Geospatial optimization of siting large-scale solar projects

USGS Publications Warehouse

Macknick, Jordan; Quinby, Ted; Caulfield, Emmet; Gerritsen, Margot; Diffendorfer, James E.; Haines, Seth S.

2014-01-01

guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.

Gala: A Python package for galactic dynamics

NASA Astrophysics Data System (ADS)

Price-Whelan, Adrian M.

2017-10-01

Gala is an Astropy-affiliated Python package for galactic dynamics. Python enables wrapping low-level languages (e.g., C) for speed without losing flexibility or ease-of-use in the user-interface. The API for Gala was designed to provide a class-based and user-friendly interface to fast (C or Cython-optimized) implementations of common operations such as gravitational potential and force evaluation, orbit integration, dynamical transformations, and chaos indicators for nonlinear dynamics. Gala also relies heavily on and interfaces well with the implementations of physical units and astronomical coordinate systems in the Astropy package (astropy.units and astropy.coordinates). Gala was designed to be used by both astronomical researchers and by students in courses on gravitational dynamics or astronomy. It has already been used in a number of scientific publications and has also been used in graduate courses on Galactic dynamics to, e.g., provide interactive visualizations of textbook material.

Cooperation stimulation strategies for peer-to-peer wireless live video-sharing social networks.

PubMed

Lin, W Sabrina; Zhao, H Vicky; Liu, K J Ray

2010-07-01

Human behavior analysis in video sharing social networks is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on video sharing systems. Users watching live streaming in the same wireless network share the same limited bandwidth of backbone connection to the Internet, thus, they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network. Every user wishes to watch video with high quality while paying as little as possible cost to help others. This paper focuses on providing incentives for user cooperation. We propose a game-theoretic framework to model user behavior and to analyze the optimal strategies for user cooperation simulation in wireless live streaming. We first analyze the Pareto optimality and the time-sensitive bargaining equilibrium of the two-person game. We then extend the solution to the multiuser scenario. We also consider potential selfish users' cheating behavior and malicious users' attacking behavior and analyze the performance of the proposed strategies with the existence of cheating users and malicious attackers. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and attack resistance, and help provide reliable services for wireless live streaming applications.

A Distributed Dynamic Programming-Based Solution for Load Management in Smart Grids

NASA Astrophysics Data System (ADS)

Zhang, Wei; Xu, Yinliang; Li, Sisi; Zhou, MengChu; Liu, Wenxin; Xu, Ying

2018-03-01

Load management is being recognized as an important option for active user participation in the energy market. Traditional load management methods usually require a centralized powerful control center and a two-way communication network between the system operators and energy end-users. The increasing user participation in smart grids may limit their applications. In this paper, a distributed solution for load management in emerging smart grids is proposed. The load management problem is formulated as a constrained optimization problem aiming at maximizing the overall utility of users while meeting the requirement for load reduction requested by the system operator, and is solved by using a distributed dynamic programming algorithm. The algorithm is implemented via a distributed framework and thus can deliver a highly desired distributed solution. It avoids the required use of a centralized coordinator or control center, and can achieve satisfactory outcomes for load management. Simulation results with various test systems demonstrate its effectiveness.

Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

PubMed

Phunchongharn, Phond; Hossain, Ekram; Camorlinga, Sergio

2011-11-01

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

Integration of Molecular Dynamics Based Predictions into the Optimization of De Novo Protein Designs: Limitations and Benefits.

PubMed

Carvalho, Henrique F; Barbosa, Arménio J M; Roque, Ana C A; Iranzo, Olga; Branco, Ricardo J F

2017-01-01

Recent advances in de novo protein design have gained considerable insight from the intrinsic dynamics of proteins, based on the integration of molecular dynamics simulations protocols on the state-of-the-art de novo protein design protocols used nowadays. With this protocol we illustrate how to set up and run a molecular dynamics simulation followed by a functional protein dynamics analysis. New users will be introduced to some useful open-source computational tools, including the GROMACS molecular dynamics simulation software package and ProDy for protein structural dynamics analysis.

A COMPARISON OF STATIC AND DYNAMIC OPTIMIZATION MUSCLE FORCE PREDICTIONS DURING WHEELCHAIR PROPULSION

PubMed Central

Morrow, Melissa M.; Rankin, Jeffery W.; Neptune, Richard R.; Kaufman, Kenton R.

2014-01-01

The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4 % Fmax error in the middle deltoid) to good (6.4 % Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction. PMID:25282075

Using ant colony optimization on the quadratic assignment problem to achieve low energy cost in geo-distributed data centers

NASA Astrophysics Data System (ADS)

Osei, Richard

There are many problems associated with operating a data center. Some of these problems include data security, system performance, increasing infrastructure complexity, increasing storage utilization, keeping up with data growth, and increasing energy costs. Energy cost differs by location, and at most locations fluctuates over time. The rising cost of energy makes it harder for data centers to function properly and provide a good quality of service. With reduced energy cost, data centers will have longer lasting servers/equipment, higher availability of resources, better quality of service, a greener environment, and reduced service and software costs for consumers. Some of the ways that data centers have tried to using to reduce energy costs include dynamically switching on and off servers based on the number of users and some predefined conditions, the use of environmental monitoring sensors, and the use of dynamic voltage and frequency scaling (DVFS), which enables processors to run at different combinations of frequencies with voltages to reduce energy cost. This thesis presents another method by which energy cost at data centers could be reduced. This method involves the use of Ant Colony Optimization (ACO) on a Quadratic Assignment Problem (QAP) in assigning user request to servers in geo-distributed data centers. In this paper, an effort to reduce data center energy cost involves the use of front portals, which handle users' requests, were used as ants to find cost effective ways to assign users requests to a server in heterogeneous geo-distributed data centers. The simulation results indicate that the ACO for Optimal Server Activation and Task Placement algorithm reduces energy cost on a small and large number of users' requests in a geo-distributed data center and its performance increases as the input data grows. In a simulation with 3 geo-distributed data centers, and user's resource request ranging from 25,000 to 25,000,000, the ACO algorithm was able to reduce energy cost on an average of $.70 per second. The ACO for Optimal Server Activation and Task Placement algorithm has proven to work as an alternative or improvement in reducing energy cost in geo-distributed data centers.

Joint optimization of regional water-power systems

NASA Astrophysics Data System (ADS)

Pereira-Cardenal, Silvio J.; Mo, Birger; Gjelsvik, Anders; Riegels, Niels D.; Arnbjerg-Nielsen, Karsten; Bauer-Gottwein, Peter

2016-06-01

Energy and water resources systems are tightly coupled; energy is needed to deliver water and water is needed to extract or produce energy. Growing pressure on these resources has raised concerns about their long-term management and highlights the need to develop integrated solutions. A method for joint optimization of water and electric power systems was developed in order to identify methodologies to assess the broader interactions between water and energy systems. The proposed method is to include water users and power producers into an economic optimization problem that minimizes the cost of power production and maximizes the benefits of water allocation, subject to constraints from the power and hydrological systems. The method was tested on the Iberian Peninsula using simplified models of the seven major river basins and the power market. The optimization problem was successfully solved using stochastic dual dynamic programming. The results showed that current water allocation to hydropower producers in basins with high irrigation productivity, and to irrigation users in basins with high hydropower productivity was sub-optimal. Optimal allocation was achieved by managing reservoirs in very distinct ways, according to the local inflow, storage capacity, hydropower productivity, and irrigation demand and productivity. This highlights the importance of appropriately representing the water users' spatial distribution and marginal benefits and costs when allocating water resources optimally. The method can handle further spatial disaggregation and can be extended to include other aspects of the water-energy nexus.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

The Normalized-Rate Iterative Algorithm: A Practical Dynamic Spectrum Management Method for DSL

NASA Astrophysics Data System (ADS)

Statovci, Driton; Nordström, Tomas; Nilsson, Rickard

2006-12-01

We present a practical solution for dynamic spectrum management (DSM) in digital subscriber line systems: the normalized-rate iterative algorithm (NRIA). Supported by a novel optimization problem formulation, the NRIA is the only DSM algorithm that jointly addresses spectrum balancing for frequency division duplexing systems and power allocation for the users sharing a common cable bundle. With a focus on being implementable rather than obtaining the highest possible theoretical performance, the NRIA is designed to efficiently solve the DSM optimization problem with the operators' business models in mind. This is achieved with the help of two types of parameters: the desired network asymmetry and the desired user priorities. The NRIA is a centralized DSM algorithm based on the iterative water-filling algorithm (IWFA) for finding efficient power allocations, but extends the IWFA by finding the achievable bitrates and by optimizing the bandplan. It is compared with three other DSM proposals: the IWFA, the optimal spectrum balancing algorithm (OSBA), and the bidirectional IWFA (bi-IWFA). We show that the NRIA achieves better bitrate performance than the IWFA and the bi-IWFA. It can even achieve performance almost as good as the OSBA, but with dramatically lower requirements on complexity. Additionally, the NRIA can achieve bitrate combinations that cannot be supported by any other DSM algorithm.

Optimizing Mars Airplane Trajectory with the Application Navigation System

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Riley, Derek

2004-01-01

Planning complex missions requires a number of programs to be executed in concert. The Application Navigation System (ANS), developed in the NAS Division, can execute many interdependent programs in a distributed environment. We show that the ANS simplifies user effort and reduces time in optimization of the trajectory of a martian airplane. We use a software package, Cart3D, to evaluate trajectories and a shortest path algorithm to determine the optimal trajectory. ANS employs the GridScape to represent the dynamic state of the available computer resources. Then, ANS uses a scheduler to dynamically assign ready task to machine resources and the GridScape for tracking available resources and forecasting completion time of running tasks. We demonstrate system capability to schedule and run the trajectory optimization application with efficiency exceeding 60% on 64 processors.

The effect of a robot-assisted surgical system on the kinematics of user movements.

PubMed

Nisky, Ilana; Hsieh, Michael H; Okamura, Allison M

2013-01-01

Teleoperated robot-assisted surgery (RAS) offers many advantages over traditional minimally invasive surgery. However, RAS has not yet realized its full potential, and it is not clear how to optimally train surgeons to use these systems. We hypothesize that the dynamics of the master manipulator impact the ability of users to make desired movements with the robot. We compared freehand and teleoperated movements of novices and experienced surgeons. To isolate the effects of dynamics from procedural knowledge, we chose simple movements rather than surgical tasks. We found statistically significant effects of teleoperation and user expertise in several aspects of motion, including target acquisition error, movement speed, and movement smoothness. Such quantitative assessment of human motor performance in RAS can impact the design of surgical robots, their control, and surgeon training methods, and eventually, improve patient outcomes.

Evolution of Query Optimization Methods

NASA Astrophysics Data System (ADS)

Hameurlain, Abdelkader; Morvan, Franck

Query optimization is the most critical phase in query processing. In this paper, we try to describe synthetically the evolution of query optimization methods from uniprocessor relational database systems to data Grid systems through parallel, distributed and data integration systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) size of the search space, (ii) type of method (static or dynamic), (iii) modification types of execution plans (re-optimization or re-scheduling), (iv) level of modification (intra-operator and/or inter-operator), (v) type of event (estimation errors, delay, user preferences), and (vi) nature of decision-making (centralized or decentralized control).

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Residential Consumption Scheduling Based on Dynamic User Profiling

NASA Astrophysics Data System (ADS)

Mangiatordi, Federica; Pallotti, Emiliano; Del Vecchio, Paolo; Capodiferro, Licia

Deployment of household appliances and of electric vehicles raises the electricity demand in the residential areas and the impact of the building's electrical power. The variations of electricity consumption across the day, may affect both the design of the electrical generation facilities and the electricity bill, mainly when a dynamic pricing is applied. This paper focuses on an energy management system able to control the day-ahead electricity demand in a residential area, taking into account both the variability of the energy production costs and the profiling of the users. The user's behavior is dynamically profiled on the basis of the tasks performed during the previous days and of the tasks foreseen for the current day. Depending on the size and on the flexibility in time of the user tasks, home inhabitants are grouped in, one over N, energy profiles, using a k-means algorithm. For a fixed energy generation cost, each energy profile is associated to a different hourly energy cost. The goal is to identify any bad user profile and to make it pay a highest bill. A bad profile example is when a user applies a lot of consumption tasks and low flexibility in task reallocation time. The proposed energy management system automatically schedules the tasks, solving a multi-objective optimization problem based on an MPSO strategy. The goals, when identifying bad users profiles, are to reduce the peak to average ratio in energy demand, and to minimize the energy costs, promoting virtuous behaviors.

Distributed Energy Resources Customer Adoption Model - Graphical User Interface, Version 2.1.8

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

Ewald, Friedrich; Stadler, Michael; Cardoso, Goncalo F

The DER-CAM Graphical User Interface has been redesigned to consist of a dynamic tree structure on the left side of the application window to allow users to quickly navigate between different data categories and views. Views can either be tables with model parameters and input data, the optimization results, or a graphical interface to draw circuit topology and visualize investment results. The model parameters and input data consist of tables where values are assigned to specific keys. The aggregation of all model parameters and input data amounts to the data required to build a DER-CAM model, and is passed tomore » the GAMS solver when users initiate the DER-CAM optimization process. Passing data to the GAMS solver relies on the use of a Java server that handles DER-CAM requests, queuing, and results delivery. This component of the DER-CAM GUI can be deployed either locally or remotely, and constitutes an intermediate step between the user data input and manipulation, and the execution of a DER-CAM optimization in the GAMS engine. The results view shows the results of the DER-CAM optimization and distinguishes between a single and a multi-objective process. The single optimization runs the DER-CAM optimization once and presents the results as a combination of summary charts and hourly dispatch profiles. The multi-objective optimization process consists of a sequence of runs initiated by the GUI, including: 1) CO2 minimization, 2) cost minimization, 3) a user defined number of points in-between objectives 1) and 2). The multi-objective results view includes both access to the detailed results of each point generated by the process as well as the generation of a Pareto Frontier graph to illustrate the trade-off between objectives. DER-CAM GUI 2.1.8 also introduces the ability to graphically generate circuit topologies, enabling support to DER-CAM 5.0.0. This feature consists of: 1) The drawing area, where users can manually create nodes and define their properties (e.g. point of common coupling, slack bus, load) and connect them through edges representing either power lines, transformers, or heat pipes, all with user defined characteristics (e.g., length, ampacity, inductance, or heat loss); 2) The tables, which display the user-defined topology in the final numerical form that will be passed to the DER-CAM optimization. Finally, the DER-CAM GUI is also deployed with a database schema that allows users to provide different energy load profiles, solar irradiance profiles, and tariff data, that can be stored locally and later used in any DER-CAM model. However, no real data will be delivered with this version.« less

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

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

Wang, Bin; Huang, Rui; Wang, Yubo

2016-05-02

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimizationmore » module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.« less

Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.

PubMed

McFarland, Dennis J; Krusienski, Dean J; Wolpaw, Jonathan R

2006-01-01

The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.

ODECS -- A computer code for the optimal design of S.I. engine control strategies

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

Arsie, I.; Pianese, C.; Rizzo, G.

1996-09-01

The computer code ODECS (Optimal Design of Engine Control Strategies) for the design of Spark Ignition engine control strategies is presented. This code has been developed starting from the author`s activity in this field, availing of some original contributions about engine stochastic optimization and dynamical models. This code has a modular structure and is composed of a user interface for the definition, the execution and the analysis of different computations performed with 4 independent modules. These modules allow the following calculations: (1) definition of the engine mathematical model from steady-state experimental data; (2) engine cycle test trajectory corresponding to amore » vehicle transient simulation test such as ECE15 or FTP drive test schedule; (3) evaluation of the optimal engine control maps with a steady-state approach; (4) engine dynamic cycle simulation and optimization of static control maps and/or dynamic compensation strategies, taking into account dynamical effects due to the unsteady fluxes of air and fuel and the influences of combustion chamber wall thermal inertia on fuel consumption and emissions. Moreover, in the last two modules it is possible to account for errors generated by a non-deterministic behavior of sensors and actuators and the related influences on global engine performances, and compute robust strategies, less sensitive to stochastic effects. In the paper the four models are described together with significant results corresponding to the simulation and the calculation of optimal control strategies for dynamic transient tests.« less

Optimal Dynamics of Intermittent Water Supply

NASA Astrophysics Data System (ADS)

Lieb, Anna; Wilkening, Jon; Rycroft, Chris

2014-11-01

In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. These consequences include degradation of distribution system components, compromised water quality, and inequitable water availability. The goal of this work is to model the important dynamics and identify operating conditions that mitigate certain negative effects of intermittent water supply. Specifically, we will look at valve parameters occurring as boundary conditions in a network model of transient, transition flow through closed pipes. Optimization will be used to find boundary values to minimize pressure gradients and ensure equitable water availability.

Game theoretic wireless resource allocation for H.264 MGS video transmission over cognitive radio networks

NASA Astrophysics Data System (ADS)

Fragkoulis, Alexandros; Kondi, Lisimachos P.; Parsopoulos, Konstantinos E.

2015-03-01

We propose a method for the fair and efficient allocation of wireless resources over a cognitive radio system network to transmit multiple scalable video streams to multiple users. The method exploits the dynamic architecture of the Scalable Video Coding extension of the H.264 standard, along with the diversity that OFDMA networks provide. We use a game-theoretic Nash Bargaining Solution (NBS) framework to ensure that each user receives the minimum video quality requirements, while maintaining fairness over the cognitive radio system. An optimization problem is formulated, where the objective is the maximization of the Nash product while minimizing the waste of resources. The problem is solved by using a Swarm Intelligence optimizer, namely Particle Swarm Optimization. Due to the high dimensionality of the problem, we also introduce a dimension-reduction technique. Our experimental results demonstrate the fairness imposed by the employed NBS framework.

Optimizing basin-scale coupled water quantity and water quality man-agement with stochastic dynamic programming

NASA Astrophysics Data System (ADS)

Davidsen, Claus; Liu, Suxia; Mo, Xingguo; Engelund Holm, Peter; Trapp, Stefan; Rosbjerg, Dan; Bauer-Gottwein, Peter

2015-04-01

Few studies address water quality in hydro-economic models, which often focus primarily on optimal allocation of water quantities. Water quality and water quantity are closely coupled, and optimal management with focus solely on either quantity or quality may cause large costs in terms of the oth-er component. In this study, we couple water quality and water quantity in a joint hydro-economic catchment-scale optimization problem. Stochastic dynamic programming (SDP) is used to minimize the basin-wide total costs arising from water allocation, water curtailment and water treatment. The simple water quality module can handle conservative pollutants, first order depletion and non-linear reactions. For demonstration purposes, we model pollutant releases as biochemical oxygen demand (BOD) and use the Streeter-Phelps equation for oxygen deficit to compute the resulting min-imum dissolved oxygen concentrations. Inelastic water demands, fixed water allocation curtailment costs and fixed wastewater treatment costs (before and after use) are estimated for the water users (agriculture, industry and domestic). If the BOD concentration exceeds a given user pollution thresh-old, the user will need to pay for pre-treatment of the water before use. Similarly, treatment of the return flow can reduce the BOD load to the river. A traditional SDP approach is used to solve one-step-ahead sub-problems for all combinations of discrete reservoir storage, Markov Chain inflow clas-ses and monthly time steps. Pollution concentration nodes are introduced for each user group and untreated return flow from the users contribute to increased BOD concentrations in the river. The pollutant concentrations in each node depend on multiple decision variables (allocation and wastewater treatment) rendering the objective function non-linear. Therefore, the pollution concen-tration decisions are outsourced to a genetic algorithm, which calls a linear program to determine the remainder of the decision variables. This hybrid formulation keeps the optimization problem computationally feasible and represents a flexible and customizable method. The method has been applied to the Ziya River basin, an economic hotspot located on the North China Plain in Northern China. The basin is subject to severe water scarcity, and the rivers are heavily polluted with wastewater and nutrients from diffuse sources. The coupled hydro-economic optimiza-tion model can be used to assess costs of meeting additional constraints such as minimum water qual-ity or to economically prioritize investments in waste water treatment facilities based on economic criteria.

Dynamic Multiple-Threshold Call Admission Control Based on Optimized Genetic Algorithm in Wireless/Mobile Networks

NASA Astrophysics Data System (ADS)

Wang, Shengling; Cui, Yong; Koodli, Rajeev; Hou, Yibin; Huang, Zhangqin

Due to the dynamics of topology and resources, Call Admission Control (CAC) plays a significant role for increasing resource utilization ratio and guaranteeing users' QoS requirements in wireless/mobile networks. In this paper, a dynamic multi-threshold CAC scheme is proposed to serve multi-class service in a wireless/mobile network. The thresholds are renewed at the beginning of each time interval to react to the changing mobility rate and network load. To find suitable thresholds, a reward-penalty model is designed, which provides different priorities between different service classes and call types through different reward/penalty policies according to network load and average call arrival rate. To speed up the running time of CAC, an Optimized Genetic Algorithm (OGA) is presented, whose components such as encoding, population initialization, fitness function and mutation etc., are all optimized in terms of the traits of the CAC problem. The simulation demonstrates that the proposed CAC scheme outperforms the similar schemes, which means the optimization is realized. Finally, the simulation shows the efficiency of OGA.

A distributed multichannel demand-adaptive P2P VoD system with optimized caching and neighbor-selection

NASA Astrophysics Data System (ADS)

Zhang, Hao; Chen, Minghua; Parekh, Abhay; Ramchandran, Kannan

2011-09-01

We design a distributed multi-channel P2P Video-on-Demand (VoD) system using "plug-and-play" helpers. Helpers are heterogenous "micro-servers" with limited storage, bandwidth and number of users they can serve simultaneously. Our proposed system has the following salient features: (1) it jointly optimizes over helper-user connection topology, video storage distribution and transmission bandwidth allocation; (2) it minimizes server load, and is adaptable to varying supply and demand patterns across multiple video channels irrespective of video popularity; and (3) it is fully distributed and requires little or no maintenance overhead. The combinatorial nature of the problem and the system demand for distributed algorithms makes the problem uniquely challenging. By utilizing Lagrangian decomposition and Markov chain approximation based arguments, we address this challenge by designing two distributed algorithms running in tandem: a primal-dual storage and bandwidth allocation algorithm and a "soft-worst-neighbor-choking" topology-building algorithm. Our scheme provably converges to a near-optimal solution, and is easy to implement in practice. Packet-level simulation results show that the proposed scheme achieves minimum sever load under highly heterogeneous combinations of supply and demand patterns, and is robust to system dynamics of user/helper churn, user/helper asynchrony, and random delays in the network.

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

PubMed

Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

PubMed Central

Sieh Kiong, Tiong; Tariqul Islam, Mohammad; Ismail, Mahamod; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859

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.

Common aero vehicle autonomous reentry trajectory optimization satisfying waypoint and no-fly zone constraints

NASA Astrophysics Data System (ADS)

Jorris, Timothy R.

2007-12-01

To support the Air Force's Global Reach concept, a Common Aero Vehicle is being designed to support the Global Strike mission. "Waypoints" are specified for reconnaissance or multiple payload deployments and "no-fly zones" are specified for geopolitical restrictions or threat avoidance. Due to time critical targets and multiple scenario analysis, an autonomous solution is preferred over a time-intensive, manually iterative one. Thus, a real-time or near real-time autonomous trajectory optimization technique is presented to minimize the flight time, satisfy terminal and intermediate constraints, and remain within the specified vehicle heating and control limitations. This research uses the Hypersonic Cruise Vehicle (HCV) as a simplified two-dimensional platform to compare multiple solution techniques. The solution techniques include a unique geometric approach developed herein, a derived analytical dynamic optimization technique, and a rapidly emerging collocation numerical approach. This up-and-coming numerical technique is a direct solution method involving discretization then dualization, with pseudospectral methods and nonlinear programming used to converge to the optimal solution. This numerical approach is applied to the Common Aero Vehicle (CAV) as the test platform for the full three-dimensional reentry trajectory optimization problem. The culmination of this research is the verification of the optimality of this proposed numerical technique, as shown for both the two-dimensional and three-dimensional models. Additionally, user implementation strategies are presented to improve accuracy and enhance solution convergence. Thus, the contributions of this research are the geometric approach, the user implementation strategies, and the determination and verification of a numerical solution technique for the optimal reentry trajectory problem that minimizes time to target while satisfying vehicle dynamics and control limitation, and heating, waypoint, and no-fly zone constraints.

The Trip Itinerary Optimization Platform: A Framework for Personalized Travel Information

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

Kwasnik, Ted; Carmichael, Scott P.; Arent, Douglas J

The New Concepts Incubator team at the National Renewable Energy Laboratory (NREL) developed a three-stage online platform for travel diary collection, personal travel plan optimization and travel itinerary visualization. In the first stage, users provide a travel diary for the previous day through an interactive map and calendar interface and survey for travel attitudes and behaviors. One or more days later, users are invited via email to engage in a second stage where they view a personal mobility dashboard displaying recommended travel itineraries generated from a novel framework that optimizes travel outcomes over a sequence of interrelated trips. A weekmore » or more after viewing these recommended travel itineraries on the dashboard, users are emailed again to engage in a third stage where they complete a final survey about travel attitudes and behaviors. A usability study of the platform conducted online showed that, in general, users found the system valuable for informing their travel decisions. A total of 274 individuals were recruited through Amazon Mechanical Turk, an online survey platform, to participate in a transportation study using this platform. On average, the platform distilled 65 feasible travel plans per individual into two recommended itineraries, each optimal according to one or more outcomes and dependent on the fixed times and locations from the travel diary. For 45 percent of users, the trip recommendation algorithm returned only a single, typically automobile-centric, itinerary because there were no other viable alternative transportation modes available. Platform users generally agreed that the dashboard was enjoyable and easy to use, and that it would be a helpful tool in adopting new travel behaviors. Users generally agreed most that the time, cost and user preferred recommendations 'made sense' to them, and were most willing to implement these itineraries. Platform users typically expressed low willingness to try the carbon and calories optimized itineraries. Of the platform users who viewed the dashboard, 13 percent reported changing their travel behavior, most adopting the time, calories or carbon optimized itineraries. While the algorithm incorporates a wealth of travel data obtained from online APIs pertaining to a travelers route such as historic traffic condition data, public transit time-tables, and bike path routes, open-ended responses from users expressed an interest in the integration of even more fine-grained traffic data and the ability to dynamically model the effect of changes in travel times. Users also commonly expressed concerns over the safety of walking and biking recommendations. Responses indicate that more information about the amenities available to cyclists and pedestrians (sidewalks, shade from trees, access to food) and routes that avoid areas of perceived elevated danger would reduce barriers to implementing these recommendations. More accurate representations of personal vehicle trips (based on vehicle make and model, implications of parking) and the identification of routes that optimize caloric intensity (seeking out elevation changes or longer walks to public transit) are promising avenues for future research.« less

Robust ADP Design for Continuous-Time Nonlinear Systems With Output Constraints.

PubMed

Fan, Bo; Yang, Qinmin; Tang, Xiaoyu; Sun, Youxian

2018-06-01

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Partial Overhaul and Initial Parallel Optimization of KINETICS, a Coupled Dynamics and Chemistry Atmosphere Model

NASA Technical Reports Server (NTRS)

Nguyen, Howard; Willacy, Karen; Allen, Mark

2012-01-01

KINETICS is a coupled dynamics and chemistry atmosphere model that is data intensive and computationally demanding. The potential performance gain from using a supercomputer motivates the adaptation from a serial version to a parallelized one. Although the initial parallelization had been done, bottlenecks caused by an abundance of communication calls between processors led to an unfavorable drop in performance. Before starting on the parallel optimization process, a partial overhaul was required because a large emphasis was placed on streamlining the code for user convenience and revising the program to accommodate the new supercomputers at Caltech and JPL. After the first round of optimizations, the partial runtime was reduced by a factor of 23; however, performance gains are dependent on the size of the data, the number of processors requested, and the computer used.

Satellite image collection optimization

NASA Astrophysics Data System (ADS)

Martin, William

2002-09-01

Imaging satellite systems represent a high capital cost. Optimizing the collection of images is critical for both satisfying customer orders and building a sustainable satellite operations business. We describe the functions of an operational, multivariable, time dynamic optimization system that maximizes the daily collection of satellite images. A graphical user interface allows the operator to quickly see the results of what if adjustments to an image collection plan. Used for both long range planning and daily collection scheduling of Space Imaging's IKONOS satellite, the satellite control and tasking (SCT) software allows collection commands to be altered up to 10 min before upload to the satellite.

Tree value system: description and assumptions.

Treesearch

D.G. Briggs

1989-01-01

TREEVAL is a microcomputer model that calculates tree or stand values and volumes based on product prices, manufacturing costs, and predicted product recovery. It was designed as an aid in evaluating management regimes. TREEVAL calculates values in either of two ways, one based on optimized tree bucking using dynamic programming and one simulating the results of user-...

Understanding checkpointing overheads on massive-scale systems : analysis of the IBM Blue Gene/P system.

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

Gupta, R.; Naik, H.; Beckman, P.

Providing fault tolerance in high-end petascale systems, consisting of millions of hardware components and complex software stacks, is becoming an increasingly challenging task. Checkpointing continues to be the most prevalent technique for providing fault tolerance in such high-end systems. Considerable research has focussed on optimizing checkpointing; however, in practice, checkpointing still involves a high-cost overhead for users. In this paper, we study the checkpointing overhead seen by various applications running on leadership-class machines like the IBM Blue Gene/P at Argonne National Laboratory. In addition to studying popular applications, we design a methodology to help users understand and intelligently choose anmore » optimal checkpointing frequency to reduce the overall checkpointing overhead incurred. In particular, we study the Grid-Based Projector-Augmented Wave application, the Carr-Parrinello Molecular Dynamics application, the Nek5000 computational fluid dynamics application and the Parallel Ocean Program application-and analyze their memory usage and possible checkpointing trends on 65,536 processors of the Blue Gene/P system.« less

Enhanced intelligence through optimized TCPED concepts for airborne ISR

NASA Astrophysics Data System (ADS)

Spitzer, M.; Kappes, E.; Böker, D.

2012-06-01

Current multinational operations show an increased demand for high quality actionable intelligence for different operational levels and users. In order to achieve sufficient availability, quality and reliability of information, various ISR assets are orchestrated within operational theatres. Especially airborne Intelligence, Surveillance and Reconnaissance (ISR) assets provide - due to their endurance, non-intrusiveness, robustness, wide spectrum of sensors and flexibility to mission changes - significant intelligence coverage of areas of interest. An efficient and balanced utilization of airborne ISR assets calls for advanced concepts for the entire ISR process framework including the Tasking, Collection, Processing, Exploitation and Dissemination (TCPED). Beyond this, the employment of current visualization concepts, shared information bases and information customer profiles, as well as an adequate combination of ISR sensors with different information age and dynamic (online) retasking process elements provides the optimization of interlinked TCPED processes towards higher process robustness, shorter process duration, more flexibility between ISR missions and, finally, adequate "entry points" for information requirements by operational users and commands. In addition, relevant Trade-offs of distributed and dynamic TCPED processes are examined and future trends are depicted.

Predicting Flows of Rarefied Gases

NASA Technical Reports Server (NTRS)

LeBeau, Gerald J.; Wilmoth, Richard G.

2005-01-01

DSMC Analysis Code (DAC) is a flexible, highly automated, easy-to-use computer program for predicting flows of rarefied gases -- especially flows of upper-atmospheric, propulsion, and vented gases impinging on spacecraft surfaces. DAC implements the direct simulation Monte Carlo (DSMC) method, which is widely recognized as standard for simulating flows at densities so low that the continuum-based equations of computational fluid dynamics are invalid. DAC enables users to model complex surface shapes and boundary conditions quickly and easily. The discretization of a flow field into computational grids is automated, thereby relieving the user of a traditionally time-consuming task while ensuring (1) appropriate refinement of grids throughout the computational domain, (2) determination of optimal settings for temporal discretization and other simulation parameters, and (3) satisfaction of the fundamental constraints of the method. In so doing, DAC ensures an accurate and efficient simulation. In addition, DAC can utilize parallel processing to reduce computation time. The domain decomposition needed for parallel processing is completely automated, and the software employs a dynamic load-balancing mechanism to ensure optimal parallel efficiency throughout the simulation.

Dynamic pricing of network goods with boundedly rational consumers.

PubMed

Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

2014-01-07

We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller's optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product's user base evolving over time and consumers basing their choices on a mixture of a myopic and a "stubborn" expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice.

Dynamic pricing of network goods with boundedly rational consumers

PubMed Central

Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

2014-01-01

We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller’s optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product’s user base evolving over time and consumers basing their choices on a mixture of a myopic and a “stubborn” expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice. PMID:24367101

ConvAn: a convergence analyzing tool for optimization of biochemical networks.

PubMed

Kostromins, Andrejs; Mozga, Ivars; Stalidzans, Egils

2012-01-01

Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Route Advising in a Dynamic Environment - A High-Tech Approach

NASA Astrophysics Data System (ADS)

Firdhous, M. F. M.; Basnayake, D. L.; Kodithuwakku, K. H. L.; Hatthalla, N. K.; Charlin, N. W.; Bandara, P. M. R. I. K.

Finding the optimal path between two locations in the Colombo city is not a straight forward task, because of the complex road system and the huge traffic jams etc. This paper presents a system to find the optimal driving direction between two locations within the Colombo city, considering road rules (one way, two ways or fully closed in both directions). The system contains three main modules - core module, web module and mobile module, additionally there are two user interfaces one for normal users and the other for administrative users. Both these interfaces can be accessed using a web browser or a GPRS enabled mobile phone. The system is developed based on the Geographic Information System (GIS) technology. GIS is considered as the best option to integrate hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. The core of the system is MapServer (MS4W) used along with the other supporting technologies such as PostGIS, PostgreSQL, pgRouting, ASP.NET and C#.

PRay - A graphical user interface for interactive visualization and modification of rayinvr models

NASA Astrophysics Data System (ADS)

Fromm, T.

2016-01-01

PRay is a graphical user interface for interactive displaying and editing of velocity models for seismic refraction. It is optimized for editing rayinvr models but can also be used as a dynamic viewer for ray tracing results from other software. The main features are the graphical editing of nodes and fast adjusting of the display (stations and phases). It can be extended by user-defined shell scripts and links to phase picking software. PRay is open source software written in the scripting language Perl, runs on Unix-like operating systems including Mac OS X and provides a version controlled source code repository for community development (https://sourceforge.net/projects/pray-plot-rayinvr/).

Context-based user grouping for multi-casting in heterogeneous radio networks

NASA Astrophysics Data System (ADS)

Mannweiler, C.; Klein, A.; Schneider, J.; Schotten, H. D.

2011-08-01

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity of social networks, these data are complemented by profile information about individual users. Making use of this information by classifying users in wireless networks enables targeted content and advertisement delivery as well as optimizing network resources, in particular bandwidth utilization, by facilitating group-based multi-casting. In this paper, we present the design and implementation of a web service for advanced user classification based on user, network, and environmental context information. The service employs simple and advanced clustering algorithms for forming classes of users. Available service functionalities include group formation, context-aware adaptation, and deletion as well as the exposure of group characteristics. Moreover, the results of a performance evaluation, where the service has been integrated in a simulator modeling user behavior in heterogeneous wireless systems, are presented.

Sandia National Laboratories environmental fluid dynamics code. Marine Hydrokinetic Module User's Manual

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

James, Scott Carlton; Roberts, Jesse D.

2014-03-01

This document describes the marine hydrokinetic (MHK) input file and subroutines for the Sandia National Laboratories Environmental Fluid Dynamics Code (SNL-EFDC), which is a combined hydrodynamic, sediment transport, and water quality model based on the Environmental Fluid Dynamics Code (EFDC) developed by John Hamrick [1], formerly sponsored by the U.S. Environmental Protection Agency, and now maintained by Tetra Tech, Inc. SNL-EFDC has been previously enhanced with the incorporation of the SEDZLJ sediment dynamics model developed by Ziegler, Lick, and Jones [2-4]. SNL-EFDC has also been upgraded to more accurately simulate algae growth with specific application to optimizing biomass in anmore » open-channel raceway for biofuels production [5]. A detailed description of the input file containing data describing the MHK device/array is provided, along with a description of the MHK FORTRAN routine. Both a theoretical description of the MHK dynamics as incorporated into SNL-EFDC and an explanation of the source code are provided. This user manual is meant to be used in conjunction with the original EFDC [6] and sediment dynamics SNL-EFDC manuals [7]. Through this document, the authors provide information for users who wish to model the effects of an MHK device (or array of devices) on a flow system with EFDC and who also seek a clear understanding of the source code, which is available from staff in the Water Power Technologies Department at Sandia National Laboratories, Albuquerque, New Mexico.« less

Linear-Quadratic-Gaussian Regulator Developed for a Magnetic Bearing

NASA Technical Reports Server (NTRS)

Choi, Benjamin B.

2002-01-01

Linear-Quadratic-Gaussian (LQG) control is a modern state-space technique for designing optimal dynamic regulators. It enables us to trade off regulation performance and control effort, and to take into account process and measurement noise. The Structural Mechanics and Dynamics Branch at the NASA Glenn Research Center has developed an LQG control for a fault-tolerant magnetic bearing suspension rig to optimize system performance and to reduce the sensor and processing noise. The LQG regulator consists of an optimal state-feedback gain and a Kalman state estimator. The first design step is to seek a state-feedback law that minimizes the cost function of regulation performance, which is measured by a quadratic performance criterion with user-specified weighting matrices, and to define the tradeoff between regulation performance and control effort. The next design step is to derive a state estimator using a Kalman filter because the optimal state feedback cannot be implemented without full state measurement. Since the Kalman filter is an optimal estimator when dealing with Gaussian white noise, it minimizes the asymptotic covariance of the estimation error.

Smart building temperature control using occupant feedback

NASA Astrophysics Data System (ADS)

Gupta, Santosh K.

This work was motivated by the problem of computing optimal commonly-agreeable thermal settings in spaces with multiple occupants. In this work we propose algorithms that take into account each occupant's preferences along with the thermal correlations between different zones in a building, to arrive at optimal thermal settings for all zones of the building in a coordinated manner. In the first part of this work we incorporate active occupant feedback to minimize aggregate user discomfort and total energy cost. User feedback is used to estimate the users comfort range, taking into account possible inaccuracies in the feedback. The control algorithm takes the energy cost into account, trading it off optimally with the aggregate user discomfort. A lumped heat transfer model based on thermal resistance and capacitance is used to model a multi-zone building. We provide a stability analysis and establish convergence of the proposed solution to a desired temperature that minimizes the sum of energy cost and aggregate user discomfort. However, for convergence to the optimal, sufficient separation between the user feedback frequency and the dynamics of the system is necessary; otherwise, the user feedback provided do not correctly reflect the effect of current control input value on user discomfort. The algorithm is further extended using singular perturbation theory to determine the minimum time between successive user feedback solicitations. Under sufficient time scale separation, we establish convergence of the proposed solution. Simulation study and experimental runs on the Watervliet based test facility demonstrates performance of the algorithm. In the second part we develop a consensus algorithm for attaining a common temperature set-point that is agreeable to all occupants of a zone in a typical multi-occupant space. The information on the comfort range functions is indeed held privately by each occupant. Using occupant differentiated dynamically adjusted prices as feedback signals, we propose a distributed solution, which ensures that a consensus is attained among all occupants upon convergence, irrespective of their temperature preferences being in coherence or conflicting. Occupants are only assumed to be rational, in that they choose their own temperature set-points so as to minimize their individual energy cost plus discomfort. We use Alternating Direction Method of Multipliers ( ADMM) to solve our consensus problem. We further establish the convergence of the proposed algorithm to the optimal thermal set point values that minimize the sum of the energy cost and the aggregate discomfort of all occupants in a multi-zone building. For simulating our consensus algorithm we use realistic building parameters based on the Watervliet test facility. The simulation study based on real world building parameters establish the validity of our theoretical model and provide insights on the dynamics of the system with a mobile user population. In the third part we present a game-theoretic (auction) mechanism, that requires occupants to "purchase" their individualized comfort levels beyond what is provided by default by the building operator. The comfort pricing policy, derived as an extension of Vickrey-Clarke-Groves (VCG) pricing, ensures incentive-compatibility of the mechanism, i.e., an occupant acting in self-interest cannot benefit from declaring their comfort function untruthfully, irrespective of the choices made by other occupants. The declared (or estimated) occupant comfort ranges (functions) are then utilized by the building operator---along with the energy cost information---to set the environment controls to optimally balance the aggregate discomfort of the occupants and the energy cost of the building operator. We use realistic building model and parameters based on our test facility to demonstrate the convergence of the actual temperatures in different zones to the desired temperatures, and provide insight to the pricing structure necessary for truthful comfort feedback from the occupants. Finally, we present an end-to-end framework designed for enabling occupant feedback collection and incorporating the feedback data towards energy efficient operation of a building. We have designed a mobile application that occupants can use on their smart phones to provide their thermal preference feedback. When relaying the occupant feedback to the central server the mobile application also uses indoor localization techniques to tie the occupant preference to their current thermal zone. Texas Instruments sensortags are used for real time zonal temperature readings. The mobile application relays the occupant preference along with the location to a central server that also hosts our learning algorithm to learn the environment and using occupant feedback calculates the optimal temperature set point. The entire process is triggered upon change of occupancy, environmental conditions, and or occupant preference. The learning algorithm is scheduled to run at regular intervals to respond dynamically to environmental and occupancy changes. We describe results from experimental studies in two different settings: a single family residential home setting and in a university based laboratory space setting. (Abstract shortened by UMI.).

User Access Management Based on Network Pricing for Social Network Applications

PubMed Central

Ma, Xingmin; Gu, Qing

2018-01-01

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. PMID:29495252

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

A two-level cache for distributed information retrieval in search engines.

PubMed

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache.

A Two-Level Cache for Distributed Information Retrieval in Search Engines

PubMed Central

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache. PMID:24363621

Remote health coaching for interactive exercise with older adults in a home environment.

PubMed

Jimison, Holly B; Hagler, Stuart; Kurillo, Gregorij; Bajcsy, Ruzena; Pavel, Misha

2015-01-01

Optimal health coaching interventions are tailored to individuals' needs, preferences, motivations, barriers, timing, and readiness to change. Technology approaches are useful in both monitoring a user's adherence to their behavior change goals and also in providing just-in-time feedback and coaching messages. User models that incorporate dynamically varying behavior change variables with algorithms that trigger tailored messages provide a framework for making health interventions more effective. These principles are applied in the described system for assisting older adults in meeting their physical exercise goals with a tailored interactive video system with just-in-time feedback and encouragement.

Program MAMO: Models for avian management optimization-user guide

USGS Publications Warehouse

Guillaumet, Alban; Paxton, Eben H.

2017-01-01

The following chapters describe the structure and code of MAMO, and walk the reader through running the different components of the program with sample data. This manual should be used alongside a computer running R, so that the reader can copy and paste code into R, observe the output, and follow along interactively. Taken together, chapters 2–4 will allow the user to replicate a simulation study investigating the consequences of climate change and two potential management actions on the population dynamics of a vulnerable and iconic Hawaiian forest bird, the ‘I‘iwi (Drepanis coccinea; hereafter IIWI).

Users manual for the Variable dimension Automatic Synthesis Program (VASP)

NASA Technical Reports Server (NTRS)

White, J. S.; Lee, H. Q.

1971-01-01

A dictionary and some problems for the Variable Automatic Synthesis Program VASP are submitted. The dictionary contains a description of each subroutine and instructions on its use. The example problems give the user a better perspective on the use of VASP for solving problems in modern control theory. These example problems include dynamic response, optimal control gain, solution of the sampled data matrix Ricatti equation, matrix decomposition, and pseudo inverse of a matrix. Listings of all subroutines are also included. The VASP program has been adapted to run in the conversational mode on the Ames 360/67 computer.

Spatial issues in user interface design from a graphic design perspective

NASA Technical Reports Server (NTRS)

Marcus, Aaron

1989-01-01

The user interface of a computer system is a visual display that provides information about the status of operations on data within the computer and control options to the user that enable adjustments to these operations. From the very beginning of computer technology the user interface was a spatial display, although its spatial features were not necessarily complex or explicitly recognized by the users. All text and nonverbal signs appeared in a virtual space generally thought of as a single flat plane of symbols. Current technology of high performance workstations permits any element of the display to appear as dynamic, multicolor, 3-D signs in a virtual 3-D space. The complexity of appearance and the user's interaction with the display provide significant challenges to the graphic designer of current and future user interfaces. In particular, spatial depiction provides many opportunities for effective communication of objects, structures, processes, navigation, selection, and manipulation. Issues are presented that are relevant to the graphic designer seeking to optimize the user interface's spatial attributes for effective visual communication.

Dynamic image fusion and general observer preference

NASA Astrophysics Data System (ADS)

Burks, Stephen D.; Doe, Joshua M.

2010-04-01

Recent developments in image fusion give the user community many options for ways of presenting the imagery to an end-user. Individuals at the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate have developed an electronic system that allows users to quickly and efficiently determine optimal image fusion algorithms and color parameters based upon collected imagery and videos from environments that are typical to observers in a military environment. After performing multiple multi-band data collections in a variety of military-like scenarios, different waveband, fusion algorithm, image post-processing, and color choices are presented to observers as an output of the fusion system. The observer preferences can give guidelines as to how specific scenarios should affect the presentation of fused imagery.

Development of the ARISTOTLE webware for cloud-based rarefied gas flow modeling

NASA Astrophysics Data System (ADS)

Deschenes, Timothy R.; Grot, Jonathan; Cline, Jason A.

2016-11-01

Rarefied gas dynamics are important for a wide variety of applications. An improvement in the ability of general users to predict these gas flows will enable optimization of current, and discovery of future processes. Despite this potential, most rarefied simulation software is designed by and for experts in the community. This has resulted in low adoption of the methods outside of the immediate RGD community. This paper outlines an ongoing effort to create a rarefied gas dynamics simulation tool that can be used by a general audience. The tool leverages a direct simulation Monte Carlo (DSMC) library that is available to the entire community and a web-based simulation process that will enable all users to take advantage of high performance computing capabilities. First, the DSMC library and simulation architecture are described. Then the DSMC library is used to predict a number of representative transient gas flows that are applicable to the rarefied gas dynamics community. The paper closes with a summary and future direction.

Translator for Optimizing Fluid-Handling Components

NASA Technical Reports Server (NTRS)

Landon, Mark; Perry, Ernest

2007-01-01

A software interface has been devised to facilitate optimization of the shapes of valves, elbows, fittings, and other components used to handle fluids under extreme conditions. This software interface translates data files generated by PLOT3D (a NASA grid-based plotting-and- data-display program) and by computational fluid dynamics (CFD) software into a format in which the files can be read by Sculptor, which is a shape-deformation- and-optimization program. Sculptor enables the user to interactively, smoothly, and arbitrarily deform the surfaces and volumes in two- and three-dimensional CFD models. Sculptor also includes design-optimization algorithms that can be used in conjunction with the arbitrary-shape-deformation components to perform automatic shape optimization. In the optimization process, the output of the CFD software is used as feedback while the optimizer strives to satisfy design criteria that could include, for example, improved values of pressure loss, velocity, flow quality, mass flow, etc.

Modeling and Optimization for Management of Intermittent Water Supply

NASA Astrophysics Data System (ADS)

Lieb, A. M.; Wilkening, J.; Rycroft, C.

2014-12-01

In many urban areas, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. These consequences include degradation of distribution system components, compromised water quality, and inequitable water availability. The goal of this work is to model the important dynamics and identify operating conditions that mitigate certain negative effects of intermittent water supply. Specifically, we will look at controlling valve parameters occurring as boundary conditions in a network model of transient, transition flow through closed pipes. Gradient-based optimization will be used to find boundary values to minimize pressure gradients and ensure equitable water availability at system endpoints.

Dynamic Pressure Microphones

NASA Astrophysics Data System (ADS)

Werner, E.

In 1876, Alexander Graham Bell described his first telephone with a microphone using magnetic induction to convert the voice input into an electric output signal. The basic principle led to a variety of designs optimized for different needs, from hearing impaired users to singers or broadcast announcers. From the various sound pressure versions, only the moving coil design is still in mass production for speech and music application.

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

NASA Astrophysics Data System (ADS)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal control policy is zero for all times). In this paper, we relax this assumption on the nominal trajectory in order to allow for controlled nominal trajectories. This allows the estimator to be iterated to obtain a more accurate nonlinear solution for both the state and control estimates. Beyond these developments to the estimator, this paper also introduces a modified distance metric for maneuver detection. The original metric used in the OCBE only accounted for the estimated control and its uncertainty. This new metric accounts for measurement deviation and a priori state deviations, such that it accounts for all three major forms of uncertainty in orbit determination. This allows the user to understand the contributions of each source of uncertainty toward the total system mismodeling so that the user can properly account for them. Together these developments create an accurate orbit determination algorithm that is automated, robust to mismodeling, and capable of detecting and reconstructing the presence of mismodeling. These qualities make this algorithm a good foundation from which to approach the problem of real-time maneuver detection and reconstruction for Space Situational Awareness applications. This is further strengthened by the algorithm's general formulation that allows it to be applied to problems with an arbitrary target and observer.

Constraint-Muse: A Soft-Constraint Based System for Music Therapy

NASA Astrophysics Data System (ADS)

Hölzl, Matthias; Denker, Grit; Meier, Max; Wirsing, Martin

Monoidal soft constraints are a versatile formalism for specifying and solving multi-criteria optimization problems with dynamically changing user preferences. We have developed a prototype tool for interactive music creation, called Constraint Muse, that uses monoidal soft constraints to ensure that a dynamically generated melody harmonizes with input from other sources. Constraint Muse provides an easy to use interface based on Nintendo Wii controllers and is intended to be used in music therapy for people with Parkinson’s disease and for children with high-functioning autism or Asperger’s syndrome.

Chance-Constrained Guidance With Non-Convex Constraints

NASA Technical Reports Server (NTRS)

Ono, Masahiro

2011-01-01

Missions to small bodies, such as comets or asteroids, require autonomous guidance for descent to these small bodies. Such guidance is made challenging by uncertainty in the position and velocity of the spacecraft, as well as the uncertainty in the gravitational field around the small body. In addition, the requirement to avoid collision with the asteroid represents a non-convex constraint that means finding the optimal guidance trajectory, in general, is intractable. In this innovation, a new approach is proposed for chance-constrained optimal guidance with non-convex constraints. Chance-constrained guidance takes into account uncertainty so that the probability of collision is below a specified threshold. In this approach, a new bounding method has been developed to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. Branch and bound enables non-convex problems to be solved efficiently to global optimality. Considering the problem of finite-horizon robust optimal control of dynamic systems under Gaussian-distributed stochastic uncertainty, with state and control constraints, a discrete-time, continuous-state linear dynamics model is assumed. Gaussian-distributed stochastic uncertainty is a more natural model for exogenous disturbances such as wind gusts and turbulence than the previously studied set-bounded models. However, with stochastic uncertainty, it is often impossible to guarantee that state constraints are satisfied, because there is typically a non-zero probability of having a disturbance that is large enough to push the state out of the feasible region. An effective framework to address robustness with stochastic uncertainty is optimization with chance constraints. These require that the probability of violating the state constraints (i.e., the probability of failure) is below a user-specified bound known as the risk bound. An example problem is to drive a car to a destination as fast as possible while limiting the probability of an accident to 10(exp -7). This framework allows users to trade conservatism against performance by choosing the risk bound. The more risk the user accepts, the better performance they can expect.

On Market-Based Coordination of Thermostatically Controlled Loads With User Preference

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

Li, Sen; Zhang, Wei; Lian, Jianming

2014-12-15

This paper presents a market-based control framework to coordinate a group of autonomous Thermostatically Controlled Loads (TCL) to achieve the system-level objectives with pricing incentives. The problem is formulated as maximizing the social welfare subject to feeder power constraint. It allows the coordinator to affect the aggregated power of a group of dynamical systems, and creates an interactive market where the users and the coordinator cooperatively determine the optimal energy allocation and energy price. The optimal pricing strategy is derived, which maximizes social welfare while respecting the feeder power constraint. The bidding strategy is also designed to compute the optimalmore » price in real time (e.g., every 5 minutes) based on local device information. The coordination framework is validated with realistic simulations in GridLab-D. Extensive simulation results demonstrate that the proposed approach effectively maximizes the social welfare and decreases power congestion at key times.« less

Tailoring vibration mode shapes using topology optimization and functionally graded material concepts

NASA Astrophysics Data System (ADS)

Montealegre Rubio, Wilfredo; Paulino, Glaucio H.; Nelli Silva, Emilio Carlos

2011-02-01

Tailoring specified vibration modes is a requirement for designing piezoelectric devices aimed at dynamic-type applications. A technique for designing the shape of specified vibration modes is the topology optimization method (TOM) which finds an optimum material distribution inside a design domain to obtain a structure that vibrates according to specified eigenfrequencies and eigenmodes. Nevertheless, when the TOM is applied to dynamic problems, the well-known grayscale or intermediate material problem arises which can invalidate the post-processing of the optimal result. Thus, a more natural way for solving dynamic problems using TOM is to allow intermediate material values. This idea leads to the functionally graded material (FGM) concept. In fact, FGMs are materials whose properties and microstructure continuously change along a specific direction. Therefore, in this paper, an approach is presented for tailoring user-defined vibration modes, by applying the TOM and FGM concepts to design functionally graded piezoelectric transducers (FGPT) and non-piezoelectric structures (functionally graded structures—FGS) in order to achieve maximum and/or minimum vibration amplitudes at certain points of the structure, by simultaneously finding the topology and material gradation function. The optimization problem is solved by using sequential linear programming. Two-dimensional results are presented to illustrate the method.

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Interplanetary Program to Optimize Simulated Trajectories (IPOST). Volume 2: Analytic manual

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D.; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Space Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization is performed using the Stanford NPSOL algorithm. IPOST structure allows subproblems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Use of DAGMan in CRAB3 to Improve the Splitting of CMS User Jobs

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

Wolf, M.; Mascheroni, M.; Woodard, A.

CRAB3 is a workload management tool used by CMS physicists to analyze data acquired by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC). Research in high energy physics often requires the analysis of large collections of files, referred to as datasets. The task is divided into jobs that are distributed among a large collection of worker nodes throughout the Worldwide LHC Computing Grid (WLCG). Splitting a large analysis task into optimally sized jobs is critical to efficient use of distributed computing resources. Jobs that are too big will have excessive runtimes and will not distributemore » the work across all of the available nodes. However, splitting the project into a large number of very small jobs is also inefficient, as each job creates additional overhead which increases load on infrastructure resources. Currently this splitting is done manually, using parameters provided by the user. However the resources needed for each job are difficult to predict because of frequent variations in the performance of the user code and the content of the input dataset. As a result, dividing a task into jobs by hand is difficult and often suboptimal. In this work we present a new feature called “automatic splitting” which removes the need for users to manually specify job splitting parameters. We discuss how HTCondor DAGMan can be used to build dynamic Directed Acyclic Graphs (DAGs) to optimize the performance of large CMS analysis jobs on the Grid. We use DAGMan to dynamically generate interconnected DAGs that estimate the processing time the user code will require to analyze each event. This is used to calculate an estimate of the total processing time per job, and a set of analysis jobs are run using this estimate as a specified time limit. Some jobs may not finish within the alloted time; they are terminated at the time limit, and the unfinished data is regrouped into smaller jobs and resubmitted.« less

Use of DAGMan in CRAB3 to improve the splitting of CMS user jobs

NASA Astrophysics Data System (ADS)

Wolf, M.; Mascheroni, M.; Woodard, A.; Belforte, S.; Bockelman, B.; Hernandez, J. M.; Vaandering, E.

2017-10-01

CRAB3 is a workload management tool used by CMS physicists to analyze data acquired by the Compact Muon Solenoid (CMS) detector at the CERN Large Hadron Collider (LHC). Research in high energy physics often requires the analysis of large collections of files, referred to as datasets. The task is divided into jobs that are distributed among a large collection of worker nodes throughout the Worldwide LHC Computing Grid (WLCG). Splitting a large analysis task into optimally sized jobs is critical to efficient use of distributed computing resources. Jobs that are too big will have excessive runtimes and will not distribute the work across all of the available nodes. However, splitting the project into a large number of very small jobs is also inefficient, as each job creates additional overhead which increases load on infrastructure resources. Currently this splitting is done manually, using parameters provided by the user. However the resources needed for each job are difficult to predict because of frequent variations in the performance of the user code and the content of the input dataset. As a result, dividing a task into jobs by hand is difficult and often suboptimal. In this work we present a new feature called “automatic splitting” which removes the need for users to manually specify job splitting parameters. We discuss how HTCondor DAGMan can be used to build dynamic Directed Acyclic Graphs (DAGs) to optimize the performance of large CMS analysis jobs on the Grid. We use DAGMan to dynamically generate interconnected DAGs that estimate the processing time the user code will require to analyze each event. This is used to calculate an estimate of the total processing time per job, and a set of analysis jobs are run using this estimate as a specified time limit. Some jobs may not finish within the alloted time; they are terminated at the time limit, and the unfinished data is regrouped into smaller jobs and resubmitted.

Dynamic Systems Analysis for Turbine Based Aero Propulsion Systems

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.

2016-01-01

The aircraft engine design process seeks to optimize the overall system-level performance, weight, and cost for a given concept. Steady-state simulations and data are used to identify trade-offs that should be balanced to optimize the system in a process known as systems analysis. These systems analysis simulations and data may not adequately capture the true performance trade-offs that exist during transient operation. Dynamic systems analysis provides the capability for assessing the dynamic tradeoffs at an earlier stage of the engine design process. The dynamic systems analysis concept, developed tools, and potential benefit are presented in this paper. To provide this capability, the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) was developed to provide the user with an estimate of the closed-loop performance (response time) and operability (high pressure compressor surge margin) for a given engine design and set of control design requirements. TTECTrA along with engine deterioration information, can be used to develop a more generic relationship between performance and operability that can impact the engine design constraints and potentially lead to a more efficient engine.

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments.

PubMed

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-10-15

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

PubMed Central

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-01-01

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates. PMID:27754456

Cache Locality Optimization for Recursive Programs

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

Lifflander, Jonathan; Krishnamoorthy, Sriram

We present an approach to optimize the cache locality for recursive programs by dynamically splicing--recursively interleaving--the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work stealing scheduler. Wemore » present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domain- specific optimizer for stencil programs.« less

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

PubMed

Suh, Myung-Kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2012-09-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

PubMed Central

Suh, Myung-kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %. PMID:27617297

Activity Monitors Help Users Get Optimum Sun Exposure

NASA Technical Reports Server (NTRS)

2015-01-01

Goddard scientist Shahid Aslam was investigating alternative methods for measuring extreme ultraviolet radiation on the Solar Dynamics Observatory when he hit upon semiconductors that measured wavelengths pertinent to human health. As a result, he and a partner established College Park, Maryland-based Sensor Sensor LLC and developed UVA+B SunFriend, a wrist monitor that lets people know when they've received their optimal amounts of sunlight for the day.

Bellman Ford algorithm - in Routing Information Protocol (RIP)

NASA Astrophysics Data System (ADS)

Krianto Sulaiman, Oris; Mahmud Siregar, Amir; Nasution, Khairuddin; Haramaini, Tasliyah

2018-04-01

In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.

Dynamic modeling and optimal joint torque coordination of advanced robotic systems

NASA Astrophysics Data System (ADS)

Kang, Hee-Jun

The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving, highly versatile, advanced robotic systems. Therefore, finally, a module based dynamic modeling algorithm is presented for the dynamic coordination of such reconfigurable modular robotic systems. A user interactive module based manipulator analysis program (MBMAP) has been coded in C language running on 4D/70 Silicon Graphics.

Multi-Satellite Scheduling Approach for Dynamic Areal Tasks Triggered by Emergent Disasters

NASA Astrophysics Data System (ADS)

Niu, X. N.; Zhai, X. J.; Tang, H.; Wu, L. X.

2016-06-01

The process of satellite mission scheduling, which plays a significant role in rapid response to emergent disasters, e.g. earthquake, is used to allocate the observation resources and execution time to a series of imaging tasks by maximizing one or more objectives while satisfying certain given constraints. In practice, the information obtained of disaster situation changes dynamically, which accordingly leads to the dynamic imaging requirement of users. We propose a satellite scheduling model to address dynamic imaging tasks triggered by emergent disasters. The goal of proposed model is to meet the emergency response requirements so as to make an imaging plan to acquire rapid and effective information of affected area. In the model, the reward of the schedule is maximized. To solve the model, we firstly present a dynamic segmenting algorithm to partition area targets. Then the dynamic heuristic algorithm embedding in a greedy criterion is designed to obtain the optimal solution. To evaluate the model, we conduct experimental simulations in the scene of Wenchuan Earthquake. The results show that the simulated imaging plan can schedule satellites to observe a wider scope of target area. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

Central East Pacific Flight Routing

NASA Technical Reports Server (NTRS)

Grabbe, Shon; Sridhar, Banavar; Kopardekar, Parimal; Cheng, Nadia

2006-01-01

With the introduction of the Federal Aviation Administration s Advanced Technology and Oceanic Procedures system at the Oakland Oceanic Center, a level of automation now exists in the oceanic environment to potentially begin accommodating increased user preferred routing requests. This paper presents the results of an initial feasibility assessment which examines the potential benefits of transitioning from the fixed Central East Pacific routes to user preferred routes. As a surrogate for the actual user-provided routing requests, a minimum-travel-time, wind-optimal dynamic programming algorithm was developed and utilized in this paper. After first describing the characteristics (e.g., origin airport, destination airport, vertical distribution and temporal distribution) of the westbound flights utilizing the Central East Pacific routes on Dec. 14-16 and 19-20, the results of both a flight-plan-based simulation and a wind-optimal-based simulation are presented. Whereas the lateral and longitudinal distribution of the aircraft trajectories in these two simulations varied dramatically, the number of simulated first-loss-of-separation events remained relatively constant. One area of concern that was uncovered in this initial analysis was a potential workload issue associated with the redistribution of traffic in the oceanic sectors due to thc prevailing wind patterns.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories.

PubMed

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories

PubMed Central

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution. PMID:29854239

E-Pad: a comfortable electrocutaneous-based tactile feedback display

NASA Astrophysics Data System (ADS)

Wang, Jiabin; Zhao, Lu; Liu, Yue; Wang, Yongtian; Cai, Yi

2018-01-01

The devices with touchscreen are becoming more popular recently; however, most of them suffer from the crucial drawbacks of lacking accurate tactile feedback. A novel electrocutaneous-based tactile device with the name of E-pad is proposed to provide a dynamic and static low-voltage feedback for touchscreen. We optimize the key parameters of the output voltage and design custom-made hardwares to guarantee a comfortable user experience. Users could move their fingers freely across the touchscreen of the proposed device to really feel virtual objects. Two preliminary experiments are conducted to evaluate the interactive performance of the proposed device and the experimental results show that the proposed device can provide a comfortable and distinct tactile feedback.

Trajectory Generation and Path Planning for Autonomous Aerobots

NASA Technical Reports Server (NTRS)

Sharma, Shivanjli; Kulczycki, Eric A.; Elfes, Alberto

2007-01-01

This paper presents global path planning algorithms for the Titan aerobot based on user defined waypoints in 2D and 3D space. The algorithms were implemented using information obtained through a planner user interface. The trajectory planning algorithms were designed to accurately represent the aerobot's characteristics, such as minimum turning radius. Additionally, trajectory planning techniques were implemented to allow for surveying of a planar area based solely on camera fields of view, airship altitude, and the location of the planar area's perimeter. The developed paths allow for planar navigation and three-dimensional path planning. These calculated trajectories are optimized to produce the shortest possible path while still remaining within realistic bounds of airship dynamics.

Real-time simulation of three-dimensional shoulder girdle and arm dynamics.

PubMed

Chadwick, Edward K; Blana, Dimitra; Kirsch, Robert F; van den Bogert, Antonie J

2014-07-01

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

AP-IO: asynchronous pipeline I/O for hiding periodic output cost in CFD simulation.

PubMed

Xiaoguang, Ren; Xinhai, Xu

2014-01-01

Computational fluid dynamics (CFD) simulation often needs to periodically output intermediate results to files in the form of snapshots for visualization or restart, which seriously impacts the performance. In this paper, we present asynchronous pipeline I/O (AP-IO) optimization scheme for the periodically snapshot output on the basis of asynchronous I/O and CFD application characteristics. In AP-IO, dedicated background I/O processes or threads are in charge of handling the file write in pipeline mode, therefore the write overhead can be hidden with more calculation than classic asynchronous I/O. We design the framework of AP-IO and implement it in OpenFOAM, providing CFD users with a user-friendly interface. Experimental results on the Tianhe-2 supercomputer demonstrate that AP-IO can achieve a good optimization effect for the periodical snapshot output in CFD application, and the effect is especially better for massively parallel CFD simulations, which can reduce the total execution time up to about 40%.

Fire modeling in a nonventilated corridor

NASA Astrophysics Data System (ADS)

Lulea, Marius Dorin; Iordache, Vlad; Năstase, Ilinca

2018-02-01

The main objective of this study was to determine the effect of fire in a nonventilated corridor. A real-scale model of a corridor has been modeled in Fire Dynamics Simulator(F.D.S.) in order to determine the evolution of indoor temperatures, the visibility and the oxygen quantities during a fire. The start time of a sprinkler has also been determined. The use of sprinklers in buildings has become a necessity and a requirement imposed by technical norms. The provision of this type of installation has become a common feature in buildings with a high fire risk, with two main effects: fire extinction and protection of structural and partition elements from high temperatures[15]. The ultimate goal is to ensure optimal conditions for saving the building users, intervention teams and maintaining the stability of the building. Low temperatures and good visibility on the escape routes during a fire are the basic conditions to ensure the optimal evacuation of users.

AP-IO: Asynchronous Pipeline I/O for Hiding Periodic Output Cost in CFD Simulation

PubMed Central

Xiaoguang, Ren; Xinhai, Xu

2014-01-01

Computational fluid dynamics (CFD) simulation often needs to periodically output intermediate results to files in the form of snapshots for visualization or restart, which seriously impacts the performance. In this paper, we present asynchronous pipeline I/O (AP-IO) optimization scheme for the periodically snapshot output on the basis of asynchronous I/O and CFD application characteristics. In AP-IO, dedicated background I/O processes or threads are in charge of handling the file write in pipeline mode, therefore the write overhead can be hidden with more calculation than classic asynchronous I/O. We design the framework of AP-IO and implement it in OpenFOAM, providing CFD users with a user-friendly interface. Experimental results on the Tianhe-2 supercomputer demonstrate that AP-IO can achieve a good optimization effect for the periodical snapshot output in CFD application, and the effect is especially better for massively parallel CFD simulations, which can reduce the total execution time up to about 40%. PMID:24955390

Game theory-based mode cooperative selection mechanism for device-to-device visible light communication

NASA Astrophysics Data System (ADS)

Liu, Yuxin; Huang, Zhitong; Li, Wei; Ji, Yuefeng

2016-03-01

Various patterns of device-to-device (D2D) communication, from Bluetooth to Wi-Fi Direct, are emerging due to the increasing requirements of information sharing between mobile terminals. This paper presents an innovative pattern named device-to-device visible light communication (D2D-VLC) to alleviate the growing traffic problem. However, the occlusion problem is a difficulty in D2D-VLC. This paper proposes a game theory-based solution in which the best-response dynamics and best-response strategies are used to realize a mode-cooperative selection mechanism. This mechanism uses system capacity as the utility function to optimize system performance and selects the optimal communication mode for each active user from three candidate modes. Moreover, the simulation and experimental results show that the mechanism can attain a significant improvement in terms of effectiveness and energy saving compared with the cases where the users communicate via only the fixed transceivers (light-emitting diode and photo diode) or via only D2D.

Interplanetary program to optimize simulated trajectories (IPOST). Volume 4: Sample cases

NASA Technical Reports Server (NTRS)

Hong, P. E.; Kent, P. D; Olson, D. W.; Vallado, C. A.

1992-01-01

The Interplanetary Program to Optimize Simulated Trajectories (IPOST) is intended to support many analysis phases, from early interplanetary feasibility studies through spacecraft development and operations. The IPOST output provides information for sizing and understanding mission impacts related to propulsion, guidance, communications, sensor/actuators, payload, and other dynamic and geometric environments. IPOST models three degree of freedom trajectory events, such as launch/ascent, orbital coast, propulsive maneuvering (impulsive and finite burn), gravity assist, and atmospheric entry. Trajectory propagation is performed using a choice of Cowell, Encke, Multiconic, Onestep, or Conic methods. The user identifies a desired sequence of trajectory events, and selects which parameters are independent (controls) and dependent (targets), as well as other constraints and the cost function. Targeting and optimization are performed using the Standard NPSOL algorithm. The IPOST structure allows sub-problems within a master optimization problem to aid in the general constrained parameter optimization solution. An alternate optimization method uses implicit simulation and collocation techniques.

Searching for superspreaders of information in real-world social media.

PubMed

Pei, Sen; Muchnik, Lev; Andrade, José S; Zheng, Zhiming; Makse, Hernán A

2014-07-03

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

NASA Astrophysics Data System (ADS)

Pei, Sen; Muchnik, Lev; Andrade, José S., Jr.; Zheng, Zhiming; Makse, Hernán A.

2014-07-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for ``viral'' information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

PubMed Central

Pei, Sen; Muchnik, Lev; Andrade, Jr., José S.; Zheng, Zhiming; Makse, Hernán A.

2014-01-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications. PMID:24989148

Experimental validation of docking and capture using space robotics testbeds

NASA Technical Reports Server (NTRS)

Spofford, John; Schmitz, Eric; Hoff, William

1991-01-01

This presentation describes the application of robotic and computer vision systems to validate docking and capture operations for space cargo transfer vehicles. Three applications are discussed: (1) air bearing systems in two dimensions that yield high quality free-flying, flexible, and contact dynamics; (2) validation of docking mechanisms with misalignment and target dynamics; and (3) computer vision technology for target location and real-time tracking. All the testbeds are supported by a network of engineering workstations for dynamic and controls analyses. Dynamic simulation of multibody rigid and elastic systems are performed with the TREETOPS code. MATRIXx/System-Build and PRO-MATLAB/Simulab are the tools for control design and analysis using classical and modern techniques such as H-infinity and LQG/LTR. SANDY is a general design tool to optimize numerically a multivariable robust compensator with a user-defined structure. Mathematica and Macsyma are used to derive symbolically dynamic and kinematic equations.

ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems.

PubMed

Niethammer, Christoph; Becker, Stefan; Bernreuther, Martin; Buchholz, Martin; Eckhardt, Wolfgang; Heinecke, Alexander; Werth, Stephan; Bungartz, Hans-Joachim; Glass, Colin W; Hasse, Hans; Vrabec, Jadran; Horsch, Martin

2014-10-14

The molecular dynamics simulation code ls1 mardyn is presented. It is a highly scalable code, optimized for massively parallel execution on supercomputing architectures and currently holds the world record for the largest molecular simulation with over four trillion particles. It enables the application of pair potentials to length and time scales that were previously out of scope for molecular dynamics simulation. With an efficient dynamic load balancing scheme, it delivers high scalability even for challenging heterogeneous configurations. Presently, multicenter rigid potential models based on Lennard-Jones sites, point charges, and higher-order polarities are supported. Due to its modular design, ls1 mardyn can be extended to new physical models, methods, and algorithms, allowing future users to tailor it to suit their respective needs. Possible applications include scenarios with complex geometries, such as fluids at interfaces, as well as nonequilibrium molecular dynamics simulation of heat and mass transfer.

Performance analysis and optimization of an advanced pharmaceutical wastewater treatment plant through a visual basic software tool (PWWT.VB).

PubMed

Pal, Parimal; Thakura, Ritwik; Chakrabortty, Sankha

2016-05-01

A user-friendly, menu-driven simulation software tool has been developed for the first time to optimize and analyze the system performance of an advanced continuous membrane-integrated pharmaceutical wastewater treatment plant. The software allows pre-analysis and manipulation of input data which helps in optimization and shows the software performance visually on a graphical platform. Moreover, the software helps the user to "visualize" the effects of the operating parameters through its model-predicted output profiles. The software is based on a dynamic mathematical model, developed for a systematically integrated forward osmosis-nanofiltration process for removal of toxic organic compounds from pharmaceutical wastewater. The model-predicted values have been observed to corroborate well with the extensive experimental investigations which were found to be consistent under varying operating conditions like operating pressure, operating flow rate, and draw solute concentration. Low values of the relative error (RE = 0.09) and high values of Willmott-d-index (d will = 0.981) reflected a high degree of accuracy and reliability of the software. This software is likely to be a very efficient tool for system design or simulation of an advanced membrane-integrated treatment plant for hazardous wastewater.

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

PubMed

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Dynamic Network Selection for Multicast Services in Wireless Cooperative Networks

NASA Astrophysics Data System (ADS)

Chen, Liang; Jin, Le; He, Feng; Cheng, Hanwen; Wu, Lenan

In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

A stream-scale model to optimize the water allocation for Small Hydropower Plants and the application to traditional systems

NASA Astrophysics Data System (ADS)

Razurel, Pierre; Niayifar, Amin; Perona, Paolo

2017-04-01

Hydropower plays an important role in supplying worldwide energy demand where it contributes to approximately 16% of global electricity production. Although hydropower, as an emission-free renewable energy, is a reliable source of energy to mitigate climate change, its development will increase river exploitation. The environmental impacts associated with both small hydropower plants (SHP) and traditional dammed systems have been found to the consequence of changing natural flow regime with other release policies, e.g. the minimal flow. Nowadays, in some countries, proportional allocation rules are also applied aiming to mimic the natural flow variability. For example, these dynamic rules are part of the environmental guidance in the United Kingdom and constitute an improvement in comparison to static rules. In a context in which the full hydropower potential might be reached in a close future, a solution to optimize the water allocation seems essential. In this work, we present a model that enables to simulate a wide range of water allocation rules (static and dynamic) for a specific hydropower plant and to evaluate their associated economic and ecological benefits. It is developed in the form of a graphical user interface (GUI) where, depending on the specific type of hydropower plant (i.e., SHP or traditional dammed system), the user is able to specify the different characteristics (e.g., hydrological data and turbine characteristics) of the studied system. As an alternative to commonly used policies, a new class of dynamic allocation functions (non-proportional repartition rules) is introduced (e.g., Razurel et al., 2016). The efficiency plot resulting from the simulations shows the environmental indicator and the energy produced for each allocation policies. The optimal water distribution rules can be identified on the Pareto's frontier, which is obtained by stochastic optimization in the case of storage systems (e.g., Niayifar and Perona, submitted) and by direct simulation for small hydropower ones (Razurel et al., 2016). Compared to proportional and constant minimal flows, economic and ecological efficiencies are found to be substantially improved in the case of using non-proportional water allocation rules for both SHP and traditional systems.

Incremental triangulation by way of edge swapping and local optimization

NASA Technical Reports Server (NTRS)

Wiltberger, N. Lyn

1994-01-01

This document is intended to serve as an installation, usage, and basic theory guide for the two dimensional triangulation software 'HARLEY' written for the Silicon Graphics IRIS workstation. This code consists of an incremental triangulation algorithm based on point insertion and local edge swapping. Using this basic strategy, several types of triangulations can be produced depending on user selected options. For example, local edge swapping criteria can be chosen which minimizes the maximum interior angle (a MinMax triangulation) or which maximizes the minimum interior angle (a MaxMin or Delaunay triangulation). It should be noted that the MinMax triangulation is generally only locally optical (not globally optimal) in this measure. The MaxMin triangulation, however, is both locally and globally optical. In addition, Steiner triangulations can be constructed by inserting new sites at triangle circumcenters followed by edge swapping based on the MaxMin criteria. Incremental insertion of sites also provides flexibility in choosing cell refinement criteria. A dynamic heap structure has been implemented in the code so that once a refinement measure is specified (i.e., maximum aspect ratio or some measure of a solution gradient for the solution adaptive grid generation) the cell with the largest value of this measure is continually removed from the top of the heap and refined. The heap refinement strategy allows the user to specify either the number of cells desired or refine the mesh until all cell refinement measures satisfy a user specified tolerance level. Since the dynamic heap structure is constantly updated, the algorithm always refines the particular cell in the mesh with the largest refinement criteria value. The code allows the user to: triangulate a cloud of prespecified points (sites), triangulate a set of prespecified interior points constrained by prespecified boundary curve(s), Steiner triangulate the interior/exterior of prespecified boundary curve(s), refine existing triangulations based on solution error measures, and partition meshes based on the Cuthill-McKee, spectral, and coordinate bisection strategies.

Analyzing checkpointing trends for applications on the IBM Blue Gene/P system.

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

Naik, H.; Gupta, R.; Beckman, P.

Current petascale systems have tens of thousands of hardware components and complex system software stacks, which increase the probability of faults occurring during the lifetime of a process. Checkpointing has been a popular method of providing fault tolerance in high-end systems. While considerable research has been done to optimize checkpointing, in practice the method still involves a high-cost overhead for users. In this paper, we study the checkpointing overhead seen by applications running on leadership-class machines such as the IBM Blue Gene/P at Argonne National Laboratory. We study various applications and design a methodology to assist users in understanding andmore » choosing checkpointing frequency and reducing the overhead incurred. In particular, we study three popular applications -- the Grid-Based Projector-Augmented Wave application, the Carr-Parrinello Molecular Dynamics application, and a Nek5000 computational fluid dynamics application -- and analyze their memory usage and possible checkpointing trends on 32,768 processors of the Blue Gene/P system.« less

What Physicists Should Know About High Performance Computing - Circa 2002

NASA Astrophysics Data System (ADS)

Frederick, Donald

2002-08-01

High Performance Computing (HPC) is a dynamic, cross-disciplinary field that traditionally has involved applied mathematicians, computer scientists, and others primarily from the various disciplines that have been major users of HPC resources - physics, chemistry, engineering, with increasing use by those in the life sciences. There is a technological dynamic that is powered by economic as well as by technical innovations and developments. This talk will discuss practical ideas to be considered when developing numerical applications for research purposes. Even with the rapid pace of development in the field, the author believes that these concepts will not become obsolete for a while, and will be of use to scientists who either are considering, or who have already started down the HPC path. These principles will be applied in particular to current parallel HPC systems, but there will also be references of value to desktop users. The talk will cover such topics as: computing hardware basics, single-cpu optimization, compilers, timing, numerical libraries, debugging and profiling tools and the emergence of Computational Grids.

Wireless Sensor Network Quality of Service Improvement on Flooding Attack Condition

NASA Astrophysics Data System (ADS)

Hartono, R.; Widyawan; Wibowo, S. B.; Purnomo, A.; Hartatik

2018-03-01

There are two methods of building communication using wireless media. The first method is building a base infrastructure as an intermediary between users. Problems that arise on this type of network infrastructure is limited space to build any network physical infrastructure and also the cost factor. The second method is to build an ad hoc network between users who will communicate. On ad hoc network, each user must be willing to send data from source to destination for the occurrence of a communication. One of network protocol in Ad Hoc, Ad hoc on demand Distance Vector (AODV), has the smallest overhead value, easier to adapt to dynamic network and has small control message. One AODV protocol’s drawback is route finding process’ security for sending the data. In this research, AODV protocol is optimized by determining Expanding Ring Search (ERS) best value. Random topology is used with variation in the number of nodes: 25, 50, 75, 100, 125 and 150 with node’s speed of 10m/s in the area of 1000m x 1000m on flooding network condition. Parameters measured are Throughput, Packet Delivery Ratio, Average Delay and Normalized Routing Load. From the test results of AODV protocol optimization with best value of Expanding Ring Search (ERS), throughput increased by 5.67%, packet delivery ratio increased by 5.73%, and as for Normalized Routing Load decreased by 4.66%. ERS optimal value for each node’s condition depending on the number of nodes on the network.

Secure Hashing of Dynamic Hand Signatures Using Wavelet-Fourier Compression with BioPhasor Mixing and [InlineEquation not available: see fulltext.] Discretization

NASA Astrophysics Data System (ADS)

Wai Kuan, Yip; Teoh, Andrew B. J.; Ngo, David C. L.

2006-12-01

We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and[InlineEquation not available: see fulltext.] discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific[InlineEquation not available: see fulltext.] discretization acts both as an error correction step as well as a real-to-binary space converter. We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of user's hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of[InlineEquation not available: see fulltext.] and[InlineEquation not available: see fulltext.] for random and skilled forgeries for stolen token (worst case) scenario, and[InlineEquation not available: see fulltext.] for both forgeries in the genuine token (optimal) scenario.

Integrated modeling environment for systems-level performance analysis of the Next-Generation Space Telescope

NASA Astrophysics Data System (ADS)

Mosier, Gary E.; Femiano, Michael; Ha, Kong; Bely, Pierre Y.; Burg, Richard; Redding, David C.; Kissil, Andrew; Rakoczy, John; Craig, Larry

1998-08-01

All current concepts for the NGST are innovative designs which present unique systems-level challenges. The goals are to outperform existing observatories at a fraction of the current price/performance ratio. Standard practices for developing systems error budgets, such as the 'root-sum-of- squares' error tree, are insufficient for designs of this complexity. Simulation and optimization are the tools needed for this project; in particular tools that integrate controls, optics, thermal and structural analysis, and design optimization. This paper describes such an environment which allows sub-system performance specifications to be analyzed parametrically, and includes optimizing metrics that capture the science requirements. The resulting systems-level design trades are greatly facilitated, and significant cost savings can be realized. This modeling environment, built around a tightly integrated combination of commercial off-the-shelf and in-house- developed codes, provides the foundation for linear and non- linear analysis on both the time and frequency-domains, statistical analysis, and design optimization. It features an interactive user interface and integrated graphics that allow highly-effective, real-time work to be done by multidisciplinary design teams. For the NGST, it has been applied to issues such as pointing control, dynamic isolation of spacecraft disturbances, wavefront sensing and control, on-orbit thermal stability of the optics, and development of systems-level error budgets. In this paper, results are presented from parametric trade studies that assess requirements for pointing control, structural dynamics, reaction wheel dynamic disturbances, and vibration isolation. These studies attempt to define requirements bounds such that the resulting design is optimized at the systems level, without attempting to optimize each subsystem individually. The performance metrics are defined in terms of image quality, specifically centroiding error and RMS wavefront error, which directly links to science requirements.

Optimal reservoir operation policies using novel nested algorithms

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri

2015-04-01

Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested optimization algorithm into the state transition that lowers the starting problem dimension and alleviates the curse of dimensionality. The algorithms can solve multi-objective optimization problems, without significantly increasing the complexity and the computational expenses. The algorithms can handle dense and irregular variable discretization, and are coded in Java as prototype applications. The three algorithms were tested at the multipurpose reservoir Knezevo of the Zletovica hydro-system located in the Republic of Macedonia, with eight objectives, including urban water supply, agriculture, ensuring ecological flow, and generation of hydropower. Because the Zletovica hydro-system is relatively complex, the novel algorithms were pushed to their limits, demonstrating their capabilities and limitations. The nSDP and nRL derived/learned the optimal reservoir policy using 45 (1951-1995) years historical data. The nSDP and nRL optimal reservoir policy was tested on 10 (1995-2005) years historical data, and compared with nDP optimal reservoir operation in the same period. The nested algorithms and optimal reservoir operation results are analysed and explained.

Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

NASA Astrophysics Data System (ADS)

Seo, Junyeong; Sung, Youngchul

2018-06-01

In this paper, an efficient transmit beam design and user scheduling method is proposed for multi-user (MU) multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) downlink, based on Pareto-optimality. The proposed beam design and user scheduling method groups simultaneously-served users into multiple clusters with practical two users in each cluster, and then applies spatical zeroforcing (ZF) across clusters to control inter-cluster interference (ICI) and Pareto-optimal beam design with successive interference cancellation (SIC) to two users in each cluster to remove interference to strong users and leverage signal-to-interference-plus-noise ratios (SINRs) of interference-experiencing weak users. The proposed method has flexibility to control the rates of strong and weak users and numerical results show that the proposed method yields good performance.

Conceptual Design Optimization of an Augmented Stability Aircraft Incorporating Dynamic Response and Actuator Constraints

NASA Technical Reports Server (NTRS)

Welstead, Jason; Crouse, Gilbert L., Jr.

2014-01-01

Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays.

PubMed

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Cooper, Emily A; Wetzstein, Gordon

2017-02-28

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one.

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays

NASA Astrophysics Data System (ADS)

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Cooper, Emily A.; Wetzstein, Gordon

2017-02-01

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one.

FRANOPP: Framework for analysis and optimization problems user's guide

NASA Technical Reports Server (NTRS)

Riley, K. M.

1981-01-01

Framework for analysis and optimization problems (FRANOPP) is a software aid for the study and solution of design (optimization) problems which provides the driving program and plotting capability for a user generated programming system. In addition to FRANOPP, the programming system also contains the optimization code CONMIN, and two user supplied codes, one for analysis and one for output. With FRANOPP the user is provided with five options for studying a design problem. Three of the options utilize the plot capability and present an indepth study of the design problem. The study can be focused on a history of the optimization process or on the interaction of variables within the design problem.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

NASA Astrophysics Data System (ADS)

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-28

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

PubMed Central

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-01-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes. PMID:27465296

Applying n-bit floating point numbers and integers, and the n-bit filter of HDF5 to reduce file sizes of remote sensing products in memory-sensitive environments

NASA Astrophysics Data System (ADS)

Zinke, Stephan

2017-02-01

Memory sensitive applications for remote sensing data require memory-optimized data types in remote sensing products. Hierarchical Data Format version 5 (HDF5) offers user defined floating point numbers and integers and the n-bit filter to create data types optimized for memory consumption. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) applies a compaction scheme to the disseminated products of the Day and Night Band (DNB) data of Suomi National Polar-orbiting Partnership (S-NPP) satellite's instrument Visible Infrared Imager Radiometer Suite (VIIRS) through the EUMETSAT Advanced Retransmission Service, converting the original 32 bits floating point numbers to user defined floating point numbers in combination with the n-bit filter for the radiance dataset of the product. The radiance dataset requires a floating point representation due to the high dynamic range of the DNB. A compression factor of 1.96 is reached by using an automatically determined exponent size and an 8 bits trailing significand and thus reducing the bandwidth requirements for dissemination. It is shown how the parameters needed for user defined floating point numbers are derived or determined automatically based on the data present in a product.

Pricing the Services in Dynamic Environment: Agent Pricing Model

NASA Astrophysics Data System (ADS)

Žagar, Drago; Rupčić, Slavko; Rimac-Drlje, Snježana

New Internet applications and services as well as new user demands open many new issues concerning dynamic management of quality of service and price for received service, respectively. The main goals of Internet service providers are to maximize profit and maintain a negotiated quality of service. From the users' perspective the main goal is to maximize ratio of received QoS and costs of service. However, achieving these objectives could become very complex if we know that Internet service users might during the session become highly dynamic and proactive. This connotes changes in user profile or network provider/s profile caused by high level of user mobility or variable level of user demands. This paper proposes a new agent based pricing architecture for serving the highly dynamic customers in context of dynamic user/network environment. The proposed architecture comprises main aspects and basic parameters that will enable objective and transparent assessment of the costs for the service those Internet users receive while dynamically change QoS demands and cost profile.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

PubMed Central

2015-01-01

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay. PMID:26237221

Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

PubMed

Malathy, E M; Muthuswamy, Vijayalakshmi

2015-01-01

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.

Flexible Demand Management under Time-Varying Prices

NASA Astrophysics Data System (ADS)

Liang, Yong

In this dissertation, the problem of flexible demand management under time-varying prices is studied. This generic problem has many applications, which usually have multiple periods in which decisions on satisfying demand need to be made, and prices in these periods are time-varying. Examples of such applications include multi-period procurement problem, operating room scheduling, and user-end demand scheduling in the Smart Grid, where the last application is used as the main motivating story throughout the dissertation. The current grid is experiencing an upgrade with lots of new designs. What is of particular interest is the idea of passing time-varying prices that reflect electricity market conditions to end users as incentives for load shifting. One key component, consequently, is the demand management system at the user-end. The objective of the system is to find the optimal trade-off between cost saving and discomfort increment resulted from load shifting. In this dissertation, we approach this problem from the following aspects: (1) construct a generic model, solve for Pareto optimal solutions, and analyze the robust solution that optimizes the worst-case payoffs, (2) extend to a distribution-free model for multiple types of demand (appliances), for which an approximate dynamic programming (ADP) approach is developed, and (3) design other efficient algorithms for practical purposes of the flexible demand management system. We first construct a novel multi-objective flexible demand management model, in which there are a finite number of periods with time-varying prices, and demand arrives in each period. In each period, the decision maker chooses to either satisfy or defer outstanding demand to minimize costs and discomfort over a certain number of periods. We consider both the deterministic model, models with stochastic demand or prices, and when only partial information about the stochastic demand or prices is known. We first analyze the stochastic optimization problem when the objective is to minimize the expected total cost and discomfort, then since the decision maker is likely to be risk-averse, and she wants to protect herself from price spikes, we study the robust optimization problem to address the risk-aversion of the decision maker. We conduct numerical studies to evaluate the price of robustness. Next, we present a detailed model that manages multiple types of flexible demand in the absence of knowledge regarding the distributions of related stochastic processes. Specifically, we consider the case in which time-varying prices with general structures are offered to users, and an energy management system for each household makes optimal energy usage, storage, and trading decisions according to the preferences of users. Because of the uncertainties associated with electricity prices, local generation, and the arrival processes of demand, we formulate a stochastic dynamic programming model, and outline a novel and tractable ADP approach to overcome the curses of dimensionality. Then, we perform numerical studies, whose results demonstrate the effectiveness of the ADP approach. At last, we propose another approximation approach based on Q-learning. In addition, we also develop another decentralization-based heuristic. Both the Q-learning approach and the heuristic make necessary assumptions on the knowledge of information, and each of them has unique advantages. We conduct numerical studies on a testing problem. The simulation results show that both the Q-learning and the decentralization based heuristic approaches work well. Lastly, we conclude the paper with some discussions on future extension directions.

Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming.

PubMed

Ibarra, Ignacio L; Melo, Francisco

2010-07-01

Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. All inquiries or comments about this software should be directed to Francisco Melo at fmelo@bio.puc.cl.

Implementing a bubble memory hierarchy system

NASA Technical Reports Server (NTRS)

Segura, R.; Nichols, C. D.

1979-01-01

This paper reports on implementation of a magnetic bubble memory in a two-level hierarchial system. The hierarchy used a major-minor loop device and RAM under microprocessor control. Dynamic memory addressing, dual bus primary memory, and hardware data modification detection are incorporated in the system to minimize access time. It is the objective of the system to incorporate the advantages of bipolar memory with that of bubble domain memory to provide a smart, optimal memory system which is easy to interface and independent of user's system.

Incorporating User Preferences Within an Optimal Traffic Flow Management Framework

NASA Technical Reports Server (NTRS)

Rios, Joseph Lucio; Sheth, Kapil S.; Guiterrez-Nolasco, Sebastian Armardo

2010-01-01

The effectiveness of future decision support tools for Traffic Flow Management in the National Airspace System will depend on two major factors: computational burden and collaboration. Previous research has focused separately on these two aspects without consideration of their interaction. In this paper, their explicit combination is examined. It is shown that when user preferences are incorporated with an optimal approach to scheduling, runtime is not adversely affected. A benefit-cost ratio is used to measure the influence of user preferences on an optimal solution. This metric shows user preferences can be accommodated without inordinately, negatively affecting the overall system delay. Specifically, incorporating user preferences will increase delays proportionally to increased user satisfaction.

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Effective user management with high strength crypto -key in dynamic group environment in cloud

NASA Astrophysics Data System (ADS)

Kumar, P. J.; Suganya, P.; Karthik, G.

2017-11-01

Cloud Clusters consists of various collections of files which are being accessed by multiple users of Cloud. The users are managed as a group and the association of the user to a particular group is dynamic in nature. Every group has a manager who handles the membership of a user to a particular group by issuing keys for encryption and decryption. Due to the dynamic nature of a user he/she may leave the group very frequently. But an attempt can be made by the user who has recently left the group to access a file maintained by that group. Key distribution becomes a critical issue while the behavior of the user is dynamic. Existing techniques to manage the users of group in terms of security and key distribution has been investigated so that to arrive at an objective to identify the scopes to increase security and key management scheme in cloud. The usage of various key combinations to measure the strength of security and efficiency of user management in dynamic cloud environment has been investigated.

Design Optimization Toolkit: Users' Manual

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

Aguilo Valentin, Miguel Alejandro

The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLABmore » command window.« less

Real-time information management environment (RIME)

NASA Astrophysics Data System (ADS)

DeCleene, Brian T.; Griffin, Sean; Matchett, Garry; Niejadlik, Richard

2000-08-01

Whereas data mining and exploitation improve the quality and quantity of information available to the user, there remains a mission requirement to assist the end-user in managing the access to this information and ensuring that the appropriate information is delivered to the right user in time to make decisions and take action. This paper discusses TASC's federated architecture to next- generation information management, contrasts the approach against emerging technologies, and quantifies the performance gains. This architecture and implementation, known as Real-time Information Management Environment (RIME), is based on two key concepts: information utility and content-based channelization. The introduction of utility allows users to express the importance and delivery requirements of their information needs in the context of their mission. Rather than competing for resources on a first-come/first-served basis, the infrastructure employs these utility functions to dynamically react to unanticipated loading by optimizing the delivered information utility. Furthermore, commander's resource policies shape these functions to ensure that resources are allocated according to military doctrine. Using information about the desired content, channelization identifies opportunities to aggregate users onto shared channels reducing redundant transmissions. Hence, channelization increases the information throughput of the system and balances sender/receiver processing load.

Issues and challenges in resource management and its interaction with levels 2/3 fusion with applications to real-world problems: an annotated perspective

NASA Astrophysics Data System (ADS)

Blasch, Erik; Kadar, Ivan; Hintz, Kenneth; Biermann, Joachim; Chong, Chee-Yee; Salerno, John; Das, Subrata

2007-04-01

Resource management (or process refinement) is critical for information fusion operations in that users, sensors, and platforms need to be informed, based on mission needs, on how to collect, process, and exploit data. To meet these growing concerns, a panel session was conducted at the International Society of Information Fusion Conference in 2006 to discuss the various issues surrounding the interaction of Resource Management with Level 2/3 Situation and Threat Assessment. This paper briefly consolidates the discussion of the invited panel panelists. The common themes include: (1) Addressing the user in system management, sensor control, and knowledge based information collection (2) Determining a standard set of fusion metrics for optimization and evaluation based on the application (3) Allowing dynamic and adaptive updating to deliver timely information needs and information rates (4) Optimizing the joint objective functions at all information fusion levels based on decision-theoretic analysis (5) Providing constraints from distributed resource mission planning and scheduling; and (6) Defining L2/3 situation entity definitions for knowledge discovery, modeling, and information projection

Geospatial Optimization of Siting Large-Scale Solar Projects

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

Macknick, Jordan; Quinby, Ted; Caulfield, Emmet

2014-03-01

Recent policy and economic conditions have encouraged a renewed interest in developing large-scale solar projects in the U.S. Southwest. However, siting large-scale solar projects is complex. In addition to the quality of the solar resource, solar developers must take into consideration many environmental, social, and economic factors when evaluating a potential site. This report describes a proof-of-concept, Web-based Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an optimization algorithm to inform discussions and decisions regarding the locations of utility-scale solar projects. Existing siting recommendations for large-scale solar projects from governmental and non-governmental organizations are not consistent withmore » each other, are often not transparent in methods, and do not take into consideration the differing priorities of stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.« less

Video personalization for usage environment

NASA Astrophysics Data System (ADS)

Tseng, Belle L.; Lin, Ching-Yung; Smith, John R.

2002-07-01

A video personalization and summarization system is designed and implemented incorporating usage environment to dynamically generate a personalized video summary. The personalization system adopts the three-tier server-middleware-client architecture in order to select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. Our semantic metadata is provided through the use of the VideoAnnEx MPEG-7 Video Annotation Tool. When the user initiates a request for content, the client communicates the MPEG-21 usage environment description along with the user query to the middleware. The middleware is powered by the personalization engine and the content adaptation engine. Our personalization engine includes the VideoSue Summarization on Usage Environment engine that selects the optimal set of desired contents according to user preferences. Afterwards, the adaptation engine performs the required transformations and compositions of the selected contents for the specific usage environment using our VideoEd Editing and Composition Tool. Finally, two personalization and summarization systems are demonstrated for the IBM Websphere Portal Server and for the pervasive PDA devices.

Joint subchannel pairing and power control for cognitive radio networks with amplify-and-forward relaying.

PubMed

Shen, Yanyan; Wang, Shuqiang; Wei, Zhiming

2014-01-01

Dynamic spectrum sharing has drawn intensive attention in cognitive radio networks. The secondary users are allowed to use the available spectrum to transmit data if the interference to the primary users is maintained at a low level. Cooperative transmission for secondary users can reduce the transmission power and thus improve the performance further. We study the joint subchannel pairing and power allocation problem in relay-based cognitive radio networks. The objective is to maximize the sum rate of the secondary user that is helped by an amplify-and-forward relay. The individual power constraints at the source and the relay, the subchannel pairing constraints, and the interference power constraints are considered. The problem under consideration is formulated as a mixed integer programming problem. By the dual decomposition method, a joint optimal subchannel pairing and power allocation algorithm is proposed. To reduce the computational complexity, two suboptimal algorithms are developed. Simulations have been conducted to verify the performance of the proposed algorithms in terms of sum rate and average running time under different conditions.

Moving-window dynamic optimization: design of stimulation profiles for walking.

PubMed

Dosen, Strahinja; Popović, Dejan B

2009-05-01

The overall goal of the research is to improve control for electrical stimulation-based assistance of walking in hemiplegic individuals. We present the simulation for generating offline input (sensors)-output (intensity of muscle stimulation) representation of walking that serves in synthesizing a rule-base for control of electrical stimulation for restoration of walking. The simulation uses new algorithm termed moving-window dynamic optimization (MWDO). The optimization criterion was to minimize the sum of the squares of tracking errors from desired trajectories with the penalty function on the total muscle efforts. The MWDO was developed in the MATLAB environment and tested using target trajectories characteristic for slow-to-normal walking recorded in healthy individual and a model with the parameters characterizing the potential hemiplegic user. The outputs of the simulation are piecewise constant intensities of electrical stimulation and trajectories generated when the calculated stimulation is applied to the model. We demonstrated the importance of this simulation by showing the outputs for healthy and hemiplegic individuals, using the same target trajectories. Results of the simulation show that the MWDO is an efficient tool for analyzing achievable trajectories and for determining the stimulation profiles that need to be delivered for good tracking.

The research of conformal optical design

NASA Astrophysics Data System (ADS)

Li, Lin; Li, Yan; Huang, Yi-fan; Du, Bao-lin

2009-07-01

Conformal optical domes are characterized as having external more elongated optical surfaces that are optimized to minimize drag, increased missile velocity and extended operational range. The outer surface of the conformal domes typically deviate greatly from spherical surface descriptions, so the inherent asymmetry of conformal surfaces leads to variations in the aberration content presented to the optical sensor as it is gimbaled across the field of regard, which degrades the sensor's ability to properly image targets of interest and then undermine the overall system performance. Consequently, the aerodynamic advantages of conformal domes cannot be realized in practical systems unless the dynamic aberration correction techniques are developed to restore adequate optical imaging capabilities. Up to now, many optical correction solutions have been researched in conformal optical design, including static aberrations corrections and dynamic aberrations corrections. There are three parts in this paper. Firstly, the combination of static and dynamic aberration correction is introduced. A system for correcting optical aberration created by a conformal dome has an outer surface and an inner surface. The optimization of the inner surface is regard as the static aberration correction; moreover, a deformable mirror is placed at the position of the secondary mirror in the two-mirror all reflective imaging system, which is the dynamic aberration correction. Secondly, the using of appropriate surface types is very important in conformal dome design. Better performing optical systems can result from surface types with adequate degrees of freedom to describe the proper corrector shape. Two surface types and the methods of using them are described, including Zernike polynomial surfaces used in correct elements and user-defined surfaces used in deformable mirror (DM). Finally, the Adaptive optics (AO) correction is presented. In order to correct the dynamical residual aberration in conformal optical design, the SPGD optimization algorithm is operated at each zoom position to calculate the optimized surface shape of the MEMS DM. The communication between MATLAB and Code V established via ActiveX technique is applied in simulation analysis.

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions.

PubMed

Saha, Tanumoy; Rathmann, Isabel; Galic, Milos

2017-07-11

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

The Role of Diverse Strategies in Sustainable Knowledge Production

PubMed Central

Wu, Lingfei; Baggio, Jacopo A.; Janssen, Marco A.

2016-01-01

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities. PMID:26934733

The Role of Diverse Strategies in Sustainable Knowledge Production.

PubMed

Wu, Lingfei; Baggio, Jacopo A; Janssen, Marco A

2016-01-01

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

QoS-aware health monitoring system using cloud-based WBANs.

PubMed

Almashaqbeh, Ghada; Hayajneh, Thaier; Vasilakos, Athanasios V; Mohd, Bassam J

2014-10-01

Wireless Body Area Networks (WBANs) are amongst the best options for remote health monitoring. However, as standalone systems WBANs have many limitations due to the large amount of processed data, mobility of monitored users, and the network coverage area. Integrating WBANs with cloud computing provides effective solutions to these problems and promotes the performance of WBANs based systems. Accordingly, in this paper we propose a cloud-based real-time remote health monitoring system for tracking the health status of non-hospitalized patients while practicing their daily activities. Compared with existing cloud-based WBAN frameworks, we divide the cloud into local one, that includes the monitored users and local medical staff, and a global one that includes the outer world. The performance of the proposed framework is optimized by reducing congestion, interference, and data delivery delay while supporting users' mobility. Several novel techniques and algorithms are proposed to accomplish our objective. First, the concept of data classification and aggregation is utilized to avoid clogging the network with unnecessary data traffic. Second, a dynamic channel assignment policy is developed to distribute the WBANs associated with the users on the available frequency channels to manage interference. Third, a delay-aware routing metric is proposed to be used by the local cloud in its multi-hop communication to speed up the reporting process of the health-related data. Fourth, the delay-aware metric is further utilized by the association protocols used by the WBANs to connect with the local cloud. Finally, the system with all the proposed techniques and algorithms is evaluated using extensive ns-2 simulations. The simulation results show superior performance of the proposed architecture in optimizing the end-to-end delay, handling the increased interference levels, maximizing the network capacity, and tracking user's mobility.

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

Systematic Propulsion Optimization Tools (SPOT)

NASA Technical Reports Server (NTRS)

Bower, Mark; Celestian, John

1992-01-01

This paper describes a computer program written by senior-level Mechanical Engineering students at the University of Alabama in Huntsville which is capable of optimizing user-defined delivery systems for carrying payloads into orbit. The custom propulsion system is designed by the user through the input of configuration, payload, and orbital parameters. The primary advantages of the software, called Systematic Propulsion Optimization Tools (SPOT), are a user-friendly interface and a modular FORTRAN 77 code designed for ease of modification. The optimization of variables in an orbital delivery system is of critical concern in the propulsion environment. The mass of the overall system must be minimized within the maximum stress, force, and pressure constraints. SPOT utilizes the Design Optimization Tools (DOT) program for the optimization techniques. The SPOT program is divided into a main program and five modules: aerodynamic losses, orbital parameters, liquid engines, solid engines, and nozzles. The program is designed to be upgraded easily and expanded to meet specific user needs. A user's manual and a programmer's manual are currently being developed to facilitate implementation and modification.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization.

PubMed

Jung, Sang-Kyu; McDonald, Karen

2011-08-16

Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization

PubMed Central

2011-01-01

Background Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. Results The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Conclusion Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net. PMID:21846353

Towards using musculoskeletal models for intelligent control of physically assistive robots.

PubMed

Carmichael, Marc G; Liu, Dikai

2011-01-01

With the increasing number of robots being developed to physically assist humans in tasks such as rehabilitation and assistive living, more intelligent and personalized control systems are desired. In this paper we propose the use of a musculoskeletal model to estimate the strength of the user, from which information can be utilized to improve control schemes in which robots physically assist humans. An optimization model is developed utilizing a musculoskeletal model to estimate human strength in a specified dynamic state. Results of this optimization as well as methods of using it to observe muscle-based weaknesses in task space are presented. Lastly potential methods and problems in incorporating this model into a robot control system are discussed.

Schedule Optimization of Imaging Missions for Multiple Satellites and Ground Stations Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Junghyun; Kim, Heewon; Chung, Hyun; Kim, Haedong; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

2018-04-01

In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multi-satellite control system comprising five satellites and three ground stations having S- and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.

Optimizing virtual reality for all users through gaze-contingent and adaptive focus displays

PubMed Central

Padmanaban, Nitish; Konrad, Robert; Stramer, Tal; Wetzstein, Gordon

2017-01-01

From the desktop to the laptop to the mobile device, personal computing platforms evolve over time. Moving forward, wearable computing is widely expected to be integral to consumer electronics and beyond. The primary interface between a wearable computer and a user is often a near-eye display. However, current generation near-eye displays suffer from multiple limitations: they are unable to provide fully natural visual cues and comfortable viewing experiences for all users. At their core, many of the issues with near-eye displays are caused by limitations in conventional optics. Current displays cannot reproduce the changes in focus that accompany natural vision, and they cannot support users with uncorrected refractive errors. With two prototype near-eye displays, we show how these issues can be overcome using display modes that adapt to the user via computational optics. By using focus-tunable lenses, mechanically actuated displays, and mobile gaze-tracking technology, these displays can be tailored to correct common refractive errors and provide natural focus cues by dynamically updating the system based on where a user looks in a virtual scene. Indeed, the opportunities afforded by recent advances in computational optics open up the possibility of creating a computing platform in which some users may experience better quality vision in the virtual world than in the real one. PMID:28193871

Dynamix: dynamic visualization by automatic selection of informative tracks from hundreds of genomic datasets.

PubMed

Monfort, Matthias; Furlong, Eileen E M; Girardot, Charles

2017-07-15

Visualization of genomic data is fundamental for gaining insights into genome function. Yet, co-visualization of a large number of datasets remains a challenge in all popular genome browsers and the development of new visualization methods is needed to improve the usability and user experience of genome browsers. We present Dynamix, a JBrowse plugin that enables the parallel inspection of hundreds of genomic datasets. Dynamix takes advantage of a priori knowledge to automatically display data tracks with signal within a genomic region of interest. As the user navigates through the genome, Dynamix automatically updates data tracks and limits all manual operations otherwise needed to adjust the data visible on screen. Dynamix also introduces a new carousel view that optimizes screen utilization by enabling users to independently scroll through groups of tracks. Dynamix is hosted at http://furlonglab.embl.de/Dynamix . charles.girardot@embl.de. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

A new dynamic tactile display for reconfigurable braille: implementation and tests.

PubMed

Motto Ros, Paolo; Dante, Vittorio; Mesin, Luca; Petetti, Erminio; Del Giudice, Paolo; Pasero, Eros

2014-01-01

Different tactile interfaces have been proposed to represent either text (braille) or, in a few cases, tactile large-area screens as replacements for visual displays. None of the implementations so far can be customized to match users' preferences, perceptual differences and skills. Optimal choices in these respects are still debated; we approach a solution by designing a flexible device allowing the user to choose key parameters of tactile transduction. We present here a new dynamic tactile display, a 8 × 8 matrix of plastic pins based on well-established and reliable piezoelectric technology to offer high resolution (pin gap 0.7mm) as well as tunable strength of the pins displacement, and refresh rate up to 50s(-1). It can reproduce arbitrary patterns, allowing it to serve the dual purpose of providing, depending on contingent user needs, tactile rendering of non-character information, and reconfigurable braille rendering. Given the relevance of the latter functionality for the expected average user, we considered testing braille encoding by volunteers a benchmark of primary importance. Tests were performed to assess the acceptance and usability with minimal training, and to check whether the offered flexibility was indeed perceived by the subject as an added value compared to conventional braille devices. Different mappings between braille dots and actual tactile pins were implemented to match user needs. Performances of eight experienced braille readers were defined as the fraction of correct identifications of rendered content. Different information contents were tested (median performance on random strings, words, sentences identification was about 75%, 85%, 98%, respectively, with a significant increase, p < 0.01), obtaining statistically significant improvements in performance during the tests (p < 0.05). Experimental results, together with qualitative ratings provided by the subjects, show a good acceptance and the effectiveness of the proposed solution.

A new dynamic tactile display for reconfigurable braille: implementation and tests

PubMed Central

Motto Ros, Paolo; Dante, Vittorio; Mesin, Luca; Petetti, Erminio; Del Giudice, Paolo; Pasero, Eros

2014-01-01

Different tactile interfaces have been proposed to represent either text (braille) or, in a few cases, tactile large-area screens as replacements for visual displays. None of the implementations so far can be customized to match users' preferences, perceptual differences and skills. Optimal choices in these respects are still debated; we approach a solution by designing a flexible device allowing the user to choose key parameters of tactile transduction. We present here a new dynamic tactile display, a 8 × 8 matrix of plastic pins based on well-established and reliable piezoelectric technology to offer high resolution (pin gap 0.7mm) as well as tunable strength of the pins displacement, and refresh rate up to 50s−1. It can reproduce arbitrary patterns, allowing it to serve the dual purpose of providing, depending on contingent user needs, tactile rendering of non-character information, and reconfigurable braille rendering. Given the relevance of the latter functionality for the expected average user, we considered testing braille encoding by volunteers a benchmark of primary importance. Tests were performed to assess the acceptance and usability with minimal training, and to check whether the offered flexibility was indeed perceived by the subject as an added value compared to conventional braille devices. Different mappings between braille dots and actual tactile pins were implemented to match user needs. Performances of eight experienced braille readers were defined as the fraction of correct identifications of rendered content. Different information contents were tested (median performance on random strings, words, sentences identification was about 75%, 85%, 98%, respectively, with a significant increase, p < 0.01), obtaining statistically significant improvements in performance during the tests (p < 0.05). Experimental results, together with qualitative ratings provided by the subjects, show a good acceptance and the effectiveness of the proposed solution. PMID:24782756

Data-oriented scheduling for PROOF

NASA Astrophysics Data System (ADS)

Xu, Neng; Guan, Wen; Wu, Sau Lan; Ganis, Gerardo

2011-12-01

The Parallel ROOT Facility - PROOF - is a distributed analysis system optimized for I/O intensive analysis tasks of HEP data. With LHC entering the analysis phase, PROOF has become a natural ingredient for computing farms at Tier3 level. These analysis facilities will typically be used by a few tenths of users, and can also be federated into a sort of analysis cloud corresponding to the Virtual Organization of the experiment. Proper scheduling is required to guarantee fair resource usage, to enforce priority policies and to optimize the throughput. In this paper we discuss an advanced priority system that we are developing for PROOF. The system has been designed to automatically adapt to unknown length of the tasks, to take into account the data location and availability (including distribution across geographically separated sites), and the {group, user} default priorities. In this system, every element - user, group, dataset, job slot and storage - gets its priority and those priorities are dynamically linked with each other. In order to tune the interplay between the various components, we have designed and started implementing a simulation application that can model various type and size of PROOF clusters. In this application a monitoring package records all the changes of them so that we can easily understand and tune the performance. We will discuss the status of our simulation and show examples of the results we are expecting from it.

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

NASA Technical Reports Server (NTRS)

Meyn, Larry A.

2018-01-01

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

On the existence of the optimal order for wavefunction extrapolation in Born-Oppenheimer molecular dynamics

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

Fang, Jun; Wang, Han, E-mail: wang-han@iapcm.ac.cn; CAEP Software Center for High Performance Numerical Simulation, Beijing

2016-06-28

Wavefunction extrapolation greatly reduces the number of self-consistent field (SCF) iterations and thus the overall computational cost of Born-Oppenheimer molecular dynamics (BOMD) that is based on the Kohn–Sham density functional theory. Going against the intuition that the higher order of extrapolation possesses a better accuracy, we demonstrate, from both theoretical and numerical perspectives, that the extrapolation accuracy firstly increases and then decreases with respect to the order, and an optimal extrapolation order in terms of minimal number of SCF iterations always exists. We also prove that the optimal order tends to be larger when using larger MD time steps ormore » more strict SCF convergence criteria. By example BOMD simulations of a solid copper system, we show that the optimal extrapolation order covers a broad range when varying the MD time step or the SCF convergence criterion. Therefore, we suggest the necessity for BOMD simulation packages to open the user interface and to provide more choices on the extrapolation order. Another factor that may influence the extrapolation accuracy is the alignment scheme that eliminates the discontinuity in the wavefunctions with respect to the atomic or cell variables. We prove the equivalence between the two existing schemes, thus the implementation of either of them does not lead to essential difference in the extrapolation accuracy.« less

Program Aids Analysis And Optimization Of Design

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Lamarsh, William J., II

1994-01-01

NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.

Occupant-responsive optimal control of smart facade systems

NASA Astrophysics Data System (ADS)

Park, Cheol-Soo

Windows provide occupants with daylight, direct sunlight, visual contact with the outside and a feeling of openness. Windows enable the use of daylighting and offer occupants a outside view. Glazing may also cause a number of problems: undesired heat gain/loss in winter. An over-lit window can cause glare, which is another major complaint by occupants. Furthermore, cold or hot window surfaces induce asymmetric thermal radiation which can result in thermal discomfort. To reduce the potential problems of window systems, double skin facades and airflow window systems have been introduced in the 1970s. They typically contain interstitial louvers and ventilation openings. The current problem with double skin facades and airflow windows is that their operation requires adequate dynamic control to reach their expected performance. Many studies have recognized that only an optimal control enables these systems to truly act as active energy savers and indoor environment controllers. However, an adequate solution for this dynamic optimization problem has thus far not been developed. The primary objective of this study is to develop occupant responsive optimal control of smart facade systems. The control could be implemented as a smart controller that operates the motorized Venetian blind system and the opening ratio of ventilation openings. The objective of the control is to combine the benefits of large windows with low energy demands for heating and cooling, while keeping visual well-being and thermal comfort at an optimal level. The control uses a simulation model with an embedded optimization routine that allows occupant interaction via the Web. An occupant can access the smart controller from a standard browser and choose a pre-defined mode (energy saving mode, visual comfort mode, thermal comfort mode, default mode, nighttime mode) or set a preferred mode (user-override mode) by moving preference sliders on the screen. The most prominent feature of these systems is the capability of dynamically reacting to the environmental input data through real-time optimization. The proposed occupant responsive optimal control of smart facade systems could provide a breakthrough in this under-developed area and lead to a renewed interest in smart facade systems.

The System Dynamics Model User Sustainability Explorer (SD-MUSE) user interface: a user-friendly tool for interpreting system dynamic models

EPA Science Inventory

Sustainability-based decision making is a challenging process that requires balancing trade-offs among social, economic, and environmental components. System Dynamic (SD) models can be useful tools to inform sustainability-based decision making because they provide a holistic co...

Software package for modeling spin-orbit motion in storage rings

NASA Astrophysics Data System (ADS)

Zyuzin, D. V.

2015-12-01

A software package providing a graphical user interface for computer experiments on the motion of charged particle beams in accelerators, as well as analysis of obtained data, is presented. The software package was tested in the framework of the international project on electric dipole moment measurement JEDI (Jülich Electric Dipole moment Investigations). The specific features of particle spin motion imply the requirement to use a cyclic accelerator (storage ring) consisting of electrostatic elements, which makes it possible to preserve horizontal polarization for a long time. Computer experiments study the dynamics of 106-109 particles in a beam during 109 turns in an accelerator (about 1012-1015 integration steps for the equations of motion). For designing an optimal accelerator structure, a large number of computer experiments on polarized beam dynamics are required. The numerical core of the package is COSY Infinity, a program for modeling spin-orbit dynamics.

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

PubMed

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

Predicting impact of multi-paths on phase change in map-based vehicular ad hoc networks

NASA Astrophysics Data System (ADS)

Rahmes, Mark; Lemieux, George; Sonnenberg, Jerome; Chester, David B.

2014-05-01

Dynamic Spectrum Access, which through its ability to adapt the operating frequency of a radio, is widely believed to be a solution to the limited spectrum problem. Mobile Ad Hoc Networks (MANETs) can extend high capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact cognitive radio employs spectrum sensing to facilitate identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We quantify optimal signal detection in map based cognitive radio networks with multiple rapidly varying phase changes and multiple orthogonal signals. Doppler shift occurs due to reflection, scattering, and rapid vehicle movement. Path propagation as well as vehicle movement produces either constructive or destructive interference with the incident wave. Our signal detection algorithms can assist the Doppler spread compensation algorithm by deciding how many phase changes in signals are present in a selected band of interest. Additionally we can populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate Dynamic Spectrum Access. We show how topography can help predict the impact of multi-paths on phase change, as well as about the prediction from dense traffic areas. Utilization of high resolution geospatial data layers in RF propagation analysis is directly applicable.

Knowledge elicitation for an operator assistant system in process control tasks

NASA Technical Reports Server (NTRS)

Boy, Guy A.

1988-01-01

A knowledge based system (KBS) methodology designed to study human machine interactions and levels of autonomy in allocation of process control tasks is presented. Users are provided with operation manuals to assist them in normal and abnormal situations. Unfortunately, operation manuals usually represent only the functioning logic of the system to be controlled. The user logic is often totally different. A method is focused on which illicits user logic to refine a KBS shell called an Operator Assistant (OA). If the OA is to help the user, it is necessary to know what level of autonomy gives the optimal performance of the overall man-machine system. For example, for diagnoses that must be carried out carefully by both the user and the OA, interactions are frequent, and processing is mostly sequential. Other diagnoses can be automated, in which the case the OA must be able to explain its reasoning in an appropriate level of detail. OA structure was used to design a working KBS called HORSES (Human Orbital Refueling System Expert System). Protocol analysis of pilots interacting with this system reveals that the a-priori analytical knowledge becomes more structured with training and the situation patterns more complex and dynamic. This approach can improve the a-priori understanding of human and automatic reasoning.

Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks

PubMed Central

Liu, Xin; Jia, Min; Gu, Xuemai; Tan, Xuezhi

2013-01-01

The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time. PMID:23604027

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence.

PubMed

Welcome, Mo; Pereverzev, Va

2014-09-01

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis.

Glycemic Allostasis during Mental Activities on Fasting in Non-alcohol Users and Alcohol Users with Different Durations of Abstinence

PubMed Central

Welcome, MO; Pereverzev, VA

2014-01-01

Glycemic allostasis is the process by which blood glucose stabilization is achieved through the balancing of glucose consumption rate and release into the blood stream under a variety of stressors. This paper reviews findings on the dynamics of glycemic levels during mental activities on fasting in non-alcohol users and alcohol users with different periods of abstinence. Referred articles for this review were searched in the databases of PubMed, Scopus, DOAJ and AJOL. The search was conducted in 2013 between January 20 and July 31. The following keywords were used in the search: alcohol action on glycemia OR brain glucose OR cognitive functions; dynamics of glycemia, dynamics of glycemia during mental activities; dynamics of glycemia on fasting; dynamics of glycemia in non-alcohol users OR alcohol users; glycemic regulation during sobriety. Analysis of the selected articles showed that glycemic allostasis during mental activities on fasting is poorly regulated in alcohol users even after a long duration of sobriety (1-4 weeks after alcohol consumption), compared to non-alcohol users. The major contributor to the maintenance of euglycemia during mental activities after the night's rest (during continuing fast) is gluconeogenesis. PMID:25364589

Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

NASA Astrophysics Data System (ADS)

Shen, Chengcheng; Shi, Honghua; Liu, Yongzhi; Li, Fen; Ding, Dewen

2016-07-01

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

The consequences of neural degeneration regarding optimal cochlear implant position in scala tympani: a model approach.

PubMed

Briaire, Jeroen J; Frijns, Johan H M

2006-04-01

Cochlear implant research endeavors to optimize the spatial selectivity, threshold and dynamic range with the objective of improving the speech perception performance of the implant user. One of the ways to achieve some of these goals is by electrode design. New cochlear implant electrode designs strive to bring the electrode contacts into close proximity to the nerve fibers in the modiolus: this is done by placing the contacts on the medial side of the array and positioning the implant against the medial wall of scala tympani. The question remains whether this is the optimal position for a cochlea with intact neural fibers and, if so, whether it is also true for a cochlea with degenerated neural fibers. In this study a computational model of the implanted human cochlea is used to investigate the optimal position of the array with respect to threshold, dynamic range and spatial selectivity for a cochlea with intact nerve fibers and for degenerated nerve fibers. In addition, the model is used to evaluate the predictive value of eCAP measurements for obtaining peri-operative information on the neural status. The model predicts improved threshold, dynamic range and spatial selectivity for the peri-modiolar position at the basal end of the cochlea, with minimal influence of neural degeneration. At the apical end of the array (1.5 cochlear turns), the dynamic range and the spatial selectivity are limited due to the occurrence of cross-turn stimulation, with the exception of the condition without neural degeneration and with the electrode array along the lateral wall of scala tympani. The eCAP simulations indicate that a large P(0) peak occurs before the N(1)P(1) complex when the fibers are not degenerated. The absence of this peak might be used as an indicator for neural degeneration.

Electrification pathways for Kenya-linking spatial electrification analysis and medium to long term energy planning

NASA Astrophysics Data System (ADS)

Moksnes, Nandi; Korkovelos, Alexandros; Mentis, Dimitrios; Howells, Mark

2017-09-01

In September 2015 UN announced 17 Sustainable Development goals (SDG) from which goal number 7 envisions universal access to modern energy services for all by 2030. In Kenya only about 46% of the population currently has access to electricity. This paper analyses hypothetical scenarios, and selected implications, investigating pathways that would allow the country to reach its electrification targets by 2030. Two modelling tools were used for the purposes of this study, namely OnSSET and OSeMOSYS. The tools were soft-linked in order to capture both the spatial and temporal dynamics of their nature. Two electricity demand scenarios were developed representing low and high end user consumption goals respectively. Indicatively, results show that geothermal, coal, hydro and natural gas would consist the optimal energy mix for the centralized national grid. However, in the case of the low demand scenario a high penetration of stand-alone systems is evident in the country, reaching out to approximately 47% of the electrified population. Increasing end user consumption leads to a shift in the optimal technology mix, with higher penetration of mini-grid technologies and grid extension.

The physics of complex systems in information and biology

NASA Astrophysics Data System (ADS)

Walker, Dylan

Citation networks have re-emerged as a topic intense interest in the complex networks community with the recent availability of large-scale data sets. The ranking of citation networks is a necessary practice as a means to improve information navigability and search. Unlike many information networks, the aging characteristics of citation networks require the development of new ranking methods. To account for strong aging characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893-2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed. Collaborative voting systems have emerged as an abundant form of real-world, complex information systems that exist in a variety of online applications. These systems are comprised of large populations of users that collectively submit and vote on objects. While the specific properties of these systems vary widely, many of them share a core set of features and dynamical behaviors that govern their evolution. We study a subset of these systems that involve material of a time-critical nature as in the popular example of news items. We consider a general model system in which articles are introduced, voted on by a population of users, and subsequently expire after a proscribed period of time. To study the interaction between popularity and quality, we introduce simple stochastic models of user behavior that approximate differing user quality and susceptibility to the common notion of popularity. We define a metric to quantify user reputation in a manner that is self-consistent, adaptable and content-blind and shows good correlation with the probability that a user behaves in an optimal fashion. We further construct a mechanism for ranking documents that take into account user reputation and provides substantial improvement in the time-critical performance of the system. The structure of complex systems have been well studied in the context of both information and biological systems. More recently, dynamics in complex systems that occur over the background of the underlying network has received a great deal of attention. In particular, the study of fluctuations in complex systems has emerged as an issue central to understanding dynamical behavior. We approach the problem of collective effects of the underlying network on dynamical fluctuations by considering the protein-protein interaction networks for the system of the living cell. We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by relatively slow changes in total concentrations (copy numbers) of interacting proteins. The second type, to which we refer to as spontaneous, is caused by quickly decaying thermodynamic deviations away from the mass-action equilibrium of the system. As such they are amenable to methods of equilibrium statistical mechanics used in our study. We investigate the effects of network connectivity on these fluctuations by comparing them to different scenarios in which the interacting pair is isolated form the rest of the network. Such comparison allows us to analytically derive upper and lower bounds on network fluctuations. The collective effects are shown to sometimes lead to relatively large amplification of spontaneous fluctuations as compared to the expectation for isolated dimers. As a consequence of this, the strength of both types of fluctuations is positively correlated with the overall network connectivity of proteins forming the complex. On the other hand, the relative amplitude of fluctuations is negatively correlated with the equilibrium concentration of the complex. Our general findings are illustrated using a curated network of protein-protein interactions and multi-protein complexes in bakers yeast with experimentally determined protein concentrations.

Optimal joint management of a coastal aquifer and a substitute resource

NASA Astrophysics Data System (ADS)

Moreaux, M.; Reynaud, A.

2004-06-01

This article characterizes the optimal joint management of a coastal aquifer and a costly water substitute. For this purpose we use a mathematical representation of the aquifer that incorporates the displacement of the interface between the seawater and the freshwater of the aquifer. We identify the spatial cost externalities created by users on each other and we show that the optimal water supply depends on the location of users. Users located in the coastal zone exclusively use the costly substitute. Those located in the more upstream area are supplied from the aquifer. At the optimum their withdrawal must take into account the cost externalities they generate on users located downstream. Last, users located in a median zone use the aquifer with a surface transportation cost. We show that the optimum can be implemented in a decentralized economy through a very simple Pigouvian tax. Finally, the optimal and decentralized extraction policies are simulated on a very simple example.

Optimization of Residual Stresses in MMC's through Process Parameter Control and the use of Heterogeneous Compensating/Compliant Interfacial Layers. OPTCOMP2 User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.

1996-01-01

A user's guide for the computer program OPTCOMP2 is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in unidirectional metal matrix composites subjected to combined thermomechanical axisymmetric loading by altering the processing history, as well as through the microstructural design of interfacial fiber coatings. The user specifies the initial architecture of the composite and the load history, with the constituent materials being elastic, plastic, viscoplastic, or as defined by the 'user-defined' constitutive model, in addition to the objective function and constraints, through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the inelastic response of a fiber/interface layer(s)/matrix concentric cylinder model where the interface layers can be either homogeneous or heterogeneous. The response of heterogeneous layers is modeled using Aboudi's three-dimensional method of cells micromechanics model. The commercial optimization package DOT is used for the nonlinear optimization problem. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Gromita: a fully integrated graphical user interface to gromacs 4.

PubMed

Sellis, Diamantis; Vlachakis, Dimitrios; Vlassi, Metaxia

2009-09-07

Gromita is a fully integrated and efficient graphical user interface (GUI) to the recently updated molecular dynamics suite Gromacs, version 4. Gromita is a cross-platform, perl/tcl-tk based, interactive front end designed to break the command line barrier and introduce a new user-friendly environment to run molecular dynamics simulations through Gromacs. Our GUI features a novel workflow interface that guides the user through each logical step of the molecular dynamics setup process, making it accessible to both advanced and novice users. This tool provides a seamless interface to the Gromacs package, while providing enhanced functionality by speeding up and simplifying the task of setting up molecular dynamics simulations of biological systems. Gromita can be freely downloaded from http://bio.demokritos.gr/gromita/.

Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network.

PubMed

Al-Mayouf, Yusor Rafid Bahar; Ismail, Mahamod; Abdullah, Nor Fadzilah; Wahab, Ainuddin Wahid Abdul; Mahdi, Omar Adil; Khan, Suleman; Choo, Kim-Kwang Raymond

2016-01-01

Vehicular ad hoc networks (VANETs) are considered an emerging technology in the industrial and educational fields. This technology is essential in the deployment of the intelligent transportation system, which is targeted to improve safety and efficiency of traffic. The implementation of VANETs can be effectively executed by transmitting data among vehicles with the use of multiple hops. However, the intrinsic characteristics of VANETs, such as its dynamic network topology and intermittent connectivity, limit data delivery. One particular challenge of this network is the possibility that the contributing node may only remain in the network for a limited time. Hence, to prevent data loss from that node, the information must reach the destination node via multi-hop routing techniques. An appropriate, efficient, and stable routing algorithm must be developed for various VANET applications to address the issues of dynamic topology and intermittent connectivity. Therefore, this paper proposes a novel routing algorithm called efficient and stable routing algorithm based on user mobility and node density (ESRA-MD). The proposed algorithm can adapt to significant changes that may occur in the urban vehicular environment. This algorithm works by selecting an optimal route on the basis of hop count and link duration for delivering data from source to destination, thereby satisfying various quality of service considerations. The validity of the proposed algorithm is investigated by its comparison with ARP-QD protocol, which works on the mechanism of optimal route finding in VANETs in urban environments. Simulation results reveal that the proposed ESRA-MD algorithm shows remarkable improvement in terms of delivery ratio, delivery delay, and communication overhead.

Optimal control, investment and utilization schemes for energy storage under uncertainty

NASA Astrophysics Data System (ADS)

Mirhosseini, Niloufar Sadat

Energy storage has the potential to offer new means for added flexibility on the electricity systems. This flexibility can be used in a number of ways, including adding value towards asset management, power quality and reliability, integration of renewable resources and energy bill savings for the end users. However, uncertainty about system states and volatility in system dynamics can complicate the question of when to invest in energy storage and how best to manage and utilize it. This work proposes models to address different problems associated with energy storage within a microgrid, including optimal control, investment, and utilization. Electric load, renewable resources output, storage technology cost and electricity day-ahead and spot prices are the factors that bring uncertainty to the problem. A number of analytical methodologies have been adopted to develop the aforementioned models. Model Predictive Control and discretized dynamic programming, along with a new decomposition algorithm are used to develop optimal control schemes for energy storage for two different levels of renewable penetration. Real option theory and Monte Carlo simulation, coupled with an optimal control approach, are used to obtain optimal incremental investment decisions, considering multiple sources of uncertainty. Two stage stochastic programming is used to develop a novel and holistic methodology, including utilization of energy storage within a microgrid, in order to optimally interact with energy market. Energy storage can contribute in terms of value generation and risk reduction for the microgrid. The integration of the models developed here are the basis for a framework which extends from long term investments in storage capacity to short term operational control (charge/discharge) of storage within a microgrid. In particular, the following practical goals are achieved: (i) optimal investment on storage capacity over time to maximize savings during normal and emergency operations; (ii) optimal market strategy of buy and sell over 24-hour periods; (iii) optimal storage charge and discharge in much shorter time intervals.

Variational Trajectory Optimization Tool Set: Technical description and user's manual

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Queen, Eric M.; Cavanaugh, Michael D.; Wetzel, Todd A.; Moerder, Daniel D.

1993-01-01

The algorithms that comprise the Variational Trajectory Optimization Tool Set (VTOTS) package are briefly described. The VTOTS is a software package for solving nonlinear constrained optimal control problems from a wide range of engineering and scientific disciplines. The VTOTS package was specifically designed to minimize the amount of user programming; in fact, for problems that may be expressed in terms of analytical functions, the user needs only to define the problem in terms of symbolic variables. This version of the VTOTS does not support tabular data; thus, problems must be expressed in terms of analytical functions. The VTOTS package consists of two methods for solving nonlinear optimal control problems: a time-domain finite-element algorithm and a multiple shooting algorithm. These two algorithms, under the VTOTS package, may be run independently or jointly. The finite-element algorithm generates approximate solutions, whereas the shooting algorithm provides a more accurate solution to the optimization problem. A user's manual, some examples with results, and a brief description of the individual subroutines are included.

Stochastic Optimization For Water Resources Allocation

NASA Astrophysics Data System (ADS)

Yamout, G.; Hatfield, K.

2003-12-01

For more than 40 years, water resources allocation problems have been addressed using deterministic mathematical optimization. When data uncertainties exist, these methods could lead to solutions that are sub-optimal or even infeasible. While optimization models have been proposed for water resources decision-making under uncertainty, no attempts have been made to address the uncertainties in water allocation problems in an integrated approach. This paper presents an Integrated Dynamic, Multi-stage, Feedback-controlled, Linear, Stochastic, and Distributed parameter optimization approach to solve a problem of water resources allocation. It attempts to capture (1) the conflict caused by competing objectives, (2) environmental degradation produced by resource consumption, and finally (3) the uncertainty and risk generated by the inherently random nature of state and decision parameters involved in such a problem. A theoretical system is defined throughout its different elements. These elements consisting mainly of water resource components and end-users are described in terms of quantity, quality, and present and future associated risks and uncertainties. Models are identified, modified, and interfaced together to constitute an integrated water allocation optimization framework. This effort is a novel approach to confront the water allocation optimization problem while accounting for uncertainties associated with all its elements; thus resulting in a solution that correctly reflects the physical problem in hand.

A quality of service negotiation procedure for distributed multimedia presentational applications

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

Hafid, A.; Bochmann, G.V.; Kerherve, B.

Most of current approaches in designing and implementing distributed multimedia (MM) presentational applications, e.g. news-on-demand, have concentrated on the performance of the continuous media file servers in terms of seek time overhead, and real-time disk scheduling; particularly the QoS negotiation mechanisms they provide are used in a rather static manner that is, these mechanisms are restricted to the evaluation of the capacity of certain system components, e.g. file server a priori known to support a specific quality of service (QoS). In contrast to those approaches, we propose a general QoS negotiation framework that supports the dynamic choice of a configurationmore » of system components to support the QoS requirements of the user of a specific application: we consider different possible system configurations and select an optimal one to provide the appropriate QoS support. In this paper we document the design and implementation of a QoS negotiation procedure for distributed MM presentational applications, such as news-on-demand. The negotiation procedure described here is an instantiation of the general framework for QoS negotiation which was developed earlier Our proposal differs in many respect with the negotiation functions provided by existing approaches: (1) the negotiation process uses an optimization approach to find a configuration of system components which supports the user requirements, (2) the negotiation process supports the negotiation of a MM document and not only a single monomedia object, (3) the QoS negotiation takes into account the cost to the user, and (4) the negotiation process may be used to support automatic adaptation to react to QoS degradations, without intervention by the user/application.« less

The emergence of Zipf's law - Spontaneous encoding optimization by users of a command language

NASA Technical Reports Server (NTRS)

Ellis, S. R.; Hitchcock, R. J.

1986-01-01

The distribution of commands issued by experienced users of a computer operating system allowing command customization tends to conform to Zipf's law. This result documents the emergence of a statistical property of natural language as users master an artificial language. Analysis of Zipf's law by Mandelbrot and Cherry shows that its emergence in the computer interaction of experienced users may be interpreted as evidence that these users optimize their encoding of commands. Accordingly, the extent to which users of a command language exhibit Zipf's law can provide a metric of the naturalness and efficiency with which that language is used.

Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum.

PubMed

Yasuma, Fumihito; Mitsunaga, Tomoo; Iso, Daisuke; Nayar, Shree K

2010-09-01

We propose the concept of a generalized assorted pixel (GAP) camera, which enables the user to capture a single image of a scene and, after the fact, control the tradeoff between spatial resolution, dynamic range and spectral detail. The GAP camera uses a complex array (or mosaic) of color filters. A major problem with using such an array is that the captured image is severely under-sampled for at least some of the filter types. This leads to reconstructed images with strong aliasing. We make four contributions in this paper: 1) we present a comprehensive optimization method to arrive at the spatial and spectral layout of the color filter array of a GAP camera. 2) We develop a novel algorithm for reconstructing the under-sampled channels of the image while minimizing aliasing artifacts. 3) We demonstrate how the user can capture a single image and then control the tradeoff of spatial resolution to generate a variety of images, including monochrome, high dynamic range (HDR) monochrome, RGB, HDR RGB, and multispectral images. 4) Finally, the performance of our GAP camera has been verified using extensive simulations that use multispectral images of real world scenes. A large database of these multispectral images has been made available at http://www1.cs.columbia.edu/CAVE/projects/gap_camera/ for use by the research community.

Numerical, Analytical, Experimental Study of Fluid Dynamic Forces in Seals Volume 6: Description of Scientific CFD Code SCISEAL

NASA Technical Reports Server (NTRS)

Athavale, Mahesh; Przekwas, Andrzej

2004-01-01

The objectives of the program were to develop computational fluid dynamics (CFD) codes and simpler industrial codes for analyzing and designing advanced seals for air-breathing and space propulsion engines. The CFD code SCISEAL is capable of producing full three-dimensional flow field information for a variety of cylindrical configurations. An implicit multidomain capability allow the division of complex flow domains to allow optimum use of computational cells. SCISEAL also has the unique capability to produce cross-coupled stiffness and damping coefficients for rotordynamic computations. The industrial codes consist of a series of separate stand-alone modules designed for expeditious parametric analyses and optimization of a wide variety of cylindrical and face seals. Coupled through a Knowledge-Based System (KBS) that provides a user-friendly Graphical User Interface (GUI), the industrial codes are PC based using an OS/2 operating system. These codes were designed to treat film seals where a clearance exists between the rotating and stationary components. Leakage is inhibited by surface roughness, small but stiff clearance films, and viscous pumping devices. The codes have demonstrated to be a valuable resource for seal development of future air-breathing and space propulsion engines.

Trust and compactness in social network groups.

PubMed

De Meo, Pasquale; Ferrara, Emilio; Rosaci, Domenico; Sarné, Giuseppe M L

2015-02-01

Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is particularly relevant. While the literature usually refers to compactness as a measure to merely determine how much members of a group are similar among each other, we argue that the mutual trustworthiness between the members should be considered as an important factor in defining such a term. In fact, trust has profound effects on the dynamics of group formation and their evolution: individuals are more likely to join with and stay in a group if they can trust other group members. In this paper, we propose a quantitative measure of group compactness that takes into account both the similarity and the trustworthiness among users, and we present an algorithm to optimize such a measure. We provide empirical results, obtained from the real social networks EPINIONS and CIAO, that compare our notion of compactness versus the traditional notion of user similarity, clearly proving the advantages of our approach.

Beam dynamics and expected performance of Sweden's new storage-ring light source: MAX IV

NASA Astrophysics Data System (ADS)

Leemann, S. C.; Andersson, Å.; Eriksson, M.; Lindgren, L.-J.; Wallén, E.; Bengtsson, J.; Streun, A.

2009-12-01

MAX IV will be Sweden’s next-generation high-performance synchrotron radiation source. The project has recently been granted funding and construction is scheduled to begin in 2010. User operation for a broad and international user community should commence in 2015. The facility is comprised of two storage rings optimized for different wavelength ranges, a linac-based short-pulse facility and a free-electron laser for the production of coherent radiation. The main radiation source of MAX IV will be a 528 m ultralow emittance storage ring operated at 3 GeV for the generation of high-brightness hard x rays. This storage ring was designed to meet the requirements of state-of-the-art insertion devices which will be installed in nineteen 5 m long dispersion-free straight sections. The storage ring is based on a novel multibend achromat design delivering an unprecedented horizontal bare lattice emittance of 0.33 nm rad and a vertical emittance below the 8 pm rad diffraction limit for 1 Å radiation. In this paper we present the beam dynamics considerations behind this storage-ring design and detail its expected unique performance.

OPTIMAL WELL LOCATOR (OWL): A SCREENING TOOL FOR EVALUATING LOCATIONS OF MONITORING WELLS: USER'S GUIDE VERSION 1.2

EPA Science Inventory

The Optimal Well Locator ( OWL) program was designed and developed by USEPA to be a screening tool to evaluate and optimize the placement of wells in long term monitoring networks at small sites. The first objective of the OWL program is to allow the user to visualize the change ...

Pricing Resources in LTE Networks through Multiobjective Optimization

PubMed Central

Lai, Yung-Liang; Jiang, Jehn-Ruey

2014-01-01

The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution. PMID:24526889

Pricing resources in LTE networks through multiobjective optimization.

PubMed

Lai, Yung-Liang; Jiang, Jehn-Ruey

2014-01-01

The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid "user churn," which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.

Playing Games with Optimal Competitive Scheduling

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Crawford, James; Khatib, Lina; Brafman, Ronen

2005-01-01

This paper is concerned with the problem of allocating a unit capacity resource to multiple users within a pre-defined time period. The resource is indivisible, so that at most one user can use it at each time instance. However, different users may use it at different times. The users have independent, selfish preferences for when and for how long they are allocated this resource. Thus, they value different resource access durations differently, and they value different time slots differently. We seek an optimal allocation schedule for this resource.

Intelligent automated surface grid generation

NASA Technical Reports Server (NTRS)

Yao, Ke-Thia; Gelsey, Andrew

1995-01-01

The goal of our research is to produce a flexible, general grid generator for automated use by other programs, such as numerical optimizers. The current trend in the gridding field is toward interactive gridding. Interactive gridding more readily taps into the spatial reasoning abilities of the human user through the use of a graphical interface with a mouse. However, a sometimes fruitful approach to generating new designs is to apply an optimizer with shape modification operators to improve an initial design. In order for this approach to be useful, the optimizer must be able to automatically grid and evaluate the candidate designs. This paper describes and intelligent gridder that is capable of analyzing the topology of the spatial domain and predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains of wide range of configurations.

Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms

PubMed Central

Yau, Kok-Lim Alvin; Poh, Geong-Sen; Chien, Su Fong; Al-Rawi, Hasan A. A.

2014-01-01

Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR. PMID:24995352

Analysis of severe storm data

NASA Technical Reports Server (NTRS)

Hickey, J. S.

1983-01-01

The Mesoscale Analysis and Space Sensor (MASS) Data Management and Analysis System developed by Atsuko Computing International (ACI) on the MASS HP-1000 Computer System within the Systems Dynamics Laboratory of the Marshall Space Flight Center is described. The MASS Data Management and Analysis System was successfully implemented and utilized daily by atmospheric scientists to graphically display and analyze large volumes of conventional and satellite derived meteorological data. The scientists can process interactively various atmospheric data (Sounding, Single Level, Gird, and Image) by utilizing the MASS (AVE80) share common data and user inputs, thereby reducing overhead, optimizing execution time, and thus enhancing user flexibility, useability, and understandability of the total system/software capabilities. In addition ACI installed eight APPLE III graphics/imaging computer terminals in individual scientist offices and integrated them into the MASS HP-1000 Computer System thus providing significant enhancement to the overall research environment.

On-board B-ISDN fast packet switching architectures. Phase 1: Study

NASA Technical Reports Server (NTRS)

Faris, Faris; Inukai, Thomas; Lee, Fred; Paul, Dilip; Shyy, Dong-Jye

1993-01-01

The broadband integrate services digital network (B-ISDN) is an emerging telecommunications technology that will meet most of the telecommunications networking needs in the mid-1990's to early next century. The satellite-based system is well positioned for providing B-ISDN service with its inherent capabilities of point-to-multipoint and broadcast transmission, virtually unlimited connectivity between any two points within a beam coverage, short deployment time of communications facility, flexible and dynamic reallocation of space segment capacity, and distance insensitive cost. On-board processing satellites, particularly in a multiple spot beam environment, will provide enhanced connectivity, better performance, optimized access and transmission link design, and lower user service cost. The following are described: the user and network aspects of broadband services; the current development status in broadband services; various satellite network architectures including system design issues; and various fast packet switch architectures and their detail designs.

Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

NASA Astrophysics Data System (ADS)

Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

2011-12-01

HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of each timestep and minimize computational overhead. Power generation for each reservoir is estimated using a 2-dimensional regression that accounts for both the available head and turbine efficiency. The object-oriented architecture makes run configuration easy to update. The dynamic model inputs include inflow and meteorological forecasts while static inputs include bathymetry data, reservoir and power generation characteristics, and topological descriptors. Ensemble forecasts of hydrological and meteorological conditions are supplied in real-time by Pacific Northwest National Laboratory and are used as a proxy for uncertainty, which is carried through the simulation and optimization process to produce output that describes the probability that different operational scenario's will be optimal. The full toolset, which includes HydroSCOPE, is currently being tested on the Feather River system in Northern California and the Upper Colorado Storage Project.

Simulation of geothermal water extraction in heterogeneous reservoirs using dynamic unstructured mesh optimisation

NASA Astrophysics Data System (ADS)

Salinas, P.; Pavlidis, D.; Jacquemyn, C.; Lei, Q.; Xie, Z.; Pain, C.; Jackson, M.

2017-12-01

It is well known that the pressure gradient into a production well increases with decreasing distance to the well. To properly capture the local pressure drawdown into the well a high grid or mesh resolution is required; moreover, the location of the well must be captured accurately. In conventional simulation models, the user must interact with the model to modify grid resolution around wells of interest, and the well location is approximated on a grid defined early in the modelling process.We report a new approach for improved simulation of near wellbore flow in reservoir scale models through the use of dynamic mesh optimisation and the recently presented double control volume finite element method. Time is discretized using an adaptive, implicit approach. Heterogeneous geologic features are represented as volumes bounded by surfaces. Within these volumes, termed geologic domains, the material properties are constant. Up-, cross- or down-scaling of material properties during dynamic mesh optimization is not required, as the properties are uniform within each geologic domain. A given model typically contains numerous such geologic domains. Wells are implicitly coupled with the domain, and the fluid flows is modelled inside the wells. The method is novel for two reasons. First, a fully unstructured tetrahedral mesh is used to discretize space, and the spatial location of the well is specified via a line vector, ensuring its location even if the mesh is modified during the simulation. The well location is therefore accurately captured, the approach allows complex well trajectories and wells with many laterals to be modelled. Second, computational efficiency is increased by use of dynamic mesh optimization, in which an unstructured mesh adapts in space and time to key solution fields (preserving the geometry of the geologic domains), such as pressure, velocity or temperature, this also increases the quality of the solutions by placing higher resolution where required to reduce an error metric based on the Hessian of the field. This allows the local pressure drawdown to be captured without user¬ driven modification of the mesh. We demonstrate that the method has wide application in reservoir ¬scale models of geothermal fields, and regional models of groundwater resources.

An integrated modeling and design tool for advanced optical spacecraft

NASA Technical Reports Server (NTRS)

Briggs, Hugh C.

1992-01-01

Consideration is given to the design and status of the Integrated Modeling of Optical Systems (IMOS) tool and to critical design issues. A multidisciplinary spacecraft design and analysis tool with support for structural dynamics, controls, thermal analysis, and optics, IMOS provides rapid and accurate end-to-end performance analysis, simulations, and optimization of advanced space-based optical systems. The requirements for IMOS-supported numerical arrays, user defined data structures, and a hierarchical data base are outlined, and initial experience with the tool is summarized. A simulation of a flexible telescope illustrates the integrated nature of the tools.

Subjective evaluation of H.265/HEVC based dynamic adaptive video streaming over HTTP (HEVC-DASH)

NASA Astrophysics Data System (ADS)

Irondi, Iheanyi; Wang, Qi; Grecos, Christos

2015-02-01

The Dynamic Adaptive Streaming over HTTP (DASH) standard is becoming increasingly popular for real-time adaptive HTTP streaming of internet video in response to unstable network conditions. Integration of DASH streaming techniques with the new H.265/HEVC video coding standard is a promising area of research. The performance of HEVC-DASH systems has been previously evaluated by a few researchers using objective metrics, however subjective evaluation would provide a better measure of the user's Quality of Experience (QoE) and overall performance of the system. This paper presents a subjective evaluation of an HEVC-DASH system implemented in a hardware testbed. Previous studies in this area have focused on using the current H.264/AVC (Advanced Video Coding) or H.264/SVC (Scalable Video Coding) codecs and moreover, there has been no established standard test procedure for the subjective evaluation of DASH adaptive streaming. In this paper, we define a test plan for HEVC-DASH with a carefully justified data set employing longer video sequences that would be sufficient to demonstrate the bitrate switching operations in response to various network condition patterns. We evaluate the end user's real-time QoE online by investigating the perceived impact of delay, different packet loss rates, fluctuating bandwidth, and the perceived quality of using different DASH video stream segment sizes on a video streaming session using different video sequences. The Mean Opinion Score (MOS) results give an insight into the performance of the system and expectation of the users. The results from this study show the impact of different network impairments and different video segments on users' QoE and further analysis and study may help in optimizing system performance.

Optimization and resilience of complex supply-demand networks

NASA Astrophysics Data System (ADS)

Zhang, Si-Ping; Huang, Zi-Gang; Dong, Jia-Qi; Eisenberg, Daniel; Seager, Thomas P.; Lai, Ying-Cheng

2015-06-01

Supply-demand processes take place on a large variety of real-world networked systems ranging from power grids and the internet to social networking and urban systems. In a modern infrastructure, supply-demand systems are constantly expanding, leading to constant increase in load requirement for resources and consequently, to problems such as low efficiency, resource scarcity, and partial system failures. Under certain conditions global catastrophe on the scale of the whole system can occur through the dynamical process of cascading failures. We investigate optimization and resilience of time-varying supply-demand systems by constructing network models of such systems, where resources are transported from the supplier sites to users through various links. Here by optimization we mean minimization of the maximum load on links, and system resilience can be characterized using the cascading failure size of users who fail to connect with suppliers. We consider two representative classes of supply schemes: load driven supply and fix fraction supply. Our findings are: (1) optimized systems are more robust since relatively smaller cascading failures occur when triggered by external perturbation to the links; (2) a large fraction of links can be free of load if resources are directed to transport through the shortest paths; (3) redundant links in the performance of the system can help to reroute the traffic but may undesirably transmit and enlarge the failure size of the system; (4) the patterns of cascading failures depend strongly upon the capacity of links; (5) the specific location of the trigger determines the specific route of cascading failure, but has little effect on the final cascading size; (6) system expansion typically reduces the efficiency; and (7) when the locations of the suppliers are optimized over a long expanding period, fewer suppliers are required. These results hold for heterogeneous networks in general, providing insights into designing optimal and resilient complex supply-demand systems that expand constantly in time.

User's manual for the BNW-I optimization code for dry-cooled power plants. Volume III. [PLCIRI

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

Braun, D.J.; Daniel, D.J.; De Mier, W.V.

1977-01-01

This appendix to User's Manual for the BNW-1 Optimization Code for Dry-Cooled Power Plants provides a listing of the BNW-I optimization code for determining, for a particular size power plant, the optimum dry cooling tower design using a plastic tube cooling surface and circular tower arrangement of the tube bundles. (LCL)

GeMS: an advanced software package for designing synthetic genes.

PubMed

Jayaraj, Sebastian; Reid, Ralph; Santi, Daniel V

2005-01-01

A user-friendly, advanced software package for gene design is described. The software comprises an integrated suite of programs-also provided as stand-alone tools-that automatically performs the following tasks in gene design: restriction site prediction, codon optimization for any expression host, restriction site inclusion and exclusion, separation of long sequences into synthesizable fragments, T(m) and stem-loop determinations, optimal oligonucleotide component design and design verification/error-checking. The output is a complete design report and a list of optimized oligonucleotides to be prepared for subsequent gene synthesis. The user interface accommodates both inexperienced and experienced users. For inexperienced users, explanatory notes are provided such that detailed instructions are not necessary; for experienced users, a streamlined interface is provided without such notes. The software has been extensively tested in the design and successful synthesis of over 400 kb of genes, many of which exceeded 5 kb in length.

User Interface Design for Dynamic Geometry Software

ERIC Educational Resources Information Center

Kortenkamp, Ulrich; Dohrmann, Christian

2010-01-01

In this article we describe long-standing user interface issues with Dynamic Geometry Software and common approaches to address them. We describe first prototypes of multi-touch-capable DGS. We also give some hints on the educational benefits of proper user interface design.

Pollution source localization in an urban water supply network based on dynamic water demand.

PubMed

Yan, Xuesong; Zhu, Zhixin; Li, Tian

2017-10-27

Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

Recommendation Systems for Geoscience Data Portals Built by Analyzing Usage Patterns

NASA Astrophysics Data System (ADS)

Crosby, C.; Nandigam, V.; Baru, C.

2009-04-01

Since its launch five years ago, the National Science Foundation-funded GEON Project (www.geongrid.org) has been providing access to a variety of geoscience data sets such as geologic maps and other geographic information system (GIS)-oriented data, paleontologic databases, gravity and magnetics data and LiDAR topography via its online portal interface. In addition to data, the GEON Portal also provides web-based tools and other resources that enable users to process and interact with data. Examples of these tools include functions to dynamically map and integrate GIS data, compute synthetic seismograms, and to produce custom digital elevation models (DEMs) with user defined parameters such as resolution. The GEON portal built on the Gridsphere-portal framework allows us to capture user interaction with the system. In addition to the site access statistics captured by tools like Google Analystics which capture hits per unit time, search key words, operating systems, browsers, and referring sites, we also record additional statistics such as which data sets are being downloaded and in what formats, processing parameters, and navigation pathways through the portal. With over four years of data now available from the GEON Portal, this record of usage is a rich resource for exploring how earth scientists discover and utilize online data sets. Furthermore, we propose that this data could ultimately be harnessed to optimize the way users interact with the data portal, design intelligent processing and data management systems, and to make recommendations on algorithm settings and other available relevant data. The paradigm of integrating popular and commonly used patterns to make recommendations to a user is well established in the world of e-commerce where users receive suggestions on books, music and other products that they may find interesting based on their website browsing and purchasing history, as well as the patterns of fellow users who have made similar selections. However, this paradigm has not yet been explored for geoscience data portals. In this presentation we will present an initial analysis of user interaction and access statistics for the GEON OpenTopography LiDAR data distribution and processing system to illustrate what they reveal about user's spatial and temporal data access patterns, data processing parameter selections, and pathways through the data portal. We also demonstrate what these usage statistics can illustrate about aspects of the data sets that are of greatest interest. Finally, we explore how these usage statistics could be used to improve the user's experience in the data portal and to optimize how data access interfaces and tools are designed and implemented.

Optimization of Residual Stresses in MMC's Using Compensating/Compliant Interfacial Layers. Part 2: OPTCOMP User's Guide

NASA Technical Reports Server (NTRS)

Pindera, Marek-Jerzy; Salzar, Robert S.; Williams, Todd O.

1994-01-01

A user's guide for the computer program OPTCOMP is presented in this report. This program provides a capability to optimize the fabrication or service-induced residual stresses in uni-directional metal matrix composites subjected to combined thermo-mechanical axisymmetric loading using compensating or compliant layers at the fiber/matrix interface. The user specifies the architecture and the initial material parameters of the interfacial region, which can be either elastic or elastoplastic, and defines the design variables, together with the objective function, the associated constraints and the loading history through a user-friendly data input interface. The optimization procedure is based on an efficient solution methodology for the elastoplastic response of an arbitrarily layered multiple concentric cylinder model that is coupled to the commercial optimization package DOT. The solution methodology for the arbitrarily layered cylinder is based on the local-global stiffness matrix formulation and Mendelson's iterative technique of successive elastic solutions developed for elastoplastic boundary-value problems. The optimization algorithm employed in DOT is based on the method of feasible directions.

Fog computing job scheduling optimization based on bees swarm

NASA Astrophysics Data System (ADS)

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

2018-04-01

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

A Human-Centered Smart Home System with Wearable-Sensor Behavior Analysis

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

Ji, Jianting; Liu, Ting; Shen, Chao

Smart home has recently attracted much research interest owing to its potential in improving the quality of human life. How to obtain user's demand is the most important and challenging task for appliance optimal scheduling in smart home, since it is highly related to user's unpredictable behavior. In this paper, a human-centered smart home system is proposed to identify user behavior, predict their demand and schedule the household appliances. Firstly, the sensor data from user's wearable devices are monitored to profile user's full-day behavior. Then, the appliance-demand matrix is constructed to predict user's demand on home environment, which is extractedmore » from the history of appliance load data and user behavior. Two simulations are designed to demonstrate user behavior identification, appliance-demand matrix construction and strategy of appliance optimal scheduling generation.« less

Optimization technique of wavefront coding system based on ZEMAX externally compiled programs

NASA Astrophysics Data System (ADS)

Han, Libo; Dong, Liquan; Liu, Ming; Zhao, Yuejin; Liu, Xiaohua

2016-10-01

Wavefront coding technique as a means of athermalization applied to infrared imaging system, the design of phase plate is the key to system performance. This paper apply the externally compiled programs of ZEMAX to the optimization of phase mask in the normal optical design process, namely defining the evaluation function of wavefront coding system based on the consistency of modulation transfer function (MTF) and improving the speed of optimization by means of the introduction of the mathematical software. User write an external program which computes the evaluation function on account of the powerful computing feature of the mathematical software in order to find the optimal parameters of phase mask, and accelerate convergence through generic algorithm (GA), then use dynamic data exchange (DDE) interface between ZEMAX and mathematical software to realize high-speed data exchanging. The optimization of the rotational symmetric phase mask and the cubic phase mask have been completed by this method, the depth of focus increases nearly 3 times by inserting the rotational symmetric phase mask, while the other system with cubic phase mask can be increased to 10 times, the consistency of MTF decrease obviously, the maximum operating temperature of optimized system range between -40°-60°. Results show that this optimization method can be more convenient to define some unconventional optimization goals and fleetly to optimize optical system with special properties due to its externally compiled function and DDE, there will be greater significance for the optimization of unconventional optical system.

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

NASA Astrophysics Data System (ADS)

Guo, Kun; Sun, Yi; Qian, Xin

2017-03-01

With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.

An Intrinsically Digital Amplification Scheme for Hearing Aids

NASA Astrophysics Data System (ADS)

Blamey, Peter J.; Macfarlane, David S.; Steele, Brenton R.

2005-12-01

Results for linear and wide-dynamic range compression were compared with a new 64-channel digital amplification strategy in three separate studies. The new strategy addresses the requirements of the hearing aid user with efficient computations on an open-platform digital signal processor (DSP). The new amplification strategy is not modeled on prior analog strategies like compression and linear amplification, but uses statistical analysis of the signal to optimize the output dynamic range in each frequency band independently. Using the open-platform DSP processor also provided the opportunity for blind trial comparisons of the different processing schemes in BTE and ITE devices of a high commercial standard. The speech perception scores and questionnaire results show that it is possible to provide improved audibility for sound in many narrow frequency bands while simultaneously improving comfort, speech intelligibility in noise, and sound quality.

A review on the mechanical design elements of ankle rehabilitation robot.

PubMed

Khalid, Yusuf M; Gouwanda, Darwin; Parasuraman, Subramanian

2015-06-01

Ankle rehabilitation robots are developed to enhance ankle strength, flexibility and proprioception after injury and to promote motor learning and ankle plasticity in patients with drop foot. This article reviews the design elements that have been incorporated into the existing robots, for example, backdrivability, safety measures and type of actuation. It also discusses numerous challenges faced by engineers in designing this robot, including robot stability and its dynamic characteristics, universal evaluation criteria to assess end-user comfort, safety and training performance and the scientific basis on the optimal rehabilitation strategies to improve ankle condition. This article can serve as a reference to design robot with better stability and dynamic characteristics and good safety measures against internal and external events. It can also serve as a guideline for the engineers to report their designs and findings. © IMechE 2015.

Detection, Evaluation, and Optimization of Optical Signals Generated by Fiber Optic Bragg Gratings Under Dynamic Excitations

NASA Technical Reports Server (NTRS)

Adamovsky, Grigory; Lekki, John; Lock, James A.

2002-01-01

The dynamic response of a fiber optic Bragg grating to mechanical vibrations is examined both theoretically and experimentally. The theoretical expressions describing the consequences of changes in the grating's reflection spectrum are derived for partially coherent beams in an interferometer. The analysis is given in terms of the dominant wavelength, optical bandwidth, and optical path difference of the interfering signals. Changes in the reflection spectrum caused by a periodic stretching and compression of the grating were experimentally measured using an unbalanced Michelson interferometer, a Michelson interferometer with a non-zero optical path difference. The interferometer's sensitivity to changes in dominant wavelength of the interfering beams was measured as a function of interferometer unbalance and was compared to theoretical predictions. The theoretical analysis enables the user to determine the optimum performance for an unbalanced interferometer.

Software package for modeling spin–orbit motion in storage rings

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

Zyuzin, D. V., E-mail: d.zyuzin@fz-juelich.de

2015-12-15

A software package providing a graphical user interface for computer experiments on the motion of charged particle beams in accelerators, as well as analysis of obtained data, is presented. The software package was tested in the framework of the international project on electric dipole moment measurement JEDI (Jülich Electric Dipole moment Investigations). The specific features of particle spin motion imply the requirement to use a cyclic accelerator (storage ring) consisting of electrostatic elements, which makes it possible to preserve horizontal polarization for a long time. Computer experiments study the dynamics of 10{sup 6}–10{sup 9} particles in a beam during 10{supmore » 9} turns in an accelerator (about 10{sup 12}–10{sup 15} integration steps for the equations of motion). For designing an optimal accelerator structure, a large number of computer experiments on polarized beam dynamics are required. The numerical core of the package is COSY Infinity, a program for modeling spin–orbit dynamics.« less

An Agent-Based Modeling Framework and Application for the Generic Nuclear Fuel Cycle

NASA Astrophysics Data System (ADS)

Gidden, Matthew J.

Key components of a novel methodology and implementation of an agent-based, dynamic nuclear fuel cycle simulator, Cyclus , are presented. The nuclear fuel cycle is a complex, physics-dependent supply chain. To date, existing dynamic simulators have not treated constrained fuel supply, time-dependent, isotopic-quality based demand, or fuel fungibility particularly well. Utilizing an agent-based methodology that incorporates sophisticated graph theory and operations research techniques can overcome these deficiencies. This work describes a simulation kernel and agents that interact with it, highlighting the Dynamic Resource Exchange (DRE), the supply-demand framework at the heart of the kernel. The key agent-DRE interaction mechanisms are described, which enable complex entity interaction through the use of physics and socio-economic models. The translation of an exchange instance to a variant of the Multicommodity Transportation Problem, which can be solved feasibly or optimally, follows. An extensive investigation of solution performance and fidelity is then presented. Finally, recommendations for future users of Cyclus and the DRE are provided.

Trajectory Optimization: OTIS 4

NASA Technical Reports Server (NTRS)

Riehl, John P.; Sjauw, Waldy K.; Falck, Robert D.; Paris, Stephen W.

2010-01-01

The latest release of the Optimal Trajectories by Implicit Simulation (OTIS4) allows users to simulate and optimize aerospace vehicle trajectories. With OTIS4, one can seamlessly generate optimal trajectories and parametric vehicle designs simultaneously. New features also allow OTIS4 to solve non-aerospace continuous time optimal control problems. The inputs and outputs of OTIS4 have been updated extensively from previous versions. Inputs now make use of objectoriented constructs, including one called a metastring. Metastrings use a greatly improved calculator and common nomenclature to reduce the user s workload. They allow for more flexibility in specifying vehicle physical models, boundary conditions, and path constraints. The OTIS4 calculator supports common mathematical functions, Boolean operations, and conditional statements. This allows users to define their own variables for use as outputs, constraints, or objective functions. The user-defined outputs can directly interface with other programs, such as spreadsheets, plotting packages, and visualization programs. Internally, OTIS4 has more explicit and implicit integration procedures, including high-order collocation methods, the pseudo-spectral method, and several variations of multiple shooting. Users may switch easily between the various methods. Several unique numerical techniques such as automated variable scaling and implicit integration grid refinement, support the integration methods. OTIS4 is also significantly more user friendly than previous versions. The installation process is nearly identical on various platforms, including Microsoft Windows, Apple OS X, and Linux operating systems. Cross-platform scripts also help make the execution of OTIS and post-processing of data easier. OTIS4 is supplied free by NASA and is subject to ITAR (International Traffic in Arms Regulations) restrictions. Users must have a Fortran compiler, and a Python interpreter is highly recommended.

Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network

PubMed Central

Al-Mayouf, Yusor Rafid Bahar; Ismail, Mahamod; Abdullah, Nor Fadzilah; Wahab, Ainuddin Wahid Abdul; Mahdi, Omar Adil; Khan, Suleman; Choo, Kim-Kwang Raymond

2016-01-01

Vehicular ad hoc networks (VANETs) are considered an emerging technology in the industrial and educational fields. This technology is essential in the deployment of the intelligent transportation system, which is targeted to improve safety and efficiency of traffic. The implementation of VANETs can be effectively executed by transmitting data among vehicles with the use of multiple hops. However, the intrinsic characteristics of VANETs, such as its dynamic network topology and intermittent connectivity, limit data delivery. One particular challenge of this network is the possibility that the contributing node may only remain in the network for a limited time. Hence, to prevent data loss from that node, the information must reach the destination node via multi-hop routing techniques. An appropriate, efficient, and stable routing algorithm must be developed for various VANET applications to address the issues of dynamic topology and intermittent connectivity. Therefore, this paper proposes a novel routing algorithm called efficient and stable routing algorithm based on user mobility and node density (ESRA-MD). The proposed algorithm can adapt to significant changes that may occur in the urban vehicular environment. This algorithm works by selecting an optimal route on the basis of hop count and link duration for delivering data from source to destination, thereby satisfying various quality of service considerations. The validity of the proposed algorithm is investigated by its comparison with ARP-QD protocol, which works on the mechanism of optimal route finding in VANETs in urban environments. Simulation results reveal that the proposed ESRA-MD algorithm shows remarkable improvement in terms of delivery ratio, delivery delay, and communication overhead. PMID:27855165

Converted and upgraded maps programmed in the newer speech processor for the first generation of multichannel cochlear implant.

PubMed

Magalhães, Ana Tereza de Matos; Goffi-Gomez, M Valéria Schmidt; Hoshino, Ana Cristina; Tsuji, Robinson Koji; Bento, Ricardo Ferreira; Brito, Rubens

2013-09-01

To identify the technological contributions of the newer version of speech processor to the first generation of multichannel cochlear implant and the satisfaction of users of the new technology. Among the new features available, we focused on the effect of the frequency allocation table, the T-SPL and C-SPL, and the preprocessing gain adjustments (adaptive dynamic range optimization). Prospective exploratory study. Cochlear implant center at hospital. Cochlear implant users of the Spectra processor with speech recognition in closed set. Seventeen patients were selected between the ages of 15 and 82 and deployed for more than 8 years. The technology update of the speech processor for the Nucleus 22. To determine Freedom's contribution, thresholds and speech perception tests were performed with the last map used with the Spectra and the maps created for Freedom. To identify the effect of the frequency allocation table, both upgraded and converted maps were programmed. One map was programmed with 25 dB T-SPL and 65 dB C-SPL and the other map with adaptive dynamic range optimization. To assess satisfaction, SADL and APHAB were used. All speech perception tests and all sound field thresholds were statistically better with the new speech processor; 64.7% of patients preferred maintaining the same frequency table that was suggested for the older processor. The sound field threshold was statistically significant at 500, 1,000, 1,500, and 2,000 Hz with 25 dB T-SPL/65 dB C-SPL. Regarding patient's satisfaction, there was a statistically significant improvement, only in the subscale of speech in noise abilities and phone use. The new technology improved the performance of patients with the first generation of multichannel cochlear implant.

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

NASA Astrophysics Data System (ADS)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

Optimal handgrip height of four-wheeled walker on various road conditions to reduce muscular load for elderly users with steady walking.

PubMed

Takanokura, Masato

2010-03-22

A four-wheeled walker is a valuable tool for assisting elderly persons with walking. The handgrip height is one of the most important factor determining the usefulness of the walker. However, the optimal handgrip height for elderly users has not been considered from a biomechanical viewpoint. In this study, the handgrip height was optimized by a two-dimensional mechanical model to reduce muscular loads in the lower body as well as in the upper body with various road conditions during steady walking. A critical height of the handgrip existed at 48% of the body height for the user regardless of gender and body dimension. A lower handgrip relieved muscular load for stooping users with a lower standing height. The stooping user pushed the handgrip strongly in the perpendicular direction by leaning the upper body on the walker. However, upright users with a higher standing height should use a four-wheeled walker with a higher handgrip for maintaining his or her upright posture. For downhill movement, the optimal handgrip height depended on the slope angle and the friction coefficient between the road and the wheels of the walker. On a low-friction downhill such as asphalt with a steeper slope angle, the user was required to maintain an erect trunk with a higher handgrip and to press on the handgrip strongly in the perpendicular direction. Movement on a low-friction road was easier for users on a flat road and an uphill road, but it compelled distinct effort from users when moving downhill. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

The Dynamics of Online User Behavior and IS Policy Implications

ERIC Educational Resources Information Center

Kim, Keehyung

2016-01-01

This dissertation, which comprises three independent essays, explores the dynamics of online user behavior and provides IS policy implications across three different applications. The first essay employs an econometric empirical analysis to examine the role of IT interventions on online users' gambling behavior, based on field data collected over…

Multifunctional Mesoscale Observing Networks.

NASA Astrophysics Data System (ADS)

Dabberdt, Walter F.; Schlatter, Thomas W.; Carr, Frederick H.; Friday, Elbert W. Joe; Jorgensen, David; Koch, Steven; Pirone, Maria; Ralph, F. Martin; Sun, Juanzhen; Welsh, Patrick; Wilson, James W.; Zou, Xiaolei

2005-07-01

More than 120 scientists, engineers, administrators, and users met on 8 10 December 2003 in a workshop format to discuss the needs for enhanced three-dimensional mesoscale observing networks. Improved networks are seen as being critical to advancing numerical and empirical modeling for a variety of mesoscale applications, including severe weather warnings and forecasts, hydrology, air-quality forecasting, chemical emergency response, transportation safety, energy management, and others. The participants shared a clear and common vision for the observing requirements: existing two-dimensional mesoscale measurement networks do not provide observations of the type, frequency, and density that are required to optimize mesoscale prediction and nowcasts. To be viable, mesoscale observing networks must serve multiple applications, and the public, private, and academic sectors must all actively participate in their design and implementation, as well as in the creation and delivery of value-added products. The mesoscale measurement challenge can best be met by an integrated approach that considers all elements of an end-to-end solution—identifying end users and their needs, designing an optimal mix of observations, defining the balance between static and dynamic (targeted or adaptive) sampling strategies, establishing long-term test beds, and developing effective implementation strategies. Detailed recommendations are provided pertaining to nowcasting, numerical prediction and data assimilation, test beds, and implementation strategies.

Combining Interactive Infrastructure Modeling and Evolutionary Algorithm Optimization for Sustainable Water Resources Design

NASA Astrophysics Data System (ADS)

Smith, R.; Kasprzyk, J. R.; Zagona, E. A.

2013-12-01

Population growth and climate change, combined with difficulties in building new infrastructure, motivate portfolio-based solutions to ensuring sufficient water supply. Powerful simulation models with graphical user interfaces (GUI) are often used to evaluate infrastructure portfolios; these GUI based models require manual modification of the system parameters, such as reservoir operation rules, water transfer schemes, or system capacities. Multiobjective evolutionary algorithm (MOEA) based optimization can be employed to balance multiple objectives and automatically suggest designs for infrastructure systems, but MOEA based decision support typically uses a fixed problem formulation (i.e., a single set of objectives, decisions, and constraints). This presentation suggests a dynamic framework for linking GUI-based infrastructure models with MOEA search. The framework begins with an initial formulation which is solved using a MOEA. Then, stakeholders can interact with candidate solutions, viewing their properties in the GUI model. This is followed by changes in the formulation which represent users' evolving understanding of exigent system properties. Our case study is built using RiverWare, an object-oriented, data-centered model that facilitates the representation of a diverse array of water resources systems. Results suggest that assumptions within the initial MOEA search are violated after investigating tradeoffs and reveal how formulations should be modified to better capture stakeholders' preferences.

The High Energy Materials Science Beamline (HEMS) at PETRA III

NASA Astrophysics Data System (ADS)

Schell, Norbert; King, Andrew; Beckmann, Felix; Ruhnau, Hans-Ulrich; Kirchhof, René; Kiehn, Rüdiger; Müller, Martin; Schreyer, Andreas

2010-06-01

The HEMS Beamline at the German high-brilliance synchrotron radiation storage ring PETRA III is fully tunable between 30 and 250 keV and optimized for sub-micrometer focusing. Approximately 70 % of the beamtime will be dedicated to Materials Research. Fundamental research will encompass metallurgy, physics and chemistry with first experiments planned for the investigation of the relationship between macroscopic and micro-structural properties of polycrystalline materials, grain-grain-interactions, and the development of smart materials or processes. For this purpose a 3D-microsctructure-mapper has been designed. Applied research for manufacturing process optimization will benefit from high flux in combination with ultra-fast detector systems allowing complex and highly dynamic in-situ studies of micro-structural transformations, e.g. during welding processes. The beamline infrastructure allows accommodation of large and heavy user provided equipment. Experiments targeting the industrial user community will be based on well established techniques with standardized evaluation, allowing full service measurements, e.g. for tomography and texture determination. The beamline consists of a five meter in-vacuum undulator, a general optics hutch, an in-house test facility and three independent experimental hutches working alternately, plus additional set-up and storage space for long-term experiments. HEMS is under commissioning as one of the first beamlines running at PETRA III.

Planning and management of cloud computing networks

NASA Astrophysics Data System (ADS)

Larumbe, Federico

The evolution of the Internet has a great impact on a big part of the population. People use it to communicate, query information, receive news, work, and as entertainment. Its extraordinary usefulness as a communication media made the number of applications and technological resources explode. However, that network expansion comes at the cost of an important power consumption. If the power consumption of telecommunication networks and data centers is considered as the power consumption of a country, it would rank at the 5 th place in the world. Furthermore, the number of servers in the world is expected to grow by a factor of 10 between 2013 and 2020. This context motivates us to study techniques and methods to allocate cloud computing resources in an optimal way with respect to cost, quality of service (QoS), power consumption, and environmental impact. The results we obtained from our test cases show that besides minimizing capital expenditures (CAPEX) and operational expenditures (OPEX), the response time can be reduced up to 6 times, power consumption by 30%, and CO2 emissions by a factor of 60. Cloud computing provides dynamic access to IT resources as a service. In this paradigm, programs are executed in servers connected to the Internet that users access from their computers and mobile devices. The first advantage of this architecture is to reduce the time of application deployment and interoperability, because a new user only needs a web browser and does not need to install software on local computers with specific operating systems. Second, applications and information are available from everywhere and with any device with an Internet access. Also, servers and IT resources can be dynamically allocated depending on the number of users and workload, a feature called elasticity. This thesis studies the resource management of cloud computing networks and is divided in three main stages. We start by analyzing the planning of cloud computing networks to get a comprehensive vision. The first question to be solved is what are the optimal data center locations. We found that the location of each data center has a big impact on cost, QoS, power consumption, and greenhouse gas emissions. An optimization problem with a multi-criteria objective function is proposed to decide jointly the optimal location of data centers and software components, link capacities, and information routing. Once the network planning has been analyzed, the problem of dynamic resource provisioning in real time is addressed. In this context, virtualization is a key technique in cloud computing because each server can be shared by multiple Virtual Machines (VMs) and the total power consumption can be reduced. In the same line of location problems, we propose a Green Cloud Broker that optimizes VM placement across multiple data centers. In fact, when multiple data centers are considered, response time can be reduced by placing VMs close to users, cost can be minimized, power consumption can be optimized by using energy efficient data centers, and CO2 emissions can be decreased by choosing data centers provided with renewable energy sources. The third stage of the analysis is the short-term management of a cloud data center. In particular, a method is proposed to assign VMs to servers by considering communication traffic among VMs. Cloud data centers receive new applications over time and these applications need on-demand resource provisioning. Each application is composed of multiple types of VMs that interact among themselves. A program called scheduler must place each new VM in a server and that impacts the QoS and power consumption. Our method places VMs that communicate among themselves in servers that are close to each other in the network topology, thus reducing communication delay and increasing the throughput available among VMs. Furthermore, the power consumption of each type of server is considered and the most efficient ones are chosen to place the VMs. The number of VMs of each application can be dynamically changed to match the workload and servers not needed in a particular period can be suspended to save energy. The methodology developed is based on Mixed Integer Programming (MIP) models to formalize the problems and use state of the art optimization solvers. Then, heuristics are developed to solve cases with more than 1,000 potential data center locations for the planning problem, 1,000 nodes for the cloud broker, and 128,000 servers for the VM placement problem. Solutions with very short optimality gaps, between 0% and 1.95%, are obtained, and execution time in the order of minutes for the planning problem and less than a second for real time cases. We consider that this thesis on resource provisioning of cloud computing networks includes important contributions on this research area, and innovative commercial applications based on the proposed methods have promising future.

Development and application of coupled system dynamics and game theory: A dynamic water conflict resolution method.

PubMed

Zomorodian, Mehdi; Lai, Sai Hin; Homayounfar, Mehran; Ibrahim, Shaliza; Pender, Gareth

2017-01-01

Conflicts over water resources can be highly dynamic and complex due to the various factors which can affect such systems, including economic, engineering, social, hydrologic, environmental and even political, as well as the inherent uncertainty involved in many of these factors. Furthermore, the conflicting behavior, preferences and goals of stakeholders can often make such conflicts even more challenging. While many game models, both cooperative and non-cooperative, have been suggested to deal with problems over utilizing and sharing water resources, most of these are based on a static viewpoint of demand points during optimization procedures. Moreover, such models are usually developed for a single reservoir system, and so are not really suitable for application to an integrated decision support system involving more than one reservoir. This paper outlines a coupled simulation-optimization modeling method based on a combination of system dynamics (SD) and game theory (GT). The method harnesses SD to capture the dynamic behavior of the water system, utilizing feedback loops between the system components in the course of the simulation. In addition, it uses GT concepts, including pure-strategy and mixed-strategy games as well as the Nash Bargaining Solution (NBS) method, to find the optimum allocation decisions over available water in the system. To test the capability of the proposed method to resolve multi-reservoir and multi-objective conflicts, two different deterministic simulation-optimization models with increasing levels of complexity were developed for the Langat River basin in Malaysia. The later is a strategic water catchment that has a range of different stakeholders and managerial bodies, which are however willing to cooperate in order to avoid unmet demand. In our first model, all water users play a dynamic pure-strategy game. The second model then adds in dynamic behaviors to reservoirs to factor in inflow uncertainty and adjust the strategies for the reservoirs using the mixed-strategy game and Markov chain methods. The two models were then evaluated against three performance indices: Reliability, Resilience and Vulnerability (R-R-V). The results showed that, while both models were well capable of dealing with conflict resolution over water resources in the Langat River basin, the second model achieved a substantially improved performance through its ability to deal with dynamicity, complexity and uncertainty in the river system.

Development and application of coupled system dynamics and game theory: A dynamic water conflict resolution method

PubMed Central

Lai, Sai Hin; Homayounfar, Mehran; Ibrahim, Shaliza; Pender, Gareth

2017-01-01

Conflicts over water resources can be highly dynamic and complex due to the various factors which can affect such systems, including economic, engineering, social, hydrologic, environmental and even political, as well as the inherent uncertainty involved in many of these factors. Furthermore, the conflicting behavior, preferences and goals of stakeholders can often make such conflicts even more challenging. While many game models, both cooperative and non-cooperative, have been suggested to deal with problems over utilizing and sharing water resources, most of these are based on a static viewpoint of demand points during optimization procedures. Moreover, such models are usually developed for a single reservoir system, and so are not really suitable for application to an integrated decision support system involving more than one reservoir. This paper outlines a coupled simulation-optimization modeling method based on a combination of system dynamics (SD) and game theory (GT). The method harnesses SD to capture the dynamic behavior of the water system, utilizing feedback loops between the system components in the course of the simulation. In addition, it uses GT concepts, including pure-strategy and mixed-strategy games as well as the Nash Bargaining Solution (NBS) method, to find the optimum allocation decisions over available water in the system. To test the capability of the proposed method to resolve multi-reservoir and multi-objective conflicts, two different deterministic simulation-optimization models with increasing levels of complexity were developed for the Langat River basin in Malaysia. The later is a strategic water catchment that has a range of different stakeholders and managerial bodies, which are however willing to cooperate in order to avoid unmet demand. In our first model, all water users play a dynamic pure-strategy game. The second model then adds in dynamic behaviors to reservoirs to factor in inflow uncertainty and adjust the strategies for the reservoirs using the mixed-strategy game and Markov chain methods. The two models were then evaluated against three performance indices: Reliability, Resilience and Vulnerability (R-R-V). The results showed that, while both models were well capable of dealing with conflict resolution over water resources in the Langat River basin, the second model achieved a substantially improved performance through its ability to deal with dynamicity, complexity and uncertainty in the river system. PMID:29216200

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

PubMed Central

Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

2017-01-01

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

PubMed

De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

2017-10-31

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

A tool for simulating parallel branch-and-bound methods

NASA Astrophysics Data System (ADS)

Golubeva, Yana; Orlov, Yury; Posypkin, Mikhail

2016-01-01

The Branch-and-Bound method is known as one of the most powerful but very resource consuming global optimization methods. Parallel and distributed computing can efficiently cope with this issue. The major difficulty in parallel B&B method is the need for dynamic load redistribution. Therefore design and study of load balancing algorithms is a separate and very important research topic. This paper presents a tool for simulating parallel Branchand-Bound method. The simulator allows one to run load balancing algorithms with various numbers of processors, sizes of the search tree, the characteristics of the supercomputer's interconnect thereby fostering deep study of load distribution strategies. The process of resolution of the optimization problem by B&B method is replaced by a stochastic branching process. Data exchanges are modeled using the concept of logical time. The user friendly graphical interface to the simulator provides efficient visualization and convenient performance analysis.

JacksonBot - Design, Simulation and Optimal Control of an Action Painting Robot

NASA Astrophysics Data System (ADS)

Raschke, Michael; Mombaur, Katja; Schubert, Alexander

We present the robotics platform JacksonBot which is capable to produce paintings inspired by the Action Painting style of Jackson Pollock. A dynamically moving robot arm splashes color from a container at the end effector on the canvas. The paintings produced by this platform rely on a combination of the algorithmic generation of robot arm motions with random effects of the splashing color. The robot can be considered as a complex and powerful tool to generate art works programmed by a user. Desired end effector motions can be prescribed either by mathematical functions, by point sequences or by data glove motions. We have evaluated the effect of different shapes of input motions on the resulting painting. In order to compute the robot joint trajectories necessary to move along a desired end effector path, we use an optimal control based approach to solve the inverse kinematics problem.

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

PubMed Central

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-01-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3. PMID:25644994

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

NASA Astrophysics Data System (ADS)

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-02-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.

An R package for the design, analysis and operation of reservoir systems

NASA Astrophysics Data System (ADS)

Turner, Sean; Ng, Jia Yi; Galelli, Stefano

2016-04-01

We present a new R package - named "reservoir" - which has been designed for rapid and easy routing of runoff through storage. The package comprises well-established tools for capacity design (e.g., the sequent peak algorithm), performance analysis (storage-yield-reliability and reliability-resilience-vulnerability analysis) and release policy optimization (Stochastic Dynamic Programming). Operating rules can be optimized for water supply, flood control and amenity objectives, as well as for maximum hydropower production. Storage-depth-area relationships are in-built, allowing users to incorporate evaporation from the reservoir surface. We demonstrate the capabilities of the software for global studies using thousands of reservoirs from the Global Reservoir and Dam (GRanD) database fed by historical monthly inflow time series from a 0.5 degree gridded global runoff dataset. The package is freely available through the Comprehensive R Archive Network (CRAN).

A Visual Basic simulation software tool for performance analysis of a membrane-based advanced water treatment plant.

PubMed

Pal, P; Kumar, R; Srivastava, N; Chaudhuri, J

2014-02-01

A Visual Basic simulation software (WATTPPA) has been developed to analyse the performance of an advanced wastewater treatment plant. This user-friendly and menu-driven software is based on the dynamic mathematical model for an industrial wastewater treatment scheme that integrates chemical, biological and membrane-based unit operations. The software-predicted results corroborate very well with the experimental findings as indicated in the overall correlation coefficient of the order of 0.99. The software permits pre-analysis and manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. It allows quick performance analysis of the whole system as well as the individual units. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for hazardous wastewater.

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

PubMed

Wang, Mengmeng; Zuo, Wanli; Wang, Ying

2015-01-01

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2016-04-01

This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to foresee future inflows depending on present and past hydrological and meteorological variables actually used by the reservoir managers to define likely inflow scenarios. A Decision Support System (DSS) was created coupling the FRB systems and the inflow prediction scheme in order to give the user a set of possible optimal releases in response to the reservoir states at the beginning of the irrigation season and the fuzzy inflow projections made using hydrological and meteorological information. The results show that the optimal DSS created using the FRB operating policies are able to increase the amount of water allocated to the users in 20 to 50 Mm3 per irrigation season with respect to the current policies. Consequently, the mechanism used to define optimal operating rules and transform them into a DSS is able to increase the water deliveries in the Jucar River Basin, combining expert criteria and optimization algorithms in an efficient way. This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. It also has received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811).

Optimization model for UDWDM-PON deployment based on physical restrictions and asymmetric user's clustering

NASA Astrophysics Data System (ADS)

Arévalo, Germán. V.; Hincapié, Roberto C.; Sierra, Javier E.

2015-09-01

UDWDM PON is a leading technology oriented to provide ultra-high bandwidth to final users while profiting the physical channels' capability. One of the main drawbacks of UDWDM technique is the fact that the nonlinear effects, like FWM, become stronger due to the close spectral proximity among channels. This work proposes a model for the optimal deployment of this type of networks taking into account the fiber length limitations imposed by physical restrictions related with the fiber's data transmission as well as the users' asymmetric distribution in a provided region. The proposed model employs the data transmission related effects in UDWDM PON as restrictions in the optimization problem and also considers the user's asymmetric clustering and the subdivision of the users region though a Voronoi geometric partition technique. Here it is considered de Voronoi dual graph, it is the Delaunay Triangulation, as the planar graph for resolving the problem related with the minimum weight of the fiber links.

CEASAW: A User-Friendly Computer Environment Analysis for the Sawmill Owner

Treesearch

Guillermo Mendoza; William Sprouse; Philip A. Araman; William G. Luppold

1991-01-01

Improved spreadsheet software capabilities have brought optimization to users with little or no background in mathematical programming. Better interface capabilities of spreadsheet models now make it possible to combine optimization models with a spreadsheet system. Sawmill production and inventory systems possess many features that make them suitable application...

Eliciting naturalistic cortical responses with a sensory prosthesis via optimized microstimulation

NASA Astrophysics Data System (ADS)

Choi, John S.; Brockmeier, Austin J.; McNiel, David B.; von Kraus, Lee M.; Príncipe, José C.; Francis, Joseph T.

2016-10-01

Objective. Lost sensations, such as touch, could one day be restored by electrical stimulation along the sensory neural pathways. Such stimulation, when informed by electronic sensors, could provide naturalistic cutaneous and proprioceptive feedback to the user. Perceptually, microstimulation of somatosensory brain regions produces localized, modality-specific sensations, and several spatiotemporal parameters have been studied for their discernibility. However, systematic methods for encoding a wide array of naturally occurring stimuli into biomimetic percepts via multi-channel microstimulation are lacking. More specifically, generating spatiotemporal patterns for explicitly evoking naturalistic neural activation has not yet been explored. Approach. We address this problem by first modeling the dynamical input-output relationship between multichannel microstimulation and downstream neural responses, and then optimizing the input pattern to reproduce naturally occurring touch responses as closely as possible. Main results. Here we show that such optimization produces responses in the S1 cortex of the anesthetized rat that are highly similar to natural, tactile-stimulus-evoked counterparts. Furthermore, information on both pressure and location of the touch stimulus was found to be highly preserved. Significance. Our results suggest that the currently presented stimulus optimization approach holds great promise for restoring naturalistic levels of sensation.

CHARMM-GUI Membrane Builder toward realistic biological membrane simulations.

PubMed

Wu, Emilia L; Cheng, Xi; Jo, Sunhwan; Rui, Huan; Song, Kevin C; Dávila-Contreras, Eder M; Qi, Yifei; Lee, Jumin; Monje-Galvan, Viviana; Venable, Richard M; Klauda, Jeffery B; Im, Wonpil

2014-10-15

CHARMM-GUI Membrane Builder, http://www.charmm-gui.org/input/membrane, is a web-based user interface designed to interactively build all-atom protein/membrane or membrane-only systems for molecular dynamics simulations through an automated optimized process. In this work, we describe the new features and major improvements in Membrane Builder that allow users to robustly build realistic biological membrane systems, including (1) addition of new lipid types, such as phosphoinositides, cardiolipin (CL), sphingolipids, bacterial lipids, and ergosterol, yielding more than 180 lipid types, (2) enhanced building procedure for lipid packing around protein, (3) reliable algorithm to detect lipid tail penetration to ring structures and protein surface, (4) distance-based algorithm for faster initial ion displacement, (5) CHARMM inputs for P21 image transformation, and (6) NAMD equilibration and production inputs. The robustness of these new features is illustrated by building and simulating a membrane model of the polar and septal regions of E. coli membrane, which contains five lipid types: CL lipids with two types of acyl chains and phosphatidylethanolamine lipids with three types of acyl chains. It is our hope that CHARMM-GUI Membrane Builder becomes a useful tool for simulation studies to better understand the structure and dynamics of proteins and lipids in realistic biological membrane environments. Copyright © 2014 Wiley Periodicals, Inc.

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

Dynamic modeling and evaluation of solid oxide fuel cell - combined heat and power system operating strategies

NASA Astrophysics Data System (ADS)

Nanaeda, Kimihiro; Mueller, Fabian; Brouwer, Jacob; Samuelsen, Scott

Operating strategies of solid oxide fuel cell (SOFC) combined heat and power (CHP) systems are developed and evaluated from a utility, and end-user perspective using a fully integrated SOFC-CHP system dynamic model that resolves the physical states, thermal integration and overall efficiency of the system. The model can be modified for any SOFC-CHP system, but the present analysis is applied to a hotel in southern California based on measured electric and heating loads. Analysis indicates that combined heat and power systems can be operated to benefit both the end-users and the utility, providing more efficient electric generation as well as grid ancillary services, namely dispatchable urban power. Design and operating strategies considered in the paper include optimal sizing of the fuel cell, thermal energy storage to dispatch heat, and operating the fuel cell to provide flexible grid power. Analysis results indicate that with a 13.1% average increase in price-of-electricity (POE), the system can provide the grid with a 50% operating range of dispatchable urban power at an overall thermal efficiency of 80%. This grid-support operating mode increases the operational flexibility of the SOFC-CHP system, which may make the technology an important utility asset for accommodating the increased penetration of intermittent renewable power.

Effectiveness Testing of Embedded User Support for U.S. Army Installation-Level Software

DTIC Science & Technology

1991-06-01

under what conditions Dynamic Help could influence performance and satisfaction. The ACIFS program was modified to provide automatic collection of all...under what conditions Dynamic Help can influence user performance and satisfaction. This chapter reports the design, implementation, and analysis of...ambiguous or is hidden in the body of the message. The ACIFS program has many user interface deficiencies, but it does allow the user to use trial and

Performance optimization of the power user electric energy data acquire system based on MOEA/D evolutionary algorithm

NASA Astrophysics Data System (ADS)

Ding, Zhongan; Gao, Chen; Yan, Shengteng; Yang, Canrong

2017-10-01

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.

A method to accurately estimate the muscular torques of human wearing exoskeletons by torque sensors.

PubMed

Hwang, Beomsoo; Jeon, Doyoung

2015-04-09

In exoskeletal robots, the quantification of the user's muscular effort is important to recognize the user's motion intentions and evaluate motor abilities. In this paper, we attempt to estimate users' muscular efforts accurately using joint torque sensor which contains the measurements of dynamic effect of human body such as the inertial, Coriolis, and gravitational torques as well as torque by active muscular effort. It is important to extract the dynamic effects of the user's limb accurately from the measured torque. The user's limb dynamics are formulated and a convenient method of identifying user-specific parameters is suggested for estimating the user's muscular torque in robotic exoskeletons. Experiments were carried out on a wheelchair-integrated lower limb exoskeleton, EXOwheel, which was equipped with torque sensors in the hip and knee joints. The proposed methods were evaluated by 10 healthy participants during body weight-supported gait training. The experimental results show that the torque sensors are to estimate the muscular torque accurately in cases of relaxed and activated muscle conditions.

S3D: An interactive surface grid generation tool

NASA Technical Reports Server (NTRS)

Luh, Raymond Ching-Chung; Pierce, Lawrence E.; Yip, David

1992-01-01

S3D, an interactive software tool for surface grid generation, is described. S3D provides the means with which a geometry definition based either on a discretized curve set or a rectangular set can be quickly processed towards the generation of a surface grid for computational fluid dynamics (CFD) applications. This is made possible as a result of implementing commonly encountered surface gridding tasks in an environment with a highly efficient and user friendly graphical interface. Some of the more advanced features of S3D include surface-surface intersections, optimized surface domain decomposition and recomposition, and automated propagation of edge distributions to surrounding grids.

The System Dynamics Model User Sustainability Explorer (SD-MUSE): a user-friendly tool for interpreting system dynamic models

EPA Science Inventory

System Dynamics (SD) models are useful for holistic integration of data to evaluate indirect and cumulative effects and inform decisions. Complex SD models can provide key insights into how decisions affect the three interconnected pillars of sustainability. However, the complexi...

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

PubMed Central

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time. PMID:26270539

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

PubMed

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Research on dynamic performance design of mobile phone application based on context awareness

NASA Astrophysics Data System (ADS)

Bo, Zhang

2018-05-01

It aims to explore the dynamic performance of different mobile phone applications and the user's cognitive differences, reduce the cognitive burden, and enhance the sense of experience. By analyzing the dynamic design performance in four different interactive contexts, and constructing the framework of information service process in the interactive context perception and the two perception principles of the cognitive consensus between designer and user, and the two kinds of knowledge in accordance with the perception principles. The analysis of the context will help users sense the dynamic performance more intuitively, so that the details of interaction will be performed more vividly and smoothly, thus enhance user's experience in the interactive process. The common perception experience enables designers and users to produce emotional resonance in different interactive contexts, and help them achieve rapid understanding of interactive content and perceive the logic and hierarchy of the content and the structure, therefore the effectiveness of mobile applications will be improved.

Automated Design Space Exploration with Aspen

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

Spafford, Kyle L.; Vetter, Jeffrey S.

Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection ofmore » costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.« less

Automated Design Space Exploration with Aspen

DOE PAGES

Spafford, Kyle L.; Vetter, Jeffrey S.

2015-01-01

Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection ofmore » costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.« less

A multi-criteria approach to camera motion design for volume data animation.

PubMed

Hsu, Wei-Hsien; Zhang, Yubo; Ma, Kwan-Liu

2013-12-01

We present an integrated camera motion design and path generation system for building volume data animations. Creating animations is an essential task in presenting complex scientific visualizations. Existing visualization systems use an established animation function based on keyframes selected by the user. This approach is limited in providing the optimal in-between views of the data. Alternatively, computer graphics and virtual reality camera motion planning is frequently focused on collision free movement in a virtual walkthrough. For semi-transparent, fuzzy, or blobby volume data the collision free objective becomes insufficient. Here, we provide a set of essential criteria focused on computing camera paths to establish effective animations of volume data. Our dynamic multi-criteria solver coupled with a force-directed routing algorithm enables rapid generation of camera paths. Once users review the resulting animation and evaluate the camera motion, they are able to determine how each criterion impacts path generation. In this paper, we demonstrate how incorporating this animation approach with an interactive volume visualization system reduces the effort in creating context-aware and coherent animations. This frees the user to focus on visualization tasks with the objective of gaining additional insight from the volume data.

Smart Building: Decision Making Architecture for Thermal Energy Management.

PubMed

Uribe, Oscar Hernández; Martin, Juan Pablo San; Garcia-Alegre, María C; Santos, Matilde; Guinea, Domingo

2015-10-30

Smart applications of the Internet of Things are improving the performance of buildings, reducing energy demand. Local and smart networks, soft computing methodologies, machine intelligence algorithms and pervasive sensors are some of the basics of energy optimization strategies developed for the benefit of environmental sustainability and user comfort. This work presents a distributed sensor-processor-communication decision-making architecture to improve the acquisition, storage and transfer of thermal energy in buildings. The developed system is implemented in a near Zero-Energy Building (nZEB) prototype equipped with a built-in thermal solar collector, where optical properties are analysed; a low enthalpy geothermal accumulation system, segmented in different temperature zones; and an envelope that includes a dynamic thermal barrier. An intelligent control of this dynamic thermal barrier is applied to reduce the thermal energy demand (heating and cooling) caused by daily and seasonal weather variations. Simulations and experimental results are presented to highlight the nZEB thermal energy reduction.

Fault-tolerant dynamic task graph scheduling

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

Kurt, Mehmet C.; Krishnamoorthy, Sriram; Agrawal, Kunal

2014-11-16

In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. We elicit from the user the basic task graph structure in terms of successor and predecessor relationships. The work stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and meta-data associated with a task get corrupted. We use this redundancy, and the knowledge of the task graph structure, to selectively recover from faults with low space andmore » time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.« less

The User Experience

ERIC Educational Resources Information Center

Schmidt, Aaron

2010-01-01

User experience (UX) is about arranging the elements of a product or service to optimize how people will interact with it. In this article, the author talks about the importance of user experience and discusses the design of user experiences in libraries. He first looks at what UX is. Then he describes three kinds of user experience design: (1)…

An intelligent agent for optimal river-reservoir system management

NASA Astrophysics Data System (ADS)

Rieker, Jeffrey D.; Labadie, John W.

2012-09-01

A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species.

Performance Trades Study for Robust Airfoil Shape Optimization

NASA Technical Reports Server (NTRS)

Li, Wu; Padula, Sharon

2003-01-01

From time to time, existing aircraft need to be redesigned for new missions with modified operating conditions such as required lift or cruise speed. This research is motivated by the needs of conceptual and preliminary design teams for smooth airfoil shapes that are similar to the baseline design but have improved drag performance over a range of flight conditions. The proposed modified profile optimization method (MPOM) modifies a large number of design variables to search for nonintuitive performance improvements, while avoiding off-design performance degradation. Given a good initial design, the MPOM generates fairly smooth airfoils that are better than the baseline without making drastic shape changes. Moreover, the MPOM allows users to gain valuable information by exploring performance trades over various design conditions. Four simulation cases of airfoil optimization in transonic viscous ow are included to demonstrate the usefulness of the MPOM as a performance trades study tool. Simulation results are obtained by solving fully turbulent Navier-Stokes equations and the corresponding discrete adjoint equations using an unstructured grid computational fluid dynamics code FUN2D.

An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

NASA Astrophysics Data System (ADS)

Shenbaga Moorthy, Rajalakshmi; Fareentaj, U.; Divya, T. K.

2017-08-01

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

User Manual and Source Code for a LAMMPS Implementation of Constant Energy Dissipative Particle Dynamics (DPD-E)

DTIC Science & Technology

2014-06-01

User Manual and Source Code for a LAMMPS Implementation of Constant Energy Dissipative Particle Dynamics (DPD-E) by James P. Larentzos...Laboratory Aberdeen Proving Ground, MD 21005-5069 ARL-SR-290 June 2014 User Manual and Source Code for a LAMMPS Implementation of Constant...3. DATES COVERED (From - To) September 2013–February 2014 4. TITLE AND SUBTITLE User Manual and Source Code for a LAMMPS Implementation of

A Secure Dynamic Identity and Chaotic Maps Based User Authentication and Key Agreement Scheme for e-Healthcare Systems.

PubMed

Li, Chun-Ta; Lee, Cheng-Chi; Weng, Chi-Yao; Chen, Song-Jhih

2016-11-01

Secure user authentication schemes in many e-Healthcare applications try to prevent unauthorized users from intruding the e-Healthcare systems and a remote user and a medical server can establish session keys for securing the subsequent communications. However, many schemes does not mask the users' identity information while constructing a login session between two or more parties, even though personal privacy of users is a significant topic for e-Healthcare systems. In order to preserve personal privacy of users, dynamic identity based authentication schemes are hiding user's real identity during the process of network communications and only the medical server knows login user's identity. In addition, most of the existing dynamic identity based authentication schemes ignore the inputs verification during login condition and this flaw may subject to inefficiency in the case of incorrect inputs in the login phase. Regarding the use of secure authentication mechanisms for e-Healthcare systems, this paper presents a new dynamic identity and chaotic maps based authentication scheme and a secure data protection approach is employed in every session to prevent illegal intrusions. The proposed scheme can not only quickly detect incorrect inputs during the phases of login and password change but also can invalidate the future use of a lost/stolen smart card. Compared the functionality and efficiency with other authentication schemes recently, the proposed scheme satisfies desirable security attributes and maintains acceptable efficiency in terms of the computational overheads for e-Healthcare systems.

Systematic Sensor Selection Strategy (S4) User Guide

NASA Technical Reports Server (NTRS)

Sowers, T. Shane

2012-01-01

This paper describes a User Guide for the Systematic Sensor Selection Strategy (S4). S4 was developed to optimally select a sensor suite from a larger pool of candidate sensors based on their performance in a diagnostic system. For aerospace systems, selecting the proper sensors is important for ensuring adequate measurement coverage to satisfy operational, maintenance, performance, and system diagnostic criteria. S4 optimizes the selection of sensors based on the system fault diagnostic approach while taking conflicting objectives such as cost, weight and reliability into consideration. S4 can be described as a general architecture structured to accommodate application-specific components and requirements. It performs combinational optimization with a user defined merit or cost function to identify optimum or near-optimum sensor suite solutions. The S4 User Guide describes the sensor selection procedure and presents an example problem using an open source turbofan engine simulation to demonstrate its application.

How to Develop a User Interface That Your Real Users Will Love

ERIC Educational Resources Information Center

Phillips, Donald

2012-01-01

A "user interface" is the part of an interactive system that bridges the user and the underlying functionality of the system. But people sometimes forget that the best interfaces will provide a platform to optimize the users' interactions so that they support and extend the users' activities in effective, useful, and usable ways. To look at it…

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

Dall'Anese, Emiliano

Past works that focused on addressing power-quality and reliability concerns related to renewable energy resources (RESs) operating with business-as-usual practices have looked at the design of Volt/VAr and Volt/Watt strategies to regulate real or reactive powers based on local voltage measurements, so that terminal voltages are within acceptable levels. These control strategies have the potential of operating at the same time scale of distribution-system dynamics, and can therefore mitigate disturbances precipitated fast time-varying loads and ambient conditions; however, they do not necessarily guarantee system-level optimality, and stability claims are mainly based on empirical evidences. On a different time scale, centralizedmore » and distributed optimal power flow (OPF) algorithms have been proposed to compute optimal steady-state inverter setpoints, so that power losses and voltage deviations are minimized and economic benefits to end-users providing ancillary services are maximized. However, traditional OPF schemes may offer decision making capabilities that do not match the dynamics of distribution systems. Particularly, during the time required to collect data from all the nodes of the network (e.g., loads), solve the OPF, and subsequently dispatch setpoints, the underlying load, ambient, and network conditions may have already changed; in this case, the DER output powers would be consistently regulated around outdated setpoints, leading to suboptimal system operation and violation of relevant electrical limits. The present work focuses on the synthesis of distributed RES-inverter controllers that leverage the opportunities for fast feedback offered by power-electronics interfaced RESs. The overarching objective is to bridge the temporal gap between long-term system optimization and real-time control, to enable seamless RES integration in large scale with stability and efficiency guarantees, while congruently pursuing system-level optimization objectives. The design of the control framework is based on suitable linear approximations of the AC power-flow equations as well as Lagrangian regularization methods. The proposed controllers enable an update of the power outputs at a time scale that is compatible with the underlying dynamics of loads and ambient conditions, and continuously drive the system operation towards OPF-based solutions.« less

Optimizing conjunctive use of surface water and groundwater resources with stochastic dynamic programming

NASA Astrophysics Data System (ADS)

Davidsen, Claus; Liu, Suxia; Mo, Xingguo; Rosbjerg, Dan; Bauer-Gottwein, Peter

2014-05-01

Optimal management of conjunctive use of surface water and groundwater has been attempted with different algorithms in the literature. In this study, a hydro-economic modelling approach to optimize conjunctive use of scarce surface water and groundwater resources under uncertainty is presented. A stochastic dynamic programming (SDP) approach is used to minimize the basin-wide total costs arising from water allocations and water curtailments. Dynamic allocation problems with inclusion of groundwater resources proved to be more complex to solve with SDP than pure surface water allocation problems due to head-dependent pumping costs. These dynamic pumping costs strongly affect the total costs and can lead to non-convexity of the future cost function. The water user groups (agriculture, industry, domestic) are characterized by inelastic demands and fixed water allocation and water supply curtailment costs. As in traditional SDP approaches, one step-ahead sub-problems are solved to find the optimal management at any time knowing the inflow scenario and reservoir/aquifer storage levels. These non-linear sub-problems are solved using a genetic algorithm (GA) that minimizes the sum of the immediate and future costs for given surface water reservoir and groundwater aquifer end storages. The immediate cost is found by solving a simple linear allocation sub-problem, and the future costs are assessed by interpolation in the total cost matrix from the following time step. Total costs for all stages, reservoir states, and inflow scenarios are used as future costs to drive a forward moving simulation under uncertain water availability. The use of a GA to solve the sub-problems is computationally more costly than a traditional SDP approach with linearly interpolated future costs. However, in a two-reservoir system the future cost function would have to be represented by a set of planes, and strict convexity in both the surface water and groundwater dimension cannot be maintained. The optimization framework based on the GA is still computationally feasible and represents a clean and customizable method. The method has been applied to the Ziya River basin, China. The basin is located on the North China Plain and is subject to severe water scarcity, which includes surface water droughts and groundwater over-pumping. The head-dependent groundwater pumping costs will enable assessment of the long-term effects of increased electricity prices on the groundwater pumping. The coupled optimization framework is used to assess realistic alternative development scenarios for the basin. In particular the potential for using electricity pricing policies to reach sustainable groundwater pumping is investigated.

Optimization Model for Web Based Multimodal Interactive Simulations.

PubMed

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-07-15

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

Optimization Model for Web Based Multimodal Interactive Simulations

PubMed Central

Halic, Tansel; Ahn, Woojin; De, Suvranu

2015-01-01

This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update. In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach. PMID:26085713

Optimal Coordination of Building Loads and Energy Storage for Power Grid and End User Services

DOE PAGES

Hao, He; Wu, Di; Lian, Jianming; ...

2017-01-18

Demand response and energy storage play a profound role in the smart grid. The focus of this study is to evaluate benefits of coordinating flexible loads and energy storage to provide power grid and end user services. We present a Generalized Battery Model (GBM) to describe the flexibility of building loads and energy storage. An optimization-based approach is proposed to characterize the parameters (power and energy limits) of the GBM for flexible building loads. We then develop optimal coordination algorithms to provide power grid and end user services such as energy arbitrage, frequency regulation, spinning reserve, as well as energymore » cost and demand charge reduction. Several case studies have been performed to demonstrate the efficacy of the GBM and coordination algorithms, and evaluate the benefits of using their flexibility for power grid and end user services. We show that optimal coordination yields significant cost savings and revenue. Moreover, the best option for power grid services is to provide energy arbitrage and frequency regulation. Finally and furthermore, when coordinating flexible loads with energy storage to provide end user services, it is recommended to consider demand charge in addition to time-of-use price in order to flatten the aggregate power profile.« less

Rural providers' access to online resources: a randomized controlled trial

PubMed Central

Hall, Laura J.; McElfresh, Karen R.; Warner, Teddy D.; Stromberg, Tiffany L.; Trost, Jaren; Jelinek, Devin A.

2016-01-01

Objective The research determined the usage and satisfaction levels with one of two point-of-care (PoC) resources among health care providers in a rural state. Methods In this randomized controlled trial, twenty-eight health care providers in rural areas were stratified by occupation and region, then randomized into either the DynaMed or the AccessMedicine study arm. Study participants were physicians, physician assistants, and nurses. A pre- and post-study survey measured participants' attitudes toward different information resources and their information-seeking activities. Medical student investigators provided training and technical support for participants. Data analyses consisted of analysis of variance (ANOVA), paired t tests, and Cohen's d statistic to compare pre- and post-study effects sizes. Results Participants in both the DynaMed and the AccessMedicine arms of the study reported increased satisfaction with their respective PoC resource, as expected. Participants in both arms also reported that they saved time in finding needed information. At baseline, both arms reported too little information available, which increased to “about right amounts of information” at the completion of the study. DynaMed users reported a Cohen's d increase of +1.50 compared to AccessMedicine users' reported use of 0.82. DynaMed users reported d2 satisfaction increases of 9.48 versus AccessMedicine satisfaction increases of 0.59 using a Cohen's d. Conclusion Participants in the DynaMed arm of the study used this clinically oriented PoC more heavily than the users of the textbook-based AccessMedicine. In terms of user satisfaction, DynaMed users reported higher levels of satisfaction than the users of AccessMedicine. PMID:26807050

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

FASTER - A tool for DSN forecasting and scheduling

NASA Technical Reports Server (NTRS)

Werntz, David; Loyola, Steven; Zendejas, Silvino

1993-01-01

FASTER (Forecasting And Scheduling Tool for Earth-based Resources) is a suite of tools designed for forecasting and scheduling JPL's Deep Space Network (DSN). The DSN is a set of antennas and other associated resources that must be scheduled for satellite communications, astronomy, maintenance, and testing. FASTER consists of MS-Windows based programs that replace two existing programs (RALPH and PC4CAST). FASTER was designed to be more flexible, maintainable, and user friendly. FASTER makes heavy use of commercial software to allow for customization by users. FASTER implements scheduling as a two pass process: the first pass calculates a predictive profile of resource utilization; the second pass uses this information to calculate a cost function used in a dynamic programming optimization step. This information allows the scheduler to 'look ahead' at activities that are not as yet scheduled. FASTER has succeeded in allowing wider access to data and tools, reducing the amount of effort expended and increasing the quality of analysis.

Using stochastic dynamic programming to support catchment-scale water resources management in China

NASA Astrophysics Data System (ADS)

Davidsen, Claus; Pereira-Cardenal, Silvio Javier; Liu, Suxia; Mo, Xingguo; Rosbjerg, Dan; Bauer-Gottwein, Peter

2013-04-01

A hydro-economic modelling approach is used to optimize reservoir management at river basin level. We demonstrate the potential of this integrated approach on the Ziya River basin, a complex basin on the North China Plain south-east of Beijing. The area is subject to severe water scarcity due to low and extremely seasonal precipitation, and the intense agricultural production is highly dependent on irrigation. Large reservoirs provide water storage for dry months while groundwater and the external South-to-North Water Transfer Project are alternative sources of water. An optimization model based on stochastic dynamic programming has been developed. The objective function is to minimize the total cost of supplying water to the users, while satisfying minimum ecosystem flow constraints. Each user group (agriculture, domestic and industry) is characterized by fixed demands, fixed water allocation costs for the different water sources (surface water, groundwater and external water) and fixed costs of water supply curtailment. The multiple reservoirs in the basin are aggregated into a single reservoir to reduce the dimensions of decisions. Water availability is estimated using a hydrological model. The hydrological model is based on the Budyko framework and is forced with 51 years of observed daily rainfall and temperature data. 23 years of observed discharge from an in-situ station located downstream a remote mountainous catchment is used for model calibration. Runoff serial correlation is described by a Markov chain that is used to generate monthly runoff scenarios to the reservoir. The optimal costs at a given reservoir state and stage were calculated as the minimum sum of immediate and future costs. Based on the total costs for all states and stages, water value tables were generated which contain the marginal value of stored water as a function of the month, the inflow state and the reservoir state. The water value tables are used to guide allocation decisions in simulation mode. The performance of the operation rules based on water value tables was evaluated. The approach was used to assess the performance of alternative development scenarios and infrastructure projects successfully in the case study region.

Equivalence between entanglement and the optimal fidelity of continuous variable teleportation.

PubMed

Adesso, Gerardo; Illuminati, Fabrizio

2005-10-07

We devise the optimal form of Gaussian resource states enabling continuous-variable teleportation with maximal fidelity. We show that a nonclassical optimal fidelity of N-user teleportation networks is necessary and sufficient for N-party entangled Gaussian resources, yielding an estimator of multipartite entanglement. The entanglement of teleportation is equivalent to the entanglement of formation in a two-user protocol, and to the localizable entanglement in a multiuser one. Finally, we show that the continuous-variable tangle, quantifying entanglement sharing in three-mode Gaussian states, is defined operationally in terms of the optimal fidelity of a tripartite teleportation network.

DNASynth: a software application to optimization of artificial gene synthesis

NASA Astrophysics Data System (ADS)

Muczyński, Jan; Nowak, Robert M.

2017-08-01

DNASynth is a client-server software application in which the client runs in a web browser. The aim of this program is to support and optimize process of artificial gene synthesizing using Ligase Chain Reaction. Thanks to LCR it is possible to obtain DNA strand coding defined by user peptide. The DNA sequence is calculated by optimization algorithm that consider optimal codon usage, minimal energy of secondary structures and minimal number of required LCR. Additionally absence of sequences characteristic for defined by user set of restriction enzymes is guaranteed. The presented software was tested on synthetic and real data.

A web based Radiation Oncology Dose Manager with a rich User Interface developed using AJAX, ruby, dynamic XHTML and the new Yahoo/EXT User Interface Library.

PubMed

Vali, Faisal; Hong, Robert

2007-10-11

With the evolution of AJAX, ruby on rails, advanced dynamic XHTML technologies and the advent of powerful user interface libraries for javascript (EXT, Yahoo User Interface Library), developers now have the ability to provide truly rich interfaces within web browsers, with reasonable effort and without third-party plugins. We designed and developed an example of such a solution. The User Interface allows radiation oncology practices to intuitively manage different dose fractionation schemes by helping estimate total dose to irradiated organs.

Comparative risk assessment and cessation information seeking among smokeless tobacco users.

PubMed

Jun, Jungmi; Nan, Xiaoli

2018-05-01

This research examined (1) smokeless tobacco users' comparative optimism in assessing the health and addiction risks of their own product in comparison with cigarettes, and (2) the effects of comparative optimism on cessation information-seeking. A nationally-representative sample from the 2015 Health Information National Trends Survey (HINTS)-FDA was employed. The analyses revealed the presence of comparative optimism in assessing both health and addiction risks among smokeless tobacco users. Comparative optimism was negatively correlated with most cessation information-seeking variables. Health bias (the health risk rating gap between the subject's own tobacco product and cigarettes) was associated with decreased intent to use cessation support. However, the health bias and addiction bias (the addiction risk rating gap between the subject's own tobacco product and cigarettes) were not consistent predictors of all cessation information-seeking variables, when covariates of socio-demographics and tobacco use status were included. In addition, positive correlations between health bias and past/recent cessation-information searches were observed. Optimisic biases may negatively influence cessation behaviors not only directly but also indirectly by influencing an important moderator, cessation information-seeking. Future interventions should prioritize dispelling the comparative optimism in perceiving risks of smokeless tobacco use, as well as provide more reliable cessation information specific to smokeless tobacco users. Copyright © 2018 Elsevier Ltd. All rights reserved.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System.

PubMed

Chinnadurai, Sunil; Selvaprabhu, Poongundran; Jeong, Yongchae; Jiang, Xueqin; Lee, Moon Ho

2017-09-18

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach's algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System

PubMed Central

Jeong, Yongchae; Jiang, Xueqin; Lee, Moon Ho

2017-01-01

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach’s algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme. PMID:28927019

Jane: a new tool for the cophylogeny reconstruction problem.

PubMed

Conow, Chris; Fielder, Daniel; Ovadia, Yaniv; Libeskind-Hadas, Ran

2010-02-03

This paper describes the theory and implementation of a new software tool, called Jane, for the study of historical associations. This problem arises in parasitology (associations of hosts and parasites), molecular systematics (associations of orderings and genes), and biogeography (associations of regions and orderings). The underlying problem is that of reconciling pairs of trees subject to biologically plausible events and costs associated with these events. Existing software tools for this problem have strengths and limitations, and the new Jane tool described here provides functionality that complements existing tools. The Jane software tool uses a polynomial time dynamic programming algorithm in conjunction with a genetic algorithm to find very good, and often optimal, solutions even for relatively large pairs of trees. The tool allows the user to provide rich timing information on both the host and parasite trees. In addition the user can limit host switch distance and specify multiple host switch costs by specifying regions in the host tree and costs for host switches between pairs of regions. Jane also provides a graphical user interface that allows the user to interactively experiment with modifications to the solutions found by the program. Jane is shown to be a useful tool for cophylogenetic reconstruction. Its functionality complements existing tools and it is therefore likely to be of use to researchers in the areas of parasitology, molecular systematics, and biogeography.

Effects of robotic manipulators on movements of novices and surgeons.

PubMed

Nisky, Ilana; Okamura, Allison M; Hsieh, Michael H

2014-07-01

Robot-assisted surgery is widely adopted for many procedures but has not realized its full potential to date. Based on human motor control theories, the authors hypothesized that the dynamics of the master manipulators impose challenges on the motor system of the user and may impair performance and slow down learning. Although studies have shown that robotic outcomes are correlated with the case experience of the surgeon, the relative contribution of cognitive versus motor skill is unknown. This study quantified the effects of da Vinci Si master manipulator dynamics on movements of novice users and experienced surgeons and suggests possible implications for training and robot design. In the reported study, six experienced robotic surgeons and ten novice nonmedical users performed movements under two conditions: teleoperation of a da Vinci Si Surgical system and freehand. A linear mixed model was applied to nine kinematic metrics (including endpoint error, movement time, peak speed, initial jerk, and deviation from a straight line) to assess the effects of teleoperation and expertise. To assess learning effects, t tests between the first and last movements of each type were used. All the users moved slower during teleoperation than during freehand movements (F(1,9343) = 345; p < 0.001). The experienced surgeons had smaller errors than the novices (F(1,14) = 36.8; p < 0.001). The straightness of movements depended on their direction (F(7,9343) = 117; p < 0.001). Learning effects were observed in all conditions. Novice users first learned the task and then the dynamics of the manipulator. The findings showed differences between the novices and the experienced surgeons for extremely simple point-to-point movements. The study demonstrated that manipulator dynamics affect user movements, suggesting that these dynamics could be improved in future robot designs. The authors showed the partial adaptation of novice users to the dynamics. Future studies are needed to evaluate whether it will be beneficial to include early training sessions dedicated to learning the dynamics of the manipulator.

A hybrid symbolic/finite-element algorithm for solving nonlinear optimal control problems

NASA Technical Reports Server (NTRS)

Bless, Robert R.; Hodges, Dewey H.

1991-01-01

The general code described is capable of solving difficult nonlinear optimal control problems by using finite elements and a symbolic manipulator. Quick and accurate solutions are obtained with a minimum for user interaction. Since no user programming is required for most problems, there are tremendous savings to be gained in terms of time and money.

Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

ERIC Educational Resources Information Center

Gelman, Andrew; Lee, Daniel; Guo, Jiqiang

2015-01-01

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…

A Data-Driven Solution for Performance Improvement

NASA Technical Reports Server (NTRS)

2002-01-01

Marketed as the "Software of the Future," Optimal Engineering Systems P.I. EXPERT(TM) technology offers statistical process control and optimization techniques that are critical to businesses looking to restructure or accelerate operations in order to gain a competitive edge. Kennedy Space Center granted Optimal Engineering Systems the funding and aid necessary to develop a prototype of the process monitoring and improvement software. Completion of this prototype demonstrated that it was possible to integrate traditional statistical quality assurance tools with robust optimization techniques in a user- friendly format that is visually compelling. Using an expert system knowledge base, the software allows the user to determine objectives, capture constraints and out-of-control processes, predict results, and compute optimal process settings.

COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

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

Sun, Y.; Borland, Michael

Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

Applying Utility Functions to Adaptation Planning for Home Automation Applications

NASA Astrophysics Data System (ADS)

Bratskas, Pyrros; Paspallis, Nearchos; Kakousis, Konstantinos; Papadopoulos, George A.

A pervasive computing environment typically comprises multiple embedded devices that may interact together and with mobile users. These users are part of the environment, and they experience it through a variety of devices embedded in the environment. This perception involves technologies which may be heterogeneous, pervasive, and dynamic. Due to the highly dynamic properties of such environments, the software systems running on them have to face problems such as user mobility, service failures, or resource and goal changes which may happen in an unpredictable manner. To cope with these problems, such systems must be autonomous and self-managed. In this chapter we deal with a special kind of a ubiquitous environment, a smart home environment, and introduce a user-preference-based model for adaptation planning. The model, which dynamically forms a set of configuration plans for resources, reasons automatically and autonomously, based on utility functions, on which plan is likely to best achieve the user's goals with respect to resource availability and user needs.

Design of 2D time-varying vector fields.

PubMed

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

A new implementation of the programming system for structural synthesis (PROSSS-2)

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.

1984-01-01

This new implementation of the PROgramming System for Structural Synthesis (PROSSS-2) combines a general-purpose finite element computer program for structural analysis, a state-of-the-art optimization program, and several user-supplied, problem-dependent computer programs. The results are flexibility of the optimization procedure, organization, and versatility of the formulation of constraints and design variables. The analysis-optimization process results in a minimized objective function, typically the mass. The analysis and optimization programs are executed repeatedly by looping through the system until the process is stopped by a user-defined termination criterion. However, some of the analysis, such as model definition, need only be one time and the results are saved for future use. The user must write some small, simple FORTRAN programs to interface between the analysis and optimization programs. One of these programs, the front processor, converts the design variables output from the optimizer into the suitable format for input into the analyzer. Another, the end processor, retrieves the behavior variables and, optionally, their gradients from the analysis program and evaluates the objective function and constraints and optionally their gradients. These quantities are output in a format suitable for input into the optimizer. These user-supplied programs are problem-dependent because they depend primarily upon which finite elements are being used in the model. PROSSS-2 differs from the original PROSSS in that the optimizer and front and end processors have been integrated into the finite element computer program. This was done to reduce the complexity and increase portability of the system, and to take advantage of the data handling features found in the finite element program.

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA Technical Reports Server (NTRS)

Rash, James

2014-01-01

NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial-optimization problems that encompasses, among many others, the problem of generating optimal space-data communications schedules.

Dynamical modeling and multi-experiment fitting with PottersWheel

PubMed Central

Maiwald, Thomas; Timmer, Jens

2008-01-01

Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization. Results: We here present the comprehensive modeling framework Potters-Wheel (PW) including novel functionalities to satisfy these requirements with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks. PW is designed as a MATLAB toolbox and includes numerous user interfaces. Deterministic and stochastic optimization routines are combined by fitting in logarithmic parameter space allowing for robust parameter calibration. Model investigation includes statistical tests for model-data-compliance, model discrimination, identifiability analysis and calculation of Hessian- and Monte-Carlo-based parameter confidence limits. A rich application programming interface is available for customization within own MATLAB code. Within an extensive performance analysis, we identified and significantly improved an integrator–optimizer pair which decreases the fitting duration for a realistic benchmark model by a factor over 3000 compared to MATLAB with optimization toolbox. Availability: PottersWheel is freely available for academic usage at http://www.PottersWheel.de/. The website contains a detailed documentation and introductory videos. The program has been intensively used since 2005 on Windows, Linux and Macintosh computers and does not require special MATLAB toolboxes. Contact: maiwald@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18614583

User's manual for the Macintosh version of PASCO

NASA Technical Reports Server (NTRS)

Lucas, S. H.; Davis, Randall C.

1991-01-01

A user's manual for Macintosh PASCO is presented. Macintosh PASCO is an Apple Macintosh version of PASCO, an existing computer code for structural analysis and optimization of longitudinally stiffened composite panels. PASCO combines a rigorous buckling analysis program with a nonlinear mathematical optimization routine to minimize panel mass. Macintosh PASCO accepts the same input as mainframe versions of PASCO. As output, Macintosh PASCO produces a text file and mode shape plots in the form of Apple Macintosh PICT files. Only the user interface for Macintosh is discussed here.

Code Optimization and Parallelization on the Origins: Looking from Users' Perspective

NASA Technical Reports Server (NTRS)

Chang, Yan-Tyng Sherry; Thigpen, William W. (Technical Monitor)

2002-01-01

Parallel machines are becoming the main compute engines for high performance computing. Despite their increasing popularity, it is still a challenge for most users to learn the basic techniques to optimize/parallelize their codes on such platforms. In this paper, we present some experiences on learning these techniques for the Origin systems at the NASA Advanced Supercomputing Division. Emphasis of this paper will be on a few essential issues (with examples) that general users should master when they work with the Origins as well as other parallel systems.

Analysis of different image-based biofeedback models for improving cycling performances

NASA Astrophysics Data System (ADS)

Bibbo, D.; Conforto, S.; Bernabucci, I.; Carli, M.; Schmid, M.; D'Alessio, T.

2012-03-01

Sport practice can take advantage from the quantitative assessment of task execution, which is strictly connected to the implementation of optimized training procedures. To this aim, it is interesting to explore the effectiveness of biofeedback training techniques. This implies a complete chain for information extraction containing instrumented devices, processing algorithms and graphical user interfaces (GUIs) to extract valuable information (i.e. kinematics, dynamics, and electrophysiology) to be presented in real-time to the athlete. In cycling, performance indexes displayed in a simple and perceivable way can help the cyclist optimize the pedaling. To this purpose, in this study four different GUIs have been designed and used in order to understand if and how a graphical biofeedback can influence the cycling performance. In particular, information related to the mechanical efficiency of pedaling is represented in each of the designed interfaces and then displayed to the user. This index is real-time calculated on the basis of the force signals exerted on the pedals during cycling. Instrumented pedals for bikes, already designed and implemented in our laboratory, have been used to measure those force components. A group of subjects underwent an experimental protocol and pedaled with (the interfaces have been used in a randomized order) and without graphical biofeedback. Preliminary results show how the effective perception of the biofeedback influences the motor performance.

Resource allocation in shared spectrum access communications for operators with diverse service requirements

NASA Astrophysics Data System (ADS)

Kibria, Mirza Golam; Villardi, Gabriel Porto; Ishizu, Kentaro; Kojima, Fumihide; Yano, Hiroyuki

2016-12-01

In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-operator and intra-operator resource allocation optimization problems. For inter-operator spectrum sharing, we present two competent approaches, namely the subcarrier gain-based sharing and fragmentation-based sharing, which carry out fair and flexible allocation of the available shareable spectrum among the operators subject to certain well-defined sharing rules, traffic demands, and channel propagation characteristics. The subcarrier gain-based spectrum sharing scheme has been found to be more efficient in terms of achieved throughput. However, the fragmentation-based sharing is more attractive in terms of computational complexity. For intra-operator resource allocation, we consider resource allocation problem with users' dissimilar service requirements, where the operator supports users with delay constraint and non-delay constraint service requirements, simultaneously. This optimization problem is a mixed-integer non-linear programming problem and non-convex, which is computationally very expensive, and the complexity grows exponentially with the number of integer variables. We propose less-complex and efficient suboptimal solution based on formulating exact linearization, linear approximation, and convexification techniques for the non-linear and/or non-convex objective functions and constraints. Extensive simulation performance analysis has been carried out that validates the efficiency of the proposed solution.

Dynamic User Interfaces for Service Oriented Architectures in Healthcare.

PubMed

Schweitzer, Marco; Hoerbst, Alexander

2016-01-01

Electronic Health Records (EHRs) play a crucial role in healthcare today. Considering a data-centric view, EHRs are very advanced as they provide and share healthcare data in a cross-institutional and patient-centered way adhering to high syntactic and semantic interoperability. However, the EHR functionalities available for the end users are rare and hence often limited to basic document query functions. Future EHR use necessitates the ability to let the users define their needed data according to a certain situation and how this data should be processed. Workflow and semantic modelling approaches as well as Web services provide means to fulfil such a goal. This thesis develops concepts for dynamic interfaces between EHR end users and a service oriented eHealth infrastructure, which allow the users to design their flexible EHR needs, modeled in a dynamic and formal way. These are used to discover, compose and execute the right Semantic Web services.

A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

PubMed

Wen, Yue; Si, Jennie; Gao, Xiang; Huang, Stephanie; Huang, He Helen

2017-09-01

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

WMT: The CSDMS Web Modeling Tool

NASA Astrophysics Data System (ADS)

Piper, M.; Hutton, E. W. H.; Overeem, I.; Syvitski, J. P.

2015-12-01

The Community Surface Dynamics Modeling System (CSDMS) has a mission to enable model use and development for research in earth surface processes. CSDMS strives to expand the use of quantitative modeling techniques, promotes best practices in coding, and advocates for the use of open-source software. To streamline and standardize access to models, CSDMS has developed the Web Modeling Tool (WMT), a RESTful web application with a client-side graphical interface and a server-side database and API that allows users to build coupled surface dynamics models in a web browser on a personal computer or a mobile device, and run them in a high-performance computing (HPC) environment. With WMT, users can: Design a model from a set of components Edit component parameters Save models to a web-accessible server Share saved models with the community Submit runs to an HPC system Download simulation results The WMT client is an Ajax application written in Java with GWT, which allows developers to employ object-oriented design principles and development tools such as Ant, Eclipse and JUnit. For deployment on the web, the GWT compiler translates Java code to optimized and obfuscated JavaScript. The WMT client is supported on Firefox, Chrome, Safari, and Internet Explorer. The WMT server, written in Python and SQLite, is a layered system, with each layer exposing a web service API: wmt-db: database of component, model, and simulation metadata and output wmt-api: configure and connect components wmt-exe: launch simulations on remote execution servers The database server provides, as JSON-encoded messages, the metadata for users to couple model components, including descriptions of component exchange items, uses and provides ports, and input parameters. Execution servers are network-accessible computational resources, ranging from HPC systems to desktop computers, containing the CSDMS software stack for running a simulation. Once a simulation completes, its output, in NetCDF, is packaged and uploaded to a data server where it is stored and from which a user can download it as a single compressed archive file.

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is presented wherein the combined effects of temperature and loading rate on the predicted response of a braided composite is investigated.

PopED lite: An optimal design software for preclinical pharmacokinetic and pharmacodynamic studies.

PubMed

Aoki, Yasunori; Sundqvist, Monika; Hooker, Andrew C; Gennemark, Peter

2016-04-01

Optimal experimental design approaches are seldom used in preclinical drug discovery. The objective is to develop an optimal design software tool specifically designed for preclinical applications in order to increase the efficiency of drug discovery in vivo studies. Several realistic experimental design case studies were collected and many preclinical experimental teams were consulted to determine the design goal of the software tool. The tool obtains an optimized experimental design by solving a constrained optimization problem, where each experimental design is evaluated using some function of the Fisher Information Matrix. The software was implemented in C++ using the Qt framework to assure a responsive user-software interaction through a rich graphical user interface, and at the same time, achieving the desired computational speed. In addition, a discrete global optimization algorithm was developed and implemented. The software design goals were simplicity, speed and intuition. Based on these design goals, we have developed the publicly available software PopED lite (http://www.bluetree.me/PopED_lite). Optimization computation was on average, over 14 test problems, 30 times faster in PopED lite compared to an already existing optimal design software tool. PopED lite is now used in real drug discovery projects and a few of these case studies are presented in this paper. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit a short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software tool can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Designing for User Cognition and Affect in Software Instructions

ERIC Educational Resources Information Center

van der Meij, Hans

2008-01-01

In this paper we examine how to design software instructions for user cognition and affect. A basic and co-user manual are compared. The first provides fundamental support for both; the latter includes a buddy to further optimize support for user affect. The basic manual was faster and judged as easier to process than the co-user manual. In…

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Modeling and Analysis of Commercial Building Electrical Loads for Demand Side Management

NASA Astrophysics Data System (ADS)

Berardino, Jonathan

In recent years there has been a push in the electric power industry for more customer involvement in the electricity markets. Traditionally the end user has played a passive role in the planning and operation of the power grid. However, many energy markets have begun opening up opportunities to consumers who wish to commit a certain amount of their electrical load under various demand side management programs. The potential benefits of more demand participation include reduced operating costs and new revenue opportunities for the consumer, as well as more reliable and secure operations for the utilities. The management of these load resources creates challenges and opportunities to the end user that were not present in previous market structures. This work examines the behavior of commercial-type building electrical loads and their capacity for supporting demand side management actions. This work is motivated by the need for accurate and dynamic tools to aid in the advancement of demand side operations. A dynamic load model is proposed for capturing the response of controllable building loads. Building-specific load forecasting techniques are developed, with particular focus paid to the integration of building management system (BMS) information. These approaches are tested using Drexel University building data. The application of building-specific load forecasts and dynamic load modeling to the optimal scheduling of multi-building systems in the energy market is proposed. Sources of potential load uncertainty are introduced in the proposed energy management problem formulation in order to investigate the impact on the resulting load schedule.

Identifying factors for optimal development of health-related websites: a delphi study among experts and potential future users.

PubMed

Schneider, Francine; van Osch, Liesbeth; de Vries, Hein

2012-02-14

The Internet has become a popular medium for offering tailored and targeted health promotion programs to the general public. However, suboptimal levels of program use in the target population limit the public health impact of these programs. Optimizing program development is considered as one of the main processes to increase usage rates. To distinguish factors potentially related to optimal development of health-related websites by involving both experts and potential users. By considering and incorporating the opinions of experts and potential users in the development process, involvement in the program is expected to increase, consequently resulting in increased appreciation, lower levels of attrition, and higher levels of sustained use. We conducted a systematic three-round Delphi study through the Internet. Both national and international experts (from the fields of health promotion, health psychology, e-communication, and technical Web design) and potential users were invited via email to participate. During this study an extensive list of factors potentially related to optimal development of health-related websites was identified, by focusing on factors related to layout, general and risk information provision, questionnaire use, additional services, and ease of use. Furthermore, we assessed the extent to which experts and potential users agreed on the importance of these factors. Differences as well as similarities among experts and potentials users were deduced. In total, 20 of 62 contacted experts participated in the first round (32% response rate); 60 of 200 contacted experts (30% response rate) and 210 potential users (95% response rate) completed the second-round questionnaire, and 32 of 60 contacted experts completed the third round (53% response rate). Results revealed important factors consented upon by experts and potential users (eg, ease of use, clear structure, and detailed health information provision), as well as differences regarding important factors consented upon by experts (eg, visual aids, self-monitoring tool, and iterative health feedback) or by potential users only (eg, bread crumb navigation and prevention of receiving spam). This study is an important first step in determining the agreed-upon factors that should be taken into account when developing online health promotion programs. The public health impact of these programs will be improved by optimizing the development process in line with these factors.

Hybrid protection algorithms based on game theory in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming

2011-12-01

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.

A web-oriented software for the optimization of pooled experiments in NGS for detection of rare mutations.

PubMed

Evangelista, Daniela; Zuccaro, Antonio; Lančinskas, Algirdas; Žilinskas, Julius; Guarracino, Mario R

2016-02-17

The cost per patient of next generation sequencing for detection of rare mutations may be significantly reduced using pooled experiments. Recently, some techniques have been proposed for the planning of pooled experiments and for the optimal allocation of patients into pools. However, the lack of a user friendly resource for planning the design of pooled experiments forces the scientists to do frequent, complex and long computations. OPENDoRM is a powerful collection of novel mathematical algorithms usable via an intuitive graphical user interface. It enables researchers to speed up the planning of their routine experiments, as well as, to support scientists without specific bioinformatics expertises. Users can automatically carry out analysis in terms of costs associated with the optimal allocation of patients in pools. They are also able to choose between three distinct pooling mathematical methods, each of which also suggests the optimal configuration for the submitted experiment. Importantly, in order to keep track of the performed experiments, users can save and export the results of their experiments in standard tabular and charts contents. OPENDoRM is a freely available web-oriented application for the planning of pooled NGS experiments, available at: http://www-labgtp.na.icar.cnr.it/OPENDoRM. Its easy and intuitive graphical user interface enables researchers to plan theirs experiments using novel algorithms, and to interactively visualize the results.

Emergent user behavior on Twitter modelled by a stochastic differential equation.

PubMed

Mollgaard, Anders; Mathiesen, Joachim

2015-01-01

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

Emergent User Behavior on Twitter Modelled by a Stochastic Differential Equation

PubMed Central

Mollgaard, Anders; Mathiesen, Joachim

2015-01-01

Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise. PMID:25955783

Preliminary studies on SMA embedded wind turbine blades for passive control of vibration

NASA Astrophysics Data System (ADS)

Haghdoust, P.; Cinquemani, S.; Lo Conte, A.

2018-03-01

Wind turbine blades are being bigger and bigger, thus requiring lightweight structures that are more flexible and thus more sensitive to dynamic excitations and to vibration problems. This paper investigates a preliminary architecture of large wind turbine blades, embedding thin sheets of SMA to passively improve their total damping. A phenomenological material model is used for simulation of strain-dependent damping in SMA materials and an user defined material model was developed for this purpose. The response of different architectures of SMA embedded blades have been investigated in the time domain to find an optimal solution in which the less amount of SMA is used while the damping of the system is maximized

DORCA computer program. Volume 1: User's guide

NASA Technical Reports Server (NTRS)

Wray, S. T., Jr.

1971-01-01

The Dynamic Operational Requirements and Cost Analysis Program (DORCA) was written to provide a top level analysis tool for NASA. DORCA relies on a man-machine interaction to optimize results based on external criteria. DORCA relies heavily on outside sources to provide cost information and vehicle performance parameters as the program does not determine these quantities but rather uses them. Given data describing missions, vehicles, payloads, containers, space facilities, schedules, cost values and costing procedures, the program computes flight schedules, cargo manifests, vehicle fleet requirements, acquisition schedules and cost summaries. The program is designed to consider the Earth Orbit, Lunar, Interplanetary and Automated Satellite Programs. A general outline of the capabilities of the program are provided.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

PubMed

Alphy, Anna; Prabakaran, S

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

PubMed Central

Alphy, Anna; Prabakaran, S.

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations. PMID:26229978

The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model

NASA Astrophysics Data System (ADS)

Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

2016-05-01

Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.

Modeling the heterogeneity of human dynamics based on the measurements of influential users in Sina Microblog

NASA Astrophysics Data System (ADS)

Wang, Chenxu; Guan, Xiaohong; Qin, Tao; Yang, Tao

2015-06-01

Online social network has become an indispensable communication tool in the information age. The development of microblog also provides us a great opportunity to study human dynamics that play a crucial role in the design of efficient communication systems. In this paper we study the characteristics of the tweeting behavior based on the data collected from Sina Microblog. The user activity level is measured to characterize how often a user posts a tweet. We find that the user activity level follows a bimodal distribution. That is, the microblog users tend to be either active or inactive. The inter-tweeting time distribution is then measured at both the aggregate and individual levels. We find that the inter-tweeting time follows a piecewise power law distribution of two tails. Furthermore, the exponents of the two tails have different correlations with the user activity level. These findings demonstrate that the dynamics of the tweeting behavior are heterogeneous in different time scales. We then develop a dynamic model co-driven by the memory and the interest mechanism to characterize the heterogeneity. The numerical simulations validate the model and verify that the short time interval tweeting behavior is driven by the memory mechanism while the long time interval behavior by the interest mechanism.

Specialty Task Force: A Strategic Component to Electronic Health Record (EHR) Optimization.

PubMed

Romero, Mary Rachel; Staub, Allison

2016-01-01

Post-implementation stage comes after an electronic health record (EHR) deployment. Analyst and end users deal with the reality that some of the concepts and designs initially planned and created may not be complementary to the workflow; creating anxiety, dissatisfaction, and failure with early adoption of system. Problems encountered during deployment are numerous and can vary from simple to complex. Redundant ticket submission creates backlog for Information Technology personnel resulting in delays in resolving concerns with EHR system. The process of optimization allows for evaluation of system and reassessment of users' needs. A solid and well executed optimization infrastructure can help minimize unexpected end-user disruptions and help tailor the system to meet regulatory agency goals and practice standards. A well device plan to resolve problems during post implementation is necessary for cost containment and to streamline communication efforts. Creating a specialty specific collaborative task force is efficacious and expedites resolution of users' concerns through a more structured process.

Constraint programming based biomarker optimization.

PubMed

Zhou, Manli; Luo, Youxi; Sun, Guoquan; Mai, Guoqin; Zhou, Fengfeng

2015-01-01

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

Dependence and resistance in community mental health care-Negotiations of user participation between staff and users.

PubMed

Femdal, I; Knutsen, I R

2017-10-01

WHAT IS KNOWN ON THE SUBJECT?: Implementation of user participation is described as a change from a paternalistic healthcare system to ideals of democratization where users' voices are heard in relational interplays with health professionals. The ideological shift involves a transition from welfare dependency and professional control towards more active service-user roles with associated rights and responsibilities. A collaborative relationship between users and professionals in mental health services is seen as important by both parties. Nevertheless, the health professionals find it challenging in practice to reorient their roles and to find productive ways to cooperate. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: This study illuminates how user participation is negotiated and involves multiple and shifting subject positions in the collaboration between users and professionals in community mental health care. By taking different positions, the relationship between users and professionals develops through dynamic interaction. This study challenges understandings of equality and implicit "truths" in user participation by illuminating subtle forms of power and dilemmas that arise in user-professional negotiations. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Instead of denying the appearance of power, it is important to question the execution of power in the interplay between users and professionals. Focusing on the negotiation processes between users and professionals is important for increasing reflection on and improving understanding of the dynamic in collaboration and speech. By focusing on negotiations, power can be used in productive ways in user-professional relationships. Introduction Implementation of user participation is considered important in today's mental health care. Research shows, however, that user participation lacks clarity and provokes uncertainty regarding shifting roles. Aim To investigate negotiation of user participation in a microstudy of interplay between users and health professionals in community mental health care. Method This qualitative study is based on semi-structured in-depth interviews, involving ten service users and ten professionals in community mental health care in Norway. The analysis is inspired by Willig's model for Foucauldian discourse analysis. Results The study illuminates the dynamic nature of user participation that arises through negotiation between users' and professionals' positions as change enablers, dependents, resisters, persuaders and knowledge holders. Discussion Discourses of user participation allow for different subject positions in mental health care. User participation also involves government and questions of power, as well as ambitions of change and control. Professionals act in different ways to make and keep users active, participating, enterprising and self-governing, and users respond and take part within the same discursive framework. Implications for practice Awareness of subjects' positions in discourses is important to increase reflection on the dynamic interplay in user-professional collaboration. © 2017 John Wiley & Sons Ltd.

Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This interactive approach gives the user the power to make optimal choices in the contrast enhancement parameters.

Software-supported USER cloning strategies for site-directed mutagenesis and DNA assembly.

PubMed

Genee, Hans Jasper; Bonde, Mads Tvillinggaard; Bagger, Frederik Otzen; Jespersen, Jakob Berg; Sommer, Morten O A; Wernersson, Rasmus; Olsen, Lars Rønn

2015-03-20

USER cloning is a fast and versatile method for engineering of plasmid DNA. We have developed a user friendly Web server tool that automates the design of optimal PCR primers for several distinct USER cloning-based applications. Our Web server, named AMUSER (Automated DNA Modifications with USER cloning), facilitates DNA assembly and introduction of virtually any type of site-directed mutagenesis by designing optimal PCR primers for the desired genetic changes. To demonstrate the utility, we designed primers for a simultaneous two-position site-directed mutagenesis of green fluorescent protein (GFP) to yellow fluorescent protein (YFP), which in a single step reaction resulted in a 94% cloning efficiency. AMUSER also supports degenerate nucleotide primers, single insert combinatorial assembly, and flexible parameters for PCR amplification. AMUSER is freely available online at http://www.cbs.dtu.dk/services/AMUSER/.

Interface Metaphors for Interactive Machine Learning

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

Jasper, Robert J.; Blaha, Leslie M.

To promote more interactive and dynamic machine learn- ing, we revisit the notion of user-interface metaphors. User-interface metaphors provide intuitive constructs for supporting user needs through interface design elements. A user-interface metaphor provides a visual or action pattern that leverages a user’s knowledge of another domain. Metaphors suggest both the visual representations that should be used in a display as well as the interactions that should be afforded to the user. We argue that user-interface metaphors can also offer a method of extracting interaction-based user feedback for use in machine learning. Metaphors offer indirect, context-based information that can be usedmore » in addition to explicit user inputs, such as user-provided labels. Implicit information from user interactions with metaphors can augment explicit user input for active learning paradigms. Or it might be leveraged in systems where explicit user inputs are more challenging to obtain. Each interaction with the metaphor provides an opportunity to gather data and learn. We argue this approach is especially important in streaming applications, where we desire machine learning systems that can adapt to dynamic, changing data.« less

Hierarchical video summarization based on context clustering

NASA Astrophysics Data System (ADS)

Tseng, Belle L.; Smith, John R.

2003-11-01

A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

Method and System for Dynamic Automated Corrections to Weather Avoidance Routes for Aircraft in En Route Airspace

NASA Technical Reports Server (NTRS)

McNally, B. David (Inventor); Erzberger, Heinz (Inventor); Sheth, Kapil (Inventor)

2015-01-01

A dynamic weather route system automatically analyzes routes for in-flight aircraft flying in convective weather regions and attempts to find more time and fuel efficient reroutes around current and predicted weather cells. The dynamic weather route system continuously analyzes all flights and provides reroute advisories that are dynamically updated in real time while the aircraft are in flight. The dynamic weather route system includes a graphical user interface that allows users to visualize, evaluate, modify if necessary, and implement proposed reroutes.

Multiantenna Relay Beamforming Design for QoS Discrimination in Two-Way Relay Networks

PubMed Central

Xiong, Ke; Zhang, Yu; Li, Dandan; Zhong, Zhangdui

2013-01-01

This paper investigates the relay beamforming design for quality of service (QoS) discrimination in two-way relay networks. The purpose is to keep legitimate two-way relay users exchange their information via a helping multiantenna relay with QoS guarantee while avoiding the exchanged information overhearing by unauthorized receiver. To this end, we propose a physical layer method, where the relay beamforming is jointly designed with artificial noise (AN) which is used to interfere in the unauthorized user's reception. We formulate the joint beamforming and AN (BFA) design into an optimization problem such that the received signal-to-interference-ratio (SINR) at the two legitimate users is over a predefined QoS threshold while limiting the received SINR at the unauthorized user which is under a certain secure threshold. The objective of the optimization problem is to seek the optimal AN and beamforming vectors to minimize the total power consumed by the relay node. Since the optimization problem is nonconvex, we solve it by using semidefinite program (SDP) relaxation. For comparison, we also study the optimal relay beamforming without using AN (BFO) under the same QoS discrimination constraints. Simulation results show that both the proposed BFA and BFO can achieve the QoS discrimination of the two-way transmission. However, the proposed BFA yields significant power savings and lower infeasible rates compared with the BFO method. PMID:24391459

EpiHosp: A web-based visualization tool enabling the exploratory analysis of complications of implantable medical devices from a nationwide hospital database.

PubMed

Ficheur, Grégoire; Ferreira Careira, Lionel; Beuscart, Régis; Chazard, Emmanuel

2015-01-01

Administrative data can be used for the surveillance of the outcomes of implantable medical devices (IMDs). The objective of this work is to build a web-based tool allowing for an exploratory analysis of time-dependent events that may occur after the implementation of an IMD. This tool should enable a pharmacoepidemiologist to explore on the fly the relationship between a given IMD and a potential outcome. This tool mine the French nationwide database of inpatient stays from 2008 to 2013. The data are preprocessed in order to optimize the queries. A web tool is developed in PHP, MySQL and Javascript. The user selects one or a group of IMD from a tree, and can filter the results using years and hospital names. Four result pages describe the selected inpatient stays: (1) temporal and demographic description, (2) a description of the geographical location of the hospital, (3) a description of the geographical place of residence of the patient and (4) a table showing the rehospitalization reasons by decreasing order of frequency. Then, the user can select one readmission reason and display dynamically the probability of readmission by mean of a Kaplan-Meier curve with confidence intervals. This tool enables to dynamically monitor the occurrence of time-dependent complications of IMD.

Deterministic Design Optimization of Structures in OpenMDAO Framework

NASA Technical Reports Server (NTRS)

Coroneos, Rula M.; Pai, Shantaram S.

2012-01-01

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks.

PubMed

Zhang, Jing; Zhou, Qingjie; Ng, Derrick Wing Kwan; Jo, Minho

2017-09-15

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity.

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks

PubMed Central

Zhou, Qingjie; Ng, Derrick Wing Kwan; Jo, Minho

2017-01-01

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity. PMID:28914818

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

Polack, F.; Silly, M.; Chauvet, C.

A new insertion device beamline is now operational on straight section 8 at the SOLEIL synchrotron radiation source in France. The beamline and the experimental station were developed to optimize the study of the dynamics of electronic and magnetic properties of materials. Here we present the main technical characteristics of the installation and the general principles behind them. The source is composed of two APPLE II type insertion devices. The monochromator with plane gratings and spherical mirrors is working in the energy range 40-1500 eV. It is equipped with VLS, VGD gratings to allow the user optimization of flux ormore » higher harmonics rejection. The observed resonance structures measured in gas phase enable us to determine the available energy resolution: a resolving power higher than 10000 is obtained at the Ar 2p, N 1s and Ne K-edges when using all the optical elements at full aperture. The total flux as a function of the measured photon energy and the characterization of the focal spot size complete the beamline characterization.« less

Launch vehicle design and GNC sizing with ASTOS

NASA Astrophysics Data System (ADS)

Cremaschi, Francesco; Winter, Sebastian; Rossi, Valerio; Wiegand, Andreas

2018-03-01

The European Space Agency (ESA) is currently involved in several activities related to launch vehicle designs (Future Launcher Preparatory Program, Ariane 6, VEGA evolutions, etc.). Within these activities, ESA has identified the importance of developing a simulation infrastructure capable of supporting the multi-disciplinary design and preliminary guidance navigation and control (GNC) design of different launch vehicle configurations. Astos Solutions has developed the multi-disciplinary optimization and launcher GNC simulation and sizing tool (LGSST) under ESA contract. The functionality is integrated in the Analysis, Simulation and Trajectory Optimization Software for space applications (ASTOS) and is intended to be used from the early design phases up to phase B1 activities. ASTOS shall enable the user to perform detailed vehicle design tasks and assessment of GNC systems, covering all aspects of rapid configuration and scenario management, sizing of stages, trajectory-dependent estimation of structural masses, rigid and flexible body dynamics, navigation, guidance and control, worst case analysis, launch safety analysis, performance analysis, and reporting.

User Access | Energy Systems Integration Facility | NREL

Science.gov Websites

User Access User Access The ESIF houses an unparalleled collection of state-of-the-art capabilities user access program, the ESIF allows researchers access to its premier laboratories in support of research and development that aims to optimize our entire energy system at full power. Requests for access

Method and System for Air Traffic Rerouting for Airspace Constraint Resolution

NASA Technical Reports Server (NTRS)

Erzberger, Heinz (Inventor); Morando, Alexander R. (Inventor); Sheth, Kapil S. (Inventor); McNally, B. David (Inventor); Clymer, Alexis A. (Inventor); Shih, Fu-tai (Inventor)

2017-01-01

A dynamic constraint avoidance route system automatically analyzes routes of aircraft flying, or to be flown, in or near constraint regions and attempts to find more time and fuel efficient reroutes around current and predicted constraints. The dynamic constraint avoidance route system continuously analyzes all flight routes and provides reroute advisories that are dynamically updated in real time. The dynamic constraint avoidance route system includes a graphical user interface that allows users to visualize, evaluate, modify if necessary, and implement proposed reroutes.

Pragmatic User Model Implementation in an Intelligent Help System.

ERIC Educational Resources Information Center

Fernandez-Manjon, Baltasar; Fernandez-Valmayor, Alfredo; Fernandez-Chamizo, Carmen

1998-01-01

Describes Aran, a knowledge-based system designed to help users deal with problems related to Unix operation. Highlights include adaptation to the individual user; user modeling knowledge; stereotypes; content of the individual user model; instantiation, acquisition, and maintenance of the individual model; dynamic acquisition of objective and…

Network-optimized congestion pricing : a parable, model and algorithm

DOT National Transportation Integrated Search

1995-05-31

This paper recites a parable, formulates a model and devises an algorithm for optimizing tolls on a road network. Such tolls induce an equilibrium traffic flow that is at once system-optimal and user-optimal. The parable introduces the network-wide c...

Civility vs. Incivility in Online Social Interactions: An Evolutionary Approach

PubMed Central

2016-01-01

Evidence is growing that forms of incivility–e.g. aggressive and disrespectful behaviors, harassment, hate speech and outrageous claims–are spreading in the population of social networking sites’ (SNS) users. Online social networks such as Facebook allow users to regularly interact with known and unknown others, who can behave either politely or rudely. This leads individuals not only to learn and adopt successful strategies for using the site, but also to condition their own behavior on that of others. Using a mean field approach, we define anevolutionary game framework to analyse the dynamics of civil and uncivil ways of interaction in online social networks and their consequences for collective welfare. Agents can choose to interact with others–politely or rudely–in SNS, or to opt out from online social networks to protect themselves from incivility. We find that, when the initial share of the population of polite users reaches a critical level, civility becomes generalized if its payoff increases more than that of incivility with the spreading of politeness in online interactions. Otherwise, the spreading of self-protective behaviors to cope with online incivility can lead the economyto non-socially optimal stationary states. JEL Codes: C61, C73, D85, O33, Z13. PsycINFO Codes: 2240, 2750. PMID:27802271

Civility vs. Incivility in Online Social Interactions: An Evolutionary Approach.

PubMed

Antoci, Angelo; Delfino, Alexia; Paglieri, Fabio; Panebianco, Fabrizio; Sabatini, Fabio

2016-01-01

Evidence is growing that forms of incivility-e.g. aggressive and disrespectful behaviors, harassment, hate speech and outrageous claims-are spreading in the population of social networking sites' (SNS) users. Online social networks such as Facebook allow users to regularly interact with known and unknown others, who can behave either politely or rudely. This leads individuals not only to learn and adopt successful strategies for using the site, but also to condition their own behavior on that of others. Using a mean field approach, we define anevolutionary game framework to analyse the dynamics of civil and uncivil ways of interaction in online social networks and their consequences for collective welfare. Agents can choose to interact with others-politely or rudely-in SNS, or to opt out from online social networks to protect themselves from incivility. We find that, when the initial share of the population of polite users reaches a critical level, civility becomes generalized if its payoff increases more than that of incivility with the spreading of politeness in online interactions. Otherwise, the spreading of self-protective behaviors to cope with online incivility can lead the economyto non-socially optimal stationary states. JEL Codes: C61, C73, D85, O33, Z13. PsycINFO Codes: 2240, 2750.

A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

PubMed

Li, Ke; Wang, Hai; Xu, Xiaolong; Du, Yu; Liu, Yuansheng; Ahmad, M Omair

2018-05-15

Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance is no longer effective. Therefore, developing an approach to evaluate and optimizing users' service perceptions has become increasingly important. Meanwhile, the development of a new sensing paradigm, mobile crowdsensing (MCS), makes it possible to evaluate and analyze the user's OTT service perception from end-user's point of view other than from the network side. In this paper, the key factors that impact users' end-to-end OTT web browsing service perception are analyzed by monitoring crowdsourced user perceptions. The intrinsic relationships among the key factors and the interactions between key quality indicators (KQI) are evaluated from several perspectives. Moreover, an analytical framework of perceptional degradation and a detailed algorithm are proposed whose goal is to identify the major factors that impact the perceptional degradation of web browsing service as well as their significance of contribution. Finally, a case study is presented to show the effectiveness of the proposed method using a dataset crowdsensed from a large number of smartphone users in a real mobile network. The proposed analytical framework forms a valuable solution for mobile network maintenance and optimization and can help improve web browsing service perception and network quality.

Estimation and detection information trade-off for x-ray system optimization

NASA Astrophysics Data System (ADS)

Cushing, Johnathan B.; Clarkson, Eric W.; Mandava, Sagar; Bilgin, Ali

2016-05-01

X-ray Computed Tomography (CT) systems perform complex imaging tasks to detect and estimate system parameters, such as a baggage imaging system performing threat detection and generating reconstructions. This leads to a desire to optimize both the detection and estimation performance of a system, but most metrics only focus on one of these aspects. When making design choices there is a need for a concise metric which considers both detection and estimation information parameters, and then provides the user with the collection of possible optimal outcomes. In this paper a graphical analysis of Estimation and Detection Information Trade-off (EDIT) will be explored. EDIT produces curves which allow for a decision to be made for system optimization based on design constraints and costs associated with estimation and detection. EDIT analyzes the system in the estimation information and detection information space where the user is free to pick their own method of calculating these measures. The user of EDIT can choose any desired figure of merit for detection information and estimation information then the EDIT curves will provide the collection of optimal outcomes. The paper will first look at two methods of creating EDIT curves. These curves can be calculated using a wide variety of systems and finding the optimal system by maximizing a figure of merit. EDIT could also be found as an upper bound of the information from a collection of system. These two methods allow for the user to choose a method of calculation which best fits the constraints of their actual system.

Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching

NASA Astrophysics Data System (ADS)

Shen, Kaiming; Yu, Wei

2018-05-01

This two-part paper develops novel methodologies for using fractional programming (FP) techniques to design and optimize communication systems. Part I of this paper proposes a new quadratic transform for FP and treats its application for continuous optimization problems. In this Part II of the paper, we study discrete problems, such as those involving user scheduling, which are considerably more difficult to solve. Unlike the continuous problems, discrete or mixed discrete-continuous problems normally cannot be recast as convex problems. In contrast to the common heuristic of relaxing the discrete variables, this work reformulates the original problem in an FP form amenable to distributed combinatorial optimization. The paper illustrates this methodology by tackling the important and challenging problem of uplink coordinated multi-cell user scheduling in wireless cellular systems. Uplink scheduling is more challenging than downlink scheduling, because uplink user scheduling decisions significantly affect the interference pattern in nearby cells. Further, the discrete scheduling variable needs to be optimized jointly with continuous variables such as transmit power levels and beamformers. The main idea of the proposed FP approach is to decouple the interaction among the interfering links, thereby permitting a distributed and joint optimization of the discrete and continuous variables with provable convergence. The paper shows that the well-known weighted minimum mean-square-error (WMMSE) algorithm can also be derived from a particular use of FP; but our proposed FP-based method significantly outperforms WMMSE when discrete user scheduling variables are involved, both in term of run-time efficiency and optimizing results.

Implicit Formulation of Muscle Dynamics in OpenSim

NASA Technical Reports Server (NTRS)

Humphreys, Brad; Dembia, Chris; Lewandowski, Beth; Van Den Bogert, Antonie

2017-01-01

Astronauts lose bone and muscle mass during spaceflight. Exercise countermeasure is the primary method for counteracting bone and muscle mass loss in space. New spacecraft exercise device concepts are currently being developed for the NASAs new crew exploration vehicle. The NASA Digital Astronaut Project (DAP) uses computational modeling to help determine if the new exercise devices will be effective as countermeasures. The NASA Digital Astronaut Project is developing the ability to utilize predictive simulation to provide insight into the change in kinematics and kinetics with a change in device and gravitational environment (1-g versus 0-g). For example, in space exercise the subject's body weight is applied in addition to the loads prescribed for musculoskeletal maintenance. How and where these loads are applied obviously directly impacts bone and tissue loads. Additionally, due to space vehicle structural requirements, exercise devices are often placed on vibration isolation systems. This changes the apparent impedance or stiffness of the device as seen by the user. Data collection under these conditions is often impractical and limited. Predictive modeling provides a means to have a virtual subject to test hypotheses. Predictive simulation provides a virtual subject for which we are able to perform studies such as sensitivity to device loading and vibration isolation without the need for laboratory kinematic or kinetic test data.Direct Collocation optimization provides an efficient means to perform task based optimization and predictive modeling. It is relatively straight forward to structure a physical exercise task in a Direct Collocation mathematical formulation: perform a motion such that you start at an initial pose, achieve a given amount of deflection i.e a squat, return to the initial pose, and minimize muscle activation cost. Direct Collocation is advantageous in that it does not require numerical integration to evaluate the objective function. Instead, the system dynamics are transformed to discrete time and the optimizer is constrained such that the solution is not considered to be a valid unless the dynamic equations are satisfied at all time points. The simulation and optimization are effectively done simultaneously. Due to the implicit integration, time steps can be more coarse than in a differential equation solver. In a gait scenario this means that that the model constraints and cost function are evaluated at 100 nodes in the gait cycle versus 10,000 integration steps in a variable-step forward dynamic simulation. Furthermore, no time is wasted on accurate simulations of movements that are far from the optimum. Constrained optimization algorithms require a Jacobian matrix that contains the partial derivatives of each of the dynamic constraints with respect to of each of the state and control variables at all time points. This is a large but sparse matrix. An implicit dynamics formulation requires computation of the dynamic residuals f as a function of the states x and their derivatives, and controls u:f(x, dxdt, u) 0If the dynamics of musculoskeletal system are formulated implicitly, the Jacobian elements are often available analytically, eliminating the need for numerical differentiation; this is obviously computationally advantageous. Additionally, implicit formulation of musculoskeletal dynamics do not suffer from singularities from low mass bodies, zero muscle activation, or other stiff system or

AUCTION MECHANISMS FOR IMPLEMENTING TRADABLE NETWORK PERMIT MARKETS

NASA Astrophysics Data System (ADS)

Wada, Kentaro; Akamatsu, Takashi

This paper proposes a new auction mechanism for implementing the tradable network permit markets. Assuming that each user makes a trip from an origin to a destination along a path in a specific time period, we design an auction mechanism that enables each user to purchase a bundle of permits corresponding to a set of links in the user's preferred path. The objective of the proposed mechanism is to achieve a socially optimal state with minimal revelation of users' private information. In order to achieve this, the mechanism employs an evolutionary approach that has an auction phase and a path capacity adjustment phase, which are repeated on a day-to-day basis. We prove that the proposed mechanism has the following desirable properties: (1) truthful bidding is the dominant strategy for each user and (2) the proposed mechanism converges to an approximate socially optimal state in the sense that the achieved value of the social surplus reaches its maximum value when the number of users is large.

Optimization of locations of diffusion spots in indoor optical wireless local area networks

NASA Astrophysics Data System (ADS)

Eltokhey, Mahmoud W.; Mahmoud, K. R.; Ghassemlooy, Zabih; Obayya, Salah S. A.

2018-03-01

In this paper, we present a novel optimization of the locations of the diffusion spots in indoor optical wireless local area networks, based on the central force optimization (CFO) scheme. The users' performance uniformity is addressed by using the CFO algorithm, and adopting different objective function's configurations, while considering maximization and minimization of the signal to noise ratio and the delay spread, respectively. We also investigate the effect of varying the objective function's weights on the system and the users' performance as part of the adaptation process. The results show that the proposed objective function configuration-based optimization procedure offers an improvement of 65% in the standard deviation of individual receivers' performance.

Optimel: Software for selecting the optimal method

NASA Astrophysics Data System (ADS)

Popova, Olga; Popov, Boris; Romanov, Dmitry; Evseeva, Marina

Optimel: software for selecting the optimal method automates the process of selecting a solution method from the optimization methods domain. Optimel features practical novelty. It saves time and money when conducting exploratory studies if its objective is to select the most appropriate method for solving an optimization problem. Optimel features theoretical novelty because for obtaining the domain a new method of knowledge structuring was used. In the Optimel domain, extended quantity of methods and their properties are used, which allows identifying the level of scientific studies, enhancing the user's expertise level, expand the prospects the user faces and opening up new research objectives. Optimel can be used both in scientific research institutes and in educational institutions.

SEEK: A FORTRAN optimization program using a feasible directions gradient search

NASA Technical Reports Server (NTRS)

Savage, M.

1995-01-01

This report describes the use of computer program 'SEEK' which works in conjunction with two user-written subroutines and an input data file to perform an optimization procedure on a user's problem. The optimization method uses a modified feasible directions gradient technique. SEEK is written in ANSI standard Fortran 77, has an object size of about 46K bytes, and can be used on a personal computer running DOS. This report describes the use of the program and discusses the optimizing method. The program use is illustrated with four example problems: a bushing design, a helical coil spring design, a gear mesh design, and a two-parameter Weibull life-reliability curve fit.

The Application of SNiPER to the JUNO Simulation

NASA Astrophysics Data System (ADS)

Lin, Tao; Zou, Jiaheng; Li, Weidong; Deng, Ziyan; Fang, Xiao; Cao, Guofu; Huang, Xingtao; You, Zhengyun; JUNO Collaboration

2017-10-01

The JUNO (Jiangmen Underground Neutrino Observatory) is a multipurpose neutrino experiment which is designed to determine neutrino mass hierarchy and precisely measure oscillation parameters. As one of the important systems, the JUNO offline software is being developed using the SNiPER software. In this proceeding, we focus on the requirements of JUNO simulation and present the working solution based on the SNiPER. The JUNO simulation framework is in charge of managing event data, detector geometries and materials, physics processes, simulation truth information etc. It glues physics generator, detector simulation and electronics simulation modules together to achieve a full simulation chain. In the implementation of the framework, many attractive characteristics of the SNiPER have been used, such as dynamic loading, flexible flow control, multiple event management and Python binding. Furthermore, additional efforts have been made to make both detector and electronics simulation flexible enough to accommodate and optimize different detector designs. For the Geant4-based detector simulation, each sub-detector component is implemented as a SNiPER tool which is a dynamically loadable and configurable plugin. So it is possible to select the detector configuration at runtime. The framework provides the event loop to drive the detector simulation and interacts with the Geant4 which is implemented as a passive service. All levels of user actions are wrapped into different customizable tools, so that user functions can be easily extended by just adding new tools. The electronics simulation has been implemented by following an event driven scheme. The SNiPER task component is used to simulate data processing steps in the electronics modules. The electronics and trigger are synchronized by triggered events containing possible physics signals. The JUNO simulation software has been released and is being used by the JUNO collaboration to do detector design optimization, event reconstruction algorithm development and physics sensitivity studies.

Best practices in the use of hybrid static-dynamic signs.

DOT National Transportation Integrated Search

2012-12-01

Static signs are traditionally used to convey messages to the road users. The need to quickly communicate up-to-date messages to the road users has given rise to the increasing use of dynamic message signs (DMS). An alternative to DMS is hybrid signs...

Development of a turbomachinery design optimization procedure using a multiple-parameter nonlinear perturbation method

NASA Technical Reports Server (NTRS)

Stahara, S. S.

1984-01-01

An investigation was carried out to complete the preliminary development of a combined perturbation/optimization procedure and associated computational code for designing optimized blade-to-blade profiles of turbomachinery blades. The overall purpose of the procedures developed is to provide demonstration of a rapid nonlinear perturbation method for minimizing the computational requirements associated with parametric design studies of turbomachinery flows. The method combines the multiple parameter nonlinear perturbation method, successfully developed in previous phases of this study, with the NASA TSONIC blade-to-blade turbomachinery flow solver, and the COPES-CONMIN optimization procedure into a user's code for designing optimized blade-to-blade surface profiles of turbomachinery blades. Results of several design applications and a documented version of the code together with a user's manual are provided.

Automatic design optimization tool for passive structural control systems

NASA Astrophysics Data System (ADS)

Mojolic, Cristian; Hulea, Radu; Parv, Bianca Roxana

2017-07-01

The present paper proposes an automatic dynamic process in order to find the parameters of the seismic isolation systems applied to large span structures. Three seismic isolation solutions are proposed for the model of the new Slatina Sport Hall. The first case uses friction pendulum system (FP), the second one uses High Damping Rubber Bearing (HDRB) and Lead Rubber Bearings, while (LRB) are used for the last case of isolation. The placement of the isolation level is at the top end of the roof supporting columns. The aim is to calculate the parameters of each isolation system so that the whole's structure first vibration periods is the one desired by the user. The model is computed with the use of SAP2000 software. In order to find the best solution for the optimization problem, an optimization process based on Genetic Algorithms (GA) has been developed in Matlab. With the use of the API (Application Programming Interface) libraries a two way link is created between the two programs in order to exchange results and link parameters. The main goal is to find the best seismic isolation method for each desired modal period so that the bending moment on the supporting columns should be minimum.

Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes.

PubMed

Liu, Ping; Li, Guodong; Liu, Xinggao

2015-09-01

Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

NASA's Earth Observing Data and Information System - Near-Term Challenges

NASA Technical Reports Server (NTRS)

Behnke, Jeanne; Mitchell, Andrew; Ramapriyan, Hampapuram

2018-01-01

NASA's Earth Observing System Data and Information System (EOSDIS) has been a central component of the NASA Earth observation program since the 1990's. EOSDIS manages data covering a wide range of Earth science disciplines including cryosphere, land cover change, polar processes, field campaigns, ocean surface, digital elevation, atmosphere dynamics and composition, and inter-disciplinary research, and many others. One of the key components of EOSDIS is a set of twelve discipline-based Distributed Active Archive Centers (DAACs) distributed across the United States. Managed by NASA's Earth Science Data and Information System (ESDIS) Project at Goddard Space Flight Center, these DAACs serve over 3 million users globally. The ESDIS Project provides the infrastructure support for EOSDIS, which includes other components such as the Science Investigator-led Processing systems (SIPS), common metadata and metrics management systems, specialized network systems, standards management, and centralized support for use of commercial cloud capabilities. Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, EOSDIS has evolved continually over the past three decades. However, many challenges remain. Challenges addressed in this paper include: growing volume and variety, achieving consistency across a diverse set of data producers, managing information about a large number of datasets, migration to a cloud computing environment, optimizing data discovery and access, incorporating user feedback from a diverse community, keeping metadata updated as data collections grow and age, and ensuring that all the content needed for understanding datasets by future users is identified and preserved.

Technological, biological, and acoustical constraints to music perception in cochlear implant users.

PubMed

Limb, Charles J; Roy, Alexis T

2014-02-01

Despite advances in technology, the ability to perceive music remains limited for many cochlear implant users. This paper reviews the technological, biological, and acoustical constraints that make music an especially challenging stimulus for cochlear implant users, while highlighting recent research efforts to overcome these shortcomings. The limitations of cochlear implant devices, which have been optimized for speech comprehension, become evident when applied to music, particularly with regards to inadequate spectral, fine-temporal, and dynamic range representation. Beyond the impoverished information transmitted by the device itself, both peripheral and central auditory nervous system deficits are seen in the presence of sensorineural hearing loss, such as auditory nerve degeneration and abnormal auditory cortex activation. These technological and biological constraints to effective music perception are further compounded by the complexity of the acoustical features of music itself that require the perceptual integration of varying rhythmic, melodic, harmonic, and timbral elements of sound. Cochlear implant users not only have difficulty perceiving spectral components individually (leading to fundamental disruptions in perception of pitch, melody, and harmony) but also display deficits with higher perceptual integration tasks required for music perception, such as auditory stream segregation. Despite these current limitations, focused musical training programs, new assessment methods, and improvements in the representation and transmission of the complex acoustical features of music through technological innovation offer the potential for significant advancements in cochlear implant-mediated music perception. Copyright © 2013 Elsevier B.V. All rights reserved.

Social Moderation and Dynamic Elements in Crowdsourced Geospatial Data: A Report on Quality Assessment, Dynamic Extensions and Mobile Device Engagement in the George Mason University Geocrowdsourcing Testbed

DTIC Science & Technology

2015-11-01

GMU) Associate Professor Dieter Pfoser describes an explosion of user generated content (UGC) available over the Internet (Pfoser 2011, Crooks et al...Crowdsourced and User - Generated Geospatial Data,” Annual (Fairfax, VA: George Mason University, November 29, 2012), http://www.dtic.mil/dtic/tr/fulltext/u2...area include GPS-enabled geosocial and 34 Dieter Pfoser, “On User - Generated

Getting the point across: exploring the effects of dynamic virtual humans in an interactive museum exhibit on user perceptions.

PubMed

Rivera-Gutierrez, Diego; Ferdig, Rick; Li, Jian; Lok, Benjamin

2014-04-01

We have created “You, M.D.”, an interactive museum exhibit in which users learn about topics in public health literacy while interacting with virtual humans. You, M.D. is equipped with a weight sensor, a height sensor and a Microsoft Kinect that gather basic user information. Conceptually, You, M.D. could use this user information to dynamically select the appearance of the virtual humans in the interaction attempting to improve learning outcomes and user perception for each particular user. For this concept to be possible, a better understanding of how different elements of the visual appearance of a virtual human affects user perceptions is required. In this paper, we present the results of an initial user study with a large sample size (n =333) ran using You, M.D. The study measured users’ reactions based on the user’s gender and body-mass index (BMI) when facing virtual humans with BMI either concordant or discordant from the user’s BMI. The results of the study indicate that concordance between the users’ BMI and the virtual human’s BMI affects male and female users differently. The results also show that female users rate virtual humans as more knowledgeable than male users rate the same virtual humans.

Collaborative Filtering Recommendation on Users' Interest Sequences.

PubMed

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Trust-Based Access Control Model from Sociological Approach in Dynamic Online Social Network Environment

PubMed Central

Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information. PMID:25374943

Trust-based access control model from sociological approach in dynamic online social network environment.

PubMed

Baek, Seungsoo; Kim, Seungjoo

2014-01-01

There has been an explosive increase in the population of the OSN (online social network) in recent years. The OSN provides users with many opportunities to communicate among friends and family. Further, it facilitates developing new relationships with previously unknown people having similar beliefs or interests. However, the OSN can expose users to adverse effects such as privacy breaches, the disclosing of uncontrolled material, and the disseminating of false information. Traditional access control models such as MAC, DAC, and RBAC are applied to the OSN to address these problems. However, these models are not suitable for the dynamic OSN environment because user behavior in the OSN is unpredictable and static access control imposes a burden on the users to change the access control rules individually. We propose a dynamic trust-based access control for the OSN to address the problems of the traditional static access control. Moreover, we provide novel criteria to evaluate trust factors such as sociological approach and evaluate a method to calculate the dynamic trust values. The proposed method can monitor negative behavior and modify access permission levels dynamically to prevent the indiscriminate disclosure of information.

Smart Building: Decision Making Architecture for Thermal Energy Management

PubMed Central

Hernández Uribe, Oscar; San Martin, Juan Pablo; Garcia-Alegre, María C.; Santos, Matilde; Guinea, Domingo

2015-01-01

Smart applications of the Internet of Things are improving the performance of buildings, reducing energy demand. Local and smart networks, soft computing methodologies, machine intelligence algorithms and pervasive sensors are some of the basics of energy optimization strategies developed for the benefit of environmental sustainability and user comfort. This work presents a distributed sensor-processor-communication decision-making architecture to improve the acquisition, storage and transfer of thermal energy in buildings. The developed system is implemented in a near Zero-Energy Building (nZEB) prototype equipped with a built-in thermal solar collector, where optical properties are analysed; a low enthalpy geothermal accumulation system, segmented in different temperature zones; and an envelope that includes a dynamic thermal barrier. An intelligent control of this dynamic thermal barrier is applied to reduce the thermal energy demand (heating and cooling) caused by daily and seasonal weather variations. Simulations and experimental results are presented to highlight the nZEB thermal energy reduction. PMID:26528978

A thermal vacuum test optimization procedure

NASA Technical Reports Server (NTRS)

Kruger, R.; Norris, H. P.

1979-01-01

An analytical model was developed that can be used to establish certain parameters of a thermal vacuum environmental test program based on an optimization of program costs. This model is in the form of a computer program that interacts with a user insofar as the input of certain parameters. The program provides the user a list of pertinent information regarding an optimized test program and graphs of some of the parameters. The model is a first attempt in this area and includes numerous simplifications. The model appears useful as a general guide and provides a way for extrapolating past performance to future missions.

SynGenics Optimization System (SynOptSys)

NASA Technical Reports Server (NTRS)

Ventresca, Carol; McMilan, Michelle L.; Globus, Stephanie

2013-01-01

The SynGenics Optimization System (SynOptSys) software application optimizes a product with respect to multiple, competing criteria using statistical Design of Experiments, Response-Surface Methodology, and the Desirability Optimization Methodology. The user is not required to be skilled in the underlying math; thus, SynOptSys can help designers and product developers overcome the barriers that prevent them from using powerful techniques to develop better pro ducts in a less costly manner. SynOpt-Sys is applicable to the design of any product or process with multiple criteria to meet, and at least two factors that influence achievement of those criteria. The user begins with a selected solution principle or system concept and a set of criteria that needs to be satisfied. The criteria may be expressed in terms of documented desirements or defined responses that the future system needs to achieve. Documented desirements can be imported into SynOptSys or created and documented directly within SynOptSys. Subsequent steps include identifying factors, specifying model order for each response, designing the experiment, running the experiment and gathering the data, analyzing the results, and determining the specifications for the optimized system. The user may also enter textual information as the project progresses. Data is easily edited within SynOptSys, and the software design enables full traceability within any step in the process, and facilitates reporting as needed. SynOptSys is unique in the way responses are defined and the nuances of the goodness associated with changes in response values for each of the responses of interest. The Desirability Optimization Methodology provides the basis of this novel feature. Moreover, this is a complete, guided design and optimization process tool with embedded math that can remain invisible to the user. It is not a standalone statistical program; it is a design and optimization system.

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

Distributed Energy Resources and Dynamic Microgrid: An Integrated Assessment

NASA Astrophysics Data System (ADS)

Shang, Duo Rick

The overall goal of this thesis is to improve understanding in terms of the benefit of DERs to both utility and to electricity end-users when integrated in power distribution system. To achieve this goal, a series of two studies was conducted to assess the value of DERs when integrated with new power paradigms. First, the arbitrage value of DERs was examined in markets with time-variant electricity pricing rates (e.g., time of use, real time pricing) under a smart grid distribution paradigm. This study uses a stochastic optimization model to estimate the potential profit from electricity price arbitrage over a five-year period. The optimization process involves two types of PHEVs (PHEV-10, and PHEV-40) under three scenarios with different assumptions on technology performance, electricity market and PHEV owner types. The simulation results indicate that expected arbitrage profit is not a viable option to engage PHEVs in dispatching and in providing ancillary services without more favorable policy and PHEV battery technologies. Subsidy or change in electricity tariff or both are needed. Second, it examined the concept of dynamic microgrid as a measure to improve distribution resilience, and estimates the prices of this emerging service. An economic load dispatch (ELD) model is developed to estimate the market-clearing price in a hypothetical community with single bid auction electricity market. The results show that the electricity market clearing price on the dynamic microgrid is predominantly decided by power output and cost of electricity of each type of DGs. At circumstances where CHP is the only source, the electricity market clearing price in the island is even cheaper than the on-grid electricity price at normal times. Integration of PHEVs in the dynamic microgrid will increase electricity market clearing prices. It demonstrates that dynamic microgrid is an economically viable alternative to enhance grid resilience.

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Modelling information dissemination under privacy concerns in social media

NASA Astrophysics Data System (ADS)

Zhu, Hui; Huang, Cheng; Lu, Rongxing; Li, Hui

2016-05-01

Social media has recently become an important platform for users to share news, express views, and post messages. However, due to user privacy preservation in social media, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this study, a general stochastic model using dynamic evolution equations was introduced to illustrate how privacy concerns impact the process of information dissemination. Extensive simulations and analyzes involving the privacy settings of general users, privileged users, and pure observers were conducted on real-world networks, and the results demonstrated that user privacy settings affect information differently. Finally, we also studied the process of information diffusion analytically and numerically with different privacy settings using two classic networks.

A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice

PubMed Central

Gao, Yuan; Zhou, Weigui; Ao, Hong; Chu, Jian; Zhou, Quan; Zhou, Bo; Wang, Kang; Li, Yi; Xue, Peng

2016-01-01

With the increasing demands for better transmission speed and robust quality of service (QoS), the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT) scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs) equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users). A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives. PMID:27077865

Dynamic characteristics of tweeting and tweet topics

NASA Astrophysics Data System (ADS)

Kwon, Hyun Woong; Choi, M. Y.; Kim, Ho Sung; Lee, Keumsook

2012-02-01

Twitter, having more than 200 million world users and more than 4 million Korean users, is still growing fast. Because Twitter users can `tweet' about any topic within the 140-character limit, and other users who follow the users and see the tweets can `retweet' them, Twitter is regarded as a new medium of transferring and sharing information. Nevertheless, the propensities of Twitter users to tweet or to retweet still remain unclear. In order to investigate these propensities, we propose a simple model for the dynamics of the total number of tweets about specific topics. We then observe that the topics can be categorized into three kinds according to predictability and sustainability: predictable events, unpredictable events, and sustainable events. Comparing model results with real data, we infer the tweet propensities motivated by external causes as well as retweet propensities.

Comparison of time-series registration methods in breast dynamic infrared imaging

NASA Astrophysics Data System (ADS)

Riyahi-Alam, S.; Agostini, V.; Molinari, F.; Knaflitz, M.

2015-03-01

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons' registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons' registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation.

The Power of Flexibility: Autonomous Agents That Conserve Energy in Commercial Buildings

NASA Astrophysics Data System (ADS)

Kwak, Jun-young

Agent-based systems for energy conservation are now a growing area of research in multiagent systems, with applications ranging from energy management and control on the smart grid, to energy conservation in residential buildings, to energy generation and dynamic negotiations in distributed rural communities. Contributing to this area, my thesis presents new agent-based models and algorithms aiming to conserve energy in commercial buildings. More specifically, my thesis provides three sets of algorithmic contributions. First, I provide online predictive scheduling algorithms to handle massive numbers of meeting/event scheduling requests considering flexibility , which is a novel concept for capturing generic user constraints while optimizing the desired objective. Second, I present a novel BM-MDP ( Bounded-parameter Multi-objective Markov Decision Problem) model and robust algorithms for multi-objective optimization under uncertainty both at the planning and execution time. The BM-MDP model and its robust algorithms are useful in (re)scheduling events to achieve energy efficiency in the presence of uncertainty over user's preferences. Third, when multiple users contribute to energy savings, fair division of credit for such savings to incentivize users for their energy saving activities arises as an important question. I appeal to cooperative game theory and specifically to the concept of Shapley value for this fair division. Unfortunately, scaling up this Shapley value computation is a major hindrance in practice. Therefore, I present novel approximation algorithms to efficiently compute the Shapley value based on sampling and partitions and to speed up the characteristic function computation. These new models have not only advanced the state of the art in multiagent algorithms, but have actually been successfully integrated within agents dedicated to energy efficiency: SAVES, TESLA and THINC. SAVES focuses on the day-to-day energy consumption of individuals and groups in commercial buildings by reactively suggesting energy conserving alternatives. TESLA takes a long-range planning perspective and optimizes overall energy consumption of a large number of group events or meetings together. THINC provides an end-to-end integration within a single agent of energy efficient scheduling, rescheduling and credit allocation. While SAVES, TESLA and THINC thus differ in their scope and applicability, they demonstrate the utility of agent-based systems in actually reducing energy consumption in commercial buildings. I evaluate my algorithms and agents using extensive analysis on data from over 110,000 real meetings/events at multiple educational buildings including the main libraries at the University of Southern California. I also provide results on simulations and real-world experiments, clearly demonstrating the power of agent technology to assist human users in saving energy in commercial buildings.

Selective Iterative Waterfilling for Digital Subscriber Lines

NASA Astrophysics Data System (ADS)

Xu, Yang; Le-Ngoc, Tho; Panigrahi, Saswat

2007-12-01

This paper presents a high-performance, low-complexity, quasi-distributed dynamic spectrum management (DSM) algorithm suitable for DSL systems. We analytically demonstrate that the rate degradation of the distributed iterative waterfilling (IW) algorithm in near-far scenarios is caused by the insufficient utilization of all available frequency and power resources due to its nature of noncooperative game theoretic formulation. Inspired by this observation, we propose the selective IW (SIW) algorithm that can considerably alleviate the performance degradation of IW by applying IW selectively to different groups of users over different frequency bands so that all the available resources can be fully utilized. For [InlineEquation not available: see fulltext.] users, the proposed SIW algorithm needs at most [InlineEquation not available: see fulltext.] times the complexity of the IW algorithm, and is much simpler than the centralized optimal spectrum balancing (OSB), while it can offer a rate performance much better than that of the IW and close to the maximum possible rate region computed by the OSB in realistic near-far DSL scenarios. Furthermore, its predominantly distributed structure makes it suitable for DSL implementation.

Kinematic path planning for space-based robotics

NASA Astrophysics Data System (ADS)

Seereeram, Sanjeev; Wen, John T.

1998-01-01

Future space robotics tasks require manipulators of significant dexterity, achievable through kinematic redundancy and modular reconfigurability, but with a corresponding complexity of motion planning. Existing research aims for full autonomy and completeness, at the expense of efficiency, generality or even user friendliness. Commercial simulators require user-taught joint paths-a significant burden for assembly tasks subject to collision avoidance, kinematic and dynamic constraints. Our research has developed a Kinematic Path Planning (KPP) algorithm which bridges the gap between research and industry to produce a powerful and useful product. KPP consists of three key components: path-space iterative search, probabilistic refinement, and an operator guidance interface. The KPP algorithm has been successfully applied to the SSRMS for PMA relocation and dual-arm truss assembly tasks. Other KPP capabilities include Cartesian path following, hybrid Cartesian endpoint/intermediate via-point planning, redundancy resolution and path optimization. KPP incorporates supervisory (operator) input at any detail to influence the solution, yielding desirable/predictable paths for multi-jointed arms, avoiding obstacles and obeying manipulator limits. This software will eventually form a marketable robotic planner suitable for commercialization in conjunction with existing robotic CAD/CAM packages.

An initial approach towards quality of service based Spectrum Trading

NASA Astrophysics Data System (ADS)

Bastidas, Carlos E. Caicedo; Vanhoy, Garret; Volos, Haris I.; Bose, Tamal

Spectrum scarcity has become an important issue as demands for higher data rates increase in diverse wireless applications and aerospace communication scenarios. To address this problem, it becomes necessary to manage radio spectrum assignment in a way that optimizes the distribution of spectrum resources among several users while taking into account the quality of service (QoS) characteristics desired by the users of spectrum. In this paper, a novel approach to managing spectrum assignment based on Spectrum Trading (ST) will be presented. Market based spectrum assignment mechanisms such as spectrum trading are of growing interest to many spectrum management agencies that are planning to increase the use of these mechanisms for spectrum management and reduce their emphasis on command and control methods. This paper presents some of our initial work into incorporating quality of service information into the mechanisms that determine how spectrum should be traded when using a spectrum exchange. Through simulations and a testbed implementation of a QoS aware spectrum exchange our results show the viability of using QoS based mechanisms in spectrum trading and in the enhancement of dynamic spectrum assignment systems.

Bee Swarm Optimization for Medical Web Information Foraging.

PubMed

Drias, Yassine; Kechid, Samir; Pasi, Gabriella

2016-02-01

The present work is related to Web intelligence and more precisely to medical information foraging. We present here a novel approach based on agents technology for information foraging. An architecture is proposed, in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and the dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The whole system offers a tool to help the user undertaking information foraging. We implemented the system using a group of cooperative reactive agents and more precisely a colony of artificial bees. In order to validate our proposal, experiments were conducted on MedlinePlus, a benchmark dedicated for research in the domain of Health. The results are promising either for those related to Web regularities and for the response time, which is very short and hence complies the real time constraint.

Multiscale Hy3S: hybrid stochastic simulation for supercomputers.

PubMed

Salis, Howard; Sotiropoulos, Vassilios; Kaznessis, Yiannis N

2006-02-24

Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems.

Computer-Assisted Traffic Engineering Using Assignment, Optimal Signal Setting, and Modal Split

DOT National Transportation Integrated Search

1978-05-01

Methods of traffic assignment, traffic signal setting, and modal split analysis are combined in a set of computer-assisted traffic engineering programs. The system optimization and user optimization traffic assignments are described. Travel time func...

Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs

PubMed Central

Moon, Bongkyo

2017-01-01

In this paper, we focus on a dynamic spectrum access strategy for Internet of Things (IoT) applications in two types of radio systems: cellular networks and cognitive radio-enabled low power wide area networks (CR-LPWANs). The spectrum channel contention between the licensed cellular networks and the unlicensed CR-LPWANs, which work with them, only takes place within the cellular radio spectrum range. Our aim is to maximize the spectrum capacity for the unlicensed users while ensuring that it never interferes with the licensed network. Therefore, in this paper we propose a dynamic spectrum access strategy for CR-LPWANs operating in both licensed and unlicensed bands. The simulation and the numerical analysis by using a matrix geometric approach for the strategy are presented. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users using IoT applications as well as keep the service quality of the licensed users independent of them. PMID:29206215

Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs.

PubMed

Moon, Bongkyo

2017-12-05

In this paper, we focus on a dynamic spectrum access strategy for Internet of Things (IoT) applications in two types of radio systems: cellular networks and cognitive radio-enabled low power wide area networks (CR-LPWANs). The spectrum channel contention between the licensed cellular networks and the unlicensed CR-LPWANs, which work with them, only takes place within the cellular radio spectrum range. Our aim is to maximize the spectrum capacity for the unlicensed users while ensuring that it never interferes with the licensed network. Therefore, in this paper we propose a dynamic spectrum access strategy for CR-LPWANs operating in both licensed and unlicensed bands. The simulation and the numerical analysis by using a matrix geometric approach for the strategy are presented. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users using IoT applications as well as keep the service quality of the licensed users independent of them.

User interface issues in supporting human-computer integrated scheduling

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Biefeld, Eric W.

1991-01-01

The topics are presented in view graph form and include the following: characteristics of Operations Mission Planner (OMP) schedule domain; OMP architecture; definition of a schedule; user interface dimensions; functional distribution; types of users; interpreting user interaction; dynamic overlays; reactive scheduling; and transitioning the interface.

Optimizing the Information Presentation on Mining Potential by using Web Services Technology with Restful Protocol

NASA Astrophysics Data System (ADS)

Abdillah, T.; Dai, R.; Setiawan, E.

2018-02-01

This study aims to develop the application of Web Services technology with RestFul Protocol to optimize the information presentation on mining potential. This study used User Interface Design approach for the information accuracy and relevance as well as the Web Service for the reliability in presenting the information. The results show that: the information accuracy and relevance regarding mining potential can be seen from the achievement of User Interface implementation in the application that is based on the following rules: The consideration of the appropriate colours and objects, the easiness of using the navigation, and users’ interaction with the applications that employs symbols and languages understood by the users; the information accuracy and relevance related to mining potential can be observed by the information presented by using charts and Tool Tip Text to help the users understand the provided chart/figure; the reliability of the information presentation is evident by the results of Web Services testing in Figure 4.5.6. This study finds out that User Interface Design and Web Services approaches (for the access of different Platform apps) are able to optimize the presentation. The results of this study can be used as a reference for software developers and Provincial Government of Gorontalo.

TAS::89 0927::TAS RECOVERY - The Lean Green Energy Controller Machine

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

Teeter, John; Wang, Gene; Moss, David

Achieving efficiency improvements and providing demand-response programs have been identified as key elements of our national energy initiative. The residential market is the largest, yet most difficult, segment to engage in efforts to meet these objectives. This project developed Energy Management System that engages the consumer and enables Smart Grid services, applications, and business processes to address this need. Our innovative solution provides smart controller providing dynamic optimization of energy consumption for the residential energy consumer. Our solution extends the technical platform to include a cloud based Internet of Things (IoT) aggregation of data sensors and actuators the go beyondmore » energy management and extend to life style services provided through compelling mobile and console based user experiences.« less

Replica Exchange Molecular Dynamics in the Age of Heterogeneous Architectures

NASA Astrophysics Data System (ADS)

Roitberg, Adrian

2014-03-01

The rise of GPU-based codes has allowed MD to reach timescales only dreamed of only 5 years ago. Even within this new paradigm there is still need for advanced sampling techniques. Modern supercomputers (e.g. Blue Waters, Titan, Keeneland) have made available to users a significant number of GPUS and CPUS, which in turn translate into amazing opportunities for dream calculations. Replica-exchange based methods can optimally use tis combination of codes and architectures to explore conformational variabilities in large systems. I will show our recent work in porting the program Amber to GPUS, and the support for replica exchange methods, where the replicated dimension could be Temperature, pH, Hamiltonian, Umbrella windows and combinations of those schemes.

A hybrid nonlinear programming method for design optimization

NASA Technical Reports Server (NTRS)

Rajan, S. D.

1986-01-01

Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.

Online optimization of storage ring nonlinear beam dynamics

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

Huang, Xiaobiao; Safranek, James

2015-08-01

We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.

Autopilot for frequency-modulation atomic force microscopy.

PubMed

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

NASA Astrophysics Data System (ADS)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

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

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri, E-mail: phsivan@tx.technion.ac.il

2015-10-15

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loopsmore » require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.« less

An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1992-01-01

The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.

A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice

PubMed Central

Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henricakson, Kristian C.; Xu, Maozeng; Wang, Yinhai

2016-01-01

This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior. PMID:26761209

Microgrid to enable optimal distributed energy retail and end-user demand response

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

Jin, Ming; Feng, Wei; Marnay, Chris

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

Microgrid to enable optimal distributed energy retail and end-user demand response

DOE PAGES

Jin, Ming; Feng, Wei; Marnay, Chris; ...

2018-06-07

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

Robust stochastic optimization for reservoir operation

NASA Astrophysics Data System (ADS)

Pan, Limeng; Housh, Mashor; Liu, Pan; Cai, Ximing; Chen, Xin

2015-01-01

Optimal reservoir operation under uncertainty is a challenging engineering problem. Application of classic stochastic optimization methods to large-scale problems is limited due to computational difficulty. Moreover, classic stochastic methods assume that the estimated distribution function or the sample inflow data accurately represents the true probability distribution, which may be invalid and the performance of the algorithms may be undermined. In this study, we introduce a robust optimization (RO) approach, Iterative Linear Decision Rule (ILDR), so as to provide a tractable approximation for a multiperiod hydropower generation problem. The proposed approach extends the existing LDR method by accommodating nonlinear objective functions. It also provides users with the flexibility of choosing the accuracy of ILDR approximations by assigning a desired number of piecewise linear segments to each uncertainty. The performance of the ILDR is compared with benchmark policies including the sampling stochastic dynamic programming (SSDP) policy derived from historical data. The ILDR solves both the single and multireservoir systems efficiently. The single reservoir case study results show that the RO method is as good as SSDP when implemented on the original historical inflows and it outperforms SSDP policy when tested on generated inflows with the same mean and covariance matrix as those in history. For the multireservoir case study, which considers water supply in addition to power generation, numerical results show that the proposed approach performs as well as in the single reservoir case study in terms of optimal value and distributional robustness.

Dynamic Appliances Scheduling in Collaborative MicroGrids System

PubMed Central

Bilil, Hasnae; Aniba, Ghassane; Gharavi, Hamid

2017-01-01

In this paper a new approach which is based on a collaborative system of MicroGrids (MG’s), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL’s), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL’s. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL’s and the second deals with the power profiles of flexible DL’s. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG’s registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach. PMID:28824226

Recent advances in integrated multidisciplinary optimization of rotorcraft

NASA Technical Reports Server (NTRS)

Adelman, Howard M.; Walsh, Joanne L.; Pritchard, Jocelyn I.

1992-01-01

A joint activity involving NASA and Army researchers at NASA LaRC to develop optimization procedures to improve the rotor blade design process by integrating appropriate disciplines and accounting for all of the important interactions among the disciplines is described. The disciplines involved include rotor aerodynamics, rotor dynamics, rotor structures, airframe dynamics, and acoustics. The work is focused on combining these five key disciplines in an optimization procedure capable of designing a rotor system to satisfy multidisciplinary design requirements. Fundamental to the plan is a three-phased approach. In phase 1, the disciplines of blade dynamics, blade aerodynamics, and blade structure are closely coupled while acoustics and airframe dynamics are decoupled and are accounted for as effective constraints on the design for the first three disciplines. In phase 2, acoustics is integrated with the first three disciplines. Finally, in phase 3, airframe dynamics is integrated with the other four disciplines. Representative results from work performed to date are described. These include optimal placement of tuning masses for reduction of blade vibratory shear forces, integrated aerodynamic/dynamic optimization, and integrated aerodynamic/dynamic/structural optimization. Examples of validating procedures are described.

Recent advances in multidisciplinary optimization of rotorcraft

NASA Technical Reports Server (NTRS)

Adelman, Howard M.; Walsh, Joanne L.; Pritchard, Jocelyn I.

1992-01-01

A joint activity involving NASA and Army researchers at NASA LaRC to develop optimization procedures to improve the rotor blade design process by integrating appropriate disciplines and accounting for all of the important interactions among the disciplines is described. The disciplines involved include rotor aerodynamics, rotor dynamics, rotor structures, airframe dynamics, and acoustics. The work is focused on combining these five key disciplines in an optimization procedure capable of designing a rotor system to satisfy multidisciplinary design requirements. Fundamental to the plan is a three-phased approach. In phase 1, the disciplines of blade dynamics, blade aerodynamics, and blade structure are closely coupled while acoustics and airframe dynamics are decoupled and are accounted for as effective constraints on the design for the first three disciplines. In phase 2, acoustics is integrated with the first three disciplines. Finally, in phase 3, airframe dynamics is integrated with the other four disciplines. Representative results from work performed to date are described. These include optimal placement of tuning masses for reduction of blade vibratory shear forces, integrated aerodynamic/dynamic optimization, and integrated aerodynamic/dynamic/structural optimization. Examples of validating procedures are described.

Percolator: Scalable Pattern Discovery in Dynamic Graphs

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

Choudhury, Sutanay; Purohit, Sumit; Lin, Peng

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walkingmore » through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.« less

Observing Consistency in Online Communication Patterns for User Re-Identification.

PubMed

Adeyemi, Ikuesan Richard; Razak, Shukor Abd; Salleh, Mazleena; Venter, Hein S

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas.

An incremental community detection method for social tagging systems using locality-sensitive hashing.

PubMed

Wu, Zhenyu; Zou, Ming

2014-10-01

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k-clique; results indicate that TASC can detect communities more efficiently and effectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Make Movies out of Your Dynamical Simulations with OGRE!

NASA Astrophysics Data System (ADS)

Tamayo, Daniel; Douglas, R. W.; Ge, H. W.; Burns, J. A.

2013-10-01

We have developed OGRE, the Orbital GRaphics Environment, an open-source project comprising a graphical user interface that allows the user to view the output from several dynamical integrators (e.g., SWIFT) that are commonly used for academic work. One can interactively vary the display speed, rotate the view and zoom the camera. This makes OGRE a great tool for students or the general public to explore accurate orbital histories that may display interesting dynamical features, e.g. the destabilization of Solar System orbits under the Nice model, or interacting pairs of exoplanets. Furthermore, OGRE allows the user to choreograph sequences of transformations as the simulation is played to generate movies for use in public talks or professional presentations. The graphical user interface is coded using Qt to ensure portability across different operating systems. OGRE will run on Linux and Mac OS X. The program is available as a self-contained executable, or as source code that the user can compile. We are continually updating the code, and hope that people who find it useful will contribute to the development of new features.

Make Movies out of Your Dynamical Simulations with OGRE!

NASA Astrophysics Data System (ADS)

Tamayo, Daniel; Douglas, R. W.; Ge, H. W.; Burns, J. A.

2014-01-01

We have developed OGRE, the Orbital GRaphics Environment, an open-source project comprising a graphical user interface that allows the user to view the output from several dynamical integrators (e.g., SWIFT) that are commonly used for academic work. One can interactively vary the display speed, rotate the view and zoom the camera. This makes OGRE a great tool for students or the general public to explore accurate orbital histories that may display interesting dynamical features, e.g. the destabilization of Solar System orbits under the Nice model, or interacting pairs of exoplanets. Furthermore, OGRE allows the user to choreograph sequences of transformations as the simulation is played to generate movies for use in public talks or professional presentations. The graphical user interface is coded using Qt to ensure portability across different operating systems. OGRE will run on Linux and Mac OS X. The program is available as a self-contained executable, or as source code that the user can compile. We are continually updating the code, and hope that people who find it useful will contribute to the development of new features.

Substance Use, Violence, and Antiretroviral Adherence: A Latent Class Analysis of Women Living with HIV in Canada.

PubMed

Carter, Allison; Roth, Eric Abella; Ding, Erin; Milloy, M-J; Kestler, Mary; Jabbari, Shahab; Webster, Kath; de Pokomandy, Alexandra; Loutfy, Mona; Kaida, Angela

2018-03-01

We used latent class analysis to identify substance use patterns for 1363 women living with HIV in Canada and assessed associations with socio-economic marginalization, violence, and sub-optimal adherence to combination antiretroviral therapy (cART). A six-class model was identified consisting of: abstainers (26.3%), Tobacco Users (8.81%), Alcohol Users (31.9%), 'Socially Acceptable' Poly-substance Users (13.9%), Illicit Poly-substance Users (9.81%) and Illicit Poly-substance Users of All Types (9.27%). Multinomial logistic regression showed that women experiencing recent violence had significantly higher odds of membership in all substance use latent classes, relative to Abstainers, while those reporting sub-optimal cART adherence had higher odds of being members of the poly-substance use classes only. Factors significantly associated with Illicit Poly-substance Users of All Types were sexual minority status, lower income, and lower resiliency. Findings underline a need for increased social and structural supports for women who use substances to support them in leading safe and healthy lives with HIV.

Three-dimensional user interfaces for scientific visualization

NASA Technical Reports Server (NTRS)

Vandam, Andries

1995-01-01

The main goal of this project is to develop novel and productive user interface techniques for creating and managing visualizations of computational fluid dynamics (CFD) datasets. We have implemented an application framework in which we can visualize computational fluid dynamics user interfaces. This UI technology allows users to interactively place visualization probes in a dataset and modify some of their parameters. We have also implemented a time-critical scheduling system which strives to maintain a constant frame-rate regardless of the number of visualization techniques. In the past year, we have published parts of this research at two conferences, the research annotation system at Visualization 1994, and the 3D user interface at UIST 1994. The real-time scheduling system has been submitted to SIGGRAPH 1995 conference. Copies of these documents are included with this report.

Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks.

PubMed

Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet

2017-06-01

This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model.

Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks

PubMed Central

Lyu, Bin; Yang, Zhen; Gui, Guan; Sari, Hikmet

2017-01-01

This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model. PMID:28587171

Odyssey, an optimized personal communications satellite system

NASA Astrophysics Data System (ADS)

Rusch, Roger J.

Personal communications places severe demands on service providers and transmission facilities. Customers are not satisfied with the current levels of service and want improvements. Among the characteristics that users seek are: lower service rates, hand held convenience, acceptable time delays, ubiquitous service, high availability, reliability, and high quality. The space industry is developing commercial space systems for providing mobile communications to personal telephones. Provision of land mobile satellite service is fundamentally different from the fixed satellite service provided by geostationary satellites. In fixed service, the earth based antennas can depend on a clear path from user to satellite. Mobile users in a terrestrial environment commonly encounter blockage due to vegetation, terrain or buildings. Consequently, high elevation angles are of premium value. TRW studied the issues and concluded that a Medium Earth Orbit constellation is the best solution for Personal Communications Satellite Service. TRW has developed Odyssey, which uses twelve satellites in medium altitude orbit to provide personal communications satellite service. The Odyssey communications system projects a multibeam antenna pattern to the Earth. The attitude control system orients the satellites to ensure constant coverage of land mass and coastal areas. Pointing can be reprogrammed by ground control to ensure optimized coverage of the desired service areas. The payload architecture features non-processing, "bent pipe" transponders and matrix amplifiers to ensure dynamic power delivery to high demand areas. Circuit capacity is 3000 circuits per satellite. Each satellite weighs 1917 kg (4226 pounds) at launch and the solar arrays provide 3126 Watts of power. Satellites are launched in pairs on Ariane, Atlas, or other vehicles. Each satellite is placed in a circular orbit at an altitude of 10,354 km. There are three orbit planes inclined at 55° to the equatorial plane. Deployment of the satellites permits phased introduction of service. After only three launches, in which two satellites are launched into each plane, continuous service can be provided to most of the world. After three more launches for a total of 12 satellites, service can be expanded to all populated regions of the Earth with path diversity to most regions. The Odyssey system is superior to both geostationary satellites and low earth orbiting satellites. Odyssey provides many benefits to the end user which are described in the paper. These include: low cost, convenience, high availability, reliability, and acceptable time delay. Odyssey exhibits benefits for telecommunications operators: simple operations, incremental, phased startup, long space segment life-time, high profitability, dynamic flexibility for adjustment and short time to market. Since submission of an FCC application in 1991, TRW has continued to explore ways to further improve the Odyssey approach by expanding coverage to the entire world and reducing the initial investment while maintaining high quality service.

Odyssey, an optimized personal communications satellite system

NASA Astrophysics Data System (ADS)

Rusch, Roger J.

Personal communications places severe demands on service providers and transmission facilities. Customers are not satisfied with the current levels of service and want improvements. Among the characteristics that users seek are: lower service rates, hand held convenience, acceptable time delays, ubiquitous service, high availability, reliability, and high quality. The space industry in developing commercial space systems for providing mobile communications to personal telephones. Provision of land mobile satellite service is fundamentally different from the fixed satellite service provided by geostationary satellites. In fixed service, the earth based antennas can depend on a clear path from user to satellite. Mobile users in a terrestrial environment commonly encounter blockage due to vegetation, terrain or buildings. Consequently, high elevation angles are of premium value. TRW studied the issues and concluded that a Medium Earth Orbit constellation is the best solution for Personal Communications Satellite Service. TRW has developed Odyssey, which uses twelve satellites in medium altitude orbit to provide personal communications satellite service. The Odyssey communications system projects a multibeam antenna pattern to the Earth. The attitude control system orients the satellites to ensure constant coverage of land mass and coastal areas. Pointing can be reprogrammed by ground control to ensure optimized coverage of the desired service areas. The payload architecture features non-processing, 'bent pipe' transponders and matrix amplifiers to ensure dynamic power delivery to high demand areas. Circuit capacity is 3000 circuits per satellite. Each satellite weighs 1917 kg (4226 pounds) at launch and the solar arrays provide 3126 watts of power. Satellites are launched in pairs on Ariane, Atlas, or other vehicles. Each satellite is placed in a circular orbit at an altitude of 10,354 km.

Arsenic removal from contaminated groundwater by membrane-integrated hybrid plant: optimization and control using Visual Basic platform.

PubMed

Chakrabortty, S; Sen, M; Pal, P

2014-03-01

A simulation software (ARRPA) has been developed in Microsoft Visual Basic platform for optimization and control of a novel membrane-integrated arsenic separation plant in the backdrop of absence of such software. The user-friendly, menu-driven software is based on a dynamic linearized mathematical model, developed for the hybrid treatment scheme. The model captures the chemical kinetics in the pre-treating chemical reactor and the separation and transport phenomena involved in nanofiltration. The software has been validated through extensive experimental investigations. The agreement between the outputs from computer simulation program and the experimental findings are excellent and consistent under varying operating conditions reflecting high degree of accuracy and reliability of the software. High values of the overall correlation coefficient (R (2) = 0.989) and Willmott d-index (0.989) are indicators of the capability of the software in analyzing performance of the plant. The software permits pre-analysis, manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. Performance analysis of the whole system as well as the individual units is possible using the tool. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for removal of arsenic from contaminated groundwater.

Optimal Decision Making in a Class of Uncertain Systems Based on Uncertain Variables

NASA Astrophysics Data System (ADS)

Bubnicki, Z.

2006-06-01

The paper is concerned with a class of uncertain systems described by relational knowledge representations with unknown parameters which are assumed to be values of uncertain variables characterized by a user in the form of certainty distributions. The first part presents the basic optimization problem consisting in finding the decision maximizing the certainty index that the requirement given by a user is satisfied. The main part is devoted to the description of the optimization problem with the given certainty threshold. It is shown how the approach presented in the paper may be applied to some problems for anticipatory systems.

Linear triangular optimization technique and pricing scheme in residential energy management systems

NASA Astrophysics Data System (ADS)

Anees, Amir; Hussain, Iqtadar; AlKhaldi, Ali Hussain; Aslam, Muhammad

2018-06-01

This paper presents a new linear optimization algorithm for power scheduling of electric appliances. The proposed system is applied in a smart home community, in which community controller acts as a virtual distribution company for the end consumers. We also present a pricing scheme between community controller and its residential users based on real-time pricing and likely block rates. The results of the proposed optimization algorithm demonstrate that by applying the anticipated technique, not only end users can minimise the consumption cost, but it can also reduce the power peak to an average ratio which will be beneficial for the utilities as well.

LoyalTracker: Visualizing Loyalty Dynamics in Search Engines.

PubMed

Shi, Conglei; Wu, Yingcai; Liu, Shixia; Zhou, Hong; Qu, Huamin

2014-12-01

The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines.

HOMER: The Micropower Optimization Model

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

Not Available

2004-03-01

HOMER, the micropower optimization model, helps users to design micropower systems for off-grid and grid-connected power applications. HOMER models micropower systems with one or more power sources including wind turbines, photovoltaics, biomass power, hydropower, cogeneration, diesel engines, cogeneration, batteries, fuel cells, and electrolyzers. Users can explore a range of design questions such as which technologies are most effective, what size should components be, how project economics are affected by changes in loads or costs, and is the renewable resource adequate.

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

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

Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

Dynamics in insulin requirements and treatment safety.

PubMed

Harper, R; Donnelly, R; Bi, Yixi; Bashan, E; Minhas, R; Hodish, I

2016-01-01

The majority of insulin users have elevated HbA1c. There is growing recognition that the low success rates are due to variations in insulin requirements. Thus, frequent dosage adjustments are needed. In practice, adjustments occur sporadically due to limited provider availability. We investigated intra-individual dynamics of insulin requirements using data from a service evaluation of the d-Nav® Insulin Guidance Service. This service facilitates automated insulin dosage adjustments, as often as needed, to achieve and maintain optimal glycemic balance. Data were collected from subjects who have been using the service for more than a year. Events of considerable and persistent decrease in insulin requirements were identified by drops in total daily insulin ≥25%. Overall, 62 patients were studied over an average period of 2.1±0.5 (mean±standard deviation) years. Stability in HbA1c was attained after ~3 quarters at 7.4%±0.2% (57.4mmol/mol±1mmol/mol). Events were identified in 56.5% of the patients. On average, each affected patient had 0.8±0.4 events per year, lasting 9.7±6.6weeks, while total daily insulin dosage decreased by 41.4±13.4%. Our findings may call attention to a major contributing factor to hypoglycemia among insulin users. In reality, insulin dosage is seldom adjusted and thus transient periods of decrease in insulin requirements and overtreatment are usually overlooked. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Program to Optimize Simulated Trajectories (POST). Volume 2: Utilization manual

NASA Technical Reports Server (NTRS)

Bauer, G. L.; Cornick, D. E.; Habeger, A. R.; Petersen, F. M.; Stevenson, R.

1975-01-01

Information pertinent to users of the program to optimize simulated trajectories (POST) is presented. The input required and output available is described for each of the trajectory and targeting/optimization options. A sample input listing and resulting output are given.

Interplay Between Energy-Market Dynamics and Physical Stability of a Smart Power Grid

NASA Astrophysics Data System (ADS)

Picozzi, Sergio; Mammoli, Andrea; Sorrentino, Francesco

2013-03-01

A smart power grid is being envisioned for the future which, among other features, should enable users to play the dual role of consumers as well as producers and traders of energy, thanks to emerging renewable energy production and energy storage technologies. As a complex dynamical system, any power grid is subject to physical instabilities. With existing grids, such instabilities tend to be caused by natural disasters, human errors, or weather-related peaks in demand. In this work we analyze the impact, upon the stability of a smart grid, of the energy-market dynamics arising from users' ability to buy from and sell energy to other users. The stability analysis of the resulting dynamical system is performed assuming different proposed models for this market of the future, and the corresponding stability regions in parameter space are identified. We test our theoretical findings by comparing them with data collected from some existing prototype systems.

Theory and Programs for Dynamic Modeling of Tree Rings from Climate

Treesearch

Paul C. van Deusen; Jennifer Koretz

1988-01-01

Computer programs written in GAUSS(TM) for IBM compatible personal computers are described that perform dynamic tree ring modeling with climate data; the underlying theory is also described. The programs and a separate users manual are available from the authors, although users must have the GAUSS software package on their personal computer. An example application of...

Data Sciences Summer Institute Topology Optimization

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

Watts, Seth

DSSI_TOPOPT is a 2D topology optimization code that designs stiff structures made of a single linear elastic material and void space. The code generates a finite element mesh of a rectangular design domain on which the user specifies displacement and load boundary conditions. The code iteratively designs a structure that minimizes the compliance (maximizes the stiffness) of the structure under the given loading, subject to an upper bound on the amount of material used. Depending on user options, the code can evaluate the performance of a user-designed structure, or create a design from scratch. Output includes the finite element mesh,more » design, and visualizations of the design.« less

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

1992-01-01

This paper describes a fully integrated aerodynamic/dynamic optimization procedure for helicopter rotor blades. The procedure combines performance and dynamics analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuver; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case the objective function involves power required (in hover, forward flight, and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

1992-01-01

A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Mixed-Strategy Chance Constrained Optimal Control

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J.

2013-01-01

This paper presents a novel chance constrained optimal control (CCOC) algorithm that chooses a control action probabilistically. A CCOC problem is to find a control input that minimizes the expected cost while guaranteeing that the probability of violating a set of constraints is below a user-specified threshold. We show that a probabilistic control approach, which we refer to as a mixed control strategy, enables us to obtain a cost that is better than what deterministic control strategies can achieve when the CCOC problem is nonconvex. The resulting mixed-strategy CCOC problem turns out to be a convexification of the original nonconvex CCOC problem. Furthermore, we also show that a mixed control strategy only needs to "mix" up to two deterministic control actions in order to achieve optimality. Building upon an iterative dual optimization, the proposed algorithm quickly converges to the optimal mixed control strategy with a user-specified tolerance.

Multidisciplinary Aerospace Systems Optimization: Computational AeroSciences (CAS) Project

NASA Technical Reports Server (NTRS)

Kodiyalam, S.; Sobieski, Jaroslaw S. (Technical Monitor)

2001-01-01

The report describes a method for performing optimization of a system whose analysis is so expensive that it is impractical to let the optimization code invoke it directly because excessive computational cost and elapsed time might result. In such situation it is imperative to have user control the number of times the analysis is invoked. The reported method achieves that by two techniques in the Design of Experiment category: a uniform dispersal of the trial design points over a n-dimensional hypersphere and a response surface fitting, and the technique of krigging. Analyses of all the trial designs whose number may be set by the user are performed before activation of the optimization code and the results are stored as a data base. That code is then executed and referred to the above data base. Two applications, one of the airborne laser system, and one of an aircraft optimization illustrate the method application.

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

NASA Astrophysics Data System (ADS)

Sutrisno; Widowati; Solikhin

2016-06-01

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

User input in iterative design for prevention product development: leveraging interdisciplinary methods to optimize effectiveness.

PubMed

Guthrie, Kate M; Rosen, Rochelle K; Vargas, Sara E; Guillen, Melissa; Steger, Arielle L; Getz, Melissa L; Smith, Kelley A; Ramirez, Jaime J; Kojic, Erna M

2017-10-01

The development of HIV-preventive topical vaginal microbicides has been challenged by a lack of sufficient adherence in later stage clinical trials to confidently evaluate effectiveness. This dilemma has highlighted the need to integrate translational research earlier in the drug development process, essentially applying behavioral science to facilitate the advances of basic science with respect to the uptake and use of biomedical prevention technologies. In the last several years, there has been an increasing recognition that the user experience, specifically the sensory experience, as well as the role of meaning-making elicited by those sensations, may play a more substantive role than previously thought. Importantly, the role of the user-their sensory perceptions, their judgements of those experiences, and their willingness to use a product-is critical in product uptake and consistent use post-marketing, ultimately realizing gains in global public health. Specifically, a successful prevention product requires an efficacious drug, an efficient drug delivery system, and an effective user. We present an integrated iterative drug development and user experience evaluation method to illustrate how user-centered formulation design can be iterated from the early stages of preclinical development to leverage the user experience. Integrating the user and their product experiences into the formulation design process may help optimize both the efficiency of drug delivery and the effectiveness of the user.

Physical interface dynamics alter how robotic exosuits augment human movement: implications for optimizing wearable assistive devices.

PubMed

Yandell, Matthew B; Quinlivan, Brendan T; Popov, Dmitry; Walsh, Conor; Zelik, Karl E

2017-05-18

Wearable assistive devices have demonstrated the potential to improve mobility outcomes for individuals with disabilities, and to augment healthy human performance; however, these benefits depend on how effectively power is transmitted from the device to the human user. Quantifying and understanding this power transmission is challenging due to complex human-device interface dynamics that occur as biological tissues and physical interface materials deform and displace under load, absorbing and returning power. Here we introduce a new methodology for quickly estimating interface power dynamics during movement tasks using common motion capture and force measurements, and then apply this method to quantify how a soft robotic ankle exosuit interacts with and transfers power to the human body during walking. We partition exosuit end-effector power (i.e., power output from the device) into power that augments ankle plantarflexion (termed augmentation power) vs. power that goes into deformation and motion of interface materials and underlying soft tissues (termed interface power). We provide empirical evidence of how human-exosuit interfaces absorb and return energy, reshaping exosuit-to-human power flow and resulting in three key consequences: (i) During exosuit loading (as applied forces increased), about 55% of exosuit end-effector power was absorbed into the interfaces. (ii) However, during subsequent exosuit unloading (as applied forces decreased) most of the absorbed interface power was returned viscoelastically. Consequently, the majority (about 75%) of exosuit end-effector work over each stride contributed to augmenting ankle plantarflexion. (iii) Ankle augmentation power (and work) was delayed relative to exosuit end-effector power, due to these interface energy absorption and return dynamics. Our findings elucidate the complexities of human-exosuit interface dynamics during transmission of power from assistive devices to the human body, and provide insight into improving the design and control of wearable robots. We conclude that in order to optimize the performance of wearable assistive devices it is important, throughout design and evaluation phases, to account for human-device interface dynamics that affect power transmission and thus human augmentation benefits.

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Ge, Fuying

1989-01-01

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

A parameters optimization method for planar joint clearance model and its application for dynamics simulation of reciprocating compressor

NASA Astrophysics Data System (ADS)

Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li

2015-05-01

In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.

CometBoards Users Manual Release 1.0

NASA Technical Reports Server (NTRS)

Guptill, James D.; Coroneos, Rula M.; Patnaik, Surya N.; Hopkins, Dale A.; Berke, Lazlo

1996-01-01

Several nonlinear mathematical programming algorithms for structural design applications are available at present. These include the sequence of unconstrained minimizations technique, the method of feasible directions, and the sequential quadratic programming technique. The optimality criteria technique and the fully utilized design concept are two other structural design methods. A project was undertaken to bring all these design methods under a common computer environment so that a designer can select any one of these tools that may be suitable for his/her application. To facilitate selection of a design algorithm, to validate and check out the computer code, and to ascertain the relative merits of the design tools, modest finite element structural analysis programs based on the concept of stiffness and integrated force methods have been coupled to each design method. The code that contains both these design and analysis tools, by reading input information from analysis and design data files, can cast the design of a structure as a minimum-weight optimization problem. The code can then solve it with a user-specified optimization technique and a user-specified analysis method. This design code is called CometBoards, which is an acronym for Comparative Evaluation Test Bed of Optimization and Analysis Routines for the Design of Structures. This manual describes for the user a step-by-step procedure for setting up the input data files and executing CometBoards to solve a structural design problem. The manual includes the organization of CometBoards; instructions for preparing input data files; the procedure for submitting a problem; illustrative examples; and several demonstration problems. A set of 29 structural design problems have been solved by using all the optimization methods available in CometBoards. A summary of the optimum results obtained for these problems is appended to this users manual. CometBoards, at present, is available for Posix-based Cray and Convex computers, Iris and Sun workstations, and the VM/CMS system.

Music preferences with hearing aids: effects of signal properties, compression settings, and listener characteristics.

PubMed

Croghan, Naomi B H; Arehart, Kathryn H; Kates, James M

2014-01-01

Current knowledge of how to design and fit hearing aids to optimize music listening is limited. Many hearing-aid users listen to recorded music, which often undergoes compression limiting (CL) in the music industry. Therefore, hearing-aid users may experience twofold effects of compression when listening to recorded music: music-industry CL and hearing-aid wide dynamic-range compression (WDRC). The goal of this study was to examine the roles of input-signal properties, hearing-aid processing, and individual variability in the perception of recorded music, with a focus on the effects of dynamic-range compression. A group of 18 experienced hearing-aid users made paired-comparison preference judgments for classical and rock music samples using simulated hearing aids. Music samples were either unprocessed before hearing-aid input or had different levels of music-industry CL. Hearing-aid conditions included linear gain and individually fitted WDRC. Combinations of four WDRC parameters were included: fast release time (50 msec), slow release time (1,000 msec), three channels, and 18 channels. Listeners also completed several psychophysical tasks. Acoustic analyses showed that CL and WDRC reduced temporal envelope contrasts, changed amplitude distributions across the acoustic spectrum, and smoothed the peaks of the modulation spectrum. Listener judgments revealed that fast WDRC was least preferred for both genres of music. For classical music, linear processing and slow WDRC were equally preferred, and the main effect of number of channels was not significant. For rock music, linear processing was preferred over slow WDRC, and three channels were preferred to 18 channels. Heavy CL was least preferred for classical music, but the amount of CL did not change the patterns of WDRC preferences for either genre. Auditory filter bandwidth as estimated from psychophysical tuning curves was associated with variability in listeners' preferences for classical music. Fast, multichannel WDRC often leads to poor music quality, whereas linear processing or slow WDRC are generally preferred. Furthermore, the effect of WDRC is more important for music preferences than music-industry CL applied to signals before the hearing-aid input stage. Variability in hearing-aid users' perceptions of music quality may be partially explained by frequency resolution abilities.

Trajectory Modulated Arc Therapy: A Fully Dynamic Delivery With Synchronized Couch and Gantry Motion Significantly Improves Dosimetric Indices Correlated With Poor Cosmesis in Accelerated Partial Breast Irradiation

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

Liang, Jieming; Atwood, Todd; Eyben, Rie von

2015-08-01

Purpose: To develop planning and delivery capabilities for linear accelerator–based nonisocentric trajectory modulated arc therapy (TMAT) and to evaluate the benefit of TMAT for accelerated partial breast irradiation (APBI) with the patient in prone position. Methods and Materials: An optimization algorithm for volumetrically modulated arc therapy (VMAT) was generalized to allow for user-defined nonisocentric TMAT trajectories combining couch rotations and translations. After optimization, XML scripts were automatically generated to program and subsequently deliver the TMAT plans. For 10 breast patients in the prone position, TMAT and 6-field noncoplanar intensity modulated radiation therapy (IMRT) plans were generated under equivalent objectives andmore » constraints. These plans were compared with regard to whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose. Results: For TMAT APBI, nonisocentric collision-free horizontal arcs with large angular span (251.5 ± 7.9°) were optimized and delivered with delivery time of ∼4.5 minutes. Percentage changes of whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose for TMAT relative to IMRT were −10.81% ± 6.91%, −27.81% ± 7.39%, −14.82% ± 9.67%, and 39.40% ± 10.53% (P≤.01). Conclusions: This is a first demonstration of end-to-end planning and delivery implementation of a fully dynamic APBI TMAT. Compared with IMRT, TMAT resulted in marked reduction of the breast tissue volume irradiated at high doses.« less

Smart LED allocation scheme for efficient multiuser visible light communication networks.

PubMed

Sewaiwar, Atul; Tiwari, Samrat Vikramaditya; Chung, Yeon Ho

2015-05-18

In a multiuser bidirectional visible light communication (VLC), a large number of LEDs or an LED array needs to be allocated in an efficient manner to ensure sustainable data rate and link quality. Moreover, in order to support an increasing or decreasing number of users in the network, the LED allocation is required to be performed dynamically. In this paper, a novel smart LED allocation scheme for efficient multiuser VLC networks is presented. The proposed scheme allocates RGB LEDs to multiple users in a dynamic and efficient fashion, while satisfying illumination requirements in an indoor environment. The smart LED array comprised of RGB LEDs is divided into sectors according to the location of the users. The allocated sectors then provide optical power concentration toward the users for efficient and reliable data transmission. An algorithm for the dynamic allocation of the LEDs is also presented. To verify its effective resource allocation feature of the proposed scheme, simulations were performed. It is found that the proposed smart LED allocation scheme provides the effect of optical beamforming toward individual users, thereby increasing the collective power concentration of the optical signals on the desirable users and resulting in significantly increased data rate, while ensuring sufficient illumination in a multiuser VLC environment.

DyNAVacS: an integrative tool for optimized DNA vaccine design.

PubMed

Harish, Nagarajan; Gupta, Rekha; Agarwal, Parul; Scaria, Vinod; Pillai, Beena

2006-07-01

DNA vaccines have slowly emerged as keystones in preventive immunology due to their versatility in inducing both cell-mediated as well as humoral immune responses. The design of an efficient DNA vaccine, involves choice of a suitable expression vector, ensuring optimal expression by codon optimization, engineering CpG motifs for enhancing immune responses and providing additional sequence signals for efficient translation. DyNAVacS is a web-based tool created for rapid and easy design of DNA vaccines. It follows a step-wise design flow, which guides the user through the various sequential steps in the design of the vaccine. Further, it allows restriction enzyme mapping, design of primers spanning user specified sequences and provides information regarding the vectors currently used for generation of DNA vaccines. The web version uses Apache HTTP server. The interface was written in HTML and utilizes the Common Gateway Interface scripts written in PERL for functionality. DyNAVacS is an integrated tool consisting of user-friendly programs, which require minimal information from the user. The software is available free of cost, as a web based application at URL: http://miracle.igib.res.in/dynavac/.

Consumer-identified barriers and strategies for optimizing technology use in the workplace.

PubMed

De Jonge, Desleigh M; Rodger, Sylvia A

2006-01-01

This article explores the experiences of 26 assistive technology (AT) users having a range of physical impairments as they optimized their use of technology in the workplace. A qualitative research design was employed using in-depth, open-ended interviews and observations of AT users in the workplace. Participants identified many factors that limited their use of technology such as discomfort and pain, limited knowledge of the technology's features, and the complexity of the technology. The amount of time required for training, limited work time available for mastery, cost of training and limitations of the training provided, resulted in an over-reliance on trial and error and informal support networks and a sense of isolation. AT users enhanced their use of technology by addressing the ergonomics of the workstation and customizing the technology to address individual needs and strategies. Other key strategies included tailored training and learning support as well as opportunities to practice using the technology and explore its features away from work demands. This research identified structures important for effective AT use in the workplace which need to be put in place to ensure that AT users are able to master and optimize their use of technology.

Robust Rate Maximization for Heterogeneous Wireless Networks under Channel Uncertainties

PubMed Central

Xu, Yongjun; Hu, Yuan; Li, Guoquan

2018-01-01

Heterogeneous wireless networks are a promising technology in next generation wireless communication networks, which has been shown to efficiently reduce the blind area of mobile communication and improve network coverage compared with the traditional wireless communication networks. In this paper, a robust power allocation problem for a two-tier heterogeneous wireless networks is formulated based on orthogonal frequency-division multiplexing technology. Under the consideration of imperfect channel state information (CSI), the robust sum-rate maximization problem is built while avoiding sever cross-tier interference to macrocell user and maintaining the minimum rate requirement of each femtocell user. To be practical, both of channel estimation errors from the femtocells to the macrocell and link uncertainties of each femtocell user are simultaneously considered in terms of outage probabilities of users. The optimization problem is analyzed under no CSI feedback with some cumulative distribution function and partial CSI with Gaussian distribution of channel estimation error. The robust optimization problem is converted into the convex optimization problem which is solved by using Lagrange dual theory and subgradient algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm by the impact of channel uncertainties on the system performance. PMID:29466315

Evaluating the User Experience of Exercising Reaching Motions With a Robot That Predicts Desired Movement Difficulty.

PubMed

Shirzad, Navid; Van der Loos, H F Machiel

2016-01-01

The notion of an optimal difficulty during practice has been articulated in many areas of cognitive psychology: flow theory, the challenge point framework, and desirable difficulties. Delivering exercises at a participant's desired difficulty has the potential to improve both motor learning and users' engagement in therapy. Motivation and engagement are among the contributing factors to the success of exercise programs. The authors previously demonstrated that error amplification can be used to introduce levels of challenge into a robotic reaching task, and that machine-learning algorithms can dynamically adjust difficulty to the desired level with 85% accuracy. Building on these findings, we present the results of a proof-of-concept study investigating the impacts of practicing under desirable difficulty conditions. A control condition with a predefined random order for difficulty levels was deemed more suitable for this study (compared to constant or continuously increasing difficulty). By practicing the task at their desirable difficulties, participants in the experimental group perceived their performance at a significantly higher level and reported lower required effort to complete the task, in comparison to a control group. Moreover, based on self-reports, participants in the experimental group were willing, on average, to continue the training session for 4.6 more training blocks (∼45 min) compared to the control group's average. This study demonstrates the efficiency of delivering the exercises at the user's desired difficulty level to improve the user's engagement in exercise tasks. Future work will focus on clinical feasibility of this approach in increasing stroke survivors' engagement in their therapy programs.

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

Ma, Biao; Yamaguchi, Keiichi; Fukuoka, Mayuko

To accelerate the logical drug design procedure, we created the program “NAGARA,” a plugin for PyMOL, and applied it to the discovery of small compounds called medical chaperones (MCs) that stabilize the cellular form of a prion protein (PrP{sup C}). In NAGARA, we constructed a single platform to unify the docking simulation (DS), free energy calculation by molecular dynamics (MD) simulation, and interfragment interaction energy (IFIE) calculation by quantum chemistry (QC) calculation. NAGARA also enables large-scale parallel computing via a convenient graphical user interface. Here, we demonstrated its performance and its broad applicability from drug discovery to lead optimization withmore » full compatibility with various experimental methods including Western blotting (WB) analysis, surface plasmon resonance (SPR), and nuclear magnetic resonance (NMR) measurements. Combining DS and WB, we discovered anti-prion activities for two compounds and tegobuvir (TGV), a non-nucleoside non-structural protein NS5B polymerase inhibitor showing activity against hepatitis C virus genotype 1. Binding profiles predicted by MD and QC are consistent with those obtained by SPR and NMR. Free energy analyses showed that these compounds stabilize the PrP{sup C} conformation by decreasing the conformational fluctuation of the PrP{sup C}. Because TGV has been already approved as a medicine, its extension to prion diseases is straightforward. Finally, we evaluated the affinities of the fragmented regions of TGV using QC and found a clue for its further optimization. By repeating WB, MD, and QC recursively, we were able to obtain the optimum lead structure. - Highlights: • NAGARA integrates docking simulation, molecular dynamics, and quantum chemistry. • We found many compounds, e.g., tegobuvir (TGV), that exhibit anti-prion activities. • We obtained insights into the action mechanism of TGV as a medical chaperone. • Using QC, we obtained useful information for optimization of the lead compound, TGV. • NAGARA is a convenient platform for drug discovery and lead optimization.« less

Optimizing the NASA Technical Report Server

NASA Technical Reports Server (NTRS)

Nelson, Michael L.; Maa, Ming-Hokng

1996-01-01

The NASA Technical Report Server (NTRS), a World Wide Web report distribution NASA technical publications service, is modified for performance enhancement, greater protocol support, and human interface optimization. Results include: Parallel database queries, significantly decreasing user access times by an average factor of 2.3; access from clients behind firewalls and/ or proxies which truncate excessively long Uniform Resource Locators (URLs); access to non-Wide Area Information Server (WAIS) databases and compatibility with the 239-50.3 protocol; and a streamlined user interface.

Distributed Combinatorial Optimization Using Privacy on Mobile Phones

NASA Astrophysics Data System (ADS)

Ono, Satoshi; Katayama, Kimihiro; Nakayama, Shigeru

This paper proposes a method for distributed combinatorial optimization which uses mobile phones as computers. In the proposed method, an ordinary computer generates solution candidates and mobile phones evaluates them by referring privacy — private information and preferences. Users therefore does not have to send their privacy to any other computers and does not have to refrain from inputting their preferences. They therefore can obtain satisfactory solution. Experimental results have showed the proposed method solved room assignment problems without sending users' privacy to a server.

TeleProbe: design and development of an efficient system for telepathology

NASA Astrophysics Data System (ADS)

Ahmed, Wamiq M.; Robinson, J. Paul; Ghafoor, Arif

2005-10-01

This paper describes an internet-based system for telepathology. This system provides support for multiple users and exploits the opportunities for optimization that arise in multi-user environment. Techniques for increasing system responsiveness by improving resource utilization and lowering network traffic are explored. Some of the proposed optimizations include an auto-focus module, client and server side caching, and request reordering. These systems can be an economic solution not only for remote pathology consultation but also for pathology and biology education.

Supercomputer optimizations for stochastic optimal control applications

NASA Technical Reports Server (NTRS)

Chung, Siu-Leung; Hanson, Floyd B.; Xu, Huihuang

1991-01-01

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.

Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information

NASA Astrophysics Data System (ADS)

Abel, Fabian; Henze, Nicola; Krause, Daniel

Tagging systems enable users to annotate resources with freely chosen keywords. The evolving bunch of tag assignments is called folksonomy and there exist already some approaches that exploit folksonomies to improve resource retrieval. In this paper, we analyze and compare graph-based ranking algorithms: FolkRank and SocialPageRank. We enhance these algorithms by exploiting the context of tags, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity itself is easy for users to perform. However, it delivers valuable semantic information about resources and their context. We present GRank that uses the context information to improve and optimize the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.

ROCOPT: A user friendly interactive code to optimize rocket structural components

NASA Technical Reports Server (NTRS)

Rule, William K.

1989-01-01

ROCOPT is a user-friendly, graphically-interfaced, microcomputer-based computer program (IBM compatible) that optimizes rocket components by minimizing the structural weight. The rocket components considered are ring stiffened truncated cones and cylinders. The applied loading is static, and can consist of any combination of internal or external pressure, axial force, bending moment, and torque. Stress margins are calculated by means of simple closed form strength of material type equations. Stability margins are determined by approximate, orthotropic-shell, closed-form equations. A modified form of Powell's method, in conjunction with a modified form of the external penalty method, is used to determine the minimum weight of the structure subject to stress and stability margin constraints, as well as user input constraints on the structural dimensions. The graphical interface guides the user through the required data prompts, explains program options and graphically displays results for easy interpretation.

Innovations in user-defined analysis: dynamic grouping and customized user datasets in VistaPHw.

PubMed

Solet, David; Glusker, Ann; Laurent, Amy; Yu, Tianji

2006-01-01

Flexible, ready access to community health assessment data is a feature of innovative Web-based data query systems. An example is VistaPHw, which provides access to Washington state data and statistics used in community health assessment. Because of its flexible analysis options, VistaPHw customizes local, population-based results to be relevant to public health decision-making. The advantages of two innovations, dynamic grouping and the Custom Data Module, are described. Dynamic grouping permits the creation of user-defined aggregations of geographic areas, age groups, race categories, and years. Standard VistaPHw measures such as rates, confidence intervals, and other statistics may then be calculated for the new groups. Dynamic grouping has provided data for major, successful grant proposals, building partnerships with local governments and organizations, and informing program planning for community organizations. The Custom Data Module allows users to prepare virtually any dataset so it may be analyzed in VistaPHw. Uses for this module may include datasets too sensitive to be placed on a Web server or datasets that are not standardized across the state. Limitations and other system needs are also discussed.

Development and pilot testing of a kneeling ultralight wheelchair design.

PubMed

Mattie, Johanne L; Leland, Danny; Borisoff, Jaimie F

2015-01-01

"Dynamic wheeled mobility" offers "on the fly" seating adjustments for wheelchair users such that various activities performed throughout the day can be matched by an appropriate seat position. While this has benefits for user participation and health, the added weight in existing dynamic wheelchairs may impact the user's ability to transport the frame, e.g. into cars. Other dynamic features to enable more participation avenues are also desirable. This paper outlines the development of a "kneeling" ultralight wheelchair design that offers dynamic wheeled mobility functionality at a weight that is comparable to many existing ultralight wheelchairs. In addition, the wheelchair's kneeling function allows a lowered seat position to facilitate low-to-the-ground tasks such as floor transfers and other activities where sustained low level reaching may be required (e.g. playing with children, changing a tire, etc.). This paper also describes the development and pilot testing of an end user evaluation protocol designed to validate the wheelchair's functionality and performance. Successful realization and commercialization of the technology would offer a novel product choice for people with mobility disabilities, and that may support daily activities, health, improved quality of life, and greater participation in the community.

Information diffusion in structured online social networks

NASA Astrophysics Data System (ADS)

Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

2015-05-01

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

Experimental Optimization of Exposure Index and Quality of Service in Wlan Networks.

PubMed

Plets, David; Vermeeren, Günter; Poorter, Eli De; Moerman, Ingrid; Goudos, Sotirios K; Luc, Martens; Wout, Joseph

2017-07-01

This paper presents the first real-life optimization of the Exposure Index (EI). A genetic optimization algorithm is developed and applied to three real-life Wireless Local Area Network scenarios in an experimental testbed. The optimization accounts for downlink, uplink and uplink of other users, for realistic duty cycles, and ensures a sufficient Quality of Service to all users. EI reductions up to 97.5% compared to a reference configuration can be achieved in a downlink-only scenario, in combination with an improved Quality of Service. Due to the dominance of uplink exposure and the lack of WiFi power control, no optimizations are possible in scenarios that also consider uplink traffic. However, future deployments that do implement WiFi power control can be successfully optimized, with EI reductions up to 86% compared to a reference configuration and an EI that is 278 times lower than optimized configurations under the absence of power control. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

NASA Technical Reports Server (NTRS)

Rash, James L.

2010-01-01

NASA's space data-communications infrastructure, the Space Network and the Ground Network, provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft via orbiting relay satellites and ground stations. An implementation of the methods and algorithms disclosed herein will be a system that produces globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary search, a class of probabilistic strategies for searching large solution spaces, constitutes the essential technology in this disclosure. Also disclosed are methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithm itself. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally, with applicability to a very broad class of combinatorial optimization problems.

Electronic processing and control system with programmable hardware

NASA Technical Reports Server (NTRS)

Alkalaj, Leon (Inventor); Fang, Wai-Chi (Inventor); Newell, Michael A. (Inventor)

1998-01-01

A computer system with reprogrammable hardware allowing dynamically allocating hardware resources for different functions and adaptability for different processors and different operating platforms. All hardware resources are physically partitioned into system-user hardware and application-user hardware depending on the specific operation requirements. A reprogrammable interface preferably interconnects the system-user hardware and application-user hardware.

Improving Data Transfer Throughput with Direct Search Optimization

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

Balaprakash, Prasanna; Morozov, Vitali; Kettimuthu, Rajkumar

2016-01-01

Improving data transfer throughput over high-speed long-distance networks has become increasingly difficult. Numerous factors such as nondeterministic congestion, dynamics of the transfer protocol, and multiuser and multitask source and destination endpoints, as well as interactions among these factors, contribute to this difficulty. A promising approach to improving throughput consists in using parallel streams at the application layer.We formulate and solve the problem of choosing the number of such streams from a mathematical optimization perspective. We propose the use of direct search methods, a class of easy-to-implement and light-weight mathematical optimization algorithms, to improve the performance of data transfers by dynamicallymore » adapting the number of parallel streams in a manner that does not require domain expertise, instrumentation, analytical models, or historic data. We apply our method to transfers performed with the GridFTP protocol, and illustrate the effectiveness of the proposed algorithm when used within Globus, a state-of-the-art data transfer tool, on productionWAN links and servers. We show that when compared to user default settings our direct search methods can achieve up to 10x performance improvement under certain conditions. We also show that our method can overcome performance degradation due to external compute and network load on source end points, a common scenario at high performance computing facilities.« less

Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

PubMed Central

Wu, Kai; Liu, Jing; Wang, Shuai

2016-01-01

Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy. PMID:27886244

Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

NASA Astrophysics Data System (ADS)

Wu, Kai; Liu, Jing; Wang, Shuai

2016-11-01

Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.

Interactive Data Exploration with Smart Drill-Down

PubMed Central

Joglekar, Manas; Garcia-Molina, Hector; Parameswaran, Aditya

2017-01-01

We present smart drill-down, an operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a rule. For instance, the rule (a, b, ⋆, 1000) tells us that there are a thousand tuples with value a in the first column and b in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are NP-Hard, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms. PMID:28210096

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

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

PubMed

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

2016-01-01

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

Mystic: Implementation of the Static Dynamic Optimal Control Algorithm for High-Fidelity, Low-Thrust Trajectory Design

NASA Technical Reports Server (NTRS)

Whiffen, Gregory J.

2006-01-01

Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.

Science Opportunity Analyzer (SOA) Version 8

NASA Technical Reports Server (NTRS)

Witoff, Robert J.; Polanskey, Carol A.; Aguinaldo, Anna Marie A.; Liu, Ning; Hofstadter, Mark D.

2013-01-01

SOA allows scientists to plan spacecraft observations. It facilitates the identification of geometrically interesting times in a spacecraft s orbit that a user can use to plan observations or instrument-driven spacecraft maneuvers. These observations can then be visualized multiple ways in both two- and three-dimensional views. When observations have been optimized within a spacecraft's flight rules, the resulting plans can be output for use by other JPL uplink tools. Now in its eighth major version, SOA improves on these capabilities in a modern and integrated fashion. SOA consists of five major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, and Data Output. Opportunity Search is a GUI-driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last-minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily assess the cost to science if flight rule changes occur. Data Output allows the user to compute ancillary data related to an observation or to a given position of the spacecraft along its trajectory. The data can be saved as a tab-delimited text file or viewed as a graph. SOA combines science planning functionality unique to both JPL and the sponsoring spacecraft. SOA is able to ingest JPL SPICE Kernels that are used to drive the tool and its computations. A Percy search engine is then included that identifies interesting time periods for the user to build observations. When observations are then built, flight-like orientation algorithms replicate spacecraft dynamics to closely simulate the flight spacecraft s dynamics. SOA v8 represents large steps forward from SOA v7 in terms of quality, reliability, maintainability, efficiency, and user experience. A tailored agile development environment has been built around SOA that provides automated unit testing, continuous build and integration, a consolidated Web-based code and documentation storage environment, modern Java enhancements, and a focus on usability

Initial state-specific photodissociation dynamics of pyrrole via 1 π σ ∗/ S 0 conical intersection initiated with optimally controlled UV-laser pulses

NASA Astrophysics Data System (ADS)

Nandipati, K. R.; Kanakati, Arun Kumar; Singh, H.; Lan, Z.; Mahapatra, S.

2017-09-01

Optimal initiation of quantum dynamics of N-H photodissociation of pyrrole on the S0-1πσ∗(1A2) coupled electronic states by UV-laser pulses in an effort to guide the subsequent dynamics to dissociation limits is studied theoretically. Specifically, the task of designing optimal laser pulses that act on initial vibrational states of the system for an effective UV-photodissociation is considered by employing optimal control theory. The associated control mechanism(s) for the initial state dependent photodissociation dynamics of pyrrole in the presence of control pulses is examined and discussed in detail. The initial conditions determine implicitly the variation in the dissociation probabilities for the two channels, upon interaction with the field. The optimal pulse corresponds to the objective fixed as maximization of overall reactive flux subject to constraints of reasonable fluence and quantum dynamics. The simple optimal pulses obtained by the use of genetic algorithm based optimization are worth an experimental implementation given the experimental relevance of πσ∗-photochemistry in recent times.

Towards a dynamic social-network-based approach for service composition in the Internet of Things

NASA Astrophysics Data System (ADS)

Xu, Wen; Hu, Zheng; Gong, Tao; Zhao, Zhengzheng

2011-12-01

The User-Generated Service (UGS) concept allows end-users to create their own services as well as to share and manage the lifecycles of these services. The current development of the Internet-of-Things (IoT) has brought new challenges to the UGS area. Creating smart services in the IoT environment requires a dynamic social network that considers the relationship between people and things. In this paper, we consider the know-how required to best organize exchanges between users and things to enhance service composition. By surveying relevant aspects including service composition technology, social networks and a recommendation system, we present the first concept of our framework to provide recommendations for a dynamic social network-based means to organize UGSs in the IoT.

Agreement Technologies for Energy Optimization at Home.

PubMed

González-Briones, Alfonso; Chamoso, Pablo; De La Prieta, Fernando; Demazeau, Yves; Corchado, Juan M

2018-05-19

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

PubMed Central

Kok, Kai Yit; Rajendran, Parvathy

2016-01-01

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630

Comparison of particle swarm optimization and differential evolution for aggregators' profit maximization in the demand response system

NASA Astrophysics Data System (ADS)

Wisittipanit, Nuttachat; Wisittipanich, Warisa

2018-07-01

Demand response (DR) refers to changes in the electricity use patterns of end-users in response to incentive payment designed to prompt lower electricity use during peak periods. Typically, there are three players in the DR system: an electric utility operator, a set of aggregators and a set of end-users. The DR model used in this study aims to minimize the operator's operational cost and offer rewards to aggregators, while profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users for altering their consumption profiles. This article presents the first application of two metaheuristics in the DR system: particle swarm optimization (PSO) and differential evolution (DE). The objective is to optimize the incentive payments during various periods to satisfy all stakeholders. The results show that DE significantly outperforms PSO, since it can attain better compensation rates, lower operational costs and higher aggregator profits.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules

PubMed Central

van der Laan, Mark J.; Petersen, Maya L.

2008-01-01

Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus. PMID:19122792

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

PubMed Central

Zhang, Dezhi; Li, Shuangyan

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

An optimal hierarchical decision model for a regional logistics network with environmental impact consideration.

PubMed

Zhang, Dezhi; Li, Shuangyan; Qin, Jin

2014-01-01

This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.

FAST copper for broadband access

NASA Astrophysics Data System (ADS)

Chiang, Mung; Huang, Jianwei; Cendrillon, Raphael; Tan, Chee Wei; Xu, Dahai

2006-10-01

FAST Copper is a multi-year, U.S. NSF funded project that started in 2004, and is jointly pursued by the research groups of Mung Chiang at Princeton University, John Cioffi at Stanford University, and Alexader Fraser at Fraser Research Lab, and in collaboration with several industrial partners including AT&T. The goal of the FAST Copper Project is to provide ubiquitous, 100 Mbps, fiber/DSL broadband access to everyone in the U.S. with a phone line. This goal will be achieved through two threads of research: dynamic and joint optimization of resources in Frequency, Amplitude, Space, and Time (thus the name 'FAST') to overcome the attenuation and crosstalk bottlenecks, and the integration of communication, networking, computation, modeling, and distributed information management and control for the multi-user twisted pair network.

Influence of Building Material Solution of Structures to Effectiveness of Real Estate Development

NASA Astrophysics Data System (ADS)

Somorová, Viera

2015-11-01

Real estate development is in its essence the development process characterized by a considerable dynamics. The purpose of the development process is the creation of buildings which can be either rented by future unknown users or sold in the real estate market. A first part of the paper is dedicated to the analysis of the parameters of buildings solutions considering the future operating costs in a phase of designing. Material solution of external structures is a main factor not only in determining the future operating costs but also in achieving the subsequent economic effectiveness of the real estate development. To determine the relationship between economic efficiency criteria and determine the optimal material variant of building constructions for the specific example is the aim of the second part of paper.

Implementation of a partitioned algorithm for simulation of large CSI problems

NASA Technical Reports Server (NTRS)

Alvin, Kenneth F.; Park, K. C.

1991-01-01

The implementation of a partitioned numerical algorithm for determining the dynamic response of coupled structure/controller/estimator finite-dimensional systems is reviewed. The partitioned approach leads to a set of coupled first and second-order linear differential equations which are numerically integrated with extrapolation and implicit step methods. The present software implementation, ACSIS, utilizes parallel processing techniques at various levels to optimize performance on a shared-memory concurrent/vector processing system. A general procedure for the design of controller and filter gains is also implemented, which utilizes the vibration characteristics of the structure to be solved. Also presented are: example problems; a user's guide to the software; the procedures and algorithm scripts; a stability analysis for the algorithm; and the source code for the parallel implementation.

The colour preference control based on two-colour combinations

NASA Astrophysics Data System (ADS)

Hong, Ji Young; Kwak, Youngshin; Park, Du-Sik; Kim, Chang Yeong

2008-02-01

This paper proposes a framework of colour preference control to satisfy the consumer's colour related emotion. A colour harmony algorithm based on two-colour combinations is developed for displaying the images with several complementary colour pairs as the relationship of two-colour combination. The colours of pixels belonging to complementary colour areas in HSV colour space are shifted toward the target hue colours and there is no colour change for the other pixels. According to the developed technique, dynamic emotions by the proposed hue conversion can be improved and the controlled output image shows improved colour emotions in the preference of the human viewer. The psychophysical experiments are conducted to investigate the optimal model parameters to produce the most pleasant image to the users in the respect of colour emotions.

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

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

Wu, Chase Qishi; Zhu, Michelle Mengxia

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models featuremore » diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.« less

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

Information Switching Processor (ISP) contention analysis and control

NASA Technical Reports Server (NTRS)

Shyy, D.; Inukai, T.

1993-01-01

Future satellite communications, as a viable means of communications and an alternative to terrestrial networks, demand flexibility and low end-user cost. On-board switching/processing satellites potentially provide these features, allowing flexible interconnection among multiple spot beams, direct to the user communications services using very small aperture terminals (VSAT's), independent uplink and downlink access/transmission system designs optimized to user's traffic requirements, efficient TDM downlink transmission, and better link performance. A flexible switching system on the satellite in conjunction with low-cost user terminals will likely benefit future satellite network users.

Observing Consistency in Online Communication Patterns for User Re-Identification

PubMed Central

Venter, Hein S.

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas. PMID:27918593

The IBM HeadTracking Pointer: improvements in vision-based pointer control.

PubMed

Kjeldsen, Rick

2008-07-01

Vision-based head trackers have been around for some years and are even beginning to be commercialized, but problems remain with respect to usability. Users without the ability to use traditional pointing devices--the intended audience of such systems--have no alternative if the automatic bootstrapping process fails. There is room for improvement in face tracking, and the pointer movement dynamics do not support accurate and efficient pointing. This paper describes the IBM HeadTracking Pointer, a system which attempts to directly address some of these issues. Head gestures are used to provide the end user a greater level of autonomous control over the system. A novel face-tracking algorithm reduces drift under variable lighting conditions, allowing the use of absolute, rather than relative, pointer positioning. Most importantly, the pointer dynamics have been designed to take into account the constraints of head-based pointing, with a non-linear gain which allows stability in fine pointer movement, high speed on long transitions and adjustability to support users with different movement dynamics. User studies have identified some difficulties with training the system and some characteristics of the pointer motion that take time to get used to, but also good user feedback and very promising performance results.

Optimizing diffusion of an online computer tailored lifestyle program: a study protocol.

PubMed

Schneider, Francine; van Osch, Liesbeth A D M; Kremers, Stef P J; Schulz, Daniela N; van Adrichem, Mathieu J G; de Vries, Hein

2011-06-20

Although the Internet is a promising medium to offer lifestyle interventions to large amounts of people at relatively low costs and effort, actual exposure rates of these interventions fail to meet the high expectations. Since public health impact of interventions is determined by intervention efficacy and level of exposure to the intervention, it is imperative to put effort in optimal dissemination. The present project attempts to optimize the dissemination process of a new online computer tailored generic lifestyle program by carefully studying the adoption process and developing a strategy to achieve sustained use of the program. A prospective study will be conducted to yield relevant information concerning the adoption process by studying the level of adoption of the program, determinants involved in adoption and characteristics of adopters and non-adopters as well as satisfied and unsatisfied users. Furthermore, a randomized control trial will be conducted to the test the effectiveness of a proactive strategy using periodic e-mail prompts in optimizing sustained use of the new program. Closely mapping the adoption process will gain insight in characteristics of adopters and non-adopters and satisfied and unsatisfied users. This insight can be used to further optimize the program by making it more suitable for a wider range of users, or to develop adjusted interventions to attract subgroups of users that are not reached or satisfied with the initial intervention. Furthermore, by studying the effect of a proactive strategy using period prompts compared to a reactive strategy to stimulate sustained use of the intervention and, possibly, behaviour change, specific recommendations on the use and the application of prompts in online lifestyle interventions can be developed. Dutch Trial Register NTR1786 and Medical Ethics Committee of Maastricht University and the University Hospital Maastricht (NL2723506809/MEC0903016).

Understanding Providers' Interaction with Graphical User Interface Pertaining to Clinical Document Usage in an Electronic Health Record System

ERIC Educational Resources Information Center

Rizvi, Rubina Fatima

2017-01-01

Despite high Electronic Health Record (EHR) system adoption rates by hospital and office-based practices, many users remain highly dissatisfied with the current state of EHRs. Sub-optimal EHR usability as a result of insufficient incorporation of User-Centered Design (UCD) approach during System Development Life Cycle process (SDLC) is considered…

Recreational System Optimization to Reduce Conflict on Public Lands

NASA Astrophysics Data System (ADS)

Shilling, Fraser; Boggs, Jennifer; Reed, Sarah

2012-09-01

In response to federal administrative rule, the Tahoe National Forest (TNF), California, USA engaged in trail-route prioritization for motorized recreation (e.g., off-highway-vehicles) and other recreation types. The prioritization was intended to identify routes that were suitable and ill-suited for maintenance in a transportation system. A recreational user survey was conducted online ( n = 813) for user preferences for trail system characteristics, recreational use patterns, and demographics. Motorized trail users and non-motorized users displayed very clear and contrasting preferences for the same system. As has been found by previous investigators, non-motorized users expressed antagonism to motorized use on the same recreational travel system, whereas motorized users either supported multiple-use routes or dismissed non-motorized recreationists' concerns. To help the TNF plan for reduced conflict, a geographic information system (GIS) based modeling approach was used to identify recreational opportunities and potential environmental impacts of all travel routes. This GIS-based approach was based on an expert-derived rule set. The rules addressed particular environmental and recreation concerns in the TNF. Route segments were identified that could be incorporated into minimal-impact networks to support various types of recreation. The combination of potential impacts and user-benefits supported an optimization approach for an appropriate recreational travel network to minimize environmental impacts and user-conflicts in a multi-purpose system.

Recreational system optimization to reduce conflict on public lands.

PubMed

Shilling, Fraser; Boggs, Jennifer; Reed, Sarah

2012-09-01

In response to federal administrative rule, the Tahoe National Forest (TNF), California, USA engaged in trail-route prioritization for motorized recreation (e.g., off-highway-vehicles) and other recreation types. The prioritization was intended to identify routes that were suitable and ill-suited for maintenance in a transportation system. A recreational user survey was conducted online (n = 813) for user preferences for trail system characteristics, recreational use patterns, and demographics. Motorized trail users and non-motorized users displayed very clear and contrasting preferences for the same system. As has been found by previous investigators, non-motorized users expressed antagonism to motorized use on the same recreational travel system, whereas motorized users either supported multiple-use routes or dismissed non-motorized recreationists' concerns. To help the TNF plan for reduced conflict, a geographic information system (GIS) based modeling approach was used to identify recreational opportunities and potential environmental impacts of all travel routes. This GIS-based approach was based on an expert-derived rule set. The rules addressed particular environmental and recreation concerns in the TNF. Route segments were identified that could be incorporated into minimal-impact networks to support various types of recreation. The combination of potential impacts and user-benefits supported an optimization approach for an appropriate recreational travel network to minimize environmental impacts and user-conflicts in a multi-purpose system.

Presentation of computer code SPIRALI for incompressible, turbulent, plane and spiral grooved cylindrical and face seals

NASA Technical Reports Server (NTRS)

Walowit, Jed A.

1994-01-01

A viewgraph presentation is made showing the capabilities of the computer code SPIRALI. Overall capabilities of SPIRALI include: computes rotor dynamic coefficients, flow, and power loss for cylindrical and face seals; treats turbulent, laminar, Couette, and Poiseuille dominated flows; fluid inertia effects are included; rotor dynamic coefficients in three (face) or four (cylindrical) degrees of freedom; includes effects of spiral grooves; user definable transverse film geometry including circular steps and grooves; independent user definable friction factor models for rotor and stator; and user definable loss coefficients for sudden expansions and contractions.

Application of the Tool for Turbine Engine Closed-Loop Transient Analysis (TTECTrA) for Dynamic Systems Analysis

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Zinnecker, Alicia M.

2014-01-01

The aircraft engine design process seeks to achieve the best overall system-level performance, weight, and cost for a given engine design. This is achieved by a complex process known as systems analysis, where steady-state simulations are used to identify trade-offs that should be balanced to optimize the system. The steady-state simulations and data on which systems analysis relies may not adequately capture the true performance trade-offs that exist during transient operation. Dynamic Systems Analysis provides the capability for assessing these trade-offs at an earlier stage of the engine design process. The concept of dynamic systems analysis and the type of information available from this analysis are presented in this paper. To provide this capability, the Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) was developed. This tool aids a user in the design of a power management controller to regulate thrust, and a transient limiter to protect the engine model from surge at a single flight condition (defined by an altitude and Mach number). Results from simulation of the closed-loop system may be used to estimate the dynamic performance of the model. This enables evaluation of the trade-off between performance and operability, or safety, in the engine, which could not be done with steady-state data alone. A design study is presented to compare the dynamic performance of two different engine models integrated with the TTECTrA software.

Dynamic pressure sensor calibration techniques offering expanded bandwidth with increased resolution

NASA Astrophysics Data System (ADS)

Wisniewiski, David

2015-03-01

Advancements in the aerospace, defense and energy markets are being made possible by increasingly more sophisticated systems and sub-systems which rely upon critical information to be conveyed from the physical environment being monitored through ever more specialized, extreme environment sensing components. One sensing parameter of particular interest is dynamic pressure measurement. Crossing the boundary of all three markets (i.e. aerospace, defense and energy) is dynamic pressure sensing which is used in research and development of gas turbine technology, and subsequently embedded into a control loop used for long-term monitoring. Applications include quantifying the effects of aircraft boundary layer ingestion into the engine inlet to provide a reliable and robust design. Another application includes optimization of combustor dynamics by "listening" to the acoustic signature so that fuel-to-air mixture can be adjusted in real-time to provide cost operating efficiencies and reduced NOx emissions. With the vast majority of pressure sensors supplied today being calibrated either statically or "quasi" statically, the dynamic response characterization of the frequency dependent sensitivity (i.e. transfer function) of the pressure sensor is noticeably absent. The shock tube has been shown to be an efficient vehicle to provide frequency response of pressure sensors from extremely high frequencies down to 500 Hz. Recent development activity has lowered this starting frequency; thereby augmenting the calibration bandwidth with increased frequency resolution so that as the pressure sensor is used in an actual test application, more understanding of the physical measurement can be ascertained by the end-user.

Estimating User Influence in Online Social Networks Subject to Information Overload

NASA Astrophysics Data System (ADS)

Li, Pei; Sun, Yunchuan; Chen, Yingwen; Tian, Zhi

2014-11-01

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

The development of a prototype intelligent user interface subsystem for NASA's scientific database systems

NASA Technical Reports Server (NTRS)

Campbell, William J.; Roelofs, Larry H.; Short, Nicholas M., Jr.

1987-01-01

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components the development of an Intelligent User Interface (IUI).The intent of the latter is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. The purpose is to support the large number of potential scientific and engineering users presently having need of space and land related research and technical data but who have little or no experience in query languages or understanding of the information content or architecture of the databases involved. This technical memorandum presents prototype Intelligent User Interface Subsystem (IUIS) using the Crustal Dynamics Project Database as a test bed for the implementation of the CRUDDES (Crustal Dynamics Expert System). The knowledge base has more than 200 rules and represents a single application view and the architectural view. Operational performance using CRUDDES has allowed nondatabase users to obtain useful information from the database previously accessible only to an expert database user or the database designer.

User's Manual for Computer Program ROTOR. [to calculate tilt-rotor aircraft dynamic characteristics

NASA Technical Reports Server (NTRS)

Yasue, M.

1974-01-01

A detailed description of a computer program to calculate tilt-rotor aircraft dynamic characteristics is presented. This program consists of two parts: (1) the natural frequencies and corresponding mode shapes of the rotor blade and wing are developed from structural data (mass distribution and stiffness distribution); and (2) the frequency response (to gust and blade pitch control inputs) and eigenvalues of the tilt-rotor dynamic system, based on the natural frequencies and mode shapes, are derived. Sample problems are included to assist the user.

Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimal Force Control of Vibro-Impact Systems for Autonomous Drilling Applications

NASA Technical Reports Server (NTRS)

Aldrich, Jack B.; Okon, Avi B.

2012-01-01

The need to maintain optimal energy efficiency is critical during the drilling operations performed on future and current planetary rover missions (see figure). Specifically, this innovation seeks to solve the following problem. Given a spring-loaded percussive drill driven by a voice-coil motor, one needs to determine the optimal input voltage waveform (periodic function) and the optimal hammering period that minimizes the dissipated energy, while ensuring that the hammer-to-rock impacts are made with sufficient (user-defined) impact velocity (or impact energy). To solve this problem, it was first observed that when voice-coil-actuated percussive drills are driven at high power, it is of paramount importance to ensure that the electrical current of the device remains in phase with the velocity of the hammer. Otherwise, negative work is performed and the drill experiences a loss of performance (i.e., reduced impact energy) and an increase in Joule heating (i.e., reduction in energy efficiency). This observation has motivated many drilling products to incorporate the standard bang-bang control approach for driving their percussive drills. However, the bang-bang control approach is significantly less efficient than the optimal energy-efficient control approach solved herein. To obtain this solution, the standard tools of classical optimal control theory were applied. It is worth noting that these tools inherently require the solution of a two-point boundary value problem (TPBVP), i.e., a system of differential equations where half the equations have unknown boundary conditions. Typically, the TPBVP is impossible to solve analytically for high-dimensional dynamic systems. However, for the case of the spring-loaded vibro-impactor, this approach yields the exact optimal control solution as the sum of four analytic functions whose coefficients are determined using a simple, easy-to-implement algorithm. Once the optimal control waveform is determined, it can be used optimally in the context of both open-loop and closed-loop control modes (using standard realtime control hardware).

Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods

NASA Astrophysics Data System (ADS)

Rogers, Adam; Safi-Harb, Samar; Fiege, Jason

2015-08-01

The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.

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.

YouGenMap: a web platform for dynamic multi-comparative mapping and visualization of genetic maps

Treesearch

Keith Batesole; Kokulapalan Wimalanathan; Lin Liu; Fan Zhang; Craig S. Echt; Chun Liang

2014-01-01

Comparative genetic maps are used in examination of genome organization, detection of conserved gene order, and exploration of marker order variations. YouGenMap is an open-source web tool that offers dynamic comparative mapping capability of users' own genetic mapping between 2 or more map sets. Users' genetic map data and optional gene annotations are...

A proposed concept for a crustal dynamics information management network

NASA Technical Reports Server (NTRS)

Lohman, G. M.; Renfrow, J. T.

1980-01-01

The findings of a requirements and feasibility analysis of the present and potential producers, users, and repositories of space-derived geodetic information are summarized. A proposed concept is presented for a crustal dynamics information management network that would apply state of the art concepts of information management technology to meet the expanding needs of the producers, users, and archivists of this geodetic information.