time delay and network design shape response patterns in biochemical negative feedback systems.

PubMed

Börsch, Anastasiya; Schaber, Jörg

2016-08-24

Negative feedback in combination with time delay can bring about both sustained oscillations and adaptive behaviour in cellular networks. Here, we study which design features of systems with delayed negative feedback shape characteristic response patterns with special emphasis on the role of time delay. To this end, we analyse generic two-dimensional delay differential equations describing the dynamics of biochemical signal-response networks. We investigate the influence of several design features on the stability of the model equilibrium, i.e., presence of auto-inhibition and/or mass conservation and the kind and/or strength of the delayed negative feedback. We show that auto-inhibition and mass conservation have a stabilizing effect, whereas increasing abruptness and decreasing feedback threshold have a de-stabilizing effect on the model equilibrium. Moreover, applying our theoretical analysis to the mammalian p53 system we show that an auto-inhibitory feedback can decouple period and amplitude of an oscillatory response, whereas the delayed feedback can not. Our theoretical framework provides insight into how time delay and design features of biochemical networks act together to elicit specific characteristic response patterns. Such insight is useful for constructing synthetic networks and controlling their behaviour in response to external stimulation.

Validation of a SysML based design for wireless sensor networks

NASA Astrophysics Data System (ADS)

Berrachedi, Amel; Rahim, Messaoud; Ioualalen, Malika; Hammad, Ahmed

2017-07-01

When developing complex systems, the requirement for the verification of the systems' design is one of the main challenges. Wireless Sensor Networks (WSNs) are examples of such systems. We address the problem of how WSNs must be designed to fulfil the system requirements. Using the SysML Language, we propose a Model Based System Engineering (MBSE) specification and verification methodology for designing WSNs. This methodology uses SysML to describe the WSNs requirements, structure and behaviour. Then, it translates the SysML elements to an analytic model, specifically, to a Deterministic Stochastic Petri Net. The proposed approach allows to design WSNs and study their behaviors and their energy performances.

Neural Network and Response Surface Methodology for Rocket Engine Component Optimization

NASA Technical Reports Server (NTRS)

Vaidyanathan, Rajkumar; Papita, Nilay; Shyy, Wei; Tucker, P. Kevin; Griffin, Lisa W.; Haftka, Raphael; Fitz-Coy, Norman; McConnaughey, Helen (Technical Monitor)

2000-01-01

The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.

The role of networks and artificial intelligence in nanotechnology design and analysis.

PubMed

Hudson, D L; Cohen, M E

2004-05-01

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Allen, Michael J.; Dibley, Ryan P.

2003-01-01

Neural networks were used to model wing bending-moment loads, torsion loads, and control surface hinge-moments of the Active Aeroelastic Wing (AAW) aircraft. Accurate loads models are required for the development of control laws designed to increase roll performance through wing twist while not exceeding load limits. Inputs to the model include aircraft rates, accelerations, and control surface positions. Neural networks were chosen to model aircraft loads because they can account for uncharacterized nonlinear effects while retaining the capability to generalize. The accuracy of the neural network models was improved by first developing linear loads models to use as starting points for network training. Neural networks were then trained with flight data for rolls, loaded reversals, wind-up-turns, and individual control surface doublets for load excitation. Generalization was improved by using gain weighting and early stopping. Results are presented for neural network loads models of four wing loads and four control surface hinge moments at Mach 0.90 and an altitude of 15,000 ft. An average model prediction error reduction of 18.6 percent was calculated for the neural network models when compared to the linear models. This paper documents the input data conditioning, input parameter selection, structure, training, and validation of the neural network models.

Intelligent Resource Management for Local Area Networks: Approach and Evolution

NASA Technical Reports Server (NTRS)

Meike, Roger

1988-01-01

The Data Management System network is a complex and important part of manned space platforms. Its efficient operation is vital to crew, subsystems and experiments. AI is being considered to aid in the initial design of the network and to augment the management of its operation. The Intelligent Resource Management for Local Area Networks (IRMA-LAN) project is concerned with the application of AI techniques to network configuration and management. A network simulation was constructed employing real time process scheduling for realistic loads, and utilizing the IEEE 802.4 token passing scheme. This simulation is an integral part of the construction of the IRMA-LAN system. From it, a causal model is being constructed for use in prediction and deep reasoning about the system configuration. An AI network design advisor is being added to help in the design of an efficient network. The AI portion of the system is planned to evolve into a dynamic network management aid. The approach, the integrated simulation, project evolution, and some initial results are described.

Reservoir characterization using core, well log, and seismic data and intelligent software

NASA Astrophysics Data System (ADS)

Soto Becerra, Rodolfo

We have developed intelligent software, Oilfield Intelligence (OI), as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. More than 1,200 lines of programming code as M-files using the language MATLAB been written. The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have been applied for identification of nonlinear systems in almost all scientific fields of humankind. Solving reservoir characterization problems is no exception. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal components and factor analysis to reduce the dimensionality and help the neural networks formulate more realistic models. We validated OI by obtaining confident models in three different oil field problems: (1) A neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas, (2) A neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) Neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas. In all cases, we compared the results from the neural network models with the results from regression statistical and non-parametric approach models. The results show that it is possible to obtain the highest cross-correlation coefficient between predicted and actual target variables, and the lowest average absolute errors using the integrated techniques of multivariate statistical analysis and neural networks in our intelligent software.

The Modeling, Simulation and Comparison of Interconnection Networks for Parallel Processing.

DTIC Science & Technology

1987-12-01

performs better at a lower hardware cost than do the single stage cube and mesh networks. As a result, the designer of a paralll pro- cessing system is...attempted, and in most cases succeeded, in designing and implementing faster. more powerful systems. Due to design innovations and technological advances...largely to the computational complexity of the algorithms executed. In the von Neumann machine, instructions must be executed in a sequential manner. Design

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

NASA Technical Reports Server (NTRS)

Mog, Robert A.

1999-01-01

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Epidemic Percolation Networks, Epidemic Outcomes, and Interventions

DOE PAGES

Kenah, Eben; Miller, Joel C.

2011-01-01

Epidemic percolation networks (EPNs) are directed random networks that can be used to analyze stochastic “Susceptible-Infectious-Removed” (SIR) and “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic models, unifying and generalizing previous uses of networks and branching processes to analyze mass-action and network-based S(E)IR models. This paper explains the fundamental concepts underlying the definition and use of EPNs, using them to build intuition about the final outcomes of epidemics. We then show how EPNs provide a novel and useful perspective on the design of vaccination strategies.

Epidemic Percolation Networks, Epidemic Outcomes, and Interventions

PubMed Central

Kenah, Eben; Miller, Joel C.

2011-01-01

Epidemic percolation networks (EPNs) are directed random networks that can be used to analyze stochastic “Susceptible-Infectious-Removed” (SIR) and “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic models, unifying and generalizing previous uses of networks and branching processes to analyze mass-action and network-based S(E)IR models. This paper explains the fundamental concepts underlying the definition and use of EPNs, using them to build intuition about the final outcomes of epidemics. We then show how EPNs provide a novel and useful perspective on the design of vaccination strategies. PMID:21437002

Developing a cross-docking network design model under uncertain environment

NASA Astrophysics Data System (ADS)

Seyedhoseini, S. M.; Rashid, Reza; Teimoury, E.

2015-06-01

Cross-docking is a logistic concept, which plays an important role in supply chain management by decreasing inventory holding, order packing, transportation costs and delivery time. Paying attention to these concerns, and importance of the congestion in cross docks, we present a mixed-integer model to optimize the location and design of cross docks at the same time to minimize the total transportation and operating costs. The model combines queuing theory for design aspects, for that matter, we consider a network of cross docks and customers where two M/M/c queues have been represented to describe operations of indoor trucks and outdoor trucks in each cross dock. To prepare a perfect illustration for performance of the model, a real case also has been examined that indicated effectiveness of the proposed model.

Design of a network for concurrent message passing systems

NASA Astrophysics Data System (ADS)

Song, Paul Y.

1988-08-01

We describe the design of the network design frame (NDF), a self-timed routing chip for a message-passing concurrent computer. The NDF uses a partitioned data path, low-voltage output drivers, and a distributed token-passing arbiter to provide a bandwidth of 450 Mbits/sec into the network. Wormhole routing and bidirectional virtual channels are used to provide low latency communications, less than 2us latency to deliver a 216 bit message across the diameter of a 1K node mess-connected machine. To support concurrent software systems, the NDF provides two logical networks, one for user messages and one for system messages. The two networks share the same set of physical wires. To facilitate the development of network nodes, the NDF is a design frame. The NDF circuitry is integrated into the pad frame of a chip leaving the center of the chip uncommitted. We define an analytic framework in which to study the effects of network size, network buffering capacity, bidirectional channels, and traffic on this class of networks. The response of the network to various combinations of these parameters are obtained through extensive simulation of the network model. Through simulation, we are able to observe the macro behavior of the network as opposed to the micro behavior of the NDF routing controller.

How does network design constrain optimal operation of intermittent water supply?

NASA Astrophysics Data System (ADS)

Lieb, Anna; Wilkening, Jon; Rycroft, Chris

2015-11-01

Urban water distribution systems do not always supply water continuously or reliably. As pipes fill and empty, pressure transients may contribute to degraded infrastructure and poor water quality. To help understand and manage this undesirable side effect of intermittent water supply--a phenomenon affecting hundreds of millions of people in cities around the world--we study the relative contributions of fixed versus dynamic properties of the network. Using a dynamical model of unsteady transition pipe flow, we study how different elements of network design, such as network geometry, pipe material, and pipe slope, contribute to undesirable pressure transients. Using an optimization framework, we then investigate to what extent network operation decisions such as supply timing and inflow rate may mitigate these effects. We characterize some aspects of network design that make them more or less amenable to operational optimization.

Nonlinear adaptive inverse control via the unified model neural network

NASA Astrophysics Data System (ADS)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Observer design for compensation of network-induced delays in integrated communication and control systems

NASA Technical Reports Server (NTRS)

Luck, R.; Ray, A.

1988-01-01

A method for compensating the effects of network-induced delays in integrated communication and control systems (ICCS) is proposed, and a finite-dimensional time-invariant ICCS model is developed. The problem of analyzing systems with time-varying and stochastic delays is circumvented by the application of a deterministic observer. For the case of controller-to-actuator delays, the observed design must rely on an extended model which represents the delays as additional states.

Analytical Models of Cross-Layer Protocol Optimization in Real-Time Wireless Sensor Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

The real-time interactions among the nodes of a wireless sensor network (WSN) to cooperatively process data from multiple sensors are modeled. Quality-of-service (QoS) metrics are associated with the quality of fused information: throughput, delay, packet error rate, etc. Multivariate point process (MVPP) models of discrete random events in WSNs establish stochastic characteristics of optimal cross-layer protocols. Discrete-event, cross-layer interactions in mobile ad hoc network (MANET) protocols have been modeled using a set of concatenated design parameters and associated resource levels by the MVPPs. Characterization of the "best" cross-layer designs for a MANET is formulated by applying the general theory of martingale representations to controlled MVPPs. Performance is described in terms of concatenated protocol parameters and controlled through conditional rates of the MVPPs. Modeling limitations to determination of closed-form solutions versus explicit iterative solutions for ad hoc WSN controls are examined.

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

PubMed

Taheri, Mahboobeh; Mohebbi, Ali

2008-08-30

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

Network-Based Visual Analysis of Tabular Data

ERIC Educational Resources Information Center

Liu, Zhicheng

2012-01-01

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look…

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Game among interdependent networks: The impact of rationality on system robustness

NASA Astrophysics Data System (ADS)

Fan, Yuhang; Cao, Gongze; He, Shibo; Chen, Jiming; Sun, Youxian

2016-12-01

Many real-world systems are composed of interdependent networks that rely on one another. Such networks are typically designed and operated by different entities, who aim at maximizing their own payoffs. There exists a game among these entities when designing their own networks. In this paper, we study the game investigating how the rational behaviors of entities impact the system robustness. We first introduce a mathematical model to quantify the interacting payoffs among varying entities. Then we study the Nash equilibrium of the game and compare it with the optimal social welfare. We reveal that the cooperation among different entities can be reached to maximize the social welfare in continuous game only when the average degree of each network is constant. Therefore, the huge gap between Nash equilibrium and optimal social welfare generally exists. The rationality of entities makes the system inherently deficient and even renders it extremely vulnerable in some cases. We analyze our model for two concrete systems with continuous strategy space and discrete strategy space, respectively. Furthermore, we uncover some factors (such as weakening coupled strength of interdependent networks, designing a suitable topology dependence of the system) that help reduce the gap and the system vulnerability.

A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

NASA Astrophysics Data System (ADS)

Kolosionis, Konstantinos; Papadopoulou, Maria P.

2017-04-01

Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

PubMed

Drewes, Rich; Zou, Quan; Goodman, Philip H

2009-01-01

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator

PubMed Central

Drewes, Rich; Zou, Quan; Goodman, Philip H.

2008-01-01

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading “glue” tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS. PMID:19506707

Optimization of robustness of interdependent network controllability by redundant design

PubMed Central

2018-01-01

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability. PMID:29438426

Designing hydrologic monitoring networks to maximize predictability of hydrologic conditions in a data assimilation system: a case study from South Florida, U.S.A

NASA Astrophysics Data System (ADS)

Flores, A. N.; Pathak, C. S.; Senarath, S. U.; Bras, R. L.

2009-12-01

Robust hydrologic monitoring networks represent a critical element of decision support systems for effective water resource planning and management. Moreover, process representation within hydrologic simulation models is steadily improving, while at the same time computational costs are decreasing due to, for instance, readily available high performance computing resources. The ability to leverage these increasingly complex models together with the data from these monitoring networks to provide accurate and timely estimates of relevant hydrologic variables within a multiple-use, managed water resources system would substantially enhance the information available to resource decision makers. Numerical data assimilation techniques provide mathematical frameworks through which uncertain model predictions can be constrained to observational data to compensate for uncertainties in the model forcings and parameters. In ensemble-based data assimilation techniques such as the ensemble Kalman Filter (EnKF), information in observed variables such as canal, marsh and groundwater stages are propagated back to the model states in a manner related to: (1) the degree of certainty in the model state estimates and observations, and (2) the cross-correlation between the model states and the observable outputs of the model. However, the ultimate degree to which hydrologic conditions can be accurately predicted in an area of interest is controlled, in part, by the configuration of the monitoring network itself. In this proof-of-concept study we developed an approach by which the design of an existing hydrologic monitoring network is adapted to iteratively improve the predictions of hydrologic conditions within an area of the South Florida Water Management District (SFWMD). The objective of the network design is to minimize prediction errors of key hydrologic states and fluxes produced by the spatially distributed Regional Simulation Model (RSM), developed specifically to simulate the hydrologic conditions in several intensively managed and hydrologically complex watersheds within the SFWMD system. In a series of synthetic experiments RSM is used to generate the notionally true hydrologic state and the relevant observational data. The EnKF is then used as the mechanism to fuse RSM hydrologic estimates with data from the candidate network. The performance of the candidate network is measured by the prediction errors of the EnKF estimates of hydrologic states, relative to the notionally true scenario. The candidate network is then adapted by relocating existing observational sites to unobserved areas where predictions of local hydrologic conditions are most uncertain and the EnKF procedure repeated. Iteration of the monitoring network continues until further improvements in EnKF-based predictions of hydrologic conditions are negligible.

Modeling polyvinyl chloride Plasma Modification by Neural Networks

NASA Astrophysics Data System (ADS)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Network Performance Evaluation Model for assessing the impacts of high-occupancy vehicle facilities

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

Janson, B.N.; Zozaya-Gorostiza, C.; Southworth, F.

1986-09-01

A model to assess the impacts of major high-occupancy vehicle (HOV) facilities on regional levels of energy consumption and vehicle air pollution emissions in urban aeas is developed and applied. This model can be used to forecast and compare the impacts of alternative HOV facility design and operation plans on traffic patterns, travel costs, model choice, travel demand, energy consumption and vehicle emissions. The model is designed to show differences in the overall impacts of alternative HOV facility types, locations and operation plans rather than to serve as a tool for detailed engineering design and traffic planning studies. The Networkmore » Performance Evaluation Model (NETPEM) combines several urban transportation planning models within a multi-modal network equilibrium framework including modules with which to define the type, location and use policy of the HOV facility to be tested, and to assess the impacts of this facility.« less

Toward Optimal Transport Networks

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.

2008-01-01

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

Robust synthetic biology design: stochastic game theory approach.

PubMed

Chen, Bor-Sen; Chang, Chia-Hung; Lee, Hsiao-Ching

2009-07-15

Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology. A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by non-linear stochastic game method directly, the Takagi-Sugeno (T-S) fuzzy model is proposed to approximate the non-linear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method. http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf.

The national stream quality accounting network: A flux-basedapproach to monitoring the water quality of large rivers

USGS Publications Warehouse

Hooper, R.P.; Aulenbach, Brent T.; Kelly, V.J.

2001-01-01

Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: The Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing.

The 3 "C" Design Model for Networked Collaborative E-Learning: A Tool for Novice Designers

ERIC Educational Resources Information Center

Bird, Len

2007-01-01

This paper outlines a model for online course design aimed at the mainstream majority of university academics rather than at the early adopters of technology. It has been developed from work at Coventry Business School where tutors have been called upon to design online modules for the first time. Like many good tools, the model's key strength is…

Treating Depression in Staff-Model Versus Network-Model Managed Care Organizations

PubMed Central

Meredith, Lisa S; Rubenstein, Lisa V; Rost, Kathryn; Ford, Daniel E; Gordon, Nancy; Nutting, Paul; Camp, Patti; Wells, Kenneth B

1999-01-01

OBJECTIVE To compare primary care providers’ depression-related knowledge, attitudes, and practices and to understand how these reports vary for providers in staff or group-model managed care organizations (MCOs) compared with network-model MCOs including independent practice associations and preferred provider organizations. DESIGN Survey of primary care providers’ depression-related practices in 1996. SETTING AND PARTICIPANTS We surveyed 410 providers, from 80 outpatient clinics, in 11 MCOs participating in four studies designed to improve the quality of depression care in primary care. MEASUREMENTS AND MAIN RESULTS We measured knowledge based on depression guidelines, attitudes (beliefs about burden, skill, and barriers) related to depression, and reported behavior. Providers in both types of MCO are equally knowledgeable about treating depression (better knowledge of pharmacologic than psychotherapeutic treatments) and perceive equivalent skills in treating depression. However, compared with network-model providers, staff/group-model providers have stronger beliefs that treating depression is burdensome to their practice. While more staff/group-model providers reported time limitations as a barrier to optimal depression treatment, more network-model providers reported limited access to mental health specialty referral as a barrier. Accordingly, these staff/group-model providers are more likely to treat patients with major depression through referral (51% vs 38%) or to assess but not treat (17% vs 7%), and network-model providers are more likely to prescribe antidepressants (57% vs 6%) as first-line treatment. CONCLUSIONS Whereas the providers from staff/group-model MCOs had greater access to and relied more on referral, the providers from network-model organizations were more likely to treat depression themselves. Given varying attitudes and behaviors, improving primary care for the treatment of depression will require unique strategies beyond enhancing technical knowledge for the two types of MCOs. PMID:9893090

ION Configuration Editor

NASA Technical Reports Server (NTRS)

Borgen, Richard L.

2013-01-01

The configuration of ION (Inter - planetary Overlay Network) network nodes is a manual task that is complex, time-consuming, and error-prone. This program seeks to accelerate this job and produce reliable configurations. The ION Configuration Editor is a model-based smart editor based on Eclipse Modeling Framework technology. An ION network designer uses this Eclipse-based GUI to construct a data model of the complete target network and then generate configurations. The data model is captured in an XML file. Intrinsic editor features aid in achieving model correctness, such as field fill-in, type-checking, lists of valid values, and suitable default values. Additionally, an explicit "validation" feature executes custom rules to catch more subtle model errors. A "survey" feature provides a set of reports providing an overview of the entire network, enabling a quick assessment of the model s completeness and correctness. The "configuration" feature produces the main final result, a complete set of ION configuration files (eight distinct file types) for each ION node in the network.

Heuristic urban transportation network design method, a multilayer coevolution approach

NASA Astrophysics Data System (ADS)

Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

2017-08-01

The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

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.

Cyber threat model for tactical radio networks

NASA Astrophysics Data System (ADS)

Kurdziel, Michael T.

2014-05-01

The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

Microeconomics-based resource allocation in overlay networks by using non-strategic behavior modeling

NASA Astrophysics Data System (ADS)

Analoui, Morteza; Rezvani, Mohammad Hossein

2011-01-01

Behavior modeling has recently been investigated for designing self-organizing mechanisms in the context of communication networks in order to exploit the natural selfishness of the users with the goal of maximizing the overall utility. In strategic behavior modeling, the users of the network are assumed to be game players who seek to maximize their utility with taking into account the decisions that the other players might make. The essential difference between the aforementioned researches and this work is that it incorporates the non-strategic decisions in order to design the mechanism for the overlay network. In this solution concept, the decisions that a peer might make does not affect the actions of the other peers at all. The theory of consumer-firm developed in microeconomics is a model of the non-strategic behavior that we have adopted in our research. Based on it, we have presented distributed algorithms for peers' "joining" and "leaving" operations. We have modeled the overlay network as a competitive economy in which the content provided by an origin server can be viewed as commodity and the origin server and the peers who multicast the content to their downside are considered as the firms. On the other hand, due to the dual role of the peers in the overlay network, they can be considered as the consumers as well. On joining to the overlay economy, each peer is provided with an income and tries to get hold of the service regardless to the behavior of the other peers. We have designed the scalable algorithms in such a way that the existence of equilibrium price (known as Walrasian equilibrium price) is guaranteed.

Multilayer Brain Networks

NASA Astrophysics Data System (ADS)

Vaiana, Michael; Muldoon, Sarah Feldt

2018-01-01

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently, multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Zhou, Jialin; Zhu, Jinqing; Li, Yunxuan; Wan, Qian

2018-03-01

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

Realistic Modeling of Wireless Network Environments

DTIC Science & Technology

2015-03-01

wireless environment, namely vehicular networks. We also made a number of improvements to an emulation-based wireless testbed to improve channel model...and the two wireless devices used in the experiment (bottom). This testbed was used for point-point vehicular wireless experiments that used the...DSRC-based vehicular networks (~5.9 GHz). We were able to meet that goal, as described below. Figure 3: DSP Card 3.3 System design and

A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.; Haller, William J.

2004-01-01

The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.

LMI designmethod for networked-based PID control

NASA Astrophysics Data System (ADS)

Souza, Fernando de Oliveira; Mozelli, Leonardo Amaral; de Oliveira, Maurício Carvalho; Palhares, Reinaldo Martinez

2016-10-01

In this paper, we propose a methodology for the design of networked PID controllers for second-order delayed processes using linear matrix inequalities. The proposed procedure takes into account time-varying delay on the plant, time-varying delays induced by the network and packed dropouts. The design is carried on entirely using a continuous-time model of the closed-loop system where time-varying delays are used to represent sampling and holding occurring in a discrete-time digital PID controller.

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis.

PubMed

Ennouri, Karim; Ayed, Rayda Ben; Hassen, Hanen Ben; Mazzarello, Maura; Ottaviani, Ennio

2015-12-01

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH₂PO₄, K₂HPO₄, FeSO₄, MnSO₄, and MgSO₄and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett-Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO₄, K2HPO₄, starch and soybean meal. Indeed, it was found that soybean meal, K₂HPO₄, KH₂PO₄and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett-Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation.

Specifications of a Simulation Model for a Local Area Network Design in Support of Stock Point Logistics Integrated Communications Environment (SPLICE).

DTIC Science & Technology

1982-10-01

class queueing system with a preemptive -resume priority service discipline, as depicted in Figure 4.2. Concerning a SPLICLAN configuration a node can...processor can be modeled as a single resource, multi-class queueing system with a preemptive -resume priority structure as the one given in Figure 4.2. An...LOCAL AREA NETWORK DESIGN IN SUPPORT OF STOCK POINT LOGISTICS INTEGRATED COMMUNICATIONS ENVIRONMENT (SPLICE) by Ioannis Th. Mastrocostopoulos October

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

Supply chain network design problem for a new market opportunity in an agile manufacturing system

NASA Astrophysics Data System (ADS)

Babazadeh, Reza; Razmi, Jafar; Ghodsi, Reza

2012-08-01

The characteristics of today's competitive environment, such as the speed with which products are designed, manufactured, and distributed, and the need for higher responsiveness and lower operational cost, are forcing companies to search for innovative ways to do business. The concept of agile manufacturing has been proposed in response to these challenges for companies. This paper copes with the strategic and tactical level decisions in agile supply chain network design. An efficient mixed-integer linear programming model that is able to consider the key characteristics of agile supply chain such as direct shipments, outsourcing, different transportation modes, discount, alliance (process and information integration) between opened facilities, and maximum waiting time of customers for deliveries is developed. In addition, in the proposed model, the capacity of facilities is determined as decision variables, which are often assumed to be fixed. Computational results illustrate that the proposed model can be applied as a power tool in agile supply chain network design as well as in the integration of strategic decisions with tactical decisions.

Design of high-speed burst mode clock and data recovery IC for passive optical network

NASA Astrophysics Data System (ADS)

Yan, Minhui; Hong, Xiaobin; Huang, Wei-Ping; Hong, Jin

2005-09-01

Design of a high bit rate burst mode clock and data recovery (BMCDR) circuit for gigabit passive optical networks (GPON) is described. A top-down design flow is established and some of the key issues related to the behavioural level modeling are addressed in consideration for the complexity of the BMCDR integrated circuit (IC). Precise implementation of Simulink behavioural model accounting for the saturation of frequency control voltage is therefore developed for the BMCDR, and the parameters of the circuit blocks can be readily adjusted and optimized based on the behavioural model. The newly designed BMCDR utilizes the 0.18um standard CMOS technology and is shown to be capable of operating at bit rate of 2.5Gbps, as well as the recovery time of one bit period in our simulation. The developed behaviour model is verified by comparing with the detailed circuit simulation.

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

PubMed

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

SSIC model: A multi-layer model for intervention of online rumors spreading

NASA Astrophysics Data System (ADS)

Tian, Ru-Ya; Zhang, Xue-Fu; Liu, Yi-Jun

2015-06-01

SIR model is a classical model to simulate rumor spreading, while the supernetwork is an effective tool for modeling complex systems. Based on the Opinion SuperNetwork involving Social Sub-network, Environmental Sub-network, Psychological Sub-network, and Viewpoint Sub-network, drawing from the modeling idea of SIR model, this paper designs super SIC model (SSIC model) and its evolution rules, and also analyzes intervention effects on public opinion of four elements of supernetwork, which are opinion agent, opinion environment, agent's psychology and viewpoint. Studies show that, the SSIC model based on supernetwork has effective intervention effects on rumor spreading. It is worth noting that (i) identifying rumor spreaders in Social Sub-network and isolating them can achieve desired intervention results, (ii) improving environmental information transparency so that the public knows as much information as possible to reduce the rumors is a feasible way to intervene, (iii) persuading wavering neutrals has better intervention effects than clarifying rumors already spread everywhere, so rumors should be intervened in properly in time by psychology counseling.

Traffic Management in ATM Networks Over Satellite Links

NASA Technical Reports Server (NTRS)

Goyal, Rohit; Jain, Raj; Goyal, Mukul; Fahmy, Sonia; Vandalore, Bobby; vonDeak, Thomas

1999-01-01

This report presents a survey of the traffic management Issues in the design and implementation of satellite Asynchronous Transfer Mode (ATM) networks. The report focuses on the efficient transport of Transmission Control Protocol (TCP) traffic over satellite ATM. First, a reference satellite ATM network architecture is presented along with an overview of the service categories available in ATM networks. A delay model for satellite networks and the major components of delay and delay variation are described. A survey of design options for TCP over Unspecified Bit Rate (UBR), Guaranteed Frame Rate (GFR) and Available Bit Rate (ABR) services in ATM is presented. The main focus is on traffic management issues. Several recommendations on the design options for efficiently carrying data services over satellite ATM networks are presented. Most of the results are based on experiments performed on Geosynchronous (GEO) latencies. Some results for Low Earth Orbits (LEO) and Medium Earth Orbit (MEO) latencies are also provided.

Double-Line-Frequency Ripple Model, Analysis & Impedance Design for Energy Stored Single-Phase Quasi-Z Source Photovoltaic System

DOE PAGES

Liang, Weihua; Liu, Yushan; Ge, Baoming; ...

2017-09-08

The battery energy stored quasi-Z-source (BESqZS) based photovoltaic (PV) power generation system combines advantages of the qZS inverter and the battery energy storage system. But, the second harmonic (2ω) power ripple degrades the system’s performance and affects the system’s design. An accurate model to analyze the 2ω ripple is very important. The existing models did not consider the battery, or assumed a symmetric qZS network with L 1=L 2 and C 1=C 2, which limits the design freedom and causes oversized impedance parameters. Our paper proposes a comprehensive model for the single-phase BES-qZS-PV inverter system, where the battery is consideredmore » and there is no restriction of L 1=L 2 and C 1=C 2. Based on the built model, a BES-qZS impedance design method is proposed to mitigate the 2ω ripple with asymmetric qZS network. Simulation and experimental results verify the proposed 2ω ripple model and impedance design method.« less

Double-Line-Frequency Ripple Model, Analysis & Impedance Design for Energy Stored Single-Phase Quasi-Z Source Photovoltaic System

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

Liang, Weihua; Liu, Yushan; Ge, Baoming

The battery energy stored quasi-Z-source (BESqZS) based photovoltaic (PV) power generation system combines advantages of the qZS inverter and the battery energy storage system. But, the second harmonic (2ω) power ripple degrades the system’s performance and affects the system’s design. An accurate model to analyze the 2ω ripple is very important. The existing models did not consider the battery, or assumed a symmetric qZS network with L 1=L 2 and C 1=C 2, which limits the design freedom and causes oversized impedance parameters. Our paper proposes a comprehensive model for the single-phase BES-qZS-PV inverter system, where the battery is consideredmore » and there is no restriction of L 1=L 2 and C 1=C 2. Based on the built model, a BES-qZS impedance design method is proposed to mitigate the 2ω ripple with asymmetric qZS network. Simulation and experimental results verify the proposed 2ω ripple model and impedance design method.« less

Multi-scale Multi-mechanism Toughening of Hydrogels

NASA Astrophysics Data System (ADS)

Zhao, Xuanhe

Hydrogels are widely used as scaffolds for tissue engineering, vehicles for drug delivery, actuators for optics and fluidics, and model extracellular matrices for biological studies. The scope of hydrogel applications, however, is often severely limited by their mechanical properties. Inspired by the mechanics and hierarchical structures of tough biological tissues, we propose that a general principle for the design of tough hydrogels is to implement two mechanisms for dissipating mechanical energy and maintaining high elasticity in hydrogels. A particularly promising strategy for the design is to integrate multiple pairs of mechanisms across multiple length scales into a hydrogel. We develop a multiscale theoretical framework to quantitatively guide the design of tough hydrogels. On the network level, we have developed micro-physical models to characterize the evolution of polymer networks under deformation. On the continuum level, we have implemented constitutive laws formulated from the network-level models into a coupled cohesive-zone and Mullins-effect model to quantitatively predict crack propagation and fracture toughness of hydrogels. Guided by the design principle and quantitative model, we will demonstrate a set of new hydrogels, based on diverse types of polymers, yet can achieve extremely high toughness superior to their natural counterparts such as cartilages. The work was supported by NSF(No. CMMI- 1253495) and ONR (No. N00014-14-1-0528).

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

PubMed

Navlakha, Saket; Barth, Alison L; Bar-Joseph, Ziv

2015-07-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

PubMed Central

Navlakha, Saket; Barth, Alison L.; Bar-Joseph, Ziv

2015-01-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains. PMID:26217933

Research on cascading failure in multilayer network with different coupling preference

NASA Astrophysics Data System (ADS)

Zhang, Yong; Jin, Lei; Wang, Xiao Juan

This paper is aimed at constructing robust multilayer networks against cascading failure. Considering link protection strategies in reality, we design a cascading failure model based on load distribution and extend it to multilayer. We use the cascading failure model to deduce the scale of the largest connected component after cascading failure, from which we can find that the performance of four kinds of load distribution strategies associates with the load ratio of the current edge to its adjacent edge. Coupling preference is a typical characteristic in multilayer networks which corresponds to the network robustness. The coupling preference of multilayer networks is divided into two forms: the coupling preference in layers and the coupling preference between layers. To analyze the relationship between the coupling preference and the multilayer network robustness, we design a construction algorithm to generate multilayer networks with different coupling preferences. Simulation results show that the load distribution based on the node betweenness performs the best. When the coupling coefficient in layers is zero, the scale-free network is the most robust. In the random network, the assortative coupling in layers is more robust than the disassortative coupling. For the coupling preference between layers, the assortative coupling between layers is more robust than the disassortative coupling both in the scale free network and the random network.

Impact of Cross-Tie Properties on the Modal Behavior of Cable Networks on Cable-Stayed Bridges

PubMed Central

Ahmad, Javaid; Ghrib, Faouzi

2015-01-01

Dynamic behaviour of cable networks is highly dependent on the installation location, stiffness, and damping of cross-ties. Thus, these are the important design parameters for a cable network. While the effects of the former two on the network response have been investigated to some extent in the past, the impact of cross-tie damping has rarely been addressed. To comprehend our knowledge of mechanics associated with cable networks, in the current study, an analytical model of a cable network will be proposed by taking into account both cross-tie stiffness and damping. In addition, the damping property of main cables in the network will also be considered in the formulation. This would allow exploring not only the effectiveness of a cross-tie design on enhancing the in-plane stiffness of a constituted cable network, but also its energy dissipation capacity. The proposed analytical model will be applied to networks with different configurations. The influence of cross-tie stiffness and damping on the modal response of various types of networks will be investigated by using the corresponding undamped rigid cross-tie network as a reference base. Results will provide valuable information on the selection of cross-tie properties to achieve more effective cable vibration control. PMID:26167539

Impact of Cross-Tie Properties on the Modal Behavior of Cable Networks on Cable-Stayed Bridges.

PubMed

Ahmad, Javaid; Cheng, Shaohong; Ghrib, Faouzi

2015-01-01

Dynamic behaviour of cable networks is highly dependent on the installation location, stiffness, and damping of cross-ties. Thus, these are the important design parameters for a cable network. While the effects of the former two on the network response have been investigated to some extent in the past, the impact of cross-tie damping has rarely been addressed. To comprehend our knowledge of mechanics associated with cable networks, in the current study, an analytical model of a cable network will be proposed by taking into account both cross-tie stiffness and damping. In addition, the damping property of main cables in the network will also be considered in the formulation. This would allow exploring not only the effectiveness of a cross-tie design on enhancing the in-plane stiffness of a constituted cable network, but also its energy dissipation capacity. The proposed analytical model will be applied to networks with different configurations. The influence of cross-tie stiffness and damping on the modal response of various types of networks will be investigated by using the corresponding undamped rigid cross-tie network as a reference base. Results will provide valuable information on the selection of cross-tie properties to achieve more effective cable vibration control.

Modeling transmission parameters of polymer microstructured fibers for applications in FTTH networks

NASA Astrophysics Data System (ADS)

Gdula, P.; Welikow, K.; Szczepański, P.; Buczyński, R.; Piramidowicz, R.

2011-10-01

This paper is focused on selected aspects of designing and modeling of transmission parameters of plastic optical fibers (POFs), considered in the context of their potential applications in optical access networks and, specifically, in Fiber-To- The-Home (FTTH) systems. The survey of state-of-the-art solutions is presented and possibility of improving transmission properties of POFs by microstructurization is discussed on the basis of the first results of numerical modeling. In particular, the microstructured POF was designed supporting propagation of limited number of modes while keeping relatively large mode area and, simultaneously, significantly lowered bending losses.

Network planning under uncertainties

NASA Astrophysics Data System (ADS)

Ho, Kwok Shing; Cheung, Kwok Wai

2008-11-01

One of the main focuses for network planning is on the optimization of network resources required to build a network under certain traffic demand projection. Traditionally, the inputs to this type of network planning problems are treated as deterministic. In reality, the varying traffic requirements and fluctuations in network resources can cause uncertainties in the decision models. The failure to include the uncertainties in the network design process can severely affect the feasibility and economics of the network. Therefore, it is essential to find a solution that can be insensitive to the uncertain conditions during the network planning process. As early as in the 1960's, a network planning problem with varying traffic requirements over time had been studied. Up to now, this kind of network planning problems is still being active researched, especially for the VPN network design. Another kind of network planning problems under uncertainties that has been studied actively in the past decade addresses the fluctuations in network resources. One such hotly pursued research topic is survivable network planning. It considers the design of a network under uncertainties brought by the fluctuations in topology to meet the requirement that the network remains intact up to a certain number of faults occurring anywhere in the network. Recently, the authors proposed a new planning methodology called Generalized Survivable Network that tackles the network design problem under both varying traffic requirements and fluctuations of topology. Although all the above network planning problems handle various kinds of uncertainties, it is hard to find a generic framework under more general uncertainty conditions that allows a more systematic way to solve the problems. With a unified framework, the seemingly diverse models and algorithms can be intimately related and possibly more insights and improvements can be brought out for solving the problem. This motivates us to seek a generic framework for solving the network planning problem under uncertainties. In addition to reviewing the various network planning problems involving uncertainties, we also propose that a unified framework based on robust optimization can be used to solve a rather large segment of network planning problem under uncertainties. Robust optimization is first introduced in the operations research literature and is a framework that incorporates information about the uncertainty sets for the parameters in the optimization model. Even though robust optimization is originated from tackling the uncertainty in the optimization process, it can serve as a comprehensive and suitable framework for tackling generic network planning problems under uncertainties. In this paper, we begin by explaining the main ideas behind the robust optimization approach. Then we demonstrate the capabilities of the proposed framework by giving out some examples of how the robust optimization framework can be applied to the current common network planning problems under uncertain environments. Next, we list some practical considerations for solving the network planning problem under uncertainties with the proposed framework. Finally, we conclude this article with some thoughts on the future directions for applying this framework to solve other network planning problems.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Neural Networks Based Approach to Enhance Space Hardware Reliability

NASA Technical Reports Server (NTRS)

Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

2011-01-01

This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

Virtual optical network mapping and core allocation in elastic optical networks using multi-core fibers

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-11-01

Virtualization technology can greatly improve the efficiency of the networks by allowing the virtual optical networks to share the resources of the physical networks. However, it will face some challenges, such as finding the efficient strategies for virtual nodes mapping, virtual links mapping and spectrum assignment. It is even more complex and challenging when the physical elastic optical networks using multi-core fibers. To tackle these challenges, we establish a constrained optimization model to determine the optimal schemes of optical network mapping, core allocation and spectrum assignment. To solve the model efficiently, tailor-made encoding scheme, crossover and mutation operators are designed. Based on these, an efficient genetic algorithm is proposed to obtain the optimal schemes of the virtual nodes mapping, virtual links mapping, core allocation. The simulation experiments are conducted on three widely used networks, and the experimental results show the effectiveness of the proposed model and algorithm.

Multi-Agent Modeling and Simulation Approach for Design and Analysis of MER Mission Operations

NASA Technical Reports Server (NTRS)

Seah, Chin; Sierhuis, Maarten; Clancey, William J.

2005-01-01

A space mission operations system is a complex network of human organizations, information and deep-space network systems and spacecraft hardware. As in other organizations, one of the problems in mission operations is managing the relationship of the mission information systems related to how people actually work (practices). Brahms, a multi-agent modeling and simulation tool, was used to model and simulate NASA's Mars Exploration Rover (MER) mission work practice. The objective was to investigate the value of work practice modeling for mission operations design. From spring 2002 until winter 2003, a Brahms modeler participated in mission systems design sessions and operations testing for the MER mission held at Jet Propulsion Laboratory (JPL). He observed how designers interacted with the Brahms tool. This paper discussed mission system designers' reactions to the simulation output during model validation and the presentation of generated work procedures. This project spurred JPL's interest in the Brahms model, but it was never included as part of the formal mission design process. We discuss why this occurred. Subsequently, we used the MER model to develop a future mission operations concept. Team members were reluctant to use the MER model, even though it appeared to be highly relevant to their effort. We describe some of the tool issues we encountered.

An architecture for designing fuzzy logic controllers using neural networks

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

Network Model-Assisted Inference from Respondent-Driven Sampling Data

PubMed Central

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328

Network Model-Assisted Inference from Respondent-Driven Sampling Data.

PubMed

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.

Embarked electrical network robust control based on singular perturbation model.

PubMed

Abdeljalil Belhaj, Lamya; Ait-Ahmed, Mourad; Benkhoris, Mohamed Fouad

2014-07-01

This paper deals with an approach of modelling in view of control for embarked networks which can be described as strongly coupled multi-sources, multi-loads systems with nonlinear and badly known characteristics. This model has to be representative of the system behaviour and easy to handle for easy regulators synthesis. As a first step, each alternator is modelled and linearized around an operating point and then it is subdivided into two lower order systems according to the singular perturbation theory. RST regulators are designed for each subsystem and tested by means of a software test-bench which allows predicting network behaviour in both steady and transient states. Finally, the designed controllers are implanted on an experimental benchmark constituted by two alternators supplying loads in order to test the dynamic performances in realistic conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Consume, Modify, Share (CMS): The Interplay between Individual Decisions and Structural Network Properties in the Diffusion of Information

PubMed Central

Koren, Hila; Kaminer, Ido

2016-01-01

Widely used information diffusion models such as Independent Cascade Model, Susceptible Infected Recovered (SIR) and others fail to acknowledge that information is constantly subject to modification. Some aspects of information diffusion are best explained by network structural characteristics while in some cases strong influence comes from individual decisions. We introduce reinvention, the ability to modify information, as an individual level decision that affects the diffusion process as a whole. Based on a combination of constructs from the Diffusion of Innovations and the Critical Mass Theories, the present study advances the CMS (consume, modify, share) model which accounts for the interplay between network structure and human behavior and interactions. The model's building blocks include processes leading up to and following the formation of a critical mass of information adopters and disseminators. We examine the formation of an inflection point, information reach, sustainability of the diffusion process and collective value creation. The CMS model is tested on two directed networks and one undirected network, assuming weak or strong ties and applying constant and relative modification schemes. While all three networks are designed for disseminating new knowledge they differ in structural properties. Our findings suggest that modification enhances the diffusion of information in networks that support undirected connections and carries the biggest effect when information is shared via weak ties. Rogers' diffusion model and traditional information contagion models are fine tuned. Our results show that modifications not only contribute to a sustainable diffusion process, but also aid information in reaching remote areas of the network. The results point to the importance of cultivating weak ties, allowing reciprocal interaction among nodes and supporting the modification of information in promoting diffusion processes. These results have theoretical and practical implications for designing networks aimed at accelerating the creation and diffusion of information. PMID:27798636

Consume, Modify, Share (CMS): The Interplay between Individual Decisions and Structural Network Properties in the Diffusion of Information.

PubMed

Koren, Hila; Kaminer, Ido; Raban, Daphne Ruth

2016-01-01

Widely used information diffusion models such as Independent Cascade Model, Susceptible Infected Recovered (SIR) and others fail to acknowledge that information is constantly subject to modification. Some aspects of information diffusion are best explained by network structural characteristics while in some cases strong influence comes from individual decisions. We introduce reinvention, the ability to modify information, as an individual level decision that affects the diffusion process as a whole. Based on a combination of constructs from the Diffusion of Innovations and the Critical Mass Theories, the present study advances the CMS (consume, modify, share) model which accounts for the interplay between network structure and human behavior and interactions. The model's building blocks include processes leading up to and following the formation of a critical mass of information adopters and disseminators. We examine the formation of an inflection point, information reach, sustainability of the diffusion process and collective value creation. The CMS model is tested on two directed networks and one undirected network, assuming weak or strong ties and applying constant and relative modification schemes. While all three networks are designed for disseminating new knowledge they differ in structural properties. Our findings suggest that modification enhances the diffusion of information in networks that support undirected connections and carries the biggest effect when information is shared via weak ties. Rogers' diffusion model and traditional information contagion models are fine tuned. Our results show that modifications not only contribute to a sustainable diffusion process, but also aid information in reaching remote areas of the network. The results point to the importance of cultivating weak ties, allowing reciprocal interaction among nodes and supporting the modification of information in promoting diffusion processes. These results have theoretical and practical implications for designing networks aimed at accelerating the creation and diffusion of information.

An Implementation of Wireless Body Area Networks for Improving Priority Data Transmission Delay.

PubMed

Gündoğdu, Köksal; Çalhan, Ali

2016-03-01

The rapid growth of wireless sensor networks has enabled the human health monitoring of patients using body sensor nodes that gather and evaluate human body parameters and movements. This study describes both simulation model and implementation of a new traffic sensitive wireless body area network by using non-preemptive priority queue discipline. A wireless body area network implementation employing TDMA is designed with three different priorities of data traffics. Besides, a coordinator node having the non-preemptive priority queue is performed in this study. We have also developed, modeled and simulated example network scenarios by using the Riverbed Modeler simulation software with the purpose of verifying the implementation results. The simulation results obtained under various network load conditions are consistent with the implementation results.

Nonlinear Maps for Design of Discrete-Time Models of Neuronal Network Dynamics

DTIC Science & Technology

2016-03-31

2016 Performance/Technic~ 03-01-2016- 03-31-2016 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Nonlinear Maps for Design of Discrete -Time Models of...simulations is to design a neuronal model in the form of difference equations that generates neuronal states in discrete moments of time. In this...responsive tiring patterns. We propose to use modern DSP ideas to develop new efficient approaches to the design of such discrete -time models for

The dynamical analysis of modified two-compartment neuron model and FPGA implementation

NASA Astrophysics Data System (ADS)

Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

2017-10-01

The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Compendium of Anomaly Detection and Reaction Tools and Projects

DTIC Science & Technology

2000-05-17

identify changes to the risk levels of business network functions based on proposed modifications. Expert can model networks as well (see special...can easily scale to support any size network from departmental systems to enterprise-wide environments. ACX is scaled with the use of a Policy Model ...Defender is a host-based intrusion detector designed for use on home or small business systems. It scans all inbound and outbound Internet traffic for

Inclusion of tank configurations as a variable in the cost optimization of branched piped-water networks

NASA Astrophysics Data System (ADS)

Hooda, Nikhil; Damani, Om

2017-06-01

The classic problem of the capital cost optimization of branched piped networks consists of choosing pipe diameters for each pipe in the network from a discrete set of commercially available pipe diameters. Each pipe in the network can consist of multiple segments of differing diameters. Water networks also consist of intermediate tanks that act as buffers between incoming flow from the primary source and the outgoing flow to the demand nodes. The network from the primary source to the tanks is called the primary network, and the network from the tanks to the demand nodes is called the secondary network. During the design stage, the primary and secondary networks are optimized separately, with the tanks acting as demand nodes for the primary network. Typically the choice of tank locations, their elevations, and the set of demand nodes to be served by different tanks is manually made in an ad hoc fashion before any optimization is done. It is desirable therefore to include this tank configuration choice in the cost optimization process itself. In this work, we explain why the choice of tank configuration is important to the design of a network and describe an integer linear program model that integrates the tank configuration to the standard pipe diameter selection problem. In order to aid the designers of piped-water networks, the improved cost optimization formulation is incorporated into our existing network design system called JalTantra.

International Neural Network Society Annual Meeting (1994) Held in San Diego, California on 5-9 June 1994. Volume 1

DTIC Science & Technology

1994-06-09

Ethics and the Soul 1-221 P. Werbos A Net Program for Natural Language Comprehension 1-863 J. Weiss Applications Oral ANN Design of Image Processing...Controlling Nonlinear Dynamic Systems Using Neuro-Fuzzy Networks 1-787 E. Teixera, G. Laforga, H. Azevedo Neural Fuzzy Logics as a Tool for Design Ecological ...Discrete Neural Network 11-466 Z. Cheng-fu Representation of Number A Theory of Mathematical Modeling 11-479 J. Cristofano An Ecological Approach to

The Design and Implementation of Network Teaching Platform Basing on .NET

NASA Astrophysics Data System (ADS)

Yanna, Ren

This paper addresses the problem that students under traditional teaching model have poor operation ability and studies in depth the network teaching platform in domestic colleges and universities, proposing the design concept of network teaching platform of NET + C # + SQL excellent course and designing the overall structure, function module and back-end database of the platform. This paper emphatically expounds the use of MD5 encryption techniques in order to solve data security problems and the assessment of student learning using ADO.NET database access technology as well as the mathematical formula. The example shows that the network teaching platform developed by using WEB application technology has higher safety and availability, and thus improves the students' operation ability.

Stock market index prediction using neural networks

NASA Astrophysics Data System (ADS)

Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

1994-03-01

A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

Advanced local area network concepts

NASA Technical Reports Server (NTRS)

Grant, Terry

1985-01-01

Development of a good model of the data traffic requirements for Local Area Networks (LANs) onboard the Space Station is the driving problem in this work. A parameterized workload model is under development. An analysis contract has been started specifically to capture the distributed processing requirements for the Space Station and then to develop a top level model to simulate how various processing scenarios can handle the workload and what data communication patterns result. A summary of the Local Area Network Extendsible Simulator 2 Requirements Specification and excerpts from a grant report on the topological design of fiber optic local area networks with application to Expressnet are given.

SFTP: A Secure and Fault-Tolerant Paradigm against Blackhole Attack in MANET

NASA Astrophysics Data System (ADS)

KumarRout, Jitendra; Kumar Bhoi, Sourav; Kumar Panda, Sanjaya

2013-02-01

Security issues in MANET are a challenging task nowadays. MANETs are vulnerable to passive attacks and active attacks because of a limited number of resources and lack of centralized authority. Blackhole attack is an attack in network layer which degrade the network performance by dropping the packets. In this paper, we have proposed a Secure Fault-Tolerant Paradigm (SFTP) which checks the Blackhole attack in the network. The three phases used in SFTP algorithm are designing of coverage area to find the area of coverage, Network Connection algorithm to design a fault-tolerant model and Route Discovery algorithm to discover the route and data delivery from source to destination. SFTP gives better network performance by making the network fault free.

The ARPANET after twenty years

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

The ARPANET began operations in 1969 with four nodes as an experiment in resource sharing among computers. It has evolved into a worldwide research network of over 60,000 nodes, influencing the design of other networks in business, education, and government. It demonstrated the speed and reliability of packet-switching networks. Its protocols have served as the models for international standards. And yet the significance of the ARPANET lies not in its technology, but in the profound alterations networking has produced in human practices. Network designers must now turn their attention to the discourses of scientific technology, business, education, and government that are being mixed together in the milieux of networking, and in particular the conflicts and misunderstandings that arise from the different world views of these discourses.

Network Design Guidance for the Global Special Operations Forces Network

DTIC Science & Technology

2014-12-01

41 a. Concepts Emerge.....................................................................42 b. Model...78 a. Culture .....................................................................................78 b... Concept for Joint Operations COIN counterinsurgency COM commercial DMA Deputy Municipal Administrator DOD Department of Defense DOS Department

Dynamic robustness of knowledge collaboration network of open source product development community

NASA Astrophysics Data System (ADS)

Zhou, Hong-Li; Zhang, Xiao-Dong

2018-01-01

As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

Decentralized supply chain network design: monopoly, duopoly and oligopoly competitions under uncertainty

NASA Astrophysics Data System (ADS)

Seyedhosseini, Seyed Mohammad; Fahimi, Kaveh; Makui, Ahmad

2017-12-01

This paper presents the competitive supply chain network design problem in which n decentralized supply chains simultaneously enter the market with no existing rival chain, shape their networks and set wholesale and retail prices in competitive mode. The customer demand is elastic and price dependent, customer utility function is based on the Hoteling model and the chains produce identical or highly substitutable products. We construct a solution algorithm based on bi-level programming and possibility theory. In the proposed bi-level model, the inner part sets the prices based on simultaneous extra- and Stackleberg intra- chains competitions, and the outer part shapes the networks in cooperative competitions. Finally, we use a real-word study to discuss the effect of the different structures of the competitors on the equilibrium solution. Moreover, sensitivity analyses are conducted and managerial insights are offered.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios

PubMed Central

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-01-01

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN. PMID:26593919

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios.

PubMed

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-11-17

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Network-driven design principles for neuromorphic systems.

PubMed

Partzsch, Johannes; Schüffny, Rene

2015-01-01

Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems.

Network-driven design principles for neuromorphic systems

PubMed Central

Partzsch, Johannes; Schüffny, Rene

2015-01-01

Synaptic connectivity is typically the most resource-demanding part of neuromorphic systems. Commonly, the architecture of these systems is chosen mainly on technical considerations. As a consequence, the potential for optimization arising from the inherent constraints of connectivity models is left unused. In this article, we develop an alternative, network-driven approach to neuromorphic architecture design. We describe methods to analyse performance of existing neuromorphic architectures in emulating certain connectivity models. Furthermore, we show step-by-step how to derive a neuromorphic architecture from a given connectivity model. For this, we introduce a generalized description for architectures with a synapse matrix, which takes into account shared use of circuit components for reducing total silicon area. Architectures designed with this approach are fitted to a connectivity model, essentially adapting to its connection density. They are guaranteeing faithful reproduction of the model on chip, while requiring less total silicon area. In total, our methods allow designers to implement more area-efficient neuromorphic systems and verify usability of the connectivity resources in these systems. PMID:26539079

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Reverse logistics system planning for recycling computers hardware: A case study

NASA Astrophysics Data System (ADS)

Januri, Siti Sarah; Zulkipli, Faridah; Zahari, Siti Meriam; Shamsuri, Siti Hajar

2014-09-01

This paper describes modeling and simulation of reverse logistics networks for collection of used computers in one of the company in Selangor. The study focuses on design of reverse logistics network for used computers recycling operation. Simulation modeling, presented in this work allows the user to analyze the future performance of the network and to understand the complex relationship between the parties involved. The findings from the simulation suggest that the model calculates processing time and resource utilization in a predictable manner. In this study, the simulation model was developed by using Arena simulation package.

Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology.

PubMed

Kirouac, D C; Onsum, M D

2013-09-04

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e71; doi:10.1038/psp.2013.38; published online 4 September 2013.

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

1999-01-01

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

NASA Astrophysics Data System (ADS)

Lugin, IV

2018-03-01

In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

78 FR 76251 - Special Conditions: Airbus, Model A350-900 Series Airplane; Electronic System Security Protection...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-17

... the comment (or signing the comment for an association, business, labor union, etc.). DOT's complete... design feature: The digital systems architecture for the Airbus Model A350-900 series airplanes is composed of several connected networks. This proposed network architecture is used for a diverse set of...

Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain

USDA-ARS?s Scientific Manuscript database

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ...

Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems

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

Aman Behal; Sunil Kumar; Goodarz Ahmadi

2007-08-05

Numerical Modeling of Solid Gas Flow, System Identification for purposes of modeling and control, and Wireless Sensor and Actor Network design were pursued as part of this project. Time series input-output data was obtained from NETL's Morgantown CFB facility courtesy of Dr. Lawrence Shadle. It was run through a nonlinear kernel estimator and nonparametric models were obtained for the system. Linear and first-order nonlinear kernels were then utilized to obtain a state-space description of the system. Neural networks were trained that performed better at capturing the plant dynamics. It is possible to use these networks to find a plant modelmore » and the inversion of this model can be used to control the system. These models allow one to compare with physics based models whose parameters can then be determined by comparing them against the available data based model. On a parallel track, Dr. Kumar designed an energy-efficient and reliable transport protocol for wireless sensor and actor networks, where the sensors could be different types of wireless sensors used in CFB based coal combustion systems and actors are more powerful wireless nodes to set up a communication network while avoiding the data congestion. Dr. Ahmadi's group studied gas solid flow in a duct. It was seen that particle concentration clearly shows a preferential distribution. The particles strongly interact with the turbulence eddies and are concentrated in narrow bands that are evolving with time. It is believed that observed preferential concentration is due to the fact that these particles are flung out of eddies by centrifugal force.« less

Research and Simulation on Application of the Mobile IP Network

NASA Astrophysics Data System (ADS)

Yibing, Deng; Wei, Hu; Minghui, Li; Feng, Gao; Junyi, Shen

The paper analysed the mobile node, home agent, and foreign agent of mobile IP network firstly, some key technique, such as mobile IP network basical principle, protocol work principle, agent discovery, registration, and IP packet transmission, were discussed. Then a network simulation model was designed, validating the characteristic of mobile IP network, and some advantages, which were brought by mobile network, were testified. Finally, the conclusion is gained: mobile IP network could realize the expectation of consumer that they can communicate with others anywhere.

System Design for Nano-Network Communications

NASA Astrophysics Data System (ADS)

ShahMohammadian, Hoda

The potential applications of nanotechnology in a wide range of areas necessities nano-networking research. Nano-networking is a new type of networking which has emerged by applying nanotechnology to communication theory. Therefore, this dissertation presents a framework for physical layer communications in a nano-network and addresses some of the pressing unsolved challenges in designing a molecular communication system. The contribution of this dissertation is proposing well-justified models for signal propagation, noise sources, optimum receiver design and synchronization in molecular communication channels. The design of any communication system is primarily based on the signal propagation channel and noise models. Using the Brownian motion and advection molecular statistics, separate signal propagation and noise models are presented for diffusion-based and flow-based molecular communication channels. It is shown that the corrupting noise of molecular channels is uncorrelated and non-stationary with a signal dependent magnitude. The next key component of any communication system is the reception and detection process. This dissertation provides a detailed analysis of the effect of the ligand-receptor binding mechanism on the received signal, and develops the first optimal receiver design for molecular communications. The bit error rate performance of the proposed receiver is evaluated and the impact of medium motion on the receiver performance is investigated. Another important feature of any communication system is synchronization. In this dissertation, the first blind synchronization algorithm is presented for the molecular communication channels. The proposed algorithm uses a non-decision directed maximum likelihood criterion for estimating the channel delay. The Cramer-Rao lower bound is also derived and the performance of the proposed synchronization algorithm is evaluated by investigating its mean square error.

Design Of Computer Based Test Using The Unified Modeling Language

NASA Astrophysics Data System (ADS)

Tedyyana, Agus; Danuri; Lidyawati

2017-12-01

The Admission selection of Politeknik Negeri Bengkalis through interest and talent search (PMDK), Joint Selection of admission test for state Polytechnics (SB-UMPN) and Independent (UM-Polbeng) were conducted by using paper-based Test (PBT). Paper Based Test model has some weaknesses. They are wasting too much paper, the leaking of the questios to the public, and data manipulation of the test result. This reasearch was Aimed to create a Computer-based Test (CBT) models by using Unified Modeling Language (UML) the which consists of Use Case diagrams, Activity diagram and sequence diagrams. During the designing process of the application, it is important to pay attention on the process of giving the password for the test questions before they were shown through encryption and description process. RSA cryptography algorithm was used in this process. Then, the questions shown in the questions banks were randomized by using the Fisher-Yates Shuffle method. The network architecture used in Computer Based test application was a client-server network models and Local Area Network (LAN). The result of the design was the Computer Based Test application for admission to the selection of Politeknik Negeri Bengkalis.

A Neural Network Design for the Estimation of Nonlinear Behavior of a Magnetically-Excited Piezoelectric Harvester

NASA Astrophysics Data System (ADS)

Çelik, Emre; Uzun, Yunus; Kurt, Erol; Öztürk, Nihat; Topaloğlu, Nurettin

2018-01-01

An application of an artificial neural network (ANN) has been implemented in this article to model the nonlinear relationship of the harvested electrical power of a recently developed piezoelectric pendulum with respect to its resistive load R L and magnetic excitation frequency f. Prediction of harvested power for a wide range is a difficult task, because it increases dramatically when f gets closer to the natural frequency f 0 of the system. The neural model of the concerned system is designed upon the basis of a standard multi-layer network with a back propagation learning algorithm. Input data, termed input patterns, to present to the network and the respective output data, termed output patterns, describing desired network output that are carefully collected from the experiment under several conditions in order to train the developed network accurately. Results have indicated that the designed ANN is an effective means for predicting the harvested power of the piezoelectric harvester as functions of R L and f with a root mean square error of 6.65 × 10-3 for training and 1.40 for different test conditions. Using the proposed approach, the harvested power can be estimated reasonably without tackling the difficulty of experimental studies and complexity of analytical formulas representing the concerned system.

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

Optical processing for future computer networks

NASA Technical Reports Server (NTRS)

Husain, A.; Haugen, P. R.; Hutcheson, L. D.; Warrior, J.; Murray, N.; Beatty, M.

1986-01-01

In the development of future data management systems, such as the NASA Space Station, a major problem represents the design and implementation of a high performance communication network which is self-correcting and repairing, flexible, and evolvable. To obtain the goal of designing such a network, it will be essential to incorporate distributed adaptive network control techniques. The present paper provides an outline of the functional and communication network requirements for the Space Station data management system. Attention is given to the mathematical representation of the operations being carried out to provide the required functionality at each layer of communication protocol on the model. The possible implementation of specific communication functions in optics is also considered.

Neural Network Prediction of New Aircraft Design Coefficients

NASA Technical Reports Server (NTRS)

Norgaard, Magnus; Jorgensen, Charles C.; Ross, James C.

1997-01-01

This paper discusses a neural network tool for more effective aircraft design evaluations during wind tunnel tests. Using a hybrid neural network optimization method, we have produced fast and reliable predictions of aerodynamical coefficients, found optimal flap settings, and flap schedules. For validation, the tool was tested on a 55% scale model of the USAF/NASA Subsonic High Alpha Research Concept aircraft (SHARC). Four different networks were trained to predict coefficients of lift, drag, moment of inertia, and lift drag ratio (C(sub L), C(sub D), C(sub M), and L/D) from angle of attack and flap settings. The latter network was then used to determine an overall optimal flap setting and for finding optimal flap schedules.

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

NASA Astrophysics Data System (ADS)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

An executable specification for the message processor in a simple combining network

NASA Technical Reports Server (NTRS)

Middleton, David

1995-01-01

While the primary function of the network in a parallel computer is to communicate data between processors, it is often useful if the network can also perform rudimentary calculations. That is, some simple processing ability in the network itself, particularly for performing parallel prefix computations, can reduce both the volume of data being communicated and the computational load on the processors proper. Unfortunately, typical implementations of such networks require a large fraction of the hardware budget, and so combining networks are viewed as being impractical. The FFP Machine has such a combining network, and various characteristics of the machine allow a good deal of simplification in the network design. Despite being simple in construction however, the network relies on many subtle details to work correctly. This paper describes an executable model of the network which will serve several purposes. It provides a complete and detailed description of the network which can substantiate its ability to support necessary functions. It provides an environment in which algorithms to be run on the network can be designed and debugged more easily than they would on physical hardware. Finally, it provides the foundation for exploring the design of the message receiving facility which connects the network to the individual processors.

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

PubMed

Interian, Yannet; Rideout, Vincent; Kearney, Vasant P; Gennatas, Efstathios; Morin, Olivier; Cheung, Joey; Solberg, Timothy; Valdes, Gilmer

2018-03-30

The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features. Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06. Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts. © 2018 American Association of Physicists in Medicine.

Cascading failures in interconnected networks with dynamical redistribution of loads

NASA Astrophysics Data System (ADS)

Zhao, Zhuang; Zhang, Peng; Yang, Hujiang

2015-09-01

Cascading failures of loads in isolated networks and coupled networks have been studied in the past few years. In most of the corresponding results, the topologies of the networks are destroyed. Here, we present an interconnected network model considering cascading failures based on the dynamic redistribution of flow in the networks. Compared with the results of single scale-free networks, we find that interconnected scale-free networks have higher vulnerability. Additionally, the network heterogeneity plays an important role in the robustness of interconnected networks under intentional attacks. Considering the effects of various coupling preferences, the results show that there are almost no differences. Finally, the application of our model to the Beijing interconnected traffic network, which consists of a subway network and a bus network, shows that the subway network suffers more damage under the attack. Moreover, the interconnected traffic network may be more exposed to damage after initial attacks on the bus network. These discussions are important for the design and optimization of interconnected networks.

Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed.

PubMed

Lopez-Coto, Israel; Ghosh, Subhomoy; Prasad, Kuldeep; Whetstone, James

2017-09-01

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k -means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Development of an end-of-life vehicle recovery model using system dynamics and future research needs

NASA Astrophysics Data System (ADS)

Mohamad-Ali, N.; Ghazilla, R. A. R.; Abdul-Rashid, S. H.; Sakundarini, N.; Ahmad-Yazid, A.; Stephenie, L.

2017-06-01

The implementation of end-of-life vehicle (ELV) recovery policy in Malaysia has led vehicle manufacturers to look at different ways to improve design and development of vehicles. Nowadays, it is crucial to incorporate end-of-life (EOL) design strategies into the vehicle design in order to enhance the effectiveness of the ELV recovery network. Although recent studies have shown that product design has a significant effect on the product recovery rate, there is a lack of studies on how EOL design strategies affects the effectiveness of ELV recovery, particularly when there are dynamic changes in the behaviour of the product recovery network. Thus, in this study, we developed a preliminary model based on the system dynamics approach in order to predict the effectiveness of ELV recovery in response to dynamic changes of various factors (including EOL design strategies) in the business environment. We developed this model based on preliminary data that we had gathered from unstructured interviews with the key stakeholders of ELV management in Malaysia. We believe that our model will greatly benefit product designers in incorporating the appropriate EOL design strategies in order to boost ELV recovery effectiveness in Malaysia.

Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

PubMed

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

PubMed Central

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

Incorporation of RAM techniques into simulation modeling

NASA Astrophysics Data System (ADS)

Nelson, S. C., Jr.; Haire, M. J.; Schryver, J. C.

1995-01-01

This work concludes that reliability, availability, and maintainability (RAM) analytical techniques can be incorporated into computer network simulation modeling to yield an important new analytical tool. This paper describes the incorporation of failure and repair information into network simulation to build a stochastic computer model to represent the RAM Performance of two vehicles being developed for the US Army: The Advanced Field Artillery System (AFAS) and the Future Armored Resupply Vehicle (FARV). The AFAS is the US Army's next generation self-propelled cannon artillery system. The FARV is a resupply vehicle for the AFAS. Both vehicles utilize automation technologies to improve the operational performance of the vehicles and reduce manpower. The network simulation model used in this work is task based. The model programmed in this application requirements a typical battle mission and the failures and repairs that occur during that battle. Each task that the FARV performs--upload, travel to the AFAS, refuel, perform tactical/survivability moves, return to logistic resupply, etc.--is modeled. Such a model reproduces a model reproduces operational phenomena (e.g., failures and repairs) that are likely to occur in actual performance. Simulation tasks are modeled as discrete chronological steps; after the completion of each task decisions are programmed that determine the next path to be followed. The result is a complex logic diagram or network. The network simulation model is developed within a hierarchy of vehicle systems, subsystems, and equipment and includes failure management subnetworks. RAM information and other performance measures are collected which have impact on design requirements. Design changes are evaluated through 'what if' questions, sensitivity studies, and battle scenario changes.

A Privacy Preservation Model for Health-Related Social Networking Sites.

PubMed

Li, Jingquan

2015-07-08

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS.

A Privacy Preservation Model for Health-Related Social Networking Sites

PubMed Central

2015-01-01

The increasing use of social networking sites (SNS) in health care has resulted in a growing number of individuals posting personal health information online. These sites may disclose users' health information to many different individuals and organizations and mine it for a variety of commercial and research purposes, yet the revelation of personal health information to unauthorized individuals or entities brings a concomitant concern of greater risk for loss of privacy among users. Many users join multiple social networks for different purposes and enter personal and other specific information covering social, professional, and health domains into other websites. Integration of multiple online and real social networks makes the users vulnerable to unintentional and intentional security threats and misuse. This paper analyzes the privacy and security characteristics of leading health-related SNS. It presents a threat model and identifies the most important threats to users and SNS providers. Building on threat analysis and modeling, this paper presents a privacy preservation model that incorporates individual self-protection and privacy-by-design approaches and uses the model to develop principles and countermeasures to protect user privacy. This study paves the way for analysis and design of privacy-preserving mechanisms on health-related SNS. PMID:26155953

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources.

PubMed

Doulamis, A D; Doulamis, N D; Kollias, S D

2003-01-01

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)-based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neural-network architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neural-network architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several real-life MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neural-network architecture presents better performance than other examined techniques.

Thermal conductivity model for nanofiber networks

NASA Astrophysics Data System (ADS)

Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

2018-02-01

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Thermal conductivity model for nanofiber networks

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

Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network ismore » revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.« less

Simulation Modeling and Performance Evaluation of Space Networks

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John

2006-01-01

In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions

Structural analysis of behavioral networks from the Internet

NASA Astrophysics Data System (ADS)

Meiss, M. R.; Menczer, F.; Vespignani, A.

2008-06-01

In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic.

CATO: a CAD tool for intelligent design of optical networks and interconnects

NASA Astrophysics Data System (ADS)

Chlamtac, Imrich; Ciesielski, Maciej; Fumagalli, Andrea F.; Ruszczyk, Chester; Wedzinga, Gosse

1997-10-01

Increasing communication speed requirements have created a great interest in very high speed optical and all-optical networks and interconnects. The design of these optical systems is a highly complex task, requiring the simultaneous optimization of various parts of the system, ranging from optical components' characteristics to access protocol techniques. Currently there are no computer aided design (CAD) tools on the market to support the interrelated design of all parts of optical communication systems, thus the designer has to rely on costly and time consuming testbed evaluations. The objective of the CATO (CAD tool for optical networks and interconnects) project is to develop a prototype of an intelligent CAD tool for the specification, design, simulation and optimization of optical communication networks. CATO allows the user to build an abstract, possible incomplete, model of the system, and determine its expected performance. Based on design constraints provided by the user, CATO will automatically complete an optimum design, using mathematical programming techniques, intelligent search methods and artificial intelligence (AI). Initial design and testing of a CATO prototype (CATO-1) has been completed recently. The objective was to prove the feasibility of combining AI techniques, simulation techniques, an optical device library and a graphical user interface into a flexible CAD tool for obtaining optimal communication network designs in terms of system cost and performance. CATO-1 is an experimental tool for designing packet-switching wavelength division multiplexing all-optical communication systems using a LAN/MAN ring topology as the underlying network. The two specific AI algorithms incorporated are simulated annealing and a genetic algorithm. CATO-1 finds the optimal number of transceivers for each network node, using an objective function that includes the cost of the devices and the overall system performance.

Application of neural networks to software quality modeling of a very large telecommunications system.

PubMed

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

Time Series Analysis of Networks: Evaluating the Effectiveness of Sanctions on Iran

DTIC Science & Technology

2013-03-27

quantitatively model the evolution and “learning” of this Iranian Network? This was the question posed to three summer apprentices at the Network Science...designated them Peripheral Players. For example, in this model the Nuclear Fuel and Procurement Company is a Broker and Bank Melli is a Peripheral Player...resource/us-sanction> [3] Al Arabiya, “Sanctions against Iran stymied efforts to produce ballistic missles : report,” Al Arabiya News, 30 July 2012. 30

Object-oriented Approach to High-level Network Monitoring and Management

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi

2000-01-01

An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.

Metabolic Network Modeling for Computer-Aided Design of Microbial Interactions

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

Song, Hyun-Seob; Nelson, William C.; Lee, Joon-Yong

Interest in applying microbial communities to biotechnology continues to increase. Successful engineering of microbial communities requires a fundamental shift in focus from enhancing metabolic capabilities in individual organisms to promoting synergistic interspecies interactions. This goal necessitates in silico tools that provide a predictive understanding of how microorganisms interact with each other and their environments. In this regard, we highlight a need for a new concept that we have termed biological computer-aided design of interactions (BioCADi). We ground this discussion within the context of metabolic network modeling.

Future aircraft networks and schedules

NASA Astrophysics Data System (ADS)

Shu, Yan

2011-07-01

Because of the importance of air transportation scheduling, the emergence of small aircraft and the vision of future fuel-efficient aircraft, this thesis has focused on the study of aircraft scheduling and network design involving multiple types of aircraft and flight services. It develops models and solution algorithms for the schedule design problem and analyzes the computational results. First, based on the current development of small aircraft and on-demand flight services, this thesis expands a business model for integrating on-demand flight services with the traditional scheduled flight services. This thesis proposes a three-step approach to the design of aircraft schedules and networks from scratch under the model. In the first step, both a frequency assignment model for scheduled flights that incorporates a passenger path choice model and a frequency assignment model for on-demand flights that incorporates a passenger mode choice model are created. In the second step, a rough fleet assignment model that determines a set of flight legs, each of which is assigned an aircraft type and a rough departure time is constructed. In the third step, a timetable model that determines an exact departure time for each flight leg is developed. Based on the models proposed in the three steps, this thesis creates schedule design instances that involve almost all the major airports and markets in the United States. The instances of the frequency assignment model created in this thesis are large-scale non-convex mixed-integer programming problems, and this dissertation develops an overall network structure and proposes iterative algorithms for solving these instances. The instances of both the rough fleet assignment model and the timetable model created in this thesis are large-scale mixed-integer programming problems, and this dissertation develops subproblem schemes for solving these instances. Based on these solution algorithms, this dissertation also presents computational results of these large-scale instances. To validate the models and solution algorithms developed, this thesis also compares the daily flight schedules that it designs with the schedules of the existing airlines. Furthermore, it creates instances that represent different economic and fuel-prices conditions and derives schedules under these different conditions. In addition, it discusses the implication of using new aircraft in the future flight schedules. Finally, future research in three areas---model, computational method, and simulation for validation---is proposed.

Modeling IoT-Based Solutions Using Human-Centric Wireless Sensor Networks

PubMed Central

Monares, Álvaro; Ochoa, Sergio F.; Santos, Rodrigo; Orozco, Javier; Meseguer, Roc

2014-01-01

The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes. PMID:25157549

Modeling IoT-based solutions using human-centric wireless sensor networks.

PubMed

Monares, Álvaro; Ochoa, Sergio F; Santos, Rodrigo; Orozco, Javier; Meseguer, Roc

2014-08-25

The Internet of Things (IoT) has inspired solutions that are already available for addressing problems in various application scenarios, such as healthcare, security, emergency support and tourism. However, there is no clear approach to modeling these systems and envisioning their capabilities at the design time. Therefore, the process of designing these systems is ad hoc and its real impact is evaluated once the solution is already implemented, which is risky and expensive. This paper proposes a modeling approach that uses human-centric wireless sensor networks to specify and evaluate models of IoT-based systems at the time of design, avoiding the need to spend time and effort on early implementations of immature designs. It allows designers to focus on the system design, leaving the implementation decisions for a next phase. The article illustrates the usefulness of this proposal through a running example, showing the design of an IoT-based solution to support the first responses during medium-sized or large urban incidents. The case study used in the proposal evaluation is based on a real train crash. The proposed modeling approach can be used to design IoT-based systems for other application scenarios, e.g., to support security operatives or monitor chronic patients in their homes.

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

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

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

DOE PAGES

Aziz, H. M. Abdul; Ukkusuri, Satish V.; Zhan, Xianyuan

2016-10-17

We know that personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. Here, we develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget inmore » the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows us to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.« less

Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks

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

Aziz, H. M. Abdul; Ukkusuri, Satish V.; Zhan, Xianyuan

We know that personal mobility carbon allowance (PMCA) schemes are designed to reduce carbon consumption from transportation networks. PMCA schemes influence the travel decision process of users and accordingly impact the system metrics including travel time and greenhouse gas (GHG) emissions. Here, we develop a multi-user class dynamic user equilibrium model to evaluate the transportation system performance when PMCA scheme is implemented. The results using Sioux-Falls test network indicate that PMCA schemes can achieve the emissions reduction goals for transportation networks. Further, users characterized by high value of travel time are found to be less sensitive to carbon budget inmore » the context of work trips. Results also show that PMCA scheme can lead to higher emissions for a path compared with the case without PMCA because of flow redistribution. The developed network equilibrium model allows us to examine the change in system states at different carbon allocation levels and to design parameters of PMCA schemes accounting for population heterogeneity.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

Toward a More Robust Pruning Procedure for MLP Networks

NASA Technical Reports Server (NTRS)

Stepniewski, Slawomir W.; Jorgensen, Charles C.

1998-01-01

Choosing a proper neural network architecture is a problem of great practical importance. Smaller models mean not only simpler designs but also lower variance for parameter estimation and network prediction. The widespread utilization of neural networks in modeling highlights an issue in human factors. The procedure of building neural models should find an appropriate level of model complexity in a more or less automatic fashion to make it less prone to human subjectivity. In this paper we present a Singular Value Decomposition based node elimination technique and enhanced implementation of the Optimal Brain Surgeon algorithm. Combining both methods creates a powerful pruning engine that can be used for tuning feedforward connectionist models. The performance of the proposed method is demonstrated by adjusting the structure of a multi-input multi-output model used to calibrate a six-component wind tunnel strain gage.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

NDEx - the Network Data Exchange, A Network Commons for Biologists | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

Network models of biology, whether curated or derived from large-scale data analysis, are critical tools in the understanding of cancer mechanisms and in the design and personalization of therapies. The NDEx Project (Network Data Exchange) will create, deploy, and maintain an open-source, web-based software platform and public website to enable scientists, organizations, and software applications to share, store, manipulate, and publish biological networks.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

NASA Astrophysics Data System (ADS)

Hao, Yufang; Xie, Shaodong

2018-03-01

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

The system of technical diagnostics of the industrial safety information network

NASA Astrophysics Data System (ADS)

Repp, P. V.

2017-01-01

This research is devoted to problems of safety of the industrial information network. Basic sub-networks, ensuring reliable operation of the elements of the industrial Automatic Process Control System, were identified. The core tasks of technical diagnostics of industrial information safety were presented. The structure of the technical diagnostics system of the information safety was proposed. It includes two parts: a generator of cyber-attacks and the virtual model of the enterprise information network. The virtual model was obtained by scanning a real enterprise network. A new classification of cyber-attacks was proposed. This classification enables one to design an efficient generator of cyber-attacks sets for testing the virtual modes of the industrial information network. The numerical method of the Monte Carlo (with LPτ - sequences of Sobol), and Markov chain was considered as the design method for the cyber-attacks generation algorithm. The proposed system also includes a diagnostic analyzer, performing expert functions. As an integrative quantitative indicator of the network reliability the stability factor (Kstab) was selected. This factor is determined by the weight of sets of cyber-attacks, identifying the vulnerability of the network. The weight depends on the frequency and complexity of cyber-attacks, the degree of damage, complexity of remediation. The proposed Kstab is an effective integral quantitative measure of the information network reliability.

Free-Energy-Based Design Policy for Robust Network Control against Environmental Fluctuation.

PubMed

Iwai, Takuya; Kominami, Daichi; Murata, Masayuki; Yomo, Tetsuya

2015-01-01

Bioinspired network control is a promising approach for realizing robust network controls. It relies on a probabilistic mechanism composed of positive and negative feedback that allows the system to eventually stabilize on the best solution. When the best solution fails due to environmental fluctuation, the system cannot keep its function until the system finds another solution again. To prevent the temporal loss of the function, the system should prepare some solution candidates and stochastically select available one from them. However, most bioinspired network controls are not designed with this issue in mind. In this paper, we propose a thermodynamics-based design policy that allows systems to retain an appropriate degree of randomness depending on the degree of environmental fluctuation, which prepares the system for the occurrence of environmental fluctuation. Furthermore, we verify the design policy by using an attractor selection model-based multipath routing to run simulation experiments.

Networking Hawaii's School Libraries.

ERIC Educational Resources Information Center

Hawaii State Dept. of Education, Honolulu. Office of Instructional Services.

This guide is designed to assist school librarians in becoming part of the planned statewide school library network in Hawaii. Approaches to the guide for librarians at all stages of planning are suggested, and an overview of the benefits, goals, steps, and historical development are provided together with a model of the networking plan. The steps…

Telecommunications Information Network: A Model for On-Demand Transfer of Medical Information. Annual Report.

ERIC Educational Resources Information Center

Lorenzi, Nancy M.; And Others

This report describes and evaluates the first year of a demonstration project to develop an on-demand telecommunications network linking four remote hospitals in southwestern Ohio to the University of Cincinnati Medical Center. The Telecommunications Information Network (TIN) is designed to allow health care professionals at those hospitals to…

Distributed Sensing and Processing: A Graphical Model Approach

DTIC Science & Technology

2005-11-30

that Ramanujan graph toplogies maximize the convergence rate of distributed detection consensus algorithms, improving over three orders of...small world type network designs. 14. SUBJECT TERMS Ramanujan graphs, sensor network topology, sensor network...that Ramanujan graphs, for which there are explicit algebraic constructions, have large eigenratios, converging much faster than structured graphs

A Model for Field Deployment of Wireless Sensor Networks (WSNs) within the Domain of Microclimate Habitat Monitoring

ERIC Educational Resources Information Center

Sanborn, Mark

2011-01-01

Wireless sensor networks (WSNs) represent a class of miniaturized information systems designed to monitor physical environments. These smart monitoring systems form collaborative networks utilizing autonomous sensing, data-collection, and processing to provide real-time analytics of observed environments. As a fundamental research area in…

Network Design: Best Practices for Alberta School Jurisdictions.

ERIC Educational Resources Information Center

Schienbein, Ralph

This report examines subsections of the computer network topology that relate to end-to-end performance and capacity planning in schools. Active star topology, Category 5 wiring, Ethernet, and intelligent devices are assumed. The report describes a model that can be used to project WAN (wide area network) connection speeds based on user traffic,…

A Survey of Middleware for Sensor and Network Virtualization

PubMed Central

Khalid, Zubair; Fisal, Norsheila; Rozaini, Mohd.

2014-01-01

Wireless Sensor Network (WSN) is leading to a new paradigm of Internet of Everything (IoE). WSNs have a wide range of applications but are usually deployed in a particular application. However, the future of WSNs lies in the aggregation and allocation of resources, serving diverse applications. WSN virtualization by the middleware is an emerging concept that enables aggregation of multiple independent heterogeneous devices, networks, radios and software platforms; and enhancing application development. WSN virtualization, middleware can further be categorized into sensor virtualization and network virtualization. Middleware for WSN virtualization poses several challenges like efficient decoupling of networks, devices and software. In this paper efforts have been put forward to bring an overview of the previous and current middleware designs for WSN virtualization, the design goals, software architectures, abstracted services, testbeds and programming techniques. Furthermore, the paper also presents the proposed model, challenges and future opportunities for further research in the middleware designs for WSN virtualization. PMID:25615737

A survey of middleware for sensor and network virtualization.

PubMed

Khalid, Zubair; Fisal, Norsheila; Rozaini, Mohd

2014-12-12

Wireless Sensor Network (WSN) is leading to a new paradigm of Internet of Everything (IoE). WSNs have a wide range of applications but are usually deployed in a particular application. However, the future of WSNs lies in the aggregation and allocation of resources, serving diverse applications. WSN virtualization by the middleware is an emerging concept that enables aggregation of multiple independent heterogeneous devices, networks, radios and software platforms; and enhancing application development. WSN virtualization, middleware can further be categorized into sensor virtualization and network virtualization. Middleware for WSN virtualization poses several challenges like efficient decoupling of networks, devices and software. In this paper efforts have been put forward to bring an overview of the previous and current middleware designs for WSN virtualization, the design goals, software architectures, abstracted services, testbeds and programming techniques. Furthermore, the paper also presents the proposed model, challenges and future opportunities for further research in the middleware designs for WSN virtualization.

Topological properties of robust biological and computational networks

PubMed Central

Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

2014-01-01

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

Extraction of business relationships in supply networks using statistical learning theory.

PubMed

Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

2016-06-01

Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

PathCase-SB architecture and database design

PubMed Central

2011-01-01

Background Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. Description PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database. Conclusions PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world. PMID:22070889

Traffic model for advanced satellite designs and experiments for ISDN services

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.; Hager, E. Paul

1991-01-01

The data base structure and fields for categorizing and storing Integrated Services Digital Network (ISDN) user characteristics is outlined. This traffic model data base will be used to exercise models of the ISDN Advanced Communication Satellite to determine design parameters and performance for the NASA Satellite Communications Applications Research (SCAR) Program.

Security and Privacy Preservation in Human-Involved Networks

NASA Astrophysics Data System (ADS)

Asher, Craig; Aumasson, Jean-Philippe; Phan, Raphael C.-W.

This paper discusses security within human-involved networks, with a focus on social networking services (SNS). We argue that more secure networks could be designed using semi-formal security models inspired from cryptography, as well as notions like that of ceremony, which exploits human-specific abilities and psychology to assist creating more secure protocols. We illustrate some of our ideas with the example of the SNS Facebook.

Support Networks of Single Puerto Rican Mothers of Children with Disabilities

ERIC Educational Resources Information Center

Correa, Vivian I.; Bonilla, Zobeida E.; Reyes-MacPherson, Maria E.

2011-01-01

The social support networks of 25 Puerto Rican single mothers of young children with disabilities were examined and compared with current models of family support for children with disabilities. This study was designed to assess the support systems of Latino single mothers in light of dominant models of family support. The Family Support Scale,…

Models of Community Learning Networks in Canada = Modeles de reseaux d'apprentissage communautaires au Canada.

ERIC Educational Resources Information Center

Human Resources Development Canada, Hull (Quebec). Office of Learning Technologies.

Canada-based community learning networks (CLNs) were examined to provide an operational definition of CLNs, design a framework for their review and analysis, and identify best practices in CLNs. Data were collected from three sources: interviews with 16 key stakeholders in CLNs, literature review, and case studies of five Canadian CLNs. The…

An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks

PubMed Central

Pani, Danilo; Meloni, Paolo; Tuveri, Giuseppe; Palumbo, Francesca; Massobrio, Paolo; Raffo, Luigi

2017-01-01

In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments. PMID:28293163

NATO Human View Architecture and Human Networks

NASA Technical Reports Server (NTRS)

Handley, Holly A. H.; Houston, Nancy P.

2010-01-01

The NATO Human View is a system architectural viewpoint that focuses on the human as part of a system. Its purpose is to capture the human requirements and to inform on how the human impacts the system design. The viewpoint contains seven static models that include different aspects of the human element, such as roles, tasks, constraints, training and metrics. It also includes a Human Dynamics component to perform simulations of the human system under design. One of the static models, termed Human Networks, focuses on the human-to-human communication patterns that occur as a result of ad hoc or deliberate team formation, especially teams distributed across space and time. Parameters of human teams that effect system performance can be captured in this model. Human centered aspects of networks, such as differences in operational tempo (sense of urgency), priorities (common goal), and team history (knowledge of the other team members), can be incorporated. The information captured in the Human Network static model can then be included in the Human Dynamics component so that the impact of distributed teams is represented in the simulation. As the NATO militaries transform to a more networked force, the Human View architecture is an important tool that can be used to make recommendations on the proper mix of technological innovations and human interactions.

Design and architecture of the Mars relay network planning and analysis framework

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Lee, C. H.

2002-01-01

In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

Coupling Network Computing Applications in Air-cooled Turbine Blades Optimization

NASA Astrophysics Data System (ADS)

Shi, Liang; Yan, Peigang; Xie, Ming; Han, Wanjin

2018-05-01

Through establishing control parameters from blade outside to inside, the parametric design of air-cooled turbine blade based on airfoil has been implemented. On the basis of fast updating structure features and generating solid model, a complex cooling system has been created. Different flow units are modeled into a complex network topology with parallel and serial connection. Applying one-dimensional flow theory, programs have been composed to get pipeline network physical quantities along flow path, including flow rate, pressure, temperature and other parameters. These inner units parameters set as inner boundary conditions for external flow field calculation program HIT-3D by interpolation, thus to achieve full field thermal coupling simulation. Referring the studies in literatures to verify the effectiveness of pipeline network program and coupling algorithm. After that, on the basis of a modified design, and with the help of iSIGHT-FD, an optimization platform had been established. Through MIGA mechanism, the target of enhancing cooling efficiency has been reached, and the thermal stress has been effectively reduced. Research work in this paper has significance for rapid deploying the cooling structure design.

Statistical assessment on a combined analysis of GRYN-ROMN-UCBN upland vegetation vital signs

USGS Publications Warehouse

Irvine, Kathryn M.; Rodhouse, Thomas J.

2014-01-01

As of 2013, Rocky Mountain and Upper Columbia Basin Inventory and Monitoring Networks have multiple years of vegetation data and Greater Yellowstone Network has three years of vegetation data and monitoring is ongoing in all three networks. Our primary objective is to assess whether a combined analysis of these data aimed at exploring correlations with climate and weather data is feasible. We summarize the core survey design elements across protocols and point out the major statistical challenges for a combined analysis at present. The dissimilarity in response designs between ROMN and UCBN-GRYN network protocols presents a statistical challenge that has not been resolved yet. However, the UCBN and GRYN data are compatible as they implement a similar response design; therefore, a combined analysis is feasible and will be pursued in future. When data collected by different networks are combined, the survey design describing the merged dataset is (likely) a complex survey design. A complex survey design is the result of combining datasets from different sampling designs. A complex survey design is characterized by unequal probability sampling, varying stratification, and clustering (see Lohr 2010 Chapter 7 for general overview). Statistical analysis of complex survey data requires modifications to standard methods, one of which is to include survey design weights within a statistical model. We focus on this issue for a combined analysis of upland vegetation from these networks, leaving other topics for future research. We conduct a simulation study on the possible effects of equal versus unequal probability selection of points on parameter estimates of temporal trend using available packages within the R statistical computing package. We find that, as written, using lmer or lm for trend detection in a continuous response and clm and clmm for visually estimated cover classes with “raw” GRTS design weights specified for the weight argument leads to substantially different results and/or computational instability. However, when only fixed effects are of interest, the survey package (svyglm and svyolr) may be suitable for a model-assisted analysis for trend. We provide possible directions for future research into combined analysis for ordinal and continuous vital sign indictors.

Cluster based architecture and network maintenance protocol for medical priority aware cognitive radio based hospital.

PubMed

Al Mamoon, Ishtiak; Muzahidul Islam, A K M; Baharun, Sabariah; Ahmed, Ashir; Komaki, Shozo

2016-08-01

Due to the rapid growth of wireless medical devices in near future, wireless healthcare services may face some inescapable issue such as medical spectrum scarcity, electromagnetic interference (EMI), bandwidth constraint, security and finally medical data communication model. To mitigate these issues, cognitive radio (CR) or opportunistic radio network enabled wireless technology is suitable for the upcoming wireless healthcare system. The up-to-date research on CR based healthcare has exposed some developments on EMI and spectrum problems. However, the investigation recommendation on system design and network model for CR enabled hospital is rare. Thus, this research designs a hierarchy based hybrid network architecture and network maintenance protocols for previously proposed CR hospital system, known as CogMed. In the previous study, the detail architecture of CogMed and its maintenance protocols were not present. The proposed architecture includes clustering concepts for cognitive base stations and non-medical devices. Two cluster head (CH selector equations are formulated based on priority of location, device, mobility rate of devices and number of accessible channels. In order to maintain the integrity of the proposed network model, node joining and node leaving protocols are also proposed. Finally, the simulation results show that the proposed network maintenance time is very low for emergency medical devices (average maintenance period 9.5 ms) and the re-clustering effects for different mobility enabled non-medical devices are also balanced.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

NASA Astrophysics Data System (ADS)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Research on TCP/IP network communication based on Node.js

NASA Astrophysics Data System (ADS)

Huang, Jing; Cai, Lixiong

2018-04-01

In the face of big data, long connection and high synchronization, TCP/IP network communication will cause performance bottlenecks due to its blocking multi-threading service model. This paper presents a method of TCP/IP network communication protocol based on Node.js. On the basis of analyzing the characteristics of Node.js architecture and asynchronous non-blocking I/O model, the principle of its efficiency is discussed, and then compare and analyze the network communication model of TCP/IP protocol to expound the reasons why TCP/IP protocol stack is widely used in network communication. Finally, according to the large data and high concurrency in the large-scale grape growing environment monitoring process, a TCP server design based on Node.js is completed. The results show that the example runs stably and efficiently.

A distributed incentive compatible pricing mechanism for P2P networks

NASA Astrophysics Data System (ADS)

Zhang, Jie; Zhao, Zheng; Xiong, Xiao; Shi, Qingwei

2007-09-01

Peer-to-Peer (P2P) systems are currently receiving considerable interest. However, as experience with P2P networks shows, the selfish behaviors of peers may lead to serious problems of P2P network, such as free-riding and white-washing. In order to solve these problems, there are increasing considerations on reputation system design in the study of P2P networks. Most of the existing works is concerning probabilistic estimation or social networks to evaluate the trustworthiness for a peer to others. However, these models can not be efficient all the time. In this paper, our aim is to provide a general mechanism that can maximize P2P networks social welfare in a way of Vickrey-Clarke-Groves family, while assuming every peer in P2P networks is rational and selfish, which means they only concern about their own outcome. This mechanism has some desirable properties using an O(n) algorithm: (1) incentive compatibility, every peer truly report its connection type; (2) individually rationality; and (3) fully decentralized, we design a multiple-principal multiple-agent model, concerning about the service provider and service requester individually.

Network Design in Close-Range Photogrammetry with Short Baseline Images

NASA Astrophysics Data System (ADS)

Barazzetti, L.

2017-08-01

The avaibility of automated software for image-based 3D modelling has changed the way people acquire images for photogrammetric applications. Short baseline images are required to match image points with SIFT-like algorithms, obtaining more images than those necessary for "old fashioned" photogrammetric projects based on manual measurements. This paper describes some considerations on network design for short baseline image sequences, especially on precision and reliability of bundle adjustment. Simulated results reveal that the large number of 3D points used for image orientation has very limited impact on network precision.

Supporting Air and Space Expeditionary Forces: Analysis of CONUS Centralized Intermediate Repair Facilities

DTIC Science & Technology

2008-01-01

designing cost -effective CIRF networks or readily comparing alterna- tive potential network designs. The RAND Corporation was asked to develop such an...optimization model that allows users to select the best mix of land- and sea-based FSLs for a given set of operational scenarios, thereby reducing costs while...27 Overview of Post-BRAC Bed-Downs and CIRF Assignments . . . . . . . . . . . . . 27 JEIM Cost

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

A model to assess the Mars Telecommunications Network relay robustness

NASA Technical Reports Server (NTRS)

Girerd, Andre R.; Meshkat, Leila; Edwards, Charles D., Jr.; Lee, Charles H.

2005-01-01

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-01-01

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-07-09

In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Video distribution system cost model

NASA Technical Reports Server (NTRS)

Gershkoff, I.; Haspert, J. K.; Morgenstern, B.

1980-01-01

A cost model that can be used to systematically identify the costs of procuring and operating satellite linked communications systems is described. The user defines a network configuration by specifying the location of each participating site, the interconnection requirements, and the transmission paths available for the uplink (studio to satellite), downlink (satellite to audience), and voice talkback (between audience and studio) segments of the network. The model uses this information to calculate the least expensive signal distribution path for each participating site. Cost estimates are broken downy by capital, installation, lease, operations and maintenance. The design of the model permits flexibility in specifying network and cost structure.

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.

CyberSecurity Monitoring Tools and Projects: A Compendium of Commercial and Government Tools and Government Research Projects

DTIC Science & Technology

2000-08-01

identify changes to the risk levels of business network functions based on proposed modifications. Expert can model networks as well (see special...network from departmental systems to enterprise-wide environments. ACX is scaled with the use of a Policy Model Database(PMDB). The PMDB is a management...This Entry February 8, 2000 Description BlackICE Defender is a host-based intrusion detector designed for use on home or small business systems. It

Oscillatory network with self-organized dynamical connections for synchronization-based image segmentation.

PubMed

Kuzmina, Margarita; Manykin, Eduard; Surina, Irina

2004-01-01

An oscillatory network of columnar architecture located in 3D spatial lattice was recently designed by the authors as oscillatory model of the brain visual cortex. Single network oscillator is a relaxational neural oscillator with internal dynamics tunable by visual image characteristics - local brightness and elementary bar orientation. It is able to demonstrate either activity state (stable undamped oscillations) or "silence" (quickly damped oscillations). Self-organized nonlocal dynamical connections of oscillators depend on oscillator activity levels and orientations of cortical receptive fields. Network performance consists in transfer into a state of clusterized synchronization. At current stage grey-level image segmentation tasks are carried out by 2D oscillatory network, obtained as a limit version of the source model. Due to supplemented network coupling strength control the 2D reduced network provides synchronization-based image segmentation. New results on segmentation of brightness and texture images presented in the paper demonstrate accurate network performance and informative visualization of segmentation results, inherent in the model.

Modeling of the Space Station Freedom data management system

NASA Technical Reports Server (NTRS)

Johnson, Marjory J.

1990-01-01

The Data Management System (DMS) is the information and communications system onboard Space Station Freedom (SSF). Extensive modeling of the DMS is being conducted throughout NASA to aid in the design and development of this vital system. Activities discussed at NASA Ames Research Center to model the DMS network infrastructure are discussed with focus on the modeling of the Fiber Distributed Data Interface (FDDI) token-ring protocol and experimental testbedding of networking aspects of the DMS.

SAINT: A combined simulation language for modeling man-machine systems

NASA Technical Reports Server (NTRS)

Seifert, D. J.

1979-01-01

SAINT (Systems Analysis of Integrated Networks of Tasks) is a network modeling and simulation technique for design and analysis of complex man machine systems. SAINT provides the conceptual framework for representing systems that consist of discrete task elements, continuous state variables, and interactions between them. It also provides a mechanism for combining human performance models and dynamic system behaviors in a single modeling structure. The SAINT technique is described and applications of the SAINT are discussed.

Observability and Controllability of Networks: Symmetry in Representations of Brains and Controllers for Epidemics

NASA Astrophysics Data System (ADS)

Schiff, Steven

Observability and controllability are essential concepts to the design of predictive observer models and feedback controllers of networked systems. We present a numerical and group representational framework, to quantify the observability and controllability of nonlinear networks with explicit symmetries that shows the connection between symmetries and nonlinear measures of observability and controllability. In addition to the topology of brain networks, we have advanced our ability to represent network nodes within the brain using conservation principles and more accurate biophysics that unifies the dynamics of spikes, seizures, and spreading depression. Lastly, we show how symmetries in controller design can be applied to infectious disease epidemics. NIH Grants 1R01EB014641, 1DP1HD086071.

Time-Centric Models For Designing Embedded Cyber-physical Systems

DTIC Science & Technology

2009-10-09

Time -centric Models For Designing Embedded Cyber- physical Systems John C. Eidson Edward A. Lee Slobodan Matic Sanjit A. Seshia Jia Zou Electrical... Time -centric Models For Designing Embedded Cyber-physical Systems ∗ John C. Eidson , Edward A. Lee, Slobodan Matic, Sanjit A. Seshia, Jia Zou...implementations, such a uniform notion of time cannot be precisely realized. Time triggered networks [10] and time synchronization [9] can be used to

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

PubMed Central

2017-01-01

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. PMID:29392184

A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

NASA Astrophysics Data System (ADS)

Niakan, F.; Vahdani, B.; Mohammadi, M.

2015-12-01

This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Case analysis online: a strategic management case model for the health industry.

PubMed

Walsh, Anne; Bearden, Eithne

2004-01-01

Despite the plethora of methods and tools available to support strategic management, the challenge for health executives in the next century will relate to their ability to access and interpret data from multiple and intricate communication networks. Integrated digital networks and satellite systems will expand the scope and ease of sharing information between business divisions, and networked systems will facilitate the use of virtual case discussions across universities. While the internet is frequently used to support clinical decisions in the healthcare industry, few executives rely upon the internetfor strategic analysis. Although electronic technologies can easily synthesize data from multiple information channels, research as well as technical issues may deter their application in strategic analysis. As digital models transform access to information, online models may become increasingly relevant in designing strategic solutions. While there are various pedagogical models available to support the strategic management process, this framework was designed to enhance strategic analysis through the application of technology and electronic research. A strategic analysis framework, which incorporated internet research and case analysis in a strategic managementcourse, is described alongwith design and application issues that emerged during the case analysis process.

A review of active learning approaches to experimental design for uncovering biological networks

PubMed Central

2017-01-01

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Nonlinear Maps for Design of Discrete Time Models of Neuronal Network Dynamics

DTIC Science & Technology

2016-02-29

Performance/Technic~ 02-01-2016- 02-29-2016 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER Nonlinear Maps for Design of Discrete -Time Models of Neuronal...neuronal model in the form of difference equations that generates neuronal states in discrete moments of time. In this approach, time step can be made...propose to use modern DSP ideas to develop new efficient approaches to the design of such discrete -time models for studies of large-scale neuronal

Smart Collision Avoidance and Hazard Routing Mechanism for Intelligent Transport Network

NASA Astrophysics Data System (ADS)

Singh, Gurpreet; Gupta, Pooja; Wahab, Mohd Helmy Abd

2017-08-01

The smart vehicular ad-hoc network is the network that consists of vehicles for smooth movement and better management of the vehicular connectivity across the given network. This research paper aims to propose a set of solution for the VANETs consisting of the automatic driven vehicles, also called as the autonomous car. Such vehicular networks are always prone to collision due to the natural or un-natural reasons which must be solved before the large-scale deployment of the autonomous transport systems. The newly designed intelligent transport movement control mechanism is based upon the intelligent data propagation along with the vehicle collision and traffic jam prevention schema [8], which may help the future designs of smart cities to become more robust and less error-prone. In the proposed model, the focus is on designing a new dynamic and robust hazard routing protocol for intelligent vehicular networks for improvement of the overall performance in various aspects. It is expected to improve the overall transmission delay as well as the number of collisions or adversaries across the vehicular network zone.

Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation

NASA Astrophysics Data System (ADS)

Aminia, A. S.

2017-09-01

Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.

Analysis and Modeling of DIII-D Experiments With OMFIT and Neural Networks

NASA Astrophysics Data System (ADS)

Meneghini, O.; Luna, C.; Smith, S. P.; Lao, L. L.; GA Theory Team

2013-10-01

The OMFIT integrated modeling framework is designed to facilitate experimental data analysis and enable integrated simulations. This talk introduces this framework and presents a selection of its applications to the DIII-D experiment. Examples include kinetic equilibrium reconstruction analysis; evaluation of MHD stability in the core and in the edge; and self-consistent predictive steady-state transport modeling. The OMFIT framework also provides the platform for an innovative approach based on neural networks to predict electron and ion energy fluxes. In our study a multi-layer feed-forward back-propagation neural network is built and trained over a database of DIII-D data. It is found that given the same parameters that the highest fidelity models use, the neural network model is able to predict to a large degree the heat transport profiles observed in the DIII-D experiments. Once the network is built, the numerical cost of evaluating the transport coefficients is virtually nonexistent, thus making the neural network model particularly well suited for plasma control and quick exploration of operational scenarios. The implementation of the neural network model and benchmark with experimental results and gyro-kinetic models will be discussed. Work supported in part by the US DOE under DE-FG02-95ER54309.

Development of task network models of human performance in microgravity

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Adam, Susan

1992-01-01

This paper discusses the utility of task-network modeling for quantifying human performance variability in microgravity. The data are gathered for: (1) improving current methodologies for assessing human performance and workload in the operational space environment; (2) developing tools for assessing alternative system designs; and (3) developing an integrated set of methodologies for the evaluation of performance degradation during extended duration spaceflight. The evaluation entailed an analysis of the Remote Manipulator System payload-grapple task performed on many shuttle missions. Task-network modeling can be used as a tool for assessing and enhancing human performance in man-machine systems, particularly for modeling long-duration manned spaceflight. Task-network modeling can be directed toward improving system efficiency by increasing the understanding of basic capabilities of the human component in the system and the factors that influence these capabilities.

Self-reconfigurable ship fluid-network modeling for simulation-based design

NASA Astrophysics Data System (ADS)

Moon, Kyungjin

Our world is filled with large-scale engineering systems, which provide various services and conveniences in our daily life. A distinctive trend in the development of today's large-scale engineering systems is the extensive and aggressive adoption of automation and autonomy that enable the significant improvement of systems' robustness, efficiency, and performance, with considerably reduced manning and maintenance costs, and the U.S. Navy's DD(X), the next-generation destroyer program, is considered as an extreme example of such a trend. This thesis pursues a modeling solution for performing simulation-based analysis in the conceptual or preliminary design stage of an intelligent, self-reconfigurable ship fluid system, which is one of the concepts of DD(X) engineering plant development. Through the investigations on the Navy's approach for designing a more survivable ship system, it is found that the current naval simulation-based analysis environment is limited by the capability gaps in damage modeling, dynamic model reconfiguration, and simulation speed of the domain specific models, especially fluid network models. As enablers of filling these gaps, two essential elements were identified in the formulation of the modeling method. The first one is the graph-based topological modeling method, which will be employed for rapid model reconstruction and damage modeling, and the second one is the recurrent neural network-based, component-level surrogate modeling method, which will be used to improve the affordability and efficiency of the modeling and simulation (M&S) computations. The integration of the two methods can deliver computationally efficient, flexible, and automation-friendly M&S which will create an environment for more rigorous damage analysis and exploration of design alternatives. As a demonstration for evaluating the developed method, a simulation model of a notional ship fluid system was created, and a damage analysis was performed. Next, the models representing different design configurations of the fluid system were created, and damage analyses were performed with them in order to find an optimal design configuration for system survivability. Finally, the benefits and drawbacks of the developed method were discussed based on the result of the demonstration.

Minimal camera networks for 3D image based modeling of cultural heritage objects.

PubMed

Alsadik, Bashar; Gerke, Markus; Vosselman, George; Daham, Afrah; Jasim, Luma

2014-03-25

3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue "Lamassu". Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883-859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm.

Minimal Camera Networks for 3D Image Based Modeling of Cultural Heritage Objects

PubMed Central

Alsadik, Bashar; Gerke, Markus; Vosselman, George; Daham, Afrah; Jasim, Luma

2014-01-01

3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue “Lamassu”. Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883–859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm. PMID:24670718

Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism

PubMed Central

Chang, Roger L; Ghamsari, Lila; Manichaikul, Ani; Hom, Erik F Y; Balaji, Santhanam; Fu, Weiqi; Shen, Yun; Hao, Tong; Palsson, Bernhard Ø; Salehi-Ashtiani, Kourosh; Papin, Jason A

2011-01-01

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. PMID:21811229

MOSES: A Matlab-based open-source stochastic epidemic simulator.

PubMed

Varol, Huseyin Atakan

2016-08-01

This paper presents an open-source stochastic epidemic simulator. Discrete Time Markov Chain based simulator is implemented in Matlab. The simulator capable of simulating SEQIJR (susceptible, exposed, quarantined, infected, isolated and recovered) model can be reduced to simpler models by setting some of the parameters (transition probabilities) to zero. Similarly, it can be extended to more complicated models by editing the source code. It is designed to be used for testing different control algorithms to contain epidemics. The simulator is also designed to be compatible with a network based epidemic simulator and can be used in the network based scheme for the simulation of a node. Simulations show the capability of reproducing different epidemic model behaviors successfully in a computationally efficient manner.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

AN INTEGRATED APPROACH TO AIR QUALITY USING IN SITU, SATELLITE, AND MODELED DATA - FOCUSED ON THE FUTURE OF EARTH OBSERVATIONS SYSTEM (EOS)

EPA Science Inventory

EPA through statutory mandates has monitored air, water, land and human health for the past several decades. The design of the ambient air monitoring networks, for the most part, has been loosely tied single-pollutant networks focused on large urban areas. These networks supply t...

Why Do You Adopt Social Networking Sites? Investigating the Driving Factors through Structural Equation Modelling

ERIC Educational Resources Information Center

Jan, Muhammad Tahir

2017-01-01

Purpose: The purpose of this paper is to investigate those factors that are associated with the adoption of social networking sites from the perspective of Muslim users residing in Malaysia. Design/methodology/approach: A complete self-administered questionnaire was collected from 223 Muslim users of social networking sites in Malaysia. Both…

Georgia Interactive Network--GaIN--for Medical Information. Final Grant Report.

ERIC Educational Resources Information Center

Rankin, Jocelyn A.

This report describes the development of the Georgia Interactive Network for Medical Information (GaIN), a project initially funded by a three-year (1983-1986) National Library of Medicine resource project grant. Designed to serve as a model network to transmit information via computer directly to health professionals, GaIN now operates through a…

A two-phase copula entropy-based multiobjective optimization approach to hydrometeorological gauge network design

NASA Astrophysics Data System (ADS)

Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Liu, Jiufu; Zou, Ying; He, Ruimin

2017-12-01

Hydrometeorological data are needed for obtaining point and areal mean, quantifying the spatial variability of hydrometeorological variables, and calibration and verification of hydrometeorological models. Hydrometeorological networks are utilized to collect such data. Since data collection is expensive, it is essential to design an optimal network based on the minimal number of hydrometeorological stations in order to reduce costs. This study proposes a two-phase copula entropy- based multiobjective optimization approach that includes: (1) copula entropy-based directional information transfer (CDIT) for clustering the potential hydrometeorological gauges into several groups, and (2) multiobjective method for selecting the optimal combination of gauges for regionalized groups. Although entropy theory has been employed for network design before, the joint histogram method used for mutual information estimation has several limitations. The copula entropy-based mutual information (MI) estimation method is shown to be more effective for quantifying the uncertainty of redundant information than the joint histogram (JH) method. The effectiveness of this approach is verified by applying to one type of hydrometeorological gauge network, with the use of three model evaluation measures, including Nash-Sutcliffe Coefficient (NSC), arithmetic mean of the negative copula entropy (MNCE), and MNCE/NSC. Results indicate that the two-phase copula entropy-based multiobjective technique is capable of evaluating the performance of regional hydrometeorological networks and can enable decision makers to develop strategies for water resources management.

Numerical design and optimization of hydraulic resistance and wall shear stress inside pressure-driven microfluidic networks.

PubMed

Damiri, Hazem Salim; Bardaweel, Hamzeh Khalid

2015-11-07

Microfluidic networks represent the milestone of microfluidic devices. Recent advancements in microfluidic technologies mandate complex designs where both hydraulic resistance and pressure drop across the microfluidic network are minimized, while wall shear stress is precisely mapped throughout the network. In this work, a combination of theoretical and modeling techniques is used to construct a microfluidic network that operates under minimum hydraulic resistance and minimum pressure drop while constraining wall shear stress throughout the network. The results show that in order to minimize the hydraulic resistance and pressure drop throughout the network while maintaining constant wall shear stress throughout the network, geometric and shape conditions related to the compactness and aspect ratio of the parent and daughter branches must be followed. Also, results suggest that while a "local" minimum hydraulic resistance can be achieved for a geometry with an arbitrary aspect ratio, a "global" minimum hydraulic resistance occurs only when the aspect ratio of that geometry is set to unity. Thus, it is concluded that square and equilateral triangular cross-sectional area microfluidic networks have the least resistance compared to all rectangular and isosceles triangular cross-sectional microfluidic networks, respectively. Precise control over wall shear stress through the bifurcations of the microfluidic network is demonstrated in this work. Three multi-generation microfluidic network designs are considered. In these three designs, wall shear stress in the microfluidic network is successfully kept constant, increased in the daughter-branch direction, or decreased in the daughter-branch direction, respectively. For the multi-generation microfluidic network with constant wall shear stress, the design guidelines presented in this work result in identical profiles of wall shear stresses not only within a single generation but also through all the generations of the microfluidic network under investigation. The results obtained in this work are consistent with previously reported data and suitable for a wide range of lab-on-chip applications.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

PubMed Central

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

Appplication of statistical mechanical methods to the modeling of social networks

NASA Astrophysics Data System (ADS)

Strathman, Anthony Robert

With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

A multi-criteria decision aid methodology to design electric vehicles public charging networks

NASA Astrophysics Data System (ADS)

Raposo, João; Rodrigues, Ana; Silva, Carlos; Dentinho, Tomaz

2015-05-01

This article presents a new multi-criteria decision aid methodology, dynamic-PROMETHEE, here used to design electric vehicle charging networks. In applying this methodology to a Portuguese city, results suggest that it is effective in designing electric vehicle charging networks, generating time and policy based scenarios, considering offer and demand and the city's urban structure. Dynamic-PROMETHE adds to the already known PROMETHEE's characteristics other useful features, such as decision memory over time, versatility and adaptability. The case study, used here to present the dynamic-PROMETHEE, served as inspiration and base to create this new methodology. It can be used to model different problems and scenarios that may present similar requirement characteristics.

Designing Cognitively Diagnostic Assessment for Algebraic Content Knowledge and Thinking Skills

ERIC Educational Resources Information Center

Zhang, Zhidong

2018-01-01

This study explored a diagnostic assessment method that emphasized the cognitive process of algebra learning. The study utilized a design and a theory-driven model to examine the content knowledge. Using the theory driven model, the thinking skills of algebra learning was also examined. A Bayesian network model was applied to represent the theory…

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Challenges of CAC in Heterogeneous Wireless Cognitive Networks

NASA Astrophysics Data System (ADS)

Wang, Jiazheng; Fu, Xiuhua

Call admission control (CAC) is known as an effective functionality in ensuring the QoS of wireless networks. The vision of next generation wireless networks has led to the development of new call admission control (CAC) algorithms specifically designed for heterogeneous wireless Cognitive networks. However, there will be a number of challenges created by dynamic spectrum access and scheduling techniques associated with the cognitive systems. In this paper for the first time, we recommend that the CAC policies should be distinguished between primary users and secondary users. The classification of different methods of cac policies in cognitive networks contexts is proposed. Although there have been some researches within the umbrella of Joint CAC and cross-layer optimization for wireless networks, the advent of the cognitive networks adds some additional problems. We present the conceptual models for joint CAC and cross-layer optimization respectively. Also, the benefit of Cognition can only be realized fully if application requirements and traffic flow contexts are determined or inferred in order to know what modes of operation and spectrum bands to use at each point in time. The process model of Cognition involved per-flow-based CAC is presented. Because there may be a number of parameters on different levels affecting a CAC decision and the conditions for accepting or rejecting a call must be computed quickly and frequently, simplicity and practicability are particularly important for designing a feasible CAC algorithm. In a word, a more thorough understanding of CAC in heterogeneous wireless cognitive networks may help one to design better CAC algorithms.

Design Sketches For Optical Crossbar Switches Intended For Large-Scale Parallel Processing Applications

NASA Astrophysics Data System (ADS)

Hartmann, Alfred; Redfield, Steve

1989-04-01

This paper discusses design of large-scale (1000x 1000) optical crossbar switching networks for use in parallel processing supercom-puters. Alternative design sketches for an optical crossbar switching network are presented using free-space optical transmission with either a beam spreading/masking model or a beam steering model for internodal communications. The performances of alternative multiple access channel communications protocol-unslotted and slotted ALOHA and carrier sense multiple access (CSMA)-are compared with the performance of the classic arbitrated bus crossbar of conventional electronic parallel computing. These comparisons indicate an almost inverse relationship between ease of implementation and speed of operation. Practical issues of optical system design are addressed, and an optically addressed, composite spatial light modulator design is presented for fabrication to arbitrarily large scale. The wide range of switch architecture, communications protocol, optical systems design, device fabrication, and system performance problems presented by these design sketches poses a serious challenge to practical exploitation of highly parallel optical interconnects in advanced computer designs.

Assessment of the service performance of drainage system and transformation of pipeline network based on urban combined sewer system model.

PubMed

Peng, Hai-Qin; Liu, Yan; Wang, Hong-Wu; Ma, Lu-Ming

2015-10-01

In recent years, due to global climate change and rapid urbanization, extreme weather events occur to the city at an increasing frequency. Waterlogging is common because of heavy rains. In this case, the urban drainage system can no longer meet the original design requirements, resulting in traffic jams and even paralysis and post a threat to urban safety. Therefore, it provides a necessary foundation for urban drainage planning and design to accurately assess the capacity of the drainage system and correctly simulate the transport effect of drainage network and the carrying capacity of drainage facilities. This study adopts InfoWorks Integrated Catchment Management (ICM) to present the two combined sewer drainage systems in Yangpu District, Shanghai (China). The model can assist the design of the drainage system. Model calibration is performed based on the historical rainfall events. The calibrated model is used for the assessment of the outlet drainage and pipe loads for the storm scenario currently existing or possibly occurring in the future. The study found that the simulation and analysis results of the drainage system model were reliable. They could fully reflect the service performance of the drainage system in the study area and provide decision-making support for regional flood control and transformation of pipeline network.

Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand.

PubMed

Lewis, Jim; Mengersen, Kerrie; Buys, Laurie; Vine, Desley; Bell, John; Morris, Peter; Ledwich, Gerard

2015-01-01

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers' peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers' location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual 'map' of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand

PubMed Central

Lewis, Jim; Mengersen, Kerrie; Buys, Laurie; Vine, Desley; Bell, John; Morris, Peter; Ledwich, Gerard

2015-01-01

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments. PMID:26226511

Design trade-offs among shunt current, pumping loss and compactness in the piping system of a multi-stack vanadium flow battery

NASA Astrophysics Data System (ADS)

Ye, Qiang; Hu, Jing; Cheng, Ping; Ma, Zhiqi

2015-11-01

Trade-off between shunt current loss and pumping loss is a major challenge in the design of the electrolyte piping network in a flow battery system. It is generally recognized that longer and thinner ducts are beneficial to reduce shunt current but detrimental to minimize pumping power. Base on the developed analog circuit model and the flow network model, we make case studies of multi-stack vanadium flow battery piping systems and demonstrate that both shunt current and electrolyte flow resistance can be simultaneously minimized by using longer and thicker ducts in the piping network. However, extremely long and/or thick ducts lead to a bulky system and may be prohibited by the stack structure. Accordingly, the intrinsic design trade-off is between system efficiency and compactness. Since multi-stack configurations bring both flexibility and complexity to the design process, we perform systematic comparisons among representative piping system designs to illustrate the complicated trade-offs among numerous parameters including stack number, intra-stack channel resistance and inter-stack pipe resistance. As the final design depends on various technical and economical requirements, this paper aims to provide guidelines rather than solutions for designers to locate the optimal trade-off points according to their specific cases.

Hybrid Communication Architectures for Distributed Smart Grid Applications

DOE PAGES

Zhang, Jianhua; Hasandka, Adarsh; Wei, Jin; ...

2018-04-09

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed amore » suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. In conclusion, the results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.« less

Hybrid Communication Architectures for Distributed Smart Grid Applications

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

Zhang, Jianhua; Hasandka, Adarsh; Wei, Jin

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed amore » suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. In conclusion, the results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.« less

Efficient experimental design for uncertainty reduction in gene regulatory networks.

PubMed

Dehghannasiri, Roozbeh; Yoon, Byung-Jun; Dougherty, Edward R

2015-01-01

An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Efficient experimental design for uncertainty reduction in gene regulatory networks

PubMed Central

2015-01-01

Background An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Conclusions Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/. PMID:26423515

Computer network environment planning and analysis

NASA Technical Reports Server (NTRS)

Dalphin, John F.

1989-01-01

The GSFC Computer Network Environment provides a broadband RF cable between campus buildings and ethernet spines in buildings for the interlinking of Local Area Networks (LANs). This system provides terminal and computer linkage among host and user systems thereby providing E-mail services, file exchange capability, and certain distributed computing opportunities. The Environment is designed to be transparent and supports multiple protocols. Networking at Goddard has a short history and has been under coordinated control of a Network Steering Committee for slightly more than two years; network growth has been rapid with more than 1500 nodes currently addressed and greater expansion expected. A new RF cable system with a different topology is being installed during summer 1989; consideration of a fiber optics system for the future will begin soon. Summmer study was directed toward Network Steering Committee operation and planning plus consideration of Center Network Environment analysis and modeling. Biweekly Steering Committee meetings were attended to learn the background of the network and the concerns of those managing it. Suggestions for historical data gathering have been made to support future planning and modeling. Data Systems Dynamic Simulator, a simulation package developed at NASA and maintained at GSFC was studied as a possible modeling tool for the network environment. A modeling concept based on a hierarchical model was hypothesized for further development. Such a model would allow input of newly updated parameters and would provide an estimation of the behavior of the network.

Comparing models for quantitative risk assessment: an application to the European Registry of foreign body injuries in children.

PubMed

Berchialla, Paola; Scarinzi, Cecilia; Snidero, Silvia; Gregori, Dario

2016-08-01

Risk Assessment is the systematic study of decisions subject to uncertain consequences. An increasing interest has been focused on modeling techniques like Bayesian Networks since their capability of (1) combining in the probabilistic framework different type of evidence including both expert judgments and objective data; (2) overturning previous beliefs in the light of the new information being received and (3) making predictions even with incomplete data. In this work, we proposed a comparison among Bayesian Networks and other classical Quantitative Risk Assessment techniques such as Neural Networks, Classification Trees, Random Forests and Logistic Regression models. Hybrid approaches, combining both Classification Trees and Bayesian Networks, were also considered. Among Bayesian Networks, a clear distinction between purely data-driven approach and combination of expert knowledge with objective data is made. The aim of this paper consists in evaluating among this models which best can be applied, in the framework of Quantitative Risk Assessment, to assess the safety of children who are exposed to the risk of inhalation/insertion/aspiration of consumer products. The issue of preventing injuries in children is of paramount importance, in particular where product design is involved: quantifying the risk associated to product characteristics can be of great usefulness in addressing the product safety design regulation. Data of the European Registry of Foreign Bodies Injuries formed the starting evidence for risk assessment. Results showed that Bayesian Networks appeared to have both the ease of interpretability and accuracy in making prediction, even if simpler models like logistic regression still performed well. © The Author(s) 2013.

Modeling of workflow-engaged networks on radiology transfers across a metro network.

PubMed

Camorlinga, Sergio; Schofield, Bruce

2006-04-01

Radiology metro networks bear the challenging proposition of interconnecting several hospitals in a region to provide a comprehensive diagnostic imaging service. Consequences of a poorly designed and implemented metro network could cause delays or no access at all when health care providers try to retrieve medical cases across the network. This could translate into limited diagnostic services to patients, resulting in negative impacts to the patients' medical treatment. A workflow-engaged network (WEN) is a new network paradigm. A WEN appreciates radiology workflows and priorities in using the network. A WEN greatly improves the network performance by guaranteeing that critical image transfers experience minimal delay. It adjusts network settings to ensure the application's requirements are met. This means that high-priority image transfers will have guaranteed and known delay times, whereas lower-priority traffic will have increased delays. This paper introduces a modeling to understand the benefits that WEN brings to a radiology metro network. The modeling uses actual data patterns and flows found in a hospital metro region. The workflows considered are based on the Integrating the Healthcare Enterprise profiles. This modeling has been applied to metropolitan workflows of a health region. The modeling helps identify the kind of metro network that supports data patterns and flows in a metro area. The results of the modeling show that a 155-Mb/s metropolitan area network (MAN) with WEN operates virtually equal to a normal 622-Mb/s MAN without WEN, with potential cost savings for leased line services measured in the millions of dollars per year.

Developmental engineering: a new paradigm for the design and manufacturing of cell-based products. Part II: from genes to networks: tissue engineering from the viewpoint of systems biology and network science.

PubMed

Lenas, Petros; Moos, Malcolm; Luyten, Frank P

2009-12-01

The field of tissue engineering is moving toward a new concept of "in vitro biomimetics of in vivo tissue development." In Part I of this series, we proposed a theoretical framework integrating the concepts of developmental biology with those of process design to provide the rules for the design of biomimetic processes. We named this methodology "developmental engineering" to emphasize that it is not the tissue but the process of in vitro tissue development that has to be engineered. To formulate the process design rules in a rigorous way that will allow a computational design, we should refer to mathematical methods to model the biological process taking place in vitro. Tissue functions cannot be attributed to individual molecules but rather to complex interactions between the numerous components of a cell and interactions between cells in a tissue that form a network. For tissue engineering to advance to the level of a technologically driven discipline amenable to well-established principles of process engineering, a scientifically rigorous formulation is needed of the general design rules so that the behavior of networks of genes, proteins, or cells that govern the unfolding of developmental processes could be related to the design parameters. Now that sufficient experimental data exist to construct plausible mathematical models of many biological control circuits, explicit hypotheses can be evaluated using computational approaches to facilitate process design. Recent progress in systems biology has shown that the empirical concepts of developmental biology that we used in Part I to extract the rules of biomimetic process design can be expressed in rigorous mathematical terms. This allows the accurate characterization of manufacturing processes in tissue engineering as well as the properties of the artificial tissues themselves. In addition, network science has recently shown that the behavior of biological networks strongly depends on their topology and has developed the necessary concepts and methods to describe it, allowing therefore a deeper understanding of the behavior of networks during biomimetic processes. These advances thus open the door to a transition for tissue engineering from a substantially empirical endeavor to a technology-based discipline comparable to other branches of engineering.

Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory.

PubMed

Shi, Bin; Jiang, Jiping; Sivakumar, Bellie; Zheng, Yi; Wang, Peng

2018-05-01

Field monitoring strategy is critical for disaster preparedness and watershed emergency environmental management. However, development of such is also highly challenging. Despite the efforts and progress thus far, no definitive guidelines or solutions are available worldwide for quantitatively designing a monitoring network in response to river chemical spill incidents, except general rules based on administrative divisions or arbitrary interpolation on routine monitoring sections. To address this gap, a novel framework for spatial-temporal network design was proposed in this study. The framework combines contaminant transport modelling with discrete entropy theory and spectral analysis. The water quality model was applied to forecast the spatio-temporal distribution of contaminant after spills and then corresponding information transfer indexes (ITIs) and Fourier approximation periodic functions were estimated as critical measures for setting sampling locations and times. The results indicate that the framework can produce scientific preparedness plans of emergency monitoring based on scenario analysis of spill risks as well as rapid design as soon as the incident happened but not prepared. The framework was applied to a hypothetical spill case based on tracer experiment and a real nitrobenzene spill incident case to demonstrate its suitability and effectiveness. The newly-designed temporal-spatial monitoring network captured major pollution information at relatively low costs. It showed obvious benefits for follow-up early-warning and treatment as well as for aftermath recovery and assessment. The underlying drivers of ITIs as well as the limitations and uncertainty of the approach were analyzed based on the case studies. Comparison with existing monitoring network design approaches, management implications, and generalized applicability were also discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modeling and performance analysis of QoS data

NASA Astrophysics Data System (ADS)

Strzeciwilk, Dariusz; Zuberek, Włodzimierz M.

2016-09-01

The article presents the results of modeling and analysis of data transmission performance on systems that support quality of service. Models are designed and tested, taking into account multiservice network architecture, i.e. supporting the transmission of data related to different classes of traffic. Studied were mechanisms of traffic shaping systems, which are based on the Priority Queuing with an integrated source of data and the various sources of data that is generated. Discussed were the basic problems of the architecture supporting QoS and queuing systems. Designed and built were models based on Petri nets, supported by temporal logics. The use of simulation tools was to verify the mechanisms of shaping traffic with the applied queuing algorithms. It is shown that temporal models of Petri nets can be effectively used in the modeling and analysis of the performance of computer networks.

Drainpipe network management information system design based on GIS and SCADA technique

NASA Astrophysics Data System (ADS)

Gu, Ze-Yu; Zhao, De-An

2011-02-01

Achieving urban drainpipe network integration of geographical information system (GIS) and supervisory control and data acquisition (SCADA) technology is described in this paper. The system design's plans are put forward, which have realized GIS and SCADA system supplementary in the technology and strengthened the model visible analysis ability. It is verified by practical cases that the system has more practical values and a good prospect.

Social Learning Networks: From Data Analytics to Active Sensing

DTIC Science & Technology

2017-10-13

time updating of user models that in turn dictate the learning path of each student . In particular, we have designed , implemented, and evaluated our...decision, unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box...social network that exists between students , instructors, and modules of learning. Between 2015 and 2017, we completed a variety of data-driven

Cell transmission model of dynamic assignment for urban rail transit networks.

PubMed

Xu, Guangming; Zhao, Shuo; Shi, Feng; Zhang, Feilian

2017-01-01

For urban rail transit network, the space-time flow distribution can play an important role in evaluating and optimizing the space-time resource allocation. For obtaining the space-time flow distribution without the restriction of schedules, a dynamic assignment problem is proposed based on the concept of continuous transmission. To solve the dynamic assignment problem, the cell transmission model is built for urban rail transit networks. The priority principle, queuing process, capacity constraints and congestion effects are considered in the cell transmission mechanism. Then an efficient method is designed to solve the shortest path for an urban rail network, which decreases the computing cost for solving the cell transmission model. The instantaneous dynamic user optimal state can be reached with the method of successive average. Many evaluation indexes of passenger flow can be generated, to provide effective support for the optimization of train schedules and the capacity evaluation for urban rail transit network. Finally, the model and its potential application are demonstrated via two numerical experiments using a small-scale network and the Beijing Metro network.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Prediction of soft soil foundation settlement in Guangxi granite area based on fuzzy neural network model

NASA Astrophysics Data System (ADS)

Luo, Junhui; Wu, Chao; Liu, Xianlin; Mi, Decai; Zeng, Fuquan; Zeng, Yongjun

2018-01-01

At present, the prediction of soft foundation settlement mostly use the exponential curve and hyperbola deferred approximation method, and the correlation between the results is poor. However, the application of neural network in this area has some limitations, and none of the models used in the existing cases adopted the TS fuzzy neural network of which calculation combines the characteristics of fuzzy system and neural network to realize the mutual compatibility methods. At the same time, the developed and optimized calculation program is convenient for engineering designers. Taking the prediction and analysis of soft foundation settlement of gully soft soil in granite area of Guangxi Guihe road as an example, the fuzzy neural network model is established and verified to explore the applicability. The TS fuzzy neural network is used to construct the prediction model of settlement and deformation, and the corresponding time response function is established to calculate and analyze the settlement of soft foundation. The results show that the prediction of short-term settlement of the model is accurate and the final settlement prediction result has certain engineering reference value.

An Interdisciplinary Approach for Designing Kinetic Models of the Ras/MAPK Signaling Pathway.

PubMed

Reis, Marcelo S; Noël, Vincent; Dias, Matheus H; Albuquerque, Layra L; Guimarães, Amanda S; Wu, Lulu; Barrera, Junior; Armelin, Hugo A

2017-01-01

We present in this article a methodology for designing kinetic models of molecular signaling networks, which was exemplarily applied for modeling one of the Ras/MAPK signaling pathways in the mouse Y1 adrenocortical cell line. The methodology is interdisciplinary, that is, it was developed in a way that both dry and wet lab teams worked together along the whole modeling process.

Network protocols for real-time applications

NASA Technical Reports Server (NTRS)

Johnson, Marjory J.

1987-01-01

The Fiber Distributed Data Interface (FDDI) and the SAE AE-9B High Speed Ring Bus (HSRB) are emerging standards for high-performance token ring local area networks. FDDI was designed to be a general-purpose high-performance network. HSRB was designed specifically for military real-time applications. A workshop was conducted at NASA Ames Research Center in January, 1987 to compare and contrast these protocols with respect to their ability to support real-time applications. This report summarizes workshop presentations and includes an independent comparison of the two protocols. A conclusion reached at the workshop was that current protocols for the upper layers of the Open Systems Interconnection (OSI) network model are inadequate for real-time applications.

Toward negative Poisson's ratio composites: Investigation of the auxetic behavior of fibrous networks

NASA Astrophysics Data System (ADS)

Tatlier, Mehmet Seha

Random fibrous can be found among natural and synthetic materials. Some of these random fibrous networks possess negative Poisson's ratio and they are extensively called auxetic materials. The governing mechanisms behind this counter intuitive property in random networks are yet to be understood and this kind of auxetic material remains widely under-explored. However, most of synthetic auxetic materials suffer from their low strength. This shortcoming can be rectified by developing high strength auxetic composites. The process of embedding auxetic random fibrous networks in a polymer matrix is an attractive alternate route to the manufacture of auxetic composites, however before such an approach can be developed, a methodology for designing fibrous networks with the desired negative Poisson's ratios must first be established. This requires an understanding of the factors which bring about negative Poisson's ratios in these materials. In this study, a numerical model is presented in order to investigate the auxetic behavior in compressed random fiber networks. Finite element analyses of three-dimensional stochastic fiber networks were performed to gain insight into the effects of parameters such as network anisotropy, network density, and degree of network compression on the out-of-plane Poisson's ratio and Young's modulus. The simulation results suggest that the compression is the critical parameter that gives rise to negative Poisson's ratio while anisotropy significantly promotes the auxetic behavior. This model can be utilized to design fibrous auxetic materials and to evaluate feasibility of developing auxetic composites by using auxetic fibrous networks as the reinforcing layer.

Concurrent enterprise: a conceptual framework for enterprise supply-chain network activities

NASA Astrophysics Data System (ADS)

Addo-Tenkorang, Richard; Helo, Petri T.; Kantola, Jussi

2017-04-01

Supply-chain management (SCM) in manufacturing industries has evolved significantly over the years. Recently, a lot more relevant research has picked up on the development of integrated solutions. Thus, seeking a collaborative optimisation of geographical, just-in-time (JIT), quality (customer demand/satisfaction) and return-on-investment (profits), aspects of organisational management and planning through 'best practice' business-process management - concepts and application; employing system tools such as certain applications/aspects of enterprise resource planning (ERP) - SCM systems information technology (IT) enablers to enhance enterprise integrated product development/concurrent engineering principles. This article assumed three main organisation theory applications in positioning its assumptions. Thus, proposing a feasible industry-specific framework not currently included within the SCOR model's level four (4) implementation level, as well as other existing SCM integration reference models such as in the MIT process handbook's - Process Interchange Format (PIF), the TOVE project, etc. which could also be replicated in other SCs. However, the wider focus of this paper's contribution will be concentrated on a complimentary proposed framework to the SCC's SCOR reference model. Quantitative empirical closed-ended questionnaires in addition to the main data collected from a qualitative empirical real-life industrial-based pilot case study were used: To propose a conceptual concurrent enterprise framework for SCM network activities. This research adopts a design structure matrix simulation approach analysis to propose an optimal enterprise SCM-networked value-adding, customised master data-management platform/portal for efficient SCM network information exchange and an effective supply-chain (SC) network systems-design teams' structure. Furthermore, social network theory analysis will be employed in a triangulation approach with statistical correlation analysis to assess the scale/level of frequency, importance, level of collaborative-ness, mutual trust as well as roles and responsibility among the enterprise SCM network for systems product development (PD) design teams' technical communication network as well as extensive literature reviews.

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Supply network configuration—A benchmarking problem

NASA Astrophysics Data System (ADS)

Brandenburg, Marcus

2018-03-01

Managing supply networks is a highly relevant task that strongly influences the competitiveness of firms from various industries. Designing supply networks is a strategic process that considerably affects the structure of the whole network. In contrast, supply networks for new products are configured without major adaptations of the existing structure, but the network has to be configured before the new product is actually launched in the marketplace. Due to dynamics and uncertainties, the resulting planning problem is highly complex. However, formal models and solution approaches that support supply network configuration decisions for new products are scant. The paper at hand aims at stimulating related model-based research. To formulate mathematical models and solution procedures, a benchmarking problem is introduced which is derived from a case study of a cosmetics manufacturer. Tasks, objectives, and constraints of the problem are described in great detail and numerical values and ranges of all problem parameters are given. In addition, several directions for future research are suggested.

Effects of topology on network evolution

NASA Astrophysics Data System (ADS)

Oikonomou, Panos; Cluzel, Philippe

2006-08-01

The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage. A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks. Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems. Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection. We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps, scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.

Robust outer synchronization between two nonlinear complex networks with parametric disturbances and mixed time-varying delays

NASA Astrophysics Data System (ADS)

Zhang, Chuan; Wang, Xingyuan; Luo, Chao; Li, Junqiu; Wang, Chunpeng

2018-03-01

In this paper, we focus on the robust outer synchronization problem between two nonlinear complex networks with parametric disturbances and mixed time-varying delays. Firstly, a general complex network model is proposed. Besides the nonlinear couplings, the network model in this paper can possess parametric disturbances, internal time-varying delay, discrete time-varying delay and distributed time-varying delay. Then, according to the robust control strategy, linear matrix inequality and Lyapunov stability theory, several outer synchronization protocols are strictly derived. Simple linear matrix controllers are designed to driver the response network synchronize to the drive network. Additionally, our results can be applied on the complex networks without parametric disturbances. Finally, by utilizing the delayed Lorenz chaotic system as the dynamics of all nodes, simulation examples are given to demonstrate the effectiveness of our theoretical results.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

PubMed

Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

2015-01-01

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

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

Davis, Michael J.; Janke, Robert

Network model detail can influence the accuracy of results from analyses of water distribution systems. Some previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregatedmore » adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. But, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

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

Davis, Michael J.; Janke, Robert

Network model detail can influence the accuracy of results from analyses of water distribution systems. Previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregated adversemore » effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. However, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

Influence of Network Model Detail on Estimated Health Effects of Drinking Water Contamination Events

DOE PAGES

Davis, Michael J.; Janke, Robert

2015-01-01

Network model detail can influence the accuracy of results from analyses of water distribution systems. Some previous work has shown the limitations of skeletonized network models when considering water quality and hydraulic effects. Loss of model detail is potentially less important for aggregated effects such as the systemwide health effects associated with a contamination event, but has received limited attention. The influence of model detail on such effects is examined here by comparing results obtained for contamination events using three large network models and several skeletonized versions of the models. Loss of model detail decreases the accuracy of estimated aggregatedmore » adverse effects related to contamination events. It has the potential to have a large negative influence on the results of consequence assessments and the design of contamination warning systems. But, the adverse influence on analysis results can be minimized by restricting attention to high percentile effects (i.e., 95th percentile or higher).« less

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

PubMed

Ranganayaki, V; Deepa, S N

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

PubMed Central

Ranganayaki, V.; Deepa, S. N.

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973

Simbrain 3.0: A flexible, visually-oriented neural network simulator.

PubMed

Tosi, Zachary; Yoshimi, Jeffrey

2016-11-01

Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Control of autonomous robot using neural networks

NASA Astrophysics Data System (ADS)

Barton, Adam; Volna, Eva

2017-07-01

The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nelson, Andrea; Aftosmis, Michael J.; Nemec, Marian; Pulliam, Thomas H.

2004-01-01

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Deep Space Network Antenna Monitoring Using Adaptive Time Series Methods and Hidden Markov Models

NASA Technical Reports Server (NTRS)

Smyth, Padhraic; Mellstrom, Jeff

1993-01-01

The Deep Space Network (DSN)(designed and operated by the Jet Propulsion Laboratory for the National Aeronautics and Space Administration (NASA) provides end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system.

The statistical mechanics of complex signaling networks: nerve growth factor signaling

NASA Astrophysics Data System (ADS)

Brown, K. S.; Hill, C. C.; Calero, G. A.; Myers, C. R.; Lee, K. H.; Sethna, J. P.; Cerione, R. A.

2004-10-01

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'

All-in-one model for designing optimal water distribution pipe networks

NASA Astrophysics Data System (ADS)

Aklog, Dagnachew; Hosoi, Yoshihiko

2017-05-01

This paper discusses the development of an easy-to-use, all-in-one model for designing optimal water distribution networks. The model combines different optimization techniques into a single package in which a user can easily choose what optimizer to use and compare the results of different optimizers to gain confidence in the performances of the models. At present, three optimization techniques are included in the model: linear programming (LP), genetic algorithm (GA) and a heuristic one-by-one reduction method (OBORM) that was previously developed by the authors. The optimizers were tested on a number of benchmark problems and performed very well in terms of finding optimal or near-optimal solutions with a reasonable computation effort. The results indicate that the model effectively addresses the issues of complexity and limited performance trust associated with previous models and can thus be used for practical purposes.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

A Flexible System for Simulating Aeronautical Telecommunication Network

NASA Technical Reports Server (NTRS)

Maly, Kurt; Overstreet, C. M.; Andey, R.

1998-01-01

At Old Dominion University, we have built Aeronautical Telecommunication Network (ATN) Simulator with NASA being the fund provider. It provides a means to evaluate the impact of modified router scheduling algorithms on the network efficiency, to perform capacity studies on various network topologies and to monitor and study various aspects of ATN through graphical user interface (GUI). In this paper we describe briefly about the proposed ATN model and our abstraction of this model. Later we describe our simulator architecture highlighting some of the design specifications, scheduling algorithms and user interface. At the end, we have provided the results of performance studies on this simulator.

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Lin; Gupta, Hoshin V.; Gao, Xiaogang; Sorooshian, Soroosh; Imam, Bisher

2002-12-01

Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.

A Prototype System for a Computer-Based Statewide Film Library Network: A Model for Operation. Final Report.

ERIC Educational Resources Information Center

Bidwell, Charles M.; Auricchio, Dominick

The project set out to establish an operational film scheduling network to improve service to New York State teachers using 16mm educational films. The Network is designed to serve local libraries located in Boards of Cooperative Educational Services (BOCES), regional libraries, and a statewide Syracuse University Film Rental Library (SUFRL). The…

An integrated GIS-based data model for multimodal urban public transportation analysis and management

NASA Astrophysics Data System (ADS)

Chen, Shaopei; Tan, Jianjun; Ray, C.; Claramunt, C.; Sun, Qinqin

2008-10-01

Diversity is one of the main characteristics of transportation data collected from multiple sources or formats, which can be extremely complex and disparate. Moreover, these multimodal transportation data are usually characterised by spatial and temporal properties. Multimodal transportation network data modelling involves both an engineering and research domain that has attracted the design of a number of spatio-temporal data models in the geographic information system (GIS). However, the application of these specific models to multimodal transportation network is still a challenging task. This research addresses this challenge from both integrated multimodal data organization and object-oriented modelling perspectives, that is, how a complex urban transportation network should be organized, represented and modeled appropriately when considering a multimodal point of view, and using object-oriented modelling method. We proposed an integrated GIS-based data model for multimodal urban transportation network that lays a foundation to enhance the multimodal transportation network analysis and management. This modelling method organizes and integrates multimodal transit network data, and supports multiple representations for spatio-temporal objects and relationship as both visual and graphic views. The data model is expressed by using a spatio-temporal object-oriented modelling method, i.e., the unified modelling language (UML) extended to spatial and temporal plug-in for visual languages (PVLs), which provides an essential support to the spatio-temporal data modelling for transportation GIS.

A model for the distribution of watermarked digital content on mobile networks

NASA Astrophysics Data System (ADS)

Frattolillo, Franco; D'Onofrio, Salvatore

2006-10-01

Although digital watermarking can be considered one of the key technologies to implement the copyright protection of digital contents distributed on the Internet, most of the content distribution models based on watermarking protocols proposed in literature have been purposely designed for fixed networks and cannot be easily adapted to mobile networks. On the contrary, the use of mobile devices currently enables new types of services and business models, and this makes the development of new content distribution models for mobile environments strategic in the current scenario of the Internet. This paper presents and discusses a distribution model of watermarked digital contents for such environments able to achieve a trade-off between the needs of efficiency and security.

A conservation planning approach to mitigate the impacts of leakage from protected area networks.

PubMed

Bode, Michael; Tulloch, Ayesha I T; Mills, Morena; Venter, Oscar; Ando, Amy W

2015-06-01

Protected area networks are designed to restrict anthropogenic pressures in areas of high biodiversity. Resource users respond by seeking to replace some or all of the lost resources from locations elsewhere in the landscape. Protected area networks thereby perturb the pattern of human pressures by displacing extractive effort from within protected areas into the broader landscape, a process known as leakage. The negative effects of leakage on conservation outcomes have been empirically documented and modeled using homogeneous descriptions of conservation landscapes. Human resource use and biodiversity vary greatly in space, however, and a theory of leakage must describe how this heterogeneity affects the magnitude, pattern, and biodiversity impacts of leakage. We combined models of household utility, adaptive human foraging, and biodiversity conservation to provide a bioeconomic model of leakage that accounts for spatial heterogeneity. Leakage had strong and divergent impacts on the performance of protected area networks, undermining biodiversity benefits but mitigating the negative impacts on local resource users. When leakage was present, our model showed that poorly designed protected area networks resulted in a substantial net loss of biodiversity. However, the effects of leakage can be mitigated if they are incorporated ex-ante into the conservation planning process. If protected areas are coupled with nonreserve policy instruments such as market subsidies, our model shows that the trade-offs between biodiversity and human well-being can be further and more directly reduced. © 2014 Society for Conservation Biology.

Auxetic metamaterials from disordered networks

NASA Astrophysics Data System (ADS)

Reid, Daniel R.; Pashine, Nidhi; Wozniak, Justin M.; Jaeger, Heinrich M.; Liu, Andrea J.; Nagel, Sidney R.; de Pablo, Juan J.

2018-02-01

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as Poisson’s ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and in the nonlinear regimes. In particular, it is shown that Poisson’s ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting 2D sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks’ susceptibility to tuning to design networks that possess a distribution of stiffer and more compliant bonds and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of unique mechanical metamaterials.

Identification and Control of Aircrafts using Multiple Models and Adaptive Critics

NASA Technical Reports Server (NTRS)

Principe, Jose C.

2007-01-01

We compared two possible implementations of local linear models for control: one approach is based on a self-organizing map (SOM) to cluster the dynamics followed by a set of linear models operating at each cluster. Therefore the gating function is hard (a single local model will represent the regional dynamics). This simplifies the controller design since there is a one to one mapping between controllers and local models. The second approach uses a soft gate using a probabilistic framework based on a Gaussian Mixture Model (also called a dynamic mixture of experts). In this approach several models may be active at a given time, we can expect a smaller number of models, but the controller design is more involved, with potentially better noise rejection characteristics. Our experiments showed that the SOM provides overall best performance in high SNRs, but the performance degrades faster than with the GMM for the same noise conditions. The SOM approach required about an order of magnitude more models than the GMM, so in terms of implementation cost, the GMM is preferable. The design of the SOM is straight forward, while the design of the GMM controllers, although still reasonable, is more involved and needs more care in the selection of the parameters. Either one of these locally linear approaches outperform global nonlinear controllers based on neural networks, such as the time delay neural network (TDNN). Therefore, in essence the local model approach warrants practical implementations. In order to call the attention of the control community for this design methodology we extended successfully the multiple model approach to PID controllers (still today the most widely used control scheme in the industry), and wrote a paper on this subject. The echo state network (ESN) is a recurrent neural network with the special characteristics that only the output parameters are trained. The recurrent connections are preset according to the problem domain and are fixed. In a nutshell, the states of the reservoir of recurrent processing elements implement a projection space, where the desired response is optimally projected. This architecture trades training efficiency by a large increase in the dimension of the recurrent layer. However, the power of the recurrent neural networks can be brought to bear on practical difficult problems. Our goal was to implement an adaptive critic architecture implementing Bellman s approach to optimal control. However, we could only characterize the ESN performance as a critic in value function evaluation, which is just one of the pieces of the overall adaptive critic controller. The results were very convincing, and the simplicity of the implementation was unparalleled.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water.

PubMed

Oh, H K; Yu, M J; Gwon, E M; Koo, J Y; Kim, S G; Koizumi, A

2004-01-01

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Implementing partnership-driven clinical federated electronic health record data sharing networks.

PubMed

Stephens, Kari A; Anderson, Nicholas; Lin, Ching-Ping; Estiri, Hossein

2016-09-01

Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Observer-Based Discrete-Time Nonnegative Edge Synchronization of Networked Systems.

PubMed

Su, Housheng; Wu, Han; Chen, Xia

2017-10-01

This paper studies the multi-input and multi-output discrete-time nonnegative edge synchronization of networked systems based on neighbors' output information. The communication relationship among the edges of networked systems is modeled by well-known line graph. Two observer-based edge synchronization algorithms are designed, for which some necessary and sufficient synchronization conditions are derived. Moreover, some computable sufficient synchronization conditions are obtained, in which the feedback matrix and the observer matrix are computed by solving the linear programming problems. We finally design several simulation examples to demonstrate the validity of the given nonnegative edge synchronization algorithms.

Efficient evaluation of wireless real-time control networks.

PubMed

Horvath, Peter; Yampolskiy, Mark; Koutsoukos, Xenofon

2015-02-11

In this paper, we present a system simulation framework for the design and performance evaluation of complex wireless cyber-physical systems. We describe the simulator architecture and the specific developments that are required to simulate cyber-physical systems relying on multi-channel, multihop mesh networks. We introduce realistic and efficient physical layer models and a system simulation methodology, which provides statistically significant performance evaluation results with low computational complexity. The capabilities of the proposed framework are illustrated in the example of WirelessHART, a centralized, real-time, multi-hop mesh network designed for industrial control and monitor applications.

Fuzzy-information-based robustness of interconnected networks against attacks and failures

NASA Astrophysics Data System (ADS)

Zhu, Qian; Zhu, Zhiliang; Wang, Yifan; Yu, Hai

2016-09-01

Cascading failure is fatal in applications and its investigation is essential and therefore became a focal topic in the field of complex networks in the last decade. In this paper, a cascading failure model is established for interconnected networks and the associated data-packet transport problem is discussed. A distinguished feature of the new model is its utilization of fuzzy information in resisting uncertain failures and malicious attacks. We numerically find that the giant component of the network after failures increases with tolerance parameter for any coupling preference and attacking ambiguity. Moreover, considering the effect of the coupling probability on the robustness of the networks, we find that the robustness of the assortative coupling and random coupling of the network model increases with the coupling probability. However, for disassortative coupling, there exists a critical phenomenon for coupling probability. In addition, a critical value that attacking information accuracy affects the network robustness is observed. Finally, as a practical example, the interconnected AS-level Internet in South Korea and Japan is analyzed. The actual data validates the theoretical model and analytic results. This paper thus provides some guidelines for preventing cascading failures in the design of architecture and optimization of real-world interconnected networks.

Toward domain-specific design environments: Some representation ideas from the telecommunications domain

NASA Technical Reports Server (NTRS)

Greenspan, Sol; Feblowitz, Mark

1992-01-01

ACME is an experimental environment for investigating new approaches to modeling and analysis of system requirements and designs. ACME is built on and extends object-oriented conceptual modeling techniques and knowledge representation and reasoning (KRR) tools. The most immediate intended use for ACME is to help represent, understand, and communicate system designs during the early stages of system planning and requirements engineering. While our research is ostensibly aimed at software systems in general, we are particularly motivated to make an impact in the telecommunications domain, especially in the area referred to as Intelligent Networks (IN's). IN systems contain the software to provide services to users of a telecommunications network (e.g., call processing services, information services, etc.) as well as the software that provides the internal infrastructure for providing the services (e.g., resource management, billing, etc.). The software includes not only systems developed by the network proprietors but also by a growing group of independent service software providers.

System-level simulation of liquid filling in microfluidic chips.

PubMed

Song, Hongjun; Wang, Yi; Pant, Kapil

2011-06-01

Liquid filling in microfluidic channels is a complex process that depends on a variety of geometric, operating, and material parameters such as microchannel geometry, flow velocity∕pressure, liquid surface tension, and contact angle of channel surface. Accurate analysis of the filling process can provide key insights into the filling time, air bubble trapping, and dead zone formation, and help evaluate trade-offs among the various design parameters and lead to optimal chip design. However, efficient modeling of liquid filling in complex microfluidic networks continues to be a significant challenge. High-fidelity computational methods, such as the volume of fluid method, are prohibitively expensive from a computational standpoint. Analytical models, on the other hand, are primarily applicable to idealized geometries and, hence, are unable to accurately capture chip level behavior of complex microfluidic systems. This paper presents a parametrized dynamic model for the system-level analysis of liquid filling in three-dimensional (3D) microfluidic networks. In our approach, a complex microfluidic network is deconstructed into a set of commonly used components, such as reservoirs, microchannels, and junctions. The components are then assembled according to their spatial layout and operating rationale to achieve a rapid system-level model. A dynamic model based on the transient momentum equation is developed to track the liquid front in the microchannels. The principle of mass conservation at the junction is used to link the fluidic parameters in the microchannels emanating from the junction. Assembly of these component models yields a set of differential and algebraic equations, which upon integration provides temporal information of the liquid filling process, particularly liquid front propagation (i.e., the arrival time). The models are used to simulate the transient liquid filling process in a variety of microfluidic constructs and in a multiplexer, representing a complex microfluidic network. The accuracy (relative error less than 7%) and orders-of-magnitude speedup (30 000X-4 000 000X) of our system-level models are verified by comparison against 3D high-fidelity numerical studies. Our findings clearly establish the utility of our models and simulation methodology for fast, reliable analysis of liquid filling to guide the design optimization of complex microfluidic networks.

Advanced ISDN satellite designs and experiments

NASA Technical Reports Server (NTRS)

Pepin, Gerard R.

1992-01-01

The research performed by GTE Government Systems and the University of Colorado in support of the NASA Satellite Communications Applications Research (SCAR) Program is summarized. Two levels of research were undertaken. The first dealt with providing interim services Integrated Services Digital Network (ISDN) satellite (ISIS) capabilities that accented basic rate ISDN with a ground control similar to that of the Advanced Communications Technology Satellite (ACTS). The ISIS Network Model development represents satellite systems like the ACTS orbiting switch. The ultimate aim is to move these ACTS ground control functions on-board the next generation of ISDN communications satellite to provide full-service ISDN satellite (FSIS) capabilities. The technical and operational parameters for the advanced ISDN communications satellite design are obtainable from the simulation of ISIS and FSIS engineering software models of the major subsystems of the ISDN communications satellite architecture. Discrete event simulation experiments would generate data for analysis against NASA SCAR performance measure and the data obtained from the ISDN satellite terminal adapter hardware (ISTA) experiments, also developed in the program. The Basic and Option 1 phases of the program are also described and include the following: literature search, traffic mode, network model, scenario specifications, performance measures definitions, hardware experiment design, hardware experiment development, simulator design, and simulator development.

Visualization, documentation, analysis, and communication of large scale gene regulatory networks

PubMed Central

Longabaugh, William J.R.; Davidson, Eric H.; Bolouri, Hamid

2009-01-01

Summary Genetic regulatory networks (GRNs) are complex, large-scale, and spatially and temporally distributed. These characteristics impose challenging demands on computational GRN modeling tools, and there is a need for custom modeling tools. In this paper, we report on our ongoing development of BioTapestry, an open source, freely available computational tool designed specifically for GRN modeling. We also outline our future development plans, and give some examples of current applications of BioTapestry. PMID:18757046

Toward Real Time Neural Net Flight Controllers

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Mah, R. W.; Ross, J.; Lu, Henry, Jr. (Technical Monitor)

1994-01-01

NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology.

PubMed

Frega, Monica; Tedesco, Mariateresa; Massobrio, Paolo; Pesce, Mattia; Martinoia, Sergio

2014-06-30

Despite the extensive use of in-vitro models for neuroscientific investigations and notwithstanding the growing field of network electrophysiology, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. These networks are usually grown onto specific rigid substrates (also with embedded electrodes) and lack of most of the constituents of the in-vivo like environment: cell morphology, cell-to-cell interaction and neuritic outgrowth in all directions. Cells in a brain region develop in a 3D space and interact with a complex multi-cellular environment and extracellular matrix. Under this perspective, 3D networks coupled to micro-transducer arrays, represent a new and powerful in-vitro model capable of better emulating in-vivo physiology. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to Micro-Electrode-Arrays (MEAs) and we show how the features of the recorded network dynamics differ from the corresponding 2D network model. Further development of the proposed 3D in-vitro model by adding embedded functionalized scaffolds might open new prospects for manipulating, stimulating and recording the neuronal activity to elucidate neurophysiological mechanisms and to design bio-hybrid microsystems.

Network dynamics of 3D engineered neuronal cultures: a new experimental model for in-vitro electrophysiology

PubMed Central

Frega, Monica; Tedesco, Mariateresa; Massobrio, Paolo; Pesce, Mattia; Martinoia, Sergio

2014-01-01

Despite the extensive use of in-vitro models for neuroscientific investigations and notwithstanding the growing field of network electrophysiology, all studies on cultured cells devoted to elucidate neurophysiological mechanisms and computational properties, are based on 2D neuronal networks. These networks are usually grown onto specific rigid substrates (also with embedded electrodes) and lack of most of the constituents of the in-vivo like environment: cell morphology, cell-to-cell interaction and neuritic outgrowth in all directions. Cells in a brain region develop in a 3D space and interact with a complex multi-cellular environment and extracellular matrix. Under this perspective, 3D networks coupled to micro-transducer arrays, represent a new and powerful in-vitro model capable of better emulating in-vivo physiology. In this work, we present a new experimental paradigm constituted by 3D hippocampal networks coupled to Micro-Electrode-Arrays (MEAs) and we show how the features of the recorded network dynamics differ from the corresponding 2D network model. Further development of the proposed 3D in-vitro model by adding embedded functionalized scaffolds might open new prospects for manipulating, stimulating and recording the neuronal activity to elucidate neurophysiological mechanisms and to design bio-hybrid microsystems. PMID:24976386

The network organization of protein interactions in the spliceosome is reproduced by the simple rules of food-web models

PubMed Central

Pires, Mathias M.; Cantor, Maurício; Guimarães, Paulo R.; de Aguiar, Marcus A. M.; dos Reis, Sérgio F.; Coltri, Patricia P.

2015-01-01

The network structure of biological systems provides information on the underlying processes shaping their organization and dynamics. Here we examined the structure of the network depicting protein interactions within the spliceosome, the macromolecular complex responsible for splicing in eukaryotic cells. We show the interactions of less connected spliceosome proteins are nested subsets of the connections of the highly connected proteins. At the same time, the network has a modular structure with groups of proteins sharing similar interaction patterns. We then investigated the role of affinity and specificity in shaping the spliceosome network by adapting a probabilistic model originally designed to reproduce food webs. This food-web model was as successful in reproducing the structure of protein interactions as it is in reproducing interactions among species. The good performance of the model suggests affinity and specificity, partially determined by protein size and the timing of association to the complex, may be determining network structure. Moreover, because network models allow building ensembles of realistic networks while encompassing uncertainty they can be useful to examine the dynamics and vulnerability of intracelullar processes. Unraveling the mechanisms organizing the spliceosome interactions is important to characterize the role of individual proteins on splicing catalysis and regulation. PMID:26443080

Underwater Wireless Sensor Networks: how do acoustic propagation models impact the performance of higher-level protocols?

PubMed

Llor, Jesús; Malumbres, Manuel P

2012-01-01

Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios.

Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols?

PubMed Central

Llor, Jesús; Malumbres, Manuel P.

2012-01-01

Several Medium Access Control (MAC) and routing protocols have been developed in the last years for Underwater Wireless Sensor Networks (UWSNs). One of the main difficulties to compare and validate the performance of different proposals is the lack of a common standard to model the acoustic propagation in the underwater environment. In this paper we analyze the evolution of underwater acoustic prediction models from a simple approach to more detailed and accurate models. Then, different high layer network protocols are tested with different acoustic propagation models in order to determine the influence of environmental parameters on the obtained results. After several experiments, we can conclude that higher-level protocols are sensitive to both: (a) physical layer parameters related to the network scenario and (b) the acoustic propagation model. Conditions like ocean surface activity, scenario location, bathymetry or floor sediment composition, may change the signal propagation behavior. So, when designing network architectures for UWSNs, the role of the physical layer should be seriously taken into account in order to assert that the obtained simulation results will be close to the ones obtained in real network scenarios. PMID:22438712

Design and development of a wireless sensor network to monitor snow depth in multiple catchments in the American River basin, California: hardware selection and sensor placement techniques

NASA Astrophysics Data System (ADS)

Kerkez, B.; Rice, R.; Glaser, S. D.; Bales, R. C.; Saksa, P. C.

2010-12-01

A 100-node wireless sensor network (WSN) was designed for the purpose of monitoring snow depth in two watersheds, spanning 3 km2 in the American River basin, in the central Sierra Nevada of California. The network will be deployed as a prototype project that will become a core element of a larger water information system for the Sierra Nevada. The site conditions range from mid-elevation forested areas to sub-alpine terrain with light forest cover. Extreme temperature and humidity fluctuations, along with heavy rain and snowfall events, create particularly challenging conditions for wireless communications. We show how statistics gathered from a previously deployed 60-node WSN, located in the Southern Sierra Critical Zone Observatory, were used to inform design. We adapted robust network hardware, manufactured by Dust Networks for highly demanding industrial monitoring, and added linear amplifiers to the radios to improve transmission distances. We also designed a custom data-logging board to interface the WSN hardware with snow-depth sensors. Due to the large distance between sensing locations, and complexity of terrain, we analyzed network statistics to select the location of repeater nodes, to create a redundant and reliable mesh. This optimized network topology will maximize transmission distances, while ensuring power-efficient network operations throughout harsh winter conditions. At least 30 of the 100 nodes will actively sense snow depth, while the remainder will act as sensor-ready repeaters in the mesh. Data from a previously conducted snow survey was used to create a Gaussian Process model of snow depth; variance estimates produced by this model were used to suggest near-optimal locations for snow-depth sensors to measure the variability across a 1 km2 grid. We compare the locations selected by the sensor placement algorithm to those made through expert opinion, and offer explanations for differences resulting from each approach.

A New Network Modeling Tool for the Ground-based Nuclear Explosion Monitoring Community

NASA Astrophysics Data System (ADS)

Merchant, B. J.; Chael, E. P.; Young, C. J.

2013-12-01

Network simulations have long been used to assess the performance of monitoring networks to detect events for such purposes as planning station deployments and network resilience to outages. The standard tool has been the SAIC-developed NetSim package. With correct parameters, NetSim can produce useful simulations; however, the package has several shortcomings: an older language (FORTRAN), an emphasis on seismic monitoring with limited support for other technologies, limited documentation, and a limited parameter set. Thus, we are developing NetMOD (Network Monitoring for Optimal Detection), a Java-based tool designed to assess the performance of ground-based networks. NetMOD's advantages include: coded in a modern language that is multi-platform, utilizes modern computing performance (e.g. multi-core processors), incorporates monitoring technologies other than seismic, and includes a well-validated default parameter set for the IMS stations. NetMOD is designed to be extendable through a plugin infrastructure, so new phenomenological models can be added. Development of the Seismic Detection Plugin is being pursued first. Seismic location and infrasound and hydroacoustic detection plugins will follow. By making NetMOD an open-release package, it can hopefully provide a common tool that the monitoring community can use to produce assessments of monitoring networks and to verify assessments made by others.

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

PubMed Central

Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

2016-01-01

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations. PMID:27242701

Complexity analysis on public transport networks of 97 large- and medium-sized cities in China

NASA Astrophysics Data System (ADS)

Tian, Zhanwei; Zhang, Zhuo; Wang, Hongfei; Ma, Li

2018-04-01

The traffic situation in Chinese urban areas is continuing to deteriorate. To make a better planning and designing of the public transport system, it is necessary to make profound research on the structure of urban public transport networks (PTNs). We investigate 97 large- and medium-sized cities’ PTNs in China, construct three types of network models — bus stop network, bus transit network and bus line network, then analyze the structural characteristics of them. It is revealed that bus stop network is small-world and scale-free, bus transit network and bus line network are both small-world. Betweenness centrality of each city’s PTN shows similar distribution pattern, although these networks’ size is various. When classifying cities according to the characteristics of PTNs or economic development level, the results are similar. It means that the development of cities’ economy and transport network has a strong correlation, PTN expands in a certain model with the development of economy.

Modeling a Million-Node Slim Fly Network Using Parallel Discrete-Event Simulation

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

Wolfe, Noah; Carothers, Christopher; Mubarak, Misbah

As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the modelmore » size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.« less

Model-based Compositional Design of Networked Control Systems

DTIC Science & Technology

2013-12-01

communication network. As described in Section 1.2, although there are several advantages in using NCS, there exist some drawbacks due to the presence of the... pendulum was used to demonstrate the approach, the results showed desirable performance in the presence of time delays. The passivity of the...certain level of performance under a wide range of failures, the designed controller is typically conservative. The drawback of this approach is that one

A selection model for accounting for publication bias in a full network meta-analysis.

PubMed

Mavridis, Dimitris; Welton, Nicky J; Sutton, Alex; Salanti, Georgia

2014-12-30

Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency. Copyright © 2014 John Wiley & Sons, Ltd.

Modelling and analysis of gene regulatory network using feedback control theory

NASA Astrophysics Data System (ADS)

El-Samad, H.; Khammash, M.

2010-01-01

Molecular pathways are a part of a remarkable hierarchy of regulatory networks that operate at all levels of organisation. These regulatory networks are responsible for much of the biological complexity within the cell. The dynamic character of these pathways and the prevalence of feedback regulation strategies in their operation make them amenable to systematic mathematical analysis using the same tools that have been used with success in analysing and designing engineering control systems. In this article, we aim at establishing this strong connection through various examples where the behaviour exhibited by gene networks is explained in terms of their underlying control strategies. We complement our analysis by a survey of mathematical techniques commonly used to model gene regulatory networks and analyse their dynamic behaviour.

In-House Communication Support System Based on the Information Propagation Model Utilizes Social Network

NASA Astrophysics Data System (ADS)

Takeuchi, Susumu; Teranishi, Yuuichi; Harumoto, Kaname; Shimojo, Shinji

Almost all companies are now utilizing computer networks to support speedier and more effective in-house information-sharing and communication. However, existing systems are designed to support communications only within the same department. Therefore, in our research, we propose an in-house communication support system which is based on the “Information Propagation Model (IPM).” The IPM is proposed to realize word-of-mouth communication in a social network, and to support information-sharing on the network. By applying the system in a real company, we found that information could be exchanged between different and unrelated departments, and such exchanges of information could help to build new relationships between the users who are apart on the social network.

Modeling and simulation of different and representative engineering problems using Network Simulation Method

PubMed Central

2018-01-01

Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model. PMID:29518121

Modeling and simulation of different and representative engineering problems using Network Simulation Method.

PubMed

Sánchez-Pérez, J F; Marín, F; Morales, J L; Cánovas, M; Alhama, F

2018-01-01

Mathematical models simulating different and representative engineering problem, atomic dry friction, the moving front problems and elastic and solid mechanics are presented in the form of a set of non-linear, coupled or not coupled differential equations. For different parameters values that influence the solution, the problem is numerically solved by the network method, which provides all the variables of the problems. Although the model is extremely sensitive to the above parameters, no assumptions are considered as regards the linearization of the variables. The design of the models, which are run on standard electrical circuit simulation software, is explained in detail. The network model results are compared with common numerical methods or experimental data, published in the scientific literature, to show the reliability of the model.

Design of robust flow processing networks with time-programmed responses

NASA Astrophysics Data System (ADS)

Kaluza, P.; Mikhailov, A. S.

2012-04-01

Can artificially designed networks reach the levels of robustness against local damage which are comparable with those of the biochemical networks of a living cell? We consider a simple model where the flow applied to an input node propagates through the network and arrives at different times to the output nodes, thus generating a pattern of coordinated responses. By using evolutionary optimization algorithms, functional networks - with required time-programmed responses - were constructed. Then, continuing the evolution, such networks were additionally optimized for robustness against deletion of individual nodes or links. In this manner, large ensembles of functional networks with different kinds of robustness were obtained, making statistical investigations and comparison of their structural properties possible. We have found that, generally, different architectures are needed for various kinds of robustness. The differences are statistically revealed, for example, in the Laplacian spectra of the respective graphs. On the other hand, motif distributions of robust networks do not differ from those of the merely functional networks; they are found to belong to the first Alon superfamily, the same as that of the gene transcription networks of single-cell organisms.

Using ecological null models to assess the potential for marine protected area networks to protect biodiversity.

PubMed

Semmens, Brice X; Auster, Peter J; Paddack, Michelle J

2010-01-27

Marine protected area (MPA) networks have been proposed as a principal method for conserving biological diversity, yet patterns of diversity may ultimately complicate or compromise the development of such networks. We show how a series of ecological null models can be applied to assemblage data across sites in order to identify non-random biological patterns likely to influence the effectiveness of MPA network design. We use fish census data from Caribbean fore-reefs as a test system and demonstrate that: 1) site assemblages were nested, such that species found on sites with relatively few species were subsets of those found on sites with relatively many species, 2) species co-occurred across sites more than expected by chance once species-habitat associations were accounted for, and 3) guilds were most evenly represented at the richest sites and richness among all guilds was correlated (i.e., species and trophic diversity were closely linked). These results suggest that the emerging Caribbean marine protected area network will likely be successful at protecting regional diversity even if planning is largely constrained by insular, inventory-based design efforts. By recasting ecological null models as tests of assemblage patterns likely to influence management action, we demonstrate how these classic tools of ecological theory can be brought to bear in applied conservation problems.

Virtual terrain: a security-based representation of a computer network

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Argauer, Brian

2008-03-01

Much research has been put forth towards detection, correlating, and prediction of cyber attacks in recent years. As this set of research progresses, there is an increasing need for contextual information of a computer network to provide an accurate situational assessment. Typical approaches adopt contextual information as needed; yet such ad hoc effort may lead to unnecessary or even conflicting features. The concept of virtual terrain is, therefore, developed and investigated in this work. Virtual terrain is a common representation of crucial information about network vulnerabilities, accessibilities, and criticalities. A virtual terrain model encompasses operating systems, firewall rules, running services, missions, user accounts, and network connectivity. It is defined as connected graphs with arc attributes defining dynamic relationships among vertices modeling network entities, such as services, users, and machines. The virtual terrain representation is designed to allow feasible development and maintenance of the model, as well as efficacy in terms of the use of the model. This paper will describe the considerations in developing the virtual terrain schema, exemplary virtual terrain models, and algorithms utilizing the virtual terrain model for situation and threat assessment.

Mathematical model of highways network optimization

NASA Astrophysics Data System (ADS)

Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

2017-12-01

The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

NASA Astrophysics Data System (ADS)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Self Improving Methods for Materials and Process Design

DTIC Science & Technology

1998-08-31

using inductive coupling techniques. The first phase of the work focuses on developing an artificial neural network learning for function approximation...developing an artificial neural network learning algorithm for time-series prediction. The third phase of the work focuses on model selection. We have

Professional Learning Networks Designed for Teacher Learning

ERIC Educational Resources Information Center

Trust, Torrey

2012-01-01

In the information age, students must learn to navigate and evaluate an expanding network of information. Highly effective teachers model this process of information analysis and knowledge acquisition by continually learning through collaboration, professional development, and studying pedagogical techniques and best practices. Many teachers have…

Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

PubMed

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

PubMed Central

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology. PMID:23555205

Modeling Signaling Networks to Advance New Cancer Therapies.

PubMed

Saez-Rodriguez, Julio; MacNamara, Aidan; Cook, Simon

2015-01-01

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

Modelling a recovery network for WEEE: a case study in Portugal.

PubMed

Gomes, Maria Isabel; Barbosa-Povoa, Ana Paula; Novais, Augusto Q

2011-07-01

The European Union directive for electric and electronic waste, published in 2003, enforced all European countries to meet some targets concerning the recycling and recovery of these products. This directive was transposed to the Portuguese legislation in 2004. Following this, a group of EEE producers set up an organization (Amb3e) whose mission was to design and manage a nationwide recovery network for WEEE, which will be the subject matter of this work. A generic MILP model is proposed to represent this network, which is applied to its design and planning, where the best locations for collection and sorting centres are chosen simultaneously with the definition of a tactical network planning. Several analyses are performed to provide further insights regarding the selection of these alternative locations. The results gave support to the company strategic expansion plans for a high number of centres to be opened and to their location near the major WEEE sources. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neurocomputing strategies in decomposition based structural design

NASA Technical Reports Server (NTRS)

Szewczyk, Z.; Hajela, P.

1993-01-01

The present paper explores the applicability of neurocomputing strategies in decomposition based structural optimization problems. It is shown that the modeling capability of a backpropagation neural network can be used to detect weak couplings in a system, and to effectively decompose it into smaller, more tractable, subsystems. When such partitioning of a design space is possible, parallel optimization can be performed in each subsystem, with a penalty term added to its objective function to account for constraint violations in all other subsystems. Dependencies among subsystems are represented in terms of global design variables, and a neural network is used to map the relations between these variables and all subsystem constraints. A vector quantization technique, referred to as a z-Network, can effectively be used for this purpose. The approach is illustrated with applications to minimum weight sizing of truss structures with multiple design constraints.

Tower-based greenhouse gas measurement network design—The National Institute of Standards and Technology North East Corridor Testbed

NASA Astrophysics Data System (ADS)

Lopez-Coto, Israel; Ghosh, Subhomoy; Prasad, Kuldeep; Whetstone, James

2017-09-01

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Designing optimal greenhouse gas observing networks that consider performance and cost

DOE PAGES

Lucas, D. D.; Yver Kwok, C.; Cameron-Smith, P.; ...

2015-06-16

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototypemore » network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH 2FCF 3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.« less

Contagion processes on the static and activity-driven coupling networks

NASA Astrophysics Data System (ADS)

Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

2016-03-01

The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence

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

Korkali, Mert; Veneman, Jason G.; Tivnan, Brian F.

Increased coupling between critical infrastructure networks, such as power and communication systems, has important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several researchers have studied models of interdependent networks and reported that increased coupling can increase vulnerability. However, these conclusions come largely from models that have substantially different mechanisms of cascading failure, relative to those found in actual power and communication networks, and that do not capture the benefits of connecting systems with complementary capabilities. In order to understand the importance of these details, this paper compares network vulnerability in simplemore » topological models and in models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid model shows a higher level of vulnerability, relative to the contagion model. Second, we compare a percolation model of topological cascading in coupled networks to three different models of power networks coupled to communication systems. Again, the more accurate models suggest very different conclusions than the percolation model. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the percolation model, in which zero coupling is optimal. Only in an extreme case, in which communication failures immediately cause grid failures, did we find that increased coupling can be harmful. Together, these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems.« less

Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence

DOE PAGES

Korkali, Mert; Veneman, Jason G.; Tivnan, Brian F.; ...

2017-03-20

Increased coupling between critical infrastructure networks, such as power and communication systems, has important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several researchers have studied models of interdependent networks and reported that increased coupling can increase vulnerability. However, these conclusions come largely from models that have substantially different mechanisms of cascading failure, relative to those found in actual power and communication networks, and that do not capture the benefits of connecting systems with complementary capabilities. In order to understand the importance of these details, this paper compares network vulnerability in simplemore » topological models and in models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid model shows a higher level of vulnerability, relative to the contagion model. Second, we compare a percolation model of topological cascading in coupled networks to three different models of power networks coupled to communication systems. Again, the more accurate models suggest very different conclusions than the percolation model. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the percolation model, in which zero coupling is optimal. Only in an extreme case, in which communication failures immediately cause grid failures, did we find that increased coupling can be harmful. Together, these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems.« less

SNM-DAT: Simulation of a heterogeneous network for nuclear border security

NASA Astrophysics Data System (ADS)

Nemzek, R.; Kenyon, G.; Koehler, A.; Lee, D. M.; Priedhorsky, W.; Raby, E. Y.

2007-08-01

We approach the problem of detecting Special Nuclear Material (SNM) smuggling across open borders by modeling a heterogeneous sensor network using an agent-based simulation. Our simulation SNM Data Analysis Tool (SNM-DAT) combines fixed seismic, metal, and radiation detectors with a mobile gamma spectrometer. Decision making within the simulation determines threat levels by combined signatures. The spectrometer is a limited-availability asset, and is only deployed for substantial threats. "Crossers" can be benign or carrying shielded SNM. Signatures and sensors are physics based, allowing us to model realistic sensor networks. The heterogeneous network provides great gains in detection efficiency compared to a radiation-only system. We can improve the simulation through better sensor and terrain models, additional signatures, and crossers that mimic actual trans-border traffic. We expect further gains in our ability to design sensor networks as we learn the emergent properties of heterogeneous detection, and potential adversary responses.

A methodology for the efficient integration of transient constraints in the design of aircraft dynamic systems

NASA Astrophysics Data System (ADS)

Phan, Leon L.

The motivation behind this thesis mainly stems from previous work performed at Hispano-Suiza (Safran Group) in the context of the European research project "Power Optimised Aircraft". Extensive testing on the COPPER Bird RTM, a test rig designed to characterize aircraft electrical networks, demonstrated the relevance of transient regimes in the design and development of dynamic systems. Transient regimes experienced by dynamic systems may have severe impacts on the operation of the aircraft. For example, the switching on of a high electrical load might cause a network voltage drop inducing a loss of power available to critical aircraft systems. These transient behaviors are thus often regulated by dynamic constraints, requiring the dynamic signals to remain within bounds whose values vary with time. The verification of these peculiar types of constraints, which generally requires high-fidelity time-domain simulation, intervenes late in the system development process, thus potentially causing costly design iterations. The research objective of this thesis is to develop a methodology that integrates the verification of dynamic constraints in the early specification of dynamic systems. In order to circumvent the inefficiencies of time-domain simulation, multivariate dynamic surrogate models of the original time-domain simulation models are generated, building on a nonlinear system identification technique using wavelet neural networks (or wavenets), which allow the multiscale nature of transient signals to be captured. However, training multivariate wavenets can become computationally prohibitive as the number of design variables increases. Therefore, an alternate approach is formulated, in which dynamic surrogate models using sigmoid-based neural networks are used to emulate the transient behavior of the envelopes of the time-domain response. Thus, in order to train the neural network, the envelopes are extracted by first separating the scales of the dynamic response, using a multiresolution analysis (MRA) based on the discrete wavelet transform. The MRA separates the dynamic response into a trend and a noise signal (ripple). The envelope of the noise is then computed with a windowing method, and recombined with the trend in order to reconstruct the global envelope of the dynamic response. The run-time efficiency of the resulting dynamic surrogate models enable the implementation of a data farming approach, in which a Monte-Carlo simulation generates time-domain behaviors of transient responses for a vast set of design and operation scenarios spanning the design and operation space. An interactive visualization environment, enabling what-if analyses, will be developed; the user can thereby instantaneously comprehend the transient response of the system (or its envelope) and its sensitivities to design and operation variables, as well as filter the design space to have it exhibit only the design scenarios verifying the dynamic constraints. The proposed methodology, along with its foundational hypotheses, are tested on the design and optimization of a 350VDC network, where a generator and its control system are concurrently designed in order to minimize the electrical losses, while ensuring that the transient undervoltage induced by peak demands in the consumption of a motor does not violate transient power quality constraints.

A proposal of optimal sampling design using a modularity strategy

NASA Astrophysics Data System (ADS)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Experimental Definition and Validation of Protein Coding Transcripts in Chlamydomonas reinhardtii

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

Kourosh Salehi-Ashtiani; Jason A. Papin

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnectedmore » metabolic networks to optimize production of the compounds of interest. Using Chlamydomonas reinhardtii as a model, we developed a systems-level methodology bridging metabolic network reconstruction with annotation and experimental verification of enzyme encoding open reading frames. We reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. Our approach to generate a predictive metabolic model integrated with cloned open reading frames, provides a cost-effective platform to generate metabolic engineering resources. While the generated resources are specific to algal systems, the approach that we have developed is not specific to algae and can be readily expanded to other microbial systems as well as higher plants and animals.« less