A Mixed Integer Linear Program for Airport Departure Scheduling
NASA Technical Reports Server (NTRS)
Gupta, Gautam; Jung, Yoon Chul
2009-01-01
Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced
2013-01-01
that these constraints can often lead to significant reductions in the gap between the optimal solution and its non-integral linear programming bound relative to the prior art as well as often substantially faster processing of moderately hard problem instances. Conclusion We provide an indication of the conditions under which such an optimal enumeration approach is likely to be feasible, suggesting that these strategies are usable for relatively large numbers of taxa, although with stricter limits on numbers of variable sites. The work thus provides methodology suitable for provably optimal solution of some harder instances that resist all prior approaches. PMID:23343437
A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem
NASA Technical Reports Server (NTRS)
Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad
2010-01-01
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
Poos, Alexandra M; Maicher, André; Dieckmann, Anna K; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer
2016-06-01
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. PMID:26908654
Poos, Alexandra M.; Maicher, André; Dieckmann, Anna K.; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer
2016-01-01
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. PMID:26908654
Li, Zukui; Ding, Ran; Floudas, Christodoulos A.
2011-01-01
Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263
Li, Y P; Huang, G H
2006-11-01
In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals can be reflected. The model can help tackle the dynamic, interactive and uncertain characteristics of the solid waste management system in the City, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Three scenarios are considered based on different waste management policies. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment or justification of the existing waste flow allocation patterns, the long-term capacity planning of the City's waste management system, and the formulation of local policies and regulations regarding waste generation and management. PMID:16678336
An Application of Parametric Mixed-Integer Linear Programming to Hydropower Development
NASA Astrophysics Data System (ADS)
Turgeon, André
1987-03-01
The problem consists in selecting the sites on the river where reservoirs and hydroelectric power plants are to be built and then determining the type and size of the projected installations. The solution obviously depends on the amount of money the utility is willing to invest, which itself is a function of what the new installations will produce. It is therefore necessary to solve the problem for all possible amounts of firm energy produced, since it is not known at the outset which production level the utility will select. This is done in the paper by a parametric mixed-integer linear programming (MILP) method whose efficiency derives from the fact that the branch-and-bound algorithm for selecting the sites to be developed (and consuming most of the computer time) is solved a minimum number of times. Between the points where the MILP problem is solved, LP parametric analysis is applied.
Automatic design of synthetic gene circuits through mixed integer non-linear programming.
Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias
2012-01-01
Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits. PMID:22536398
Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach
NASA Astrophysics Data System (ADS)
Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin
2014-09-01
Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.
PySP : modeling and solving stochastic mixed-integer programs in Python.
Woodruff, David L.; Watson, Jean-Paul
2010-08-01
Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.
NASA Astrophysics Data System (ADS)
Sawyer, Charles S.; Ahlfeld, David P.; King, Alan J.
1995-05-01
A three-dimensional groundwater flow management model for making decisions on the design of hydrodynamic control of a groundwater flow system using a combination of extraction and/or injection wells is developed. The model takes into account constraints imposed on the system to stop the horizontal spread of contaminants and to ensure a net upward flow in areas where downward vertical gradients exist. The mathematical formulation of the groundwater remediation problem as a mixed-integer model and the strategy for solving the model are presented. Numerical results are presented for the Toms River Plant site, which is modeled as a five-layer aquifer system with interconnecting aquitards. A sensitivity analysis on the relative magnitude of the continuous operating costs and the fixed-charge costs is also presented.
Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A
2016-03-01
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. PMID:26756402
DRIESSEN,BRIAN; SADEGH,NADER
2000-04-25
This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.
NASA Astrophysics Data System (ADS)
Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang
2008-12-01
Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.
Mixed integer programming model for optimizing the layout of an ICU vehicle
2009-01-01
Background This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). Methods The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. Results As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. Conclusion The outcome was highly satisfactory. Indeed, the final design served as a basis
NASA Astrophysics Data System (ADS)
Irmeilyana, Puspita, Fitri Maya; Indrawati
2016-02-01
The pricing for wireless networks is developed by considering linearity factors, elasticity price and price factors. Mixed Integer Nonlinear Programming of wireless pricing model is proposed as the nonlinear programming problem that can be solved optimally using LINGO 13.0. The solutions are expected to give some information about the connections between the acceptance factor and the price. Previous model worked on the model that focuses on bandwidth as the QoS attribute. The models attempt to maximize the total price for a connection based on QoS parameter. The QoS attributes used will be the bandwidth and the end to end delay that affect the traffic. The maximum goal to maximum price is achieved when the provider determine the requirement for the increment or decrement of price change due to QoS change and amount of QoS value.
Winebrake, James J; Corbett, James J; Wang, Chengfeng; Farrell, Alexander E; Woods, Pippa
2005-04-01
Emissions from passenger ferries operating in urban harbors may contribute significantly to emissions inventories and commuter exposure to air pollution. In particular, ferries are problematic because of high emissions of oxides of nitrogen (NOx) and particulate matter (PM) from primarily unregulated diesel engines. This paper explores technical solutions to reduce pollution from passenger ferries operating in the New York-New Jersey Harbor. The paper discusses and demonstrates a mixed-integer, non-linear programming model used to identify optimal control strategies for meeting NOx and PM reduction targets for 45 privately owned commuter ferries in the harbor. Results from the model can be used by policy-makers to craft programs aimed at achieving least-cost reduction targets. PMID:15887889
Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.
2015-01-01
Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881
Armutlu, Pelin; Ozdemir, Muhittin E; Uney-Yuksektepe, Fadime; Kavakli, I Halil; Turkay, Metin
2008-01-01
Background A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed. PMID:18834515
NASA Astrophysics Data System (ADS)
Guo, P.; Huang, G. H.; Li, Y. P.
2010-01-01
In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.
Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems. PMID:22122384
Ko, Andi Setiady; Chang, Ni-Bin
2008-07-01
Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time. PMID:17395362
ERIC Educational Resources Information Center
Han, Kyung T.; Rudner, Lawrence M.
2014-01-01
This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…
Mixed-Integer Formulations for Constellation Scheduling
NASA Astrophysics Data System (ADS)
Valicka, C.; Hart, W.; Rintoul, M.
Remote sensing systems have expanded the set of capabilities available for and critical to national security. Cooperating, high-fidelity sensing systems and growing mission applications have exponentially increased the set of potential schedules. A definitive lack of advanced tools places an increased burden on operators, as planning and scheduling remain largely manual tasks. This is particularly true in time-critical planning activities where operators aim to accomplish a large number of missions through optimal utilization of single or multiple sensor systems. Automated scheduling through identification and comparison of alternative schedules remains a challenging problem applicable across all remote sensing systems. Previous approaches focused on a subset of sensor missions and do not consider ad-hoc tasking. We have begun development of a robust framework that leverages the Pyomo optimization modeling language for the design of a tool to assist sensor operators planning under the constraints of multiple concurrent missions and uncertainty. Our scheduling models have been formulated to address the stochastic nature of ad-hoc tasks inserted under a variety of scenarios. Operator experience is being leveraged to select appropriate model objectives. Successful development of the framework will include iterative development of high-fidelity mission models that consider and expose various schedule performance metrics. Creating this tool will aid time-critical scheduling by increasing planning efficiency, clarifying the value of alternative modalities uniquely provided by multi-sensor systems, and by presenting both sets of organized information to operators. Such a tool will help operators more quickly and fully utilize sensing systems, a high interest objective within the current remote sensing operations community. Preliminary results for mixed-integer programming formulations of a sensor scheduling problem will be presented. Assumptions regarding sensor geometry
Solution of Mixed-Integer Programming Problems on the XT5
Hartman-Baker, Rebecca J; Busch, Ingrid Karin; Hilliard, Michael R; Middleton, Richard S; Schultze, Michael
2009-01-01
In this paper, we describe our experience with solving difficult mixed-integer linear programming problems (MILPs) on the petaflop Cray XT5 system at the National Center for Computational Sciences at Oak Ridge National Laboratory. We describe the algorithmic, software, and hardware needs for solving MILPs and present the results of using PICO, an open-source, parallel, mixed-integer linear programming solver developed at Sandia National Laboratories, to solve canonical MILPs as well as problems of interest arising from the logistics and supply chain management field.
Smalley, Hannah K; Keskinocak, Pinar; Swann, Julie; Hinman, Alan
2015-11-17
In addition to improved sanitation, hygiene, and better access to safe water, oral cholera vaccines can help to control the spread of cholera in the short term. However, there is currently no systematic method for determining the best allocation of oral cholera vaccines to minimize disease incidence in a population where the disease is endemic and resources are limited. We present a mathematical model for optimally allocating vaccines in a region under varying levels of demographic and incidence data availability. The model addresses the questions of where, when, and how many doses of vaccines to send. Considering vaccine efficacies (which may vary based on age and the number of years since vaccination), we analyze distribution strategies which allocate vaccines over multiple years. Results indicate that, given appropriate surveillance data, targeting age groups and regions with the highest disease incidence should be the first priority, followed by other groups primarily in order of disease incidence, as this approach is the most life-saving and cost-effective. A lack of detailed incidence data results in distribution strategies which are not cost-effective and can lead to thousands more deaths from the disease. The mathematical model allows for what-if analysis for various vaccine distribution strategies by providing the ability to easily vary parameters such as numbers and sizes of regions and age groups, risk levels, vaccine price, vaccine efficacy, production capacity and budget. PMID:26458806
Solving mixed integer nonlinear programming problems using spiral dynamics optimization algorithm
NASA Astrophysics Data System (ADS)
Kania, Adhe; Sidarto, Kuntjoro Adji
2016-02-01
Many engineering and practical problem can be modeled by mixed integer nonlinear programming. This paper proposes to solve the problem with modified spiral dynamics inspired optimization method of Tamura and Yasuda. Four test cases have been examined, including problem in engineering and sport. This method succeeds in obtaining the optimal result in all test cases.
NASA Astrophysics Data System (ADS)
Skulovich, Olya; Bent, Russell; Judi, David; Perelman, Lina Sela; Ostfeld, Avi
2015-06-01
Despite their potential catastrophic impact, transients are often ignored or presented ad hoc when designing water distribution systems. To address this problem, we introduce a new piece-wise function fitting model that is integrated with mixed integer programming to optimally place and size surge tanks for transient control. The key features of the algorithm are a model-driven discretization of the search space, a linear approximation nonsmooth system response surface to transients, and a mixed integer linear programming optimization. Results indicate that high quality solutions can be obtained within a reasonable number of function evaluations and demonstrate the computational effectiveness of the approach through two case studies. The work investigates one type of surge control devices (closed surge tank) for a specified set of transient events. The performance of the algorithm relies on the assumption that there exists a smooth relationship between the objective function and tank size. Results indicate the potential of the approach for the optimal surge control design in water systems.
Constrained spacecraft reorientation using mixed integer convex programming
NASA Astrophysics Data System (ADS)
Tam, Margaret; Glenn Lightsey, E.
2016-10-01
A constrained attitude guidance (CAG) system is developed using convex optimization to autonomously achieve spacecraft pointing objectives while meeting the constraints imposed by on-board hardware. These constraints include bounds on the control input and slew rate, as well as pointing constraints imposed by the sensors. The pointing constraints consist of inclusion and exclusion cones that dictate permissible orientations of the spacecraft in order to keep objects in or out of the field of view of the sensors. The optimization scheme drives a body vector towards a target inertial vector along a trajectory that consists solely of permissible orientations in order to achieve the desired attitude for a given mission mode. The non-convex rotational kinematics are handled by discretization, which also ensures that the quaternion stays unity norm. In order to guarantee an admissible path, the pointing constraints are relaxed. Depending on how strict the pointing constraints are, the degree of relaxation is tuneable. The use of binary variables permits the inclusion of logical expressions in the pointing constraints in the case that a set of sensors has redundancies. The resulting mixed integer convex programming (MICP) formulation generates a steering law that can be easily integrated into an attitude determination and control (ADC) system. A sample simulation of the system is performed for the Bevo-2 satellite, including disturbance torques and actuator dynamics which are not modeled by the controller. Simulation results demonstrate the robustness of the system to disturbances while meeting the mission requirements with desirable performance characteristics.
NASA Astrophysics Data System (ADS)
Li, J. C.; Gong, B.; Wang, H. G.
2016-08-01
Optimal development of shale gas fields involves designing a most productive fracturing network for hydraulic stimulation processes and operating wells appropriately throughout the production time. A hydraulic fracturing network design-determining well placement, number of fracturing stages, and fracture lengths-is defined by specifying a set of integer ordered blocks to drill wells and create fractures in a discrete shale gas reservoir model. The well control variables such as bottom hole pressures or production rates for well operations are real valued. Shale gas development problems, therefore, can be mathematically formulated with mixed-integer optimization models. A shale gas reservoir simulator is used to evaluate the production performance for a hydraulic fracturing and well control plan. To find the optimal fracturing design and well operation is challenging because the problem is a mixed integer optimization problem and entails computationally expensive reservoir simulation. A dynamic simplex interpolation-based alternate subspace (DSIAS) search method is applied for mixed integer optimization problems associated with shale gas development projects. The optimization performance is demonstrated with the example case of the development of the Barnett Shale field. The optimization results of DSIAS are compared with those of a pattern search algorithm.
Gorissen, Bram L; den Hertog, Dick; Hoffmann, Aswin L
2013-02-21
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of the existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 s, which confirms earlier results. We propose an iterative procedure for QP that allows us to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model and produces better plans than the non-iterative QP. We formulate a new dose-volume-based model that maximizes V(100%) while satisfying pre-set DVH criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 s and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between the objective value and the clinical plan quality is weak in the existing dose-based models. PMID:23363622
Multi-objective mixed integer strategy for the optimisation of biological networks.
Sendín, J O H; Exler, O; Banga, J R
2010-05-01
In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation. PMID:20500003
Guo, P; Huang, G H
2009-01-01
In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada. PMID:19800164
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
NASA Technical Reports Server (NTRS)
Cheung, Kar-Ming; Lee, Charles H.
2012-01-01
We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.
Mixed Integer Programming and Heuristic Scheduling for Space Communication
NASA Technical Reports Server (NTRS)
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.
Optimization Research of Generation Investment Based on Linear Programming Model
NASA Astrophysics Data System (ADS)
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
Optimal control of polymer flooding based on mixed-integer iterative dynamic programming
NASA Astrophysics Data System (ADS)
Lei, Yang; Li, Shurong; Zhang, Xiaodong; Zhang, Qiang; Guo, Lanlei
2011-11-01
Polymer flooding is one of the most important technologies for enhanced oil recovery. In this article, a mixed-integer optimal control model of distributed parameter systems (DPS) for the injection strategies is established, which involves the performance index as maximum of the profit, the governing equations as the fluid flow equations of polymer flooding and some inequalities constraints, such as polymer concentration and injection amount limitation. The control variables are the volume size, the injection concentration of each slug and the terminal flooding time. For the constant injection rate, the slug size is determined by the integer time stage length, and thus the integer variables are introduced in the DPS. To cope with the optimal control problem (OCP) of this DPS, a mixed-integer iterative dynamic programming incorporating a special truncation procedure to handle integer restrictions on stage lengths is proposed. First, the OCP with variable time stage lengths is transformed into a fixed time stage problem by introducing a normalised time variable. Then, the optimisation procedure is carried out at each stage and preceded backwards in a systematic way. Finally, the numerical results of an example illustrate the effectiveness of the proposed method.
Orbital rendezvous mission planning using mixed integer nonlinear programming
NASA Astrophysics Data System (ADS)
Zhang, Jin; Tang, Guo-jin; Luo, Ya-Zhong; Li, Hai-yang
2011-04-01
The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates single phase parameters and phase connecting parameters simultaneously, is proposed to improve the rendezvous mission's overall performance. The design variables are composed of integers and continuous-valued numbers. The integer part consists of the parameters for station-keeping and sensor-switching, the number of maneuvers in each rendezvous phase and the number of repeating periods to start the rendezvous mission. The continuous part consists of the orbital transfer time and the station-keeping duration. The objective function is a combination of the propellant consumed, the sun angle which represents the power available, and the terminal precision of each rendezvous phase. The operational requirements for the spacecraft-ground communication, sun illumination and the sensor transition are considered. The simple genetic algorithm, which is a combination of the integer-coded and real-coded genetic algorithm, is chosen to obtain the optimal solution. A practical rendezvous mission planning problem is solved by the proposed method. The results show that the method proposed can solve the integral rendezvous mission planning problem effectively, and the solution obtained can satisfy the operational constraints and has a good overall performance.
Linderoth, Jeff T.; Luedtke, James R.
2013-05-30
The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.
A Mixed-Integer Optimization Framework for De Novo Peptide Identification
DiMaggio, Peter A.
2009-01-01
A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. PMID:19412358
A DSN optimal spacecraft scheduling model
NASA Technical Reports Server (NTRS)
Webb, W. A.
1982-01-01
A computer model is described which uses mixed-integer linear programming to provide optimal DSN spacecraft schedules given a mission set and specified scheduling requirements. A solution technique is proposed which uses Bender's Method and a heuristic starting algorithm.
Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming
Bravo, Héctor Corrada; Wright, Stephen; Eng, Kevin H.; Keles, Sündüz; Wahba, Grace
2011-01-01
We present a novel method for estimating tree-structured covariance matrices directly from observed continuous data. Specifically, we estimate a covariance matrix from observations of p continuous random variables encoding a stochastic process over a tree with p leaves. A representation of these classes of matrices as linear combinations of rank-one matrices indicating object partitions is used to formulate estimation as instances of well-studied numerical optimization problems. In particular, our estimates are based on projection, where the covariance estimate is the nearest tree-structured covariance matrix to an observed sample covariance matrix. The problem is posed as a linear or quadratic mixed-integer program (MIP) where a setting of the integer variables in the MIP specifies a set of tree topologies of the structured covariance matrix. We solve these problems to optimality using efficient and robust existing MIP solvers. We present a case study in phylogenetic analysis of gene expression and a simulation study comparing our method to distance-based tree estimating procedures. PMID:22081761
An optimal spacecraft scheduling model for the NASA deep space network
NASA Technical Reports Server (NTRS)
Webb, W. A.
1985-01-01
A computer model is described which uses mixed-integer linear programming to provide optimal DSN spacecraft schedules given a mission set and specified scheduling requirements. A solution technique is proposed which uses Bender's method and a heuristic starting algorithm.
Synchronic interval Gaussian mixed-integer programming for air quality management.
Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong
2015-12-15
To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can
Designing cost-effective biopharmaceutical facilities using mixed-integer optimization.
Liu, Songsong; Simaria, Ana S; Farid, Suzanne S; Papageorgiou, Lazaros G
2013-01-01
Chromatography operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. This becomes even more critical with increasing product titers that result in higher mass loads onto chromatography columns, potentially causing capacity bottlenecks. In this work, a mixed-integer nonlinear programming (MINLP) model was created and applied to an industrially relevant case study to optimize the design of a facility by determining the most cost-effective chromatography equipment sizing strategies for the production of mAbs. Furthermore, the model was extended to evaluate the ability of a fixed facility to cope with higher product titers up to 15 g/L. Examination of the characteristics of the optimal chromatography sizing strategies across different titer values enabled the identification of the maximum titer that the facility could handle using a sequence of single column chromatography steps as well as multi-column steps. The critical titer levels for different ratios of upstream to dowstream trains where multiple parallel columns per step resulted in the removal of facility bottlenecks were identified. Different facility configurations in terms of number of upstream trains were considered and the trade-off between their cost and ability to handle higher titers was analyzed. The case study insights demonstrate that the proposed modeling approach, combining MINLP models with visualization tools, is a valuable decision-support tool for the design of cost-effective facility configurations and to aid facility fit decisions. 2013. PMID:23956206
Mixed-integer programming methods for transportation and power generation problems
NASA Astrophysics Data System (ADS)
Damci Kurt, Pelin
This dissertation conducts theoretical and computational research to solve challenging problems in application areas such as supply chain and power systems. The first part of the dissertation studies a transportation problem with market choice (TPMC) which is a variant of the classical transportation problem in which suppliers with limited capacities have a choice of which demands (markets) to satisfy. We show that TPMC is strongly NP-complete. We consider a version of the problem with a service level constraint on the maximum number of markets that can be rejected and show that if the original problem is polynomial, its cardinality-constrained version is also polynomial. We propose valid inequalities for mixed-integer cover and knapsack sets with variable upper bound constraints, which appear as substructures of TPMC and use them in a branch-and-cut algorithm to solve this problem. The second part of this dissertation studies a unit commitment (UC) problem in which the goal is to minimize the operational cost of power generators over a time period subject to physical constraints while satisfying demand. We provide several exponential classes of multi-period ramping and multi-period variable upper bound inequalities. We prove the strength of these inequalities and describe polynomial-time separation algorithms. Computational results show the effectiveness of the proposed inequalities when used as cuts in a branch-and-cut algorithm to solve the UC problem. The last part of this dissertation investigates the effects of uncertain wind power on the UC problem. A two-stage robust model and a three-stage stochastic program are compared.
NASA Astrophysics Data System (ADS)
Yin, Sisi; Nishi, Tatsushi
2014-11-01
Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.
NASA Astrophysics Data System (ADS)
Wang, Bin; Chiang, Hsiao-Dong
Many applications of smart grid can be formulated as constrained optimization problems. Because of the discrete controls involved in power systems, these problems are essentially mixed-integer nonlinear programs. In this paper, we review the Trust-Tech-based methodology for solving mixed-integer nonlinear optimization. Specifically, we have developed a two-stage Trust-Tech-based methodology to systematically compute all the local optimal solutions for constrained mixed-integer nonlinear programming (MINLP) problems. In the first stage, for a given MINLP problem this methodology starts with the construction of a new, continuous, unconstrained problem through relaxation and the penalty function method. A corresponding dynamical system is then constructed to search for a set of local optimal solutions for the unconstrained problem. In the second stage, a reduced constrained NLP is defined for each local optimal solution by determining and fixing the values of integral variables of the MINLP problem. The Trust-Tech-based method is used to compute a set of local optimal solutions for these reduced NLP problems, from which the optimal solution of the original MINLP problem is determined. A numerical simulation of several testing problems is provided to illustrate the effectiveness of our proposed method.
Comparison of penalty functions on a penalty approach to mixed-integer optimization
NASA Astrophysics Data System (ADS)
Francisco, Rogério B.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2016-06-01
In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the `erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.
Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs
Gade, Dinakar; Hackebeil, Gabriel; Ryan, Sarah M.; Watson, Jean -Paul; Wets, Roger J.-B.; Woodruff, David L.
2016-04-02
We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorithm by using dual prices that are calculated during execution of the standard PHA. In conclusion, we report computational results on stochastic unit commitment and stochastic server location problem instances, and explore the relationship between key PHA parameters and the quality of the resulting lower bounds.
Optimization of a wood dryer kiln using the mixed integer programming technique: A case study
Gustafsson, S.I.
1999-07-01
When wood is to be utilized as a raw material for furniture, buildings, etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination of monitored values from an actual drying kiln it has been possible to optimize the use of steam and electricity using the so called mixed integer programming technique. Owing to the operating schedule for the drying kiln it has been necessary to divide the drying process in very short time intervals, i.e., a number of minutes. Since a drying cycle takes about two or three weeks, a considerable mathematical problem is presented and this has to be solved.
NASA Astrophysics Data System (ADS)
Baran, Ismet; Tutum, Cem C.; Hattel, Jesper H.
2013-08-01
In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.
A Two-Stage Stochastic Mixed-Integer Programming Approach to the Smart House Scheduling Problem
NASA Astrophysics Data System (ADS)
Ozoe, Shunsuke; Tanaka, Yoichi; Fukushima, Masao
A “Smart House” is a highly energy-optimized house equipped with photovoltaic systems (PV systems), electric battery systems, fuel cell cogeneration systems (FC systems), electric vehicles (EVs) and so on. Smart houses are attracting much attention recently thanks to their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC system and an EV. We formulate the problem as a mixed integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method.
Munguia, Lluis-Miquel; Oxberry, Geoffrey; Rajan, Deepak
2016-05-01
Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve furthermore » as more functionality is added in the future.« less
NASA Astrophysics Data System (ADS)
Shoemaker, Christine; Wan, Ying
2016-04-01
Optimization of nonlinear water resources management issues which have a mixture of fixed (e.g. construction cost for a well) and variable (e.g. cost per gallon of water pumped) costs has been not well addressed because prior algorithms for the resulting nonlinear mixed integer problems have required many groundwater simulations (with different configurations of decision variable), especially when the solution space is multimodal. In particular heuristic methods like genetic algorithms have often been used in the water resources area, but they require so many groundwater simulations that only small systems have been solved. Hence there is a need to have a method that reduces the number of expensive groundwater simulations. A recently published algorithm for nonlinear mixed integer programming using surrogates was shown in this study to greatly reduce the computational effort for obtaining accurate answers to problems involving fixed costs for well construction as well as variable costs for pumping because of a substantial reduction in the number of groundwater simulations required to obtain an accurate answer. Results are presented for a US EPA hazardous waste site. The nonlinear mixed integer surrogate algorithm is general and can be used on other problems arising in hydrology with open source codes in Matlab and python ("pySOT" in Bitbucket).
Guo, P.; Huang, G.H.
2010-03-15
In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their
Non-linear Total Energy Optimisation of a Fleet of Power Plants
NASA Astrophysics Data System (ADS)
Nolle, Lars; Biegler-König, Friedrich; Deeskow, Peter
In order to optimise the energy production in a fleet of power plants, it is necessary to solve a mixed integer optimisation problem. Traditionally, the continuous parts of the problem are linearized and a Simplex scheme is applied. Alternatively, heuristic "bionic" optimisation methods can be used without having to linearize the problem. Weare going to demonstrate this approach by modelling power plant blocks with fast Neural Networks and optimising the operation of multi-block power plants over one day with Simulated Annealing.
Linear models: permutation methods
Cade, B.S.
2005-01-01
Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...
Zou, Meng; Zhang, Peng-Jun; Wen, Xin-Yu; Chen, Luonan; Tian, Ya-Ping; Wang, Yong
2015-07-15
Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we propose a novel method, MILP_k, to identify serum-based multi-biomarker panel to distinguish colorectal cancers (CRC) from benign colorectal tumors. Specifically, the multi-biomarker panel detection problem is modeled by a mixed integer programming to maximize the classification accuracy. Then we measured the serum profiling data for 101 CRC patients and 95 benign patients. The 61 biomarkers were analyzed individually and further their combinations by our method. We discovered 4 biomarkers as the optimal small multi-biomarker panel, including known CRC biomarkers CEA and IL-10 as well as novel biomarkers IMA and NSE. This multi-biomarker panel obtains leave-one-out cross-validation (LOOCV) accuracy to 0.7857 by nearest centroid classifier. An independent test of this panel by support vector machine (SVM) with threefold cross validation gets an AUC 0.8438. This greatly improves the predictive accuracy by 20% over the single best biomarker. Further extension of this 4-biomarker panel to a larger 13-biomarker panel improves the LOOCV to 0.8673 with independent AUC 0.8437. Comparison with the exhaustive search method shows that our method dramatically reduces the searching time by 1000-fold. Experiments on the early cancer stage samples reveal two panel of biomarkers and show promising accuracy. The proposed method allows us to select the subset of biomarkers with best accuracy to distinguish case and control samples given the number of selected biomarkers. Both receiver operating characteristic curve and precision-recall curve show our method's consistent performance gain in accuracy. Our method
NASA Astrophysics Data System (ADS)
Purnomo, Muhammad Ridwan Andi; Satrio Wiwoho, Yoga
2016-01-01
Facility layout becomes one of production system factor that should be managed well, as it is designated for the location of production. In managing the layout, designing the layout by considering the optimal layout condition that supports the work condition is essential. One of the method for facility layout optimization is Mixed Integer Programming (MIP). In this study, the MIP is solved using Lingo 9.0 software and considering quantitative and qualitative objectives to be achieved simultaneously: minimizing material handling cost, maximizing closeness rating, and minimizing re-layout cost. The research took place in Rekayasa Wangdi as a make to order company, focusing on the making of concrete brick dough stirring machine with 10 departments involved. The result shows an improvement in the new layout for 333,72 points of objective value compared with the initial layout. As the conclusion, the proposed MIP is proven to be used to model facility layout problem under multi objective consideration for a more realistic look.
Villante, F. L.; Ricci, B.
2010-05-01
We present a new approach to studying the properties of the Sun. We consider small variations of the physical and chemical properties of the Sun with respect to standard solar model predictions and we linearize the structure equations to relate them to the properties of the solar plasma. By assuming that the (variation of) present solar composition can be estimated from the (variation of) nuclear reaction rates and elemental diffusion efficiency in the present Sun, we obtain a linear system of ordinary differential equations which can be used to calculate the response of the Sun to an arbitrary modification of the input parameters (opacity, cross sections, etc.). This new approach is intended to be a complement to the traditional methods for solar model (SM) calculation and allows us to investigate in a more efficient and transparent way the role of parameters and assumptions in SM construction. We verify that these linear solar models recover the predictions of the traditional SMs with a high level of accuracy.
Parameterized Linear Longitudinal Airship Model
NASA Technical Reports Server (NTRS)
Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph
2010-01-01
A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics
Puerto Rico water resources planning model program description
Moody, D.W.; Maddock, Thomas; Karlinger, M.R.; Lloyd, J.J.
1973-01-01
Because the use of the Mathematical Programming System -Extended (MPSX) to solve large linear and mixed integer programs requires the preparation of many input data cards, a matrix generator program to produce the MPSX input data from a much more limited set of data may expedite the use of the mixed integer programming optimization technique. The Model Definition and Control Program (MODCQP) is intended to assist a planner in preparing MPSX input data for the Puerto Rico Water Resources Planning Model. The model utilizes a mixed-integer mathematical program to identify a minimum present cost set of water resources projects (diversions, reservoirs, ground-water fields, desalinization plants, water treatment plants, and inter-basin transfers of water) which will meet a set of future water demands and to determine their sequence of construction. While MODCOP was specifically written to generate MPSX input data for the planning model described in this report, the program can be easily modified to reflect changes in the model's mathematical structure.
LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL
NASA Technical Reports Server (NTRS)
Duke, E. L.
1994-01-01
The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of
NASA Astrophysics Data System (ADS)
Rashid, Kashif; Ambani, Saumil; Cetinkaya, Eren
2013-02-01
Many real-world optimization problems comprise objective functions that are based on the output of one or more simulation models. As these underlying processes can be time and computation intensive, the objective function is deemed expensive to evaluate. While methods to alleviate this cost in the optimization procedure have been explored previously, less attention has been given to the treatment of expensive constraints. This article presents a methodology for treating expensive simulation-based nonlinear constraints alongside an expensive simulation-based objective function using adaptive radial basis function techniques. Specifically, a multiquadric radial basis function approximation scheme is developed, together with a robust training method, to model not only the costly objective function, but also each expensive simulation-based constraint defined in the problem. The article presents the methodology developed for expensive nonlinear constrained optimization problems comprising both continuous and integer variables. Results from various test cases, both analytical and simulation-based, are presented.
Computing Linear Mathematical Models Of Aircraft
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1991-01-01
Derivation and Definition of Linear Aircraft Model (LINEAR) computer program provides user with powerful, and flexible, standard, documented, and verified software tool for linearization of mathematical models of aerodynamics of aircraft. Intended for use in software tool to drive linear analysis of stability and design of control laws for aircraft. Capable of both extracting such linearized engine effects as net thrust, torque, and gyroscopic effects, and including these effects in linear model of system. Designed to provide easy selection of state, control, and observation variables used in particular model. Also provides flexibility of allowing alternate formulations of both state and observation equations. Written in FORTRAN.
Baliban, Richard C; Dimaggio, Peter A; Plazas-Mayorca, Mariana D; Garcia, Benjamin A; Floudas, Christodoulos A
2012-09-01
A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu. PMID:22788846
Nonlinear Modeling by Assembling Piecewise Linear Models
NASA Technical Reports Server (NTRS)
Yao, Weigang; Liou, Meng-Sing
2013-01-01
To preserve nonlinearity of a full order system over a parameters range of interest, we propose a simple modeling approach by assembling a set of piecewise local solutions, including the first-order Taylor series terms expanded about some sampling states. The work by Rewienski and White inspired our use of piecewise linear local solutions. The assembly of these local approximations is accomplished by assigning nonlinear weights, through radial basis functions in this study. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving at different Mach numbers and pitching motions, under which the flow exhibits prominent nonlinear behaviors. All results confirm that our nonlinear model is accurate and stable for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robustness-accurate for inputs considerably different from the base trajectory in form and magnitude. This modeling preserves nonlinearity of the problems considered in a rather simple and accurate manner.
Generalized Linear Models in Family Studies
ERIC Educational Resources Information Center
Wu, Zheng
2005-01-01
Generalized linear models (GLMs), as defined by J. A. Nelder and R. W. M. Wedderburn (1972), unify a class of regression models for categorical, discrete, and continuous response variables. As an extension of classical linear models, GLMs provide a common body of theory and methodology for some seemingly unrelated models and procedures, such as…
Linear Logistic Test Modeling with R
ERIC Educational Resources Information Center
Baghaei, Purya; Kubinger, Klaus D.
2015-01-01
The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…
Composite Linear Models | Division of Cancer Prevention
By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty examples from the literature. |
Investigating data envelopment analysis model with potential improvement for integer output values
NASA Astrophysics Data System (ADS)
Hussain, Mushtaq Taleb; Ramli, Razamin; Khalid, Ruzelan
2015-12-01
The decrement of input proportions in DEA model is associated with its input reduction. This reduction is apparently good for economy since it could reduce unnecessary cost resources. However, in some situations the reduction of relevant inputs such as labour could create social problems. Such inputs should thus be maintained or increased. This paper develops an advanced radial DEA model dealing with mixed integer linear programming to improve integer output values through the combination of inputs. The model can deal with real input values and integer output values. This model is valuable for situations dealing with input combination to improve integer output values as faced by most organizations.
Non-linear Models for Longitudinal Data
Serroyen, Jan; Molenberghs, Geert; Verbeke, Geert; Davidian, Marie
2009-01-01
While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees. PMID:20160890
A MILP-Based Distribution Optimal Power Flow Model for Microgrid Operation
Liu, Guodong; Starke, Michael R; Zhang, Xiaohu; Tomsovic, Kevin
2016-01-01
This paper proposes a distribution optimal power flow (D-OPF) model for the operation of microgrids. The proposed model minimizes not only the operating cost, including fuel cost, purchasing cost and demand charge, but also several performance indices, including voltage deviation, network power loss and power factor. It co-optimizes the real and reactive power form distributed generators (DGs) and batteries considering their capacity and power factor limits. The D-OPF is formulated as a mixed-integer linear programming (MILP). Numerical simulation results show the effectiveness of the proposed model.
Menu-Driven Solver Of Linear-Programming Problems
NASA Technical Reports Server (NTRS)
Viterna, L. A.; Ferencz, D.
1992-01-01
Program assists inexperienced user in formulating linear-programming problems. A Linear Program Solver (ALPS) computer program is full-featured LP analysis program. Solves plain linear-programming problems as well as more-complicated mixed-integer and pure-integer programs. Also contains efficient technique for solution of purely binary linear-programming problems. Written entirely in IBM's APL2/PC software, Version 1.01. Packed program contains licensed material, property of IBM (copyright 1988, all rights reserved).
Aircraft engine mathematical model - linear system approach
NASA Astrophysics Data System (ADS)
Rotaru, Constantin; Roateşi, Simona; Cîrciu, Ionicǎ
2016-06-01
This paper examines a simplified mathematical model of the aircraft engine, based on the theory of linear and nonlinear systems. The dynamics of the engine was represented by a linear, time variant model, near a nominal operating point within a finite time interval. The linearized equations were expressed in a matrix form, suitable for the incorporation in the MAPLE program solver. The behavior of the engine was included in terms of variation of the rotational speed following a deflection of the throttle. The engine inlet parameters can cover a wide range of altitude and Mach numbers.
On Estimation of Partially Linear Transformation Models.
Lu, Wenbin; Zhang, Hao Helen
2010-06-01
We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. PMID:20802823
On Estimation of Partially Linear Transformation Models
Lu, Wenbin; Zhang, Hao Helen
2010-01-01
We study a general class of partially linear transformation models, which extend linear transformation models by incorporating nonlinear covariate effects in survival data analysis. A new martingale-based estimating equation approach, consisting of both global and kernel-weighted local estimation equations, is developed for estimating the parametric and nonparametric covariate effects in a unified manner. We show that with a proper choice of the kernel bandwidth parameter, one can obtain the consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established as well. We further suggest a simple resampling method to estimate the asymptotic variance of the linear estimates and show its effectiveness. To facilitate the implementation of the new procedure, an iterative algorithm is developed. Numerical examples are given to illustrate the finite-sample performance of the procedure. PMID:20802823
Numerical Based Linear Model for Dipole Magnets
Li,Y.; Krinsky, S.; Rehak, M.
2009-05-04
In this paper, we discuss an algorithm for constructing a numerical linear optics model for dipole magnets from a 3D field map. The difference between the numerical model and K. Brown's analytic approach is investigated and clarified. It was found that the optics distortion due to the dipoles' fringe focusing must be properly taken into account to accurately determine the chromaticities. In NSLS-II, there are normal dipoles with 35-mm gap and dipoles for infrared sources with 90-mm gap. This linear model of the dipole magnets is applied to the NSLS-II lattice design to match optics parameters between the DBA cells having dipoles with different gaps.
Congeneric Models and Levine's Linear Equating Procedures.
ERIC Educational Resources Information Center
Brennan, Robert L.
In 1955, R. Levine introduced two linear equating procedures for the common-item non-equivalent populations design. His procedures make the same assumptions about true scores; they differ in terms of the nature of the equating function used. In this paper, two parameterizations of a classical congeneric model are introduced to model the variables…
ERIC Educational Resources Information Center
Ker, H. W.
2014-01-01
Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…
Orthogonal nilpotent superfields from linear models
NASA Astrophysics Data System (ADS)
Kallosh, Renata; Karlsson, Anna; Mosk, Benjamin; Murli, Divyanshu
2016-05-01
We derive supersymmetry/supergravity models with constrained orthogonal nilpotent superfields from the linear models in the formal limit where the masses of the sgoldstino, inflatino and sinflaton tend to infinity. The case where the sinflaton mass remains finite leads to a model with a `relaxed' constraint, where the sinflaton remains an independent field. Our procedure is equivalent to a requirement that some of the components of the curvature of the moduli space tend to infinity.
Managing Clustered Data Using Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Linear Mixed Models: Gum and Beyond
NASA Astrophysics Data System (ADS)
Arendacká, Barbora; Täubner, Angelika; Eichstädt, Sascha; Bruns, Thomas; Elster, Clemens
2014-04-01
In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.
NASA Technical Reports Server (NTRS)
Ferencz, Donald C.; Viterna, Larry A.
1991-01-01
ALPS is a computer program which can be used to solve general linear program (optimization) problems. ALPS was designed for those who have minimal linear programming (LP) knowledge and features a menu-driven scheme to guide the user through the process of creating and solving LP formulations. Once created, the problems can be edited and stored in standard DOS ASCII files to provide portability to various word processors or even other linear programming packages. Unlike many math-oriented LP solvers, ALPS contains an LP parser that reads through the LP formulation and reports several types of errors to the user. ALPS provides a large amount of solution data which is often useful in problem solving. In addition to pure linear programs, ALPS can solve for integer, mixed integer, and binary type problems. Pure linear programs are solved with the revised simplex method. Integer or mixed integer programs are solved initially with the revised simplex, and the completed using the branch-and-bound technique. Binary programs are solved with the method of implicit enumeration. This manual describes how to use ALPS to create, edit, and solve linear programming problems. Instructions for installing ALPS on a PC compatible computer are included in the appendices along with a general introduction to linear programming. A programmers guide is also included for assistance in modifying and maintaining the program.
Inverse Modeling Via Linearized Functional Minimization
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Wohlberg, B.; Vesselinov, V. V.; Tartakovsky, D. M.
2014-12-01
We present a novel parameter estimation methodology for transient models of geophysical systems with uncertain, spatially distributed, heterogeneous and piece-wise continuous parameters.The methodology employs a bayesian approach to propose an inverse modeling problem for the spatial configuration of the model parameters.The likelihood of the configuration is formulated using sparse measurements of both model parameters and transient states.We propose using total variation regularization (TV) as the prior reflecting the heterogeneous, piece-wise continuity assumption on the parameter distribution.The maximum a posteriori (MAP) estimator of the parameter configuration is then computed by minimizing the negative bayesian log-posterior using a linearized functional minimization approach. The computation of the MAP estimator is a large-dimensional nonlinear minimization problem with two sources of nonlinearity: (1) the TV operator, and (2) the nonlinear relation between states and parameters provided by the model's governing equations.We propose a a hybrid linearized functional minimization (LFM) algorithm in two stages to efficiently treat both sources of nonlinearity.The relation between states and parameters is linearized, resulting in a linear minimization sub-problem equipped with the TV operator; this sub-problem is then minimized using the Alternating Direction Method of Multipliers (ADMM). The methodology is illustrated with a transient saturated groundwater flow application in a synthetic domain, stimulated by external point-wise loadings representing aquifer pumping, together with an array of discrete measurements of hydraulic conductivity and transient measurements of hydraulic head.We show that our inversion strategy is able to recover the overall large-scale features of the parameter configuration, and that the reconstruction is improved by the addition of transient information of the state variable.
User's manual for LINEAR, a FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Patterson, Brian P.; Antoniewicz, Robert F.
1987-01-01
This report documents a FORTRAN program that provides a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
Linear transport models for adsorbing solutes
NASA Astrophysics Data System (ADS)
Roth, K.; Jury, W. A.
1993-04-01
A unified linear theory for the transport of adsorbing solutes through soils is presented and applied to analyze movement of napropamide through undisturbed soil columns. The transport characteristics of the soil are expressed in terms of the travel time distribution of the mobile phase which is then used to incorporate local interaction processes. This approach permits the analysis of all linear transport processes, not only the small subset for which a differential description is known. From a practical point of view, it allows the direct use of measured concentrations or fluxes of conservative solutes to characterize the mobile phase without first subjecting them to any model. For complicated flow regimes, this may vastly improve the identification of models and estimation of their parameters for the local adsorption processes.
Nonlinear damping and quasi-linear modelling.
Elliott, S J; Ghandchi Tehrani, M; Langley, R S
2015-09-28
The mechanism of energy dissipation in mechanical systems is often nonlinear. Even though there may be other forms of nonlinearity in the dynamics, nonlinear damping is the dominant source of nonlinearity in a number of practical systems. The analysis of such systems is simplified by the fact that they show no jump or bifurcation behaviour, and indeed can often be well represented by an equivalent linear system, whose damping parameters depend on the form and amplitude of the excitation, in a 'quasi-linear' model. The diverse sources of nonlinear damping are first reviewed in this paper, before some example systems are analysed, initially for sinusoidal and then for random excitation. For simplicity, it is assumed that the system is stable and that the nonlinear damping force depends on the nth power of the velocity. For sinusoidal excitation, it is shown that the response is often also almost sinusoidal, and methods for calculating the amplitude are described based on the harmonic balance method, which is closely related to the describing function method used in control engineering. For random excitation, several methods of analysis are shown to be equivalent. In general, iterative methods need to be used to calculate the equivalent linear damper, since its value depends on the system's response, which itself depends on the value of the equivalent linear damper. The power dissipation of the equivalent linear damper, for both sinusoidal and random cases, matches that dissipated by the nonlinear damper, providing both a firm theoretical basis for this modelling approach and clear physical insight. Finally, practical examples of nonlinear damping are discussed: in microspeakers, vibration isolation, energy harvesting and the mechanical response of the cochlea. PMID:26303921
B-737 Linear Autoland Simulink Model
NASA Technical Reports Server (NTRS)
Belcastro, Celeste (Technical Monitor); Hogge, Edward F.
2004-01-01
The Linear Autoland Simulink model was created to be a modular test environment for testing of control system components in commercial aircraft. The input variables, physical laws, and referenced frames used are summarized. The state space theory underlying the model is surveyed and the location of the control actuators described. The equations used to realize the Dryden gust model to simulate winds and gusts are derived. A description of the pseudo-random number generation method used in the wind gust model is included. The longitudinal autopilot, lateral autopilot, automatic throttle autopilot, engine model and automatic trim devices are considered as subsystems. The experience in converting the Airlabs FORTRAN aircraft control system simulation to a graphical simulation tool (Matlab/Simulink) is described.
Reduced Order ODE Model for Linear Contrails
NASA Astrophysics Data System (ADS)
Inamdar, A. R.; Lele, S. K.; Jacobson, M. Z.
2015-12-01
It is widely acknowledged that the large uncertainties in predictions of climate impact of linear contrails stem from inadequate parametrization of contrails in GCMs. But, the parameter space on which contrail dynamics and optical properties depend is very large and spanning it using high fidelity LES is prohibitively expensive. This study leverages the large dataset of LES done so far to understand the most important physical process that governs the evolution of contrails in different stages of its life and proposes a simple, low-cost and robust ODE model to capture the evolution of quantities of interest such as ice mass, vortex downwash and contrail cross-sectional dimensions. A direct consequence of modeling the contrail using parameters impacting the most important physical process is the reduction of the original parameter space to only those groupings of parameters that impact linear contrails independently. We are able to capture the most prominent features of the contrail at every stage of the life of the contrail - the induction of the jet exhaust by the trailing vortex pair, the vortex downwash and eventual destruction and the subsequent spreading of the contrail by ambient turbulence. A simplified version of GATOR-GCMOM - a GCM - is initialized using inputs from the new ODE model to test if the inclusion of the impact of the aforementioned parameter groups has significant persistent effects. Results from the GATOR-GCMOM box model calculations show which parameter groupings show persistent effects.
User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.
1988-01-01
An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
Estimating population trends with a linear model
Bart, J.; Collins, B.; Morrison, R.I.G.
2003-01-01
We describe a simple and robust method for estimating trends in population size. The method may be used with Breeding Bird Survey data, aerial surveys, point counts, or any other program of repeated surveys at permanent locations. Surveys need not be made at each location during each survey period. The method differs from most existing methods in being design based, rather than model based. The only assumptions are that the nominal sampling plan is followed and that sample size is large enough for use of the t-distribution. Simulations based on two bird data sets from natural populations showed that the point estimate produced by the linear model was essentially unbiased even when counts varied substantially and 25% of the complete data set was missing. The estimating-equation approach, often used to analyze Breeding Bird Survey data, performed similarly on one data set but had substantial bias on the second data set, in which counts were highly variable. The advantages of the linear model are its simplicity, flexibility, and that it is self-weighting. A user-friendly computer program to carry out the calculations is available from the senior author.
Linear modelling of attentional resource allocation
NASA Technical Reports Server (NTRS)
Pierce, B.
1978-01-01
Eight subjects time-shared performance of two compensatory tracking tasks under conditions when both were of constant difficulty, and when the control order of one task (designated primary) was varied over time within a trial. On line performance feedback was presented on half of the trials. The data are interpreted in terms of a linear model of the operator's attention allocation system, and suggest that this allocation is strongly suboptimal. Furthermore, the limitations in reallocating attentional resources between tasks, in response to difficulty fluctuations were not reduced by augmented performance feedback. Some characteristics of the allocation system are described, and reasons for its limitations suggested.
Wealth redistribution in conservative linear kinetic models
NASA Astrophysics Data System (ADS)
Toscani, G.
2009-10-01
We introduce and discuss kinetic models for wealth distribution which include both taxation and uniform redistribution. The evolution of the continuous density of wealth obeys a linear Boltzmann equation where the background density represents the action of an external subject on the taxation mechanism. The case in which the mean wealth is conserved is analyzed in full details, by recovering the analytical form of the steady states. These states are probability distributions of convergent random series of a special structure, called perpetuities. Among others, Gibbs distribution appears as steady state in case of total taxation and uniform redistribution.
The Piece Wise Linear Reactive Flow Model
Vitello, P; Souers, P C
2005-08-18
For non-ideal explosives a wide range of behavior is observed in experiments dealing with differing sizes and geometries. A predictive detonation model must be able to reproduce many phenomena including such effects as: variations in the detonation velocity with the radial diameter of rate sticks; slowing of the detonation velocity around gentle corners; production of dead zones for abrupt corner turning; failure of small diameter rate sticks; and failure for rate sticks with sufficiently wide cracks. Most models have been developed to explain one effect at a time. Often, changes are made in the input parameters used to fit each succeeding case with the implication that this is sufficient for the model to be valid over differing regimes. We feel that it is important to develop a model that is able to fit experiments with one set of parameters. To address this we are creating a new generation of models that are able to produce better fitting to individual data sets than prior models and to simultaneous fit distinctly different regimes of experiments. Presented here are details of our new Piece Wise Linear reactive flow model applied to LX-17.
Model Selection with the Linear Mixed Model for Longitudinal Data
ERIC Educational Resources Information Center
Ryoo, Ji Hoon
2011-01-01
Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…
Information metric from a linear sigma model.
Miyamoto, U; Yahikozawa, S
2012-05-01
The idea that a space-time metric emerges as a Fisher-Rao "information metric" of instanton moduli space has been examined in several field theories, such as the Yang-Mills theories and nonlinear σ models. In this paper, we report that the flat Euclidean or Minkowskian metric, rather than an anti-de Sitter metric that generically emerges from instanton moduli spaces, can be obtained as the Fisher-Rao metric from a nontrivial solution of the massive Klein-Gordon field (a linear σ model). This realization of the flat space from the simple field theory would be useful to investigate the ideas that relate the space-time geometry with the information geometry. PMID:23004729
Modeling patterns in data using linear and related models
Engelhardt, M.E.
1996-06-01
This report considers the use of linear models for analyzing data related to reliability and safety issues of the type usually associated with nuclear power plants. The report discusses some of the general results of linear regression analysis, such as the model assumptions and properties of the estimators of the parameters. The results are motivated with examples of operational data. Results about the important case of a linear regression model with one covariate are covered in detail. This case includes analysis of time trends. The analysis is applied with two different sets of time trend data. Diagnostic procedures and tests for the adequacy of the model are discussed. Some related methods such as weighted regression and nonlinear models are also considered. A discussion of the general linear model is also included. Appendix A gives some basic SAS programs and outputs for some of the analyses discussed in the body of the report. Appendix B is a review of some of the matrix theoretic results which are useful in the development of linear models.
Averaging models for linear piezostructural systems
NASA Astrophysics Data System (ADS)
Kim, W.; Kurdila, A. J.; Stepanyan, V.; Inman, D. J.; Vignola, J.
2009-03-01
In this paper, we consider a linear piezoelectric structure which employs a fast-switched, capacitively shunted subsystem to yield a tunable vibration absorber or energy harvester. The dynamics of the system is modeled as a hybrid system, where the switching law is considered as a control input and the ambient vibration is regarded as an external disturbance. It is shown that under mild assumptions of existence and uniqueness of the solution of this hybrid system, averaging theory can be applied, provided that the original system dynamics is periodic. The resulting averaged system is controlled by the duty cycle of a driven pulse-width modulated signal. The response of the averaged system approximates the performance of the original fast-switched linear piezoelectric system. It is analytically shown that the averaging approximation can be used to predict the electromechanically coupled system modal response as a function of the duty cycle of the input switching signal. This prediction is experimentally validated for the system consisting of a piezoelectric bimorph connected to an electromagnetic exciter. Experimental results show that the analytical predictions are observed in practice over a fixed "effective range" of switching frequencies. The same experiments show that the response of the switched system is insensitive to an increase in switching frequency above the effective frequency range.
NASA Astrophysics Data System (ADS)
Bostan, Mohamad; Hadi Afshar, Mohamad; Khadem, Majed
2015-04-01
This article proposes a hybrid linear programming (LP-LP) methodology for the simultaneous optimal design and operation of groundwater utilization systems. The proposed model is an extension of an earlier LP-LP model proposed by the authors for the optimal operation of a set of existing wells. The proposed model can be used to optimally determine the number, configuration and pumping rates of the operational wells out of potential wells with fixed locations to minimize the total cost of utilizing a two-dimensional confined aquifer under steady-state flow conditions. The model is able to take into account the well installation, piping and pump installation costs in addition to the operational costs, including the cost of energy and maintenance. The solution to the problem is defined by well locations and their pumping rates, minimizing the total cost while satisfying a downstream demand, lower/upper bound on the pumping rates, and lower/upper bound on the water level drawdown at the wells. A discretized version of the differential equation governing the flow is first embedded into the model formulation as a set of additional constraints. The resulting mixed-integer highly constrained nonlinear optimization problem is then decomposed into two subproblems with different sets of decision variables, one with a piezometric head and the other with the operational well locations and the corresponding pumping rates. The binary variables representing the well locations are approximated by a continuous variable leading to two LP subproblems. Having started with a random value for all decision variables, the two subproblems are solved iteratively until convergence is achieved. The performance and ability of the proposed method are tested against a hypothetical problem from the literature and the results are presented and compared with those obtained using a mixed-integer nonlinear programming method. The results show the efficiency and effectiveness of the proposed method for
Numerical linearized MHD model of flapping oscillations
NASA Astrophysics Data System (ADS)
Korovinskiy, D. B.; Ivanov, I. B.; Semenov, V. S.; Erkaev, N. V.; Kiehas, S. A.
2016-06-01
Kink-like magnetotail flapping oscillations in a Harris-like current sheet with earthward growing normal magnetic field component Bz are studied by means of time-dependent 2D linearized MHD numerical simulations. The dispersion relation and two-dimensional eigenfunctions are obtained. The results are compared with analytical estimates of the double-gradient model, which are found to be reliable for configurations with small Bz up to values ˜ 0.05 of the lobe magnetic field. Coupled with previous results, present simulations confirm that the earthward/tailward growth direction of the Bz component acts as a switch between stable/unstable regimes of the flapping mode, while the mode dispersion curve is the same in both cases. It is confirmed that flapping oscillations may be triggered by a simple Gaussian initial perturbation of the Vz velocity.
Testing Linear Models for Ability Parameters in Item Response Models
ERIC Educational Resources Information Center
Glas, Cees A. W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum likelihood framework. They are explicitly formulated…
ERIC Educational Resources Information Center
Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.
2009-01-01
This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
ERIC Educational Resources Information Center
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
Non linear behaviour of cell tensegrity models
NASA Astrophysics Data System (ADS)
Alippi, A.; Bettucci, A.; Biagioni, A.; Conclusio, D.; D'Orazio, A.; Germano, M.; Passeri, D.
2012-05-01
Tensegrity models for the cytoskeleton structure of living cells is largely used nowadays for interpreting the biochemical response of living tissues to mechanical stresses. Microtubules, microfilaments and filaments are the microscopic cell counterparts of struts (microtubules) and cables (microfilaments and filaments) in the macroscopic world: the formers oppose to compression, the latters to tension, thus yielding an overall structure, light and highly deformable. Specific cell surface receptors, such as integrins, act as the coupling elements that transmit the outside mechanical stress state into the cell body. Reversible finite deformations of tensegrity structures have been widely demonstrated experimentally and in a number of living cell simulations. In the present paper, the bistability behaviour of two general models, the linear bar oscillator and the icosahedron, is studied, as they are both obtained from mathematical simulation, the former, and from larger scale experiments, the latter. The discontinuity in the frequency response of the oscillation amplitude and the lateral bending of the resonance curves are put in evidence, as it grows larger as the driving amplitude increases, respectively.
Linearized Functional Minimization for Inverse Modeling
Wohlberg, Brendt; Tartakovsky, Daniel M.; Dentz, Marco
2012-06-21
Heterogeneous aquifers typically consist of multiple lithofacies, whose spatial arrangement significantly affects flow and transport. The estimation of these lithofacies is complicated by the scarcity of data and by the lack of a clear correlation between identifiable geologic indicators and attributes. We introduce a new inverse-modeling approach to estimate both the spatial extent of hydrofacies and their properties from sparse measurements of hydraulic conductivity and hydraulic head. Our approach is to minimize a functional defined on the vectors of values of hydraulic conductivity and hydraulic head fields defined on regular grids at a user-determined resolution. This functional is constructed to (i) enforce the relationship between conductivity and heads provided by the groundwater flow equation, (ii) penalize deviations of the reconstructed fields from measurements where they are available, and (iii) penalize reconstructed fields that are not piece-wise smooth. We develop an iterative solver for this functional that exploits a local linearization of the mapping from conductivity to head. This approach provides a computationally efficient algorithm that rapidly converges to a solution. A series of numerical experiments demonstrates the robustness of our approach.
Guo, P; Huang, G H
2009-01-01
In this study, a solid waste decision-support system was developed for the long-term planning of waste management in the City of Regina, Canada. Interactions among various system components, objectives, and constraints will be analyzed. Issues concerning planning for cost-effective diversion and prolongation of the landfill will be addressed. Decisions of system-capacity expansion and waste allocation within a multi-facility, multi-option, and multi-period context will be obtained. The obtained results would provide useful information and decision-support for the City's solid waste management and planning. In the application, four scenarios are considered. Through the above scenario analyses under different waste-management policies, useful decision support for the City's solid waste managers and decision makers was generated. Analyses for the effects of varied policies (for allowable waste flows to different facilities) under 35 and 50% diversion goals were also undertaken. Tradeoffs among system cost and constraint-violation risk were analyzed. Generally, a policy with lower allowable waste-flow levels corresponded to a lower system cost under advantageous conditions but, at the same time, a higher penalty when such allowances were violated. A policy with higher allowable flow levels corresponded to a higher cost under disadvantageous conditions. The modeling results were useful for (i) scheduling adequate time and capacity for long-term planning of the facility development and/or expansion in the city's waste management system, (ii) adjusting of the existing waste flow allocation patterns to satisfy the city's diversion goal, and (iii) generating of desired policies for managing the city's waste generation, collection and disposal. PMID:19818549
Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models.
Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad
2016-02-01
In the current research, the muscle equivalent linear damping coefficient which is introduced as the force-velocity relation in a muscle model and the corresponding time constant are investigated. In order to reach this goal, a 1D skeletal muscle model was used. Two characterizations of this model using a linear force-stiffness relationship (Hill-type model) and a nonlinear one have been implemented. The OpenSim platform was used for verification of the model. The isometric activation has been used for the simulation. The equivalent linear damping and the time constant of each model were extracted by using the results obtained from the simulation. The results provide a better insight into the characteristics of each model. It is found that the nonlinear models had a response rate closer to the reality compared to the Hill-type models. PMID:26837750
Recent Updates to the GEOS-5 Linear Model
NASA Technical Reports Server (NTRS)
Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul
2014-01-01
Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.
Rakheja, S; Gurram, R; Gouw, G J
1993-10-01
Hand-arm vibration (HAV) models serve as an effective tool to assess the vibration characteristics of the hand-tool system and to evaluate the attenuation performance of vibration isolation mechanisms. This paper describes a methodology to identify the parameters of HAV models, whether linear or nonlinear, using mechanical impedance data and a nonlinear programming based optimization technique. Three- and four-degrees-of-freedom (DOF) linear, piecewise linear and nonlinear HAV models are formulated and analyzed to yield impedance characteristics in the 5-1000 Hz frequency range. A local equivalent linearization algorithm, based upon the principle of energy similarity, is implemented to simulate the nonlinear HAV models. Optimization methods are employed to identify the model parameters, such that the magnitude and phase errors between the computed and measured impedance characteristics are minimum in the entire frequency range. The effectiveness of the proposed method is demonstrated through derivations of models that correlate with the measured X-axis impedance characteristics of the hand-arm system, proposed by ISO. The results of the study show that a linear model cannot predict the impedance characteristics in the entire frequency range, while a piecewise linear model yields an accurate estimation. PMID:8253830
Linear and Nonlinear Models of Agenda Setting in Television.
ERIC Educational Resources Information Center
Brosius, Hans-Bernd; Kepplinger, Hans Mathias
1992-01-01
A content analysis of major German television news shows and 53 weekly surveys on 16 issues were used to compare linear and nonlinear models as ways to describe the relationship between media coverage and the public agenda. Results indicate that nonlinear models are in some cases superior to linear models in terms of explained variance. (34…
Tried and True: Springing into Linear Models
ERIC Educational Resources Information Center
Darling, Gerald
2012-01-01
In eighth grade, students usually learn about forces in science class and linear relationships in math class, crucial topics that form the foundation for further study in science and engineering. An activity that links these two fundamental concepts involves measuring the distance a spring stretches as a function of how much weight is suspended…
Three-Dimensional Modeling in Linear Regression.
ERIC Educational Resources Information Center
Herman, James D.
Linear regression examines the relationship between one or more independent (predictor) variables and a dependent variable. By using a particular formula, regression determines the weights needed to minimize the error term for a given set of predictors. With one predictor variable, the relationship between the predictor and the dependent variable…
An analytically linearized helicopter model with improved modeling accuracy
NASA Technical Reports Server (NTRS)
Jensen, Patrick T.; Curtiss, H. C., Jr.; Mckillip, Robert M., Jr.
1991-01-01
An analytically linearized model for helicopter flight response including rotor blade dynamics and dynamic inflow, that was recently developed, was studied with the objective of increasing the understanding, the ease of use, and the accuracy of the model. The mathematical model is described along with a description of the UH-60A Black Hawk helicopter and flight test used to validate the model. To aid in utilization of the model for sensitivity analysis, a new, faster, and more efficient implementation of the model was developed. It is shown that several errors in the mathematical modeling of the system caused a reduction in accuracy. These errors in rotor force resolution, trim force and moment calculation, and rotor inertia terms were corrected along with improvements to the programming style and documentation. Use of a trim input file to drive the model is examined. Trim file errors in blade twist, control input phase angle, coning and lag angles, main and tail rotor pitch, and uniform induced velocity, were corrected. Finally, through direct comparison of the original and corrected model responses to flight test data, the effect of the corrections on overall model output is shown.
Development of a Linear Stirling Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC s non-linear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
Descriptive Linear modeling of steady-state visual evoked response
NASA Technical Reports Server (NTRS)
Levison, W. H.; Junker, A. M.; Kenner, K.
1986-01-01
A study is being conducted to explore use of the steady state visual-evoke electrocortical response as an indicator of cognitive task loading. Application of linear descriptive modeling to steady state Visual Evoked Response (VER) data is summarized. Two aspects of linear modeling are reviewed: (1) unwrapping the phase-shift portion of the frequency response, and (2) parsimonious characterization of task-loading effects in terms of changes in model parameters. Model-based phase unwrapping appears to be most reliable in applications, such as manual control, where theoretical models are available. Linear descriptive modeling of the VER has not yet been shown to provide consistent and readily interpretable results.
NASA Astrophysics Data System (ADS)
Moraga, RocíO.; GarcíA-GonzáLez, Javier; Parrilla, Ernesto; Nogales, Sergio
2007-04-01
In a competitive environment, operation and planning decisions of generating units are decentralized. Therefore the management of hydroelectric generation resources requires the development of advanced planning and scheduling tools adapted to the particular needs of each company. This paper presents a method for considering natural water transfers through a pipeline in the context of a midterm hydro scheduling model. The main complexity of gravitational transfer modeling resides in considering the nonlinear relation between the water levels in the connected reservoirs and the transfer flow. The methodology proposed consists first in simplifying the problem by means of a change of variables, subsequently using a piecewise linear approximation of the transfer flow equation in order to consider it within a mixed integer linear programming tool, and ultimately adjusting the final solution. The proposed methodology is currently being used to manage the Sil River hydro basin in the northwest of Spain, with satisfactory results, as shown in the case study.
Analysis of Modeling Assumptions used in Production Cost Models for Renewable Integration Studies
Stoll, Brady; Brinkman, Gregory; Townsend, Aaron; Bloom, Aaron
2016-01-01
Renewable energy integration studies have been published for many different regions exploring the question of how higher penetration of renewable energy will impact the electric grid. These studies each make assumptions about the systems they are analyzing; however the effect of many of these assumptions has not been yet been examined and published. In this paper we analyze the impact of modeling assumptions in renewable integration studies, including the optimization method used (linear or mixed-integer programming) and the temporal resolution of the dispatch stage (hourly or sub-hourly). We analyze each of these assumptions on a large and a small system and determine the impact of each assumption on key metrics including the total production cost, curtailment of renewables, CO2 emissions, and generator starts and ramps. Additionally, we identified the impact on these metrics if a four-hour ahead commitment step is included before the dispatch step and the impact of retiring generators to reduce the degree to which the system is overbuilt. We find that the largest effect of these assumptions is at the unit level on starts and ramps, particularly for the temporal resolution, and saw a smaller impact at the aggregate level on system costs and emissions. For each fossil fuel generator type we measured the average capacity started, average run-time per start, and average number of ramps. Linear programming results saw up to a 20% difference in number of starts and average run time of traditional generators, and up to a 4% difference in the number of ramps, when compared to mixed-integer programming. Utilizing hourly dispatch instead of sub-hourly dispatch saw no difference in coal or gas CC units for either start metric, while gas CT units had a 5% increase in the number of starts and 2% increase in the average on-time per start. The number of ramps decreased up to 44%. The smallest effect seen was on the CO2 emissions and total production cost, with a 0.8% and 0
Topology changing transitions in supersymmetric linear σ-models
NASA Astrophysics Data System (ADS)
Ryang, Shijong
1995-02-01
We analyze the two-dimensional supersymmetric linear σ-model with U(1) gauge symmetries that includes a Calabi-Yau phase and a possible Landau-Ginzburg phase. We demonstrate the topology changing transitions among the generic vacua of various linear σ-models. In the supersymmetric transition the determinantal contraction naturally arises.
Response of a rotorcraft model with damping non-linearities
NASA Astrophysics Data System (ADS)
Tongue, B. H.
1985-11-01
The linearized equations of motion of a helicopter in contact with the ground have solutions which can be linearly stable or unstable, depending on the system parameters. The present study includes physical non-linearities in the helicopter model. This allows one to determine if a steady-state response exists and, if so, what the frequency and amplitude of the oscillations will be. In this way, one can determine how serious the linearly unstable operating regime is and whether destructive oscillations are possible when the system is in the linearly stable regime. The present analysis applies to helicopters having fully articulated rotors.
A linear model of population dynamics
NASA Astrophysics Data System (ADS)
Lushnikov, A. A.; Kagan, A. I.
2016-08-01
The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).
Employment of CB models for non-linear dynamic analysis
NASA Technical Reports Server (NTRS)
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
Humans use internal models to estimate gravity and linear acceleration.
Merfeld, D M; Zupan, L; Peterka, R J
1999-04-15
Because sensory systems often provide ambiguous information, neural processes must exist to resolve these ambiguities. It is likely that similar neural processes are used by different sensory systems. For example, many tasks require neural processing to distinguish linear acceleration from gravity, but Einstein's equivalence principle states that all linear accelerometers must measure both linear acceleration and gravity. Here we investigate whether the brain uses internal models, defined as neural systems that mimic physical principles, to help estimate linear acceleration and gravity. Internal models may be used in motor contro, sensorimotor integration and sensory processing, but direct experimental evidence for such models is limited. To determine how humans process ambiguous gravity and linear acceleration cues, subjects were tilted after being rotated at a constant velocity about an Earth-vertical axis. We show that the eye movements evoked by this post-rotational tilt include a response component that compensates for the estimated linear acceleration even when no actual linear acceleration occurs. These measured responses are consistent with our internal model predictions that the nervous system can develop a non-zero estimate of linear acceleration even when no true linear acceleration is present. PMID:10217143
Linear radiation model for phase of thermal emission spectroscopy
NASA Astrophysics Data System (ADS)
Bennett, Ted D.; Yu, Fengling
2005-11-01
A linear radiation model is developed that overcomes the analytical complexity in phase of thermal emission spectroscopy. It is shown that the linear radiation model can result in a simple algebraic relation between the phase of thermal emission and four coating properties, enabling these properties to be determined by nonlinear regression analysis of experimental measurements. Suitability of the linear radiation model to various measurement conditions is explored, and the model is applied to the phase of thermal emission measurements performed on a thermal barrier coating.
Non-linear transformer modeling and simulation
Archer, W.E.; Deveney, M.F.; Nagel, R.L.
1994-08-01
Transformers models for simulation with Pspice and Analogy`s Saber are being developed using experimental B-H Loop and network analyzer measurements. The models are evaluated for accuracy and convergence using several test circuits. Results are presented which demonstrate the effects on circuit performance from magnetic core losses eddy currents and mechanical stress on the magnetic cores.
A unifying review of linear gaussian models.
Roweis, S; Ghahramani, Z
1999-02-15
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models. PMID:9950734
A multi-period optimization model for energy planning with CO(2) emission consideration.
Mirzaesmaeeli, H; Elkamel, A; Douglas, P L; Croiset, E; Gupta, M
2010-05-01
A novel deterministic multi-period mixed-integer linear programming (MILP) model for the power generation planning of electric systems is described and evaluated in this paper. The model is developed with the objective of determining the optimal mix of energy supply sources and pollutant mitigation options that meet a specified electricity demand and CO(2) emission targets at minimum cost. Several time-dependent parameters are included in the model formulation; they include forecasted energy demand, fuel price variability, construction lead time, conservation initiatives, and increase in fixed operational and maintenance costs over time. The developed model is applied to two case studies. The objective of the case studies is to examine the economical, structural, and environmental effects that would result if the electricity sector was required to reduce its CO(2) emissions to a specified limit. PMID:20149519
On-line control models for the Stanford Linear Collider
Sheppard, J.C.; Helm, R.H.; Lee, M.J.; Woodley, M.D.
1983-03-01
Models for computer control of the SLAC three-kilometer linear accelerator and damping rings have been developed as part of the control system for the Stanford Linear Collider. Some of these models have been tested experimentally and implemented in the control program for routine linac operations. This paper will describe the development and implementation of these models, as well as some of the operational results.
Neural network modelling of non-linear hydrological relationships
NASA Astrophysics Data System (ADS)
Abrahart, R. J.; See, L. M.
2007-09-01
Two recent studies have suggested that neural network modelling offers no worthwhile improvements in comparison to the application of weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. The potential of an artificial neural network to perform simple non-linear hydrological transformations under controlled conditions is examined in this paper. Eight neural network models were developed: four full or partial emulations of a recognised non-linear hydrological rainfall-runoff model; four solutions developed on an identical set of inputs and a calculated runoff coefficient output. The use of different input combinations enabled the competencies of solutions developed on a reduced number of parameters to be assessed. The selected hydrological model had a limited number of inputs and contained no temporal component. The modelling process was based on a set of random inputs that had a uniform distribution and spanned a modest range of possibilities. The initial cloning operations permitted a direct comparison to be performed with the equation-based relationship. It also provided more general information about the power of a neural network to replicate mathematical equations and model modest non-linear relationships. The second group of experiments explored a different relationship that is of hydrological interest; the target surface contained a stronger set of non-linear properties and was more challenging. Linear modelling comparisons were performed against traditional least squares multiple linear regression solutions developed on identical datasets. The reported results demonstrate that neural networks are capable of modelling non-linear hydrological processes and are therefore appropriate tools for hydrological modelling.
Non-linear protocell models: synchronization and chaos
NASA Astrophysics Data System (ADS)
Filisetti, A.; Serra, R.; Carletti, T.; Villani, M.; Poli, I.
2010-09-01
We consider generic protocells models allowing linear and non-linear kinetics for the main involved chemical reactions. We are interested in understanding if and how the protocell division and the metabolism do synchronise to give rise to sustainable evolution of the protocell.
Derivation and definition of a linear aircraft model
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1988-01-01
A linear aircraft model for a rigid aircraft of constant mass flying over a flat, nonrotating earth is derived and defined. The derivation makes no assumptions of reference trajectory or vehicle symmetry. The linear system equations are derived and evaluated along a general trajectory and include both aircraft dynamics and observation variables.
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
ERIC Educational Resources Information Center
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Recent History Functional Linear Models for Sparse Longitudinal Data
Kim, Kion; Şentürk, Damla; Li, Runze
2011-01-01
We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology. PMID:21691421
Nonlinear Submodels Of Orthogonal Linear Models
ERIC Educational Resources Information Center
Bechtel, Gordon G.
1973-01-01
It is the purpose of this paper to suggest the orthogonal analysis of variance as a device for simplifying either the analytic or iterative problem of finding LS (least squares) estimates for the parameters of particular nonlinear models. (Author/RK)
Genetic parameters for racing records in trotters using linear and generalized linear models.
Suontama, M; van der Werf, J H J; Juga, J; Ojala, M
2012-09-01
Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success
Linear functional minimization for inverse modeling
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Wohlberg, B. E.; Vesselinov, V. V.; Tartakovsky, D. M.
2015-06-01
We present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulic head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.
Bond models in linear and nonlinear optics
NASA Astrophysics Data System (ADS)
Aspnes, D. E.
2015-08-01
Bond models, also known as polarizable-point or mechanical models, have a long history in optics, starting with the Clausius-Mossotti relation but more accurately originating with Ewald's largely forgotten work in 1912. These models describe macroscopic phenomena such as dielectric functions and nonlinear-optical (NLO) susceptibilities in terms of the physics that takes place in real space, in real time, on the atomic scale. Their strengths lie in the insights that they provide and the questions that they raise, aspects that are often obscured by quantum-mechanical treatments. Statics versions were used extensively in the late 1960's and early 1970's to correlate NLO susceptibilities among bulk materials. Interest in NLO applications revived with the 2002 work of Powell et al., who showed that a fully anisotropic version reduced by more than a factor of 2 the relatively large number of parameters necessary to describe secondharmonic- generation (SHG) data for Si(111)/SiO2 interfaces. Attention now is focused on the exact physical meaning of these parameters, and to the extent that they represent actual physical quantities.
Failure of Tube Models to Predict the Linear Rheology of Star/Linear Blends
NASA Astrophysics Data System (ADS)
Hall, Ryan; Desai, Priyanka; Kang, Beomgoo; Katzarova, Maria; Huang, Qifan; Lee, Sanghoon; Chang, Taihyun; Venerus, David; Mays, Jimmy; Schieber, Jay; Larson, Ronald
We compare predictions of two of the most advanced versions of the tube model, namely the Hierarchical model by Wang et al. (J. Rheol. 54:223, 2010) and the BOB (branch-on-branch) model by Das et al. (J. Rheol. 50:207-234, 2006), against linear viscoelastic data on blends of monodisperse star and monodisperse linear polybutadiene polymers. The star was carefully synthesized/characterized by temperature gradient interaction chromatography, and rheological data in the high frequency region were obtained through time-temperature superposition. We found massive failures of both the Hierarchical and BOB models to predict the terminal relaxation behavior of the star/linear blends, despite their success in predicting the rheology of the pure star and pure linear. This failure occurred regardless of the choices made concerning constraint release, such as assuming arm retraction in fat or skinny tubes, or allowing for disentanglement relaxation to cut off the constraint release Rouse process at long times. The failures call into question whether constraint release can be described as a combination of constraint release Rouse processes and dynamic tube dilation within a canonical tube model of entanglement interactions.
An optimization model for energy generation and distribution in a dynamic facility
NASA Technical Reports Server (NTRS)
Lansing, F. L.
1981-01-01
An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.
Dilatonic non-linear sigma models and Ricci flow extensions
NASA Astrophysics Data System (ADS)
Carfora, M.; Marzuoli, A.
2016-09-01
We review our recent work describing, in terms of the Wasserstein geometry over the space of probability measures, the embedding of the Ricci flow in the renormalization group flow for dilatonic non-linear sigma models.
Model checking for linear temporal logic: An efficient implementation
NASA Technical Reports Server (NTRS)
Sherman, Rivi; Pnueli, Amir
1990-01-01
This report provides evidence to support the claim that model checking for linear temporal logic (LTL) is practically efficient. Two implementations of a linear temporal logic model checker is described. One is based on transforming the model checking problem into a satisfiability problem; the other checks an LTL formula for a finite model by computing the cross-product of the finite state transition graph of the program with a structure containing all possible models for the property. An experiment was done with a set of mutual exclusion algorithms and tested safety and liveness under fairness for these algorithms.
NASA Astrophysics Data System (ADS)
Sun, Xiaoqiang; Cai, Yingfeng; Wang, Shaohua; Liu, Yanling; Chen, Long
2016-01-01
The control problems associated with vehicle height adjustment of electronically controlled air suspension (ECAS) still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical (MLD) modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller (HMPC) is tuned based on online mixed-integer quadratic optimization (MIQP). Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology (MPT), thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.
ERIC Educational Resources Information Center
Esteley, Cristina; Villarreal, Monica; Alagia, Humberto
2004-01-01
This research report presents a study of the work of agronomy majors in which an extension of linear models to non-linear contexts can be observed. By linear models we mean the model y=a.x+b, some particular representations of direct proportionality and the diagram for the rule of three. Its presence and persistence in different types of problems…
Modelling Childhood Growth Using Fractional Polynomials and Linear Splines
Tilling, Kate; Macdonald-Wallis, Corrie; Lawlor, Debbie A.; Hughes, Rachael A.; Howe, Laura D.
2014-01-01
Background There is increasing emphasis in medical research on modelling growth across the life course and identifying factors associated with growth. Here, we demonstrate multilevel models for childhood growth either as a smooth function (using fractional polynomials) or a set of connected linear phases (using linear splines). Methods We related parental social class to height from birth to 10 years of age in 5,588 girls from the Avon Longitudinal Study of Parents and Children (ALSPAC). Multilevel fractional polynomial modelling identified the best-fitting model as being of degree 2 with powers of the square root of age, and the square root of age multiplied by the log of age. The multilevel linear spline model identified knot points at 3, 12 and 36 months of age. Results Both the fractional polynomial and linear spline models show an initially fast rate of growth, which slowed over time. Both models also showed that there was a disparity in length between manual and non-manual social class infants at birth, which decreased in magnitude until approximately 1 year of age and then increased. Conclusions Multilevel fractional polynomials give a more realistic smooth function, and linear spline models are easily interpretable. Each can be used to summarise individual growth trajectories and their relationships with individual-level exposures. PMID:25413651
Phase II monitoring of auto-correlated linear profiles using linear mixed model
NASA Astrophysics Data System (ADS)
Narvand, A.; Soleimani, P.; Raissi, Sadigh
2013-05-01
In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto-correlated linear and nonlinear profiles in recent years. In the present paper, we use the linear mixed models to account autocorrelation within observations which is gathered on phase II of the monitoring process. We undertake that the structure of correlated linear profiles simultaneously has both random and fixed effects. The work enhanced a Hotelling's T 2 statistic, a multivariate exponential weighted moving average (MEWMA), and a multivariate cumulative sum (MCUSUM) control charts to monitor process. We also compared their performances, in terms of average run length criterion, and designated that the proposed control charts schemes could effectively act in detecting shifts in process parameters. Finally, the results are applied on a real case study in an agricultural field.
Generation of linear dynamic models from a digital nonlinear simulation
NASA Technical Reports Server (NTRS)
Daniele, C. J.; Krosel, S. M.
1979-01-01
The results and methodology used to derive linear models from a nonlinear simulation are presented. It is shown that averaged positive and negative perturbations in the state variables can reduce numerical errors in finite difference, partial derivative approximations and, in the control inputs, can better approximate the system response in both directions about the operating point. Both explicit and implicit formulations are addressed. Linear models are derived for the F 100 engine, and comparisons of transients are made with the nonlinear simulation. The problem of startup transients in the nonlinear simulation in making these comparisons is addressed. Also, reduction of the linear models is investigated using the modal and normal techniques. Reduced-order models of the F 100 are derived and compared with the full-state models.
Computer modeling of batteries from non-linear circuit elements
NASA Technical Reports Server (NTRS)
Waaben, S.; Federico, J.; Moskowitz, I.
1983-01-01
A simple non-linear circuit model for battery behavior is given. It is based on time-dependent features of the well-known PIN change storage diode, whose behavior is described by equations similar to those associated with electrochemical cells. The circuit simulation computer program ADVICE was used to predict non-linear response from a topological description of the battery analog built from advice components. By a reasonable choice of one set of parameters, the circuit accurately simulates a wide spectrum of measured non-linear battery responses to within a few millivolts.
Confirming the Lanchestrian linear-logarithmic model of attrition
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and final force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. 37 refs., 73 figs., 68 tabs.
Non-Linear Finite Element Modeling of THUNDER Piezoelectric Actuators
NASA Technical Reports Server (NTRS)
Taleghani, Barmac K.; Campbell, Joel F.
1999-01-01
A NASTRAN non-linear finite element model has been developed for predicting the dome heights of THUNDER (THin Layer UNimorph Ferroelectric DrivER) piezoelectric actuators. To analytically validate the finite element model, a comparison was made with a non-linear plate solution using Von Karmen's approximation. A 500 volt input was used to examine the actuator deformation. The NASTRAN finite element model was also compared with experimental results. Four groups of specimens were fabricated and tested. Four different input voltages, which included 120, 160, 200, and 240 Vp-p with a 0 volts offset, were used for this comparison.
Dynamic modeling of electrochemical systems using linear graph theory
NASA Astrophysics Data System (ADS)
Dao, Thanh-Son; McPhee, John
An electrochemical cell is a multidisciplinary system which involves complex chemical, electrical, and thermodynamical processes. The primary objective of this paper is to develop a linear graph-theoretical modeling for the dynamic description of electrochemical systems through the representation of the system topologies. After a brief introduction to the topic and a review of linear graphs, an approach to develop linear graphs for electrochemical systems using a circuitry representation is discussed, followed in turn by the use of the branch and chord transformation techniques to generate final dynamic equations governing the system. As an example, the application of linear graph theory to modeling a nickel metal hydride (NiMH) battery will be presented. Results show that not only the number of equations are reduced significantly, but also the linear graph model simulates faster compared to the original lumped parameter model. The approach presented in this paper can be extended to modeling complex systems such as an electric or hybrid electric vehicle where a battery pack is interconnected with other components in many different domains.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
ASSESSING THE ACCURACY OF THE LINEARIZED LANGMUIR MODEL
Technology Transfer Automated Retrieval System (TEKTRAN)
One of the most commonly used models for describing phosphorus (P) sorption to soils is the nonlinear Langmuir model. To avoid the difficulties in fitting the nonlinear Langmuir equation to sorption data, linearized versions are commonly used. Although concerns have been raised in the past regarding...
Optical linear algebra processors - Noise and error-source modeling
NASA Technical Reports Server (NTRS)
Casasent, D.; Ghosh, A.
1985-01-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)
We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...
A supply chain network design model for biomass co-firing in coal-fired power plants
Md. S. Roni; Sandra D. Eksioglu; Erin Searcy; Krishna Jha
2014-01-01
We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.
PID controller design for trailer suspension based on linear model
NASA Astrophysics Data System (ADS)
Kushairi, S.; Omar, A. R.; Schmidt, R.; Isa, A. A. Mat; Hudha, K.; Azizan, M. A.
2015-05-01
A quarter of an active trailer suspension system having the characteristics of a double wishbone type was modeled as a complex multi-body dynamic system in MSC.ADAMS. Due to the complexity of the model, a linearized version is considered in this paper. A model reduction technique is applied to the linear model, resulting in a reduced-order model. Based on this simplified model, a Proportional-Integral-Derivative (PID) controller was designed in MATLAB/Simulink environment; primarily to reduce excessive roll motions and thus improving the ride comfort. Simulation results show that the output signal closely imitates the input signal in multiple cases - demonstrating the effectiveness of the controller.
Functional linear models for association analysis of quantitative traits.
Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao
2013-11-01
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. PMID:24130119
Confirming the Lanchestrian linear-logarithmic model of attrition
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and final force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. The implications of these findings are potentially far-reaching. Two-sided daily attrition data on a large number of battles is needed to absolutely confirm these results. Such a confirmation will require that numerous computer conflict models containing square and linear law based attrition algorithms be reexamined. It is conceivable that complex mixed, heterogeneous, square plus linear law algorithms may produce the same results as a homogeneous mixed linear-logarithmic law algorithm; however, such an occurrence is by no means assured. Even without such absolute confirmation, the results of this research allow the analysis of combat data for the effects of training, weather, leadership, and other human factors, unencumbered by the force size effects.
Multikernel linear mixed models for complex phenotype prediction.
Weissbrod, Omer; Geiger, Dan; Rosset, Saharon
2016-07-01
Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy. PMID:27302636
Parallel, iterative solution of sparse linear systems: Models and architectures
NASA Technical Reports Server (NTRS)
Reed, D. A.; Patrick, M. L.
1984-01-01
A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.
A linear model of stationary elevator traveling and compensation cables
NASA Astrophysics Data System (ADS)
Zhu, W. D.; Ren, H.
2013-06-01
Based on a recent asymptotic analysis of a nonlinear model of a slack cable, a computationally efficient, linear model is developed for calculating the natural frequencies, mode shapes, and dynamic responses of stationary elevator traveling and compensation cables. The linear cable model consists of two vertical cable segments connected by a half-circular lower loop. The two vertical cable segments are modeled as a string with a variable tension due to the weight of the cable. The horizontal displacements of the cable segments consist of boundary-induced displacements and relative elastic displacements, where the boundary-induced displacements are interpolated from the displacements of the two lower ends of the cable segments, and the relative elastic displacements satisfy the corresponding homogeneous boundary conditions of the cable segments. The horizontal displacement of the lower loop is interpolated from those of the two lower ends of the two cable segments, and the bending stiffness of the lower loop is modeled by a spring with a constant stiffness, which can be calculated from the nonlinear model. Given a car position, the natural frequencies and mode shapes of an elevator traveling or compensation cable are calculated using the linear model and compared with those from the nonlinear model. The calculated natural frequencies are also compared with those from a full-scale experiment. In addition, the dynamic responses of a cable under a boundary excitation are calculated and compared with those from the nonlinear model. There is a good agreement between the predictions from the linear and nonlinear models and between the measured natural frequencies from the full-scale experiment and the corresponding calculated ones.
The determination of third order linear models from a seventh order nonlinear jet engine model
NASA Technical Reports Server (NTRS)
Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex
1989-01-01
Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.
NASA Astrophysics Data System (ADS)
Ikegami, Takashi; Iwafune, Yumiko; Ogimoto, Kazuhiko
The high penetration of variable renewable generation such as Photovoltaic (PV) systems will cause the issue of supply-demand imbalance in a whole power system. The activation of the residential power usage, storage and generation by sophisticated scheduling and control using the Home Energy Management System (HEMS) will be needed to balance power supply and demand in the near future. In order to evaluate the applicability of the HEMS as a distributed controller for local and system-wide supply-demand balances, we developed an optimum operation scheduling model of domestic electric appliances using the mixed integer linear programming. Applying this model to several houses with dynamic electricity prices reflecting the power balance of the total power system, it was found that the adequate changes in electricity prices bring about the shift of residential power usages to control the amount of the reverse power flow due to excess PV generation.
Identification of linear system models and state estimators for controls
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen
1992-01-01
The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.
Asymptotic behavior of coupled linear systems modeling suspension bridges
NASA Astrophysics Data System (ADS)
Dell'Oro, Filippo; Giorgi, Claudio; Pata, Vittorino
2015-06-01
We consider the coupled linear system describing the vibrations of a string-beam system related to the well-known Lazer-McKenna suspension bridge model. For ɛ > 0 and k > 0, the decay properties of the solution semigroup are discussed in dependence of the nonnegative parameters γ and h, which are responsible for the damping effects.
BF Models in Dual Formulations of Linearized Gravity
Bizdadea, Constantin; Cioroianu, Eugen M.; Danehkar, Ashbiz; Iordache, Marius; Saliu, Solange O.; Sararu, Silviu C.
2009-05-22
The case of couplings in D = 5 between a simple, maximal BF model and the dual formulation of linearized gravity is considered. All the possible interactions are exhausted by means of computing the 'free' local BRST cohomology in ghost number zero.
Linear network representation of multistate models of transport.
Sandblom, J; Ring, A; Eisenman, G
1982-05-01
By introducing external driving forces in rate-theory models of transport we show how the Eyring rate equations can be transformed into Ohm's law with potentials that obey Kirchhoff's second law. From such a formalism the state diagram of a multioccupancy multicomponent system can be directly converted into linear network with resistors connecting nodal (branch) points and with capacitances connecting each nodal point with a reference point. The external forces appear as emf or current generators in the network. This theory allows the algebraic methods of linear network theory to be used in solving the flux equations for multistate models and is particularly useful for making proper simplifying approximation in models of complex membrane structure. Some general properties of linear network representation are also deduced. It is shown, for instance, that Maxwell's reciprocity relationships of linear networks lead directly to Onsager's relationships in the near equilibrium region. Finally, as an example of the procedure, the equivalent circuit method is used to solve the equations for a few transport models. PMID:7093425
Use of Linear Models for Thermal Processing Acidified Foods
Technology Transfer Automated Retrieval System (TEKTRAN)
Acidified vegetable products with a pH above 3.3 must be pasteurized to assure the destruction of acid resistant pathogenic bacteria. The times and temperatures needed to assure a five log reduction by pasteurization have previously been determined using a non-linear (Weibull) model. Recently, the F...
Mathematical modelling and linear stability analysis of laser fusion cutting
NASA Astrophysics Data System (ADS)
Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg; Thombansen, Ulrich
2016-06-01
A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process' amount of dynamic behavior.
Evaluating Faculty Salary Equity Using Hierarchical Linear Modeling.
ERIC Educational Resources Information Center
Stapleton, Laura M.; Lissitz, Robert W.
This paper presents results from a comparison of the multiple regression (MR) approach to examining faculty salary equity (with clusters for the various disciplines) and hierarchical linear modeling (HLM) for the same problem. The comparison was done in two steps. First, a practical example of applying both techniques, using empirical data, is…
A SEMIPARAMETRIC BAYESIAN MODEL FOR CIRCULAR-LINEAR REGRESSION
We present a Bayesian approach to regress a circular variable on a linear predictor. The regression coefficients are assumed to have a nonparametric distribution with a Dirichlet process prior. The semiparametric Bayesian approach gives added flexibility to the model and is usefu...
Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Wagler, Amy E.
2014-01-01
Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…
A General Linear Model Approach to Adjusting the Cumulative GPA.
ERIC Educational Resources Information Center
Young, John W.
A general linear model (GLM), using least-squares techniques, was used to develop a criterion measure to replace freshman year grade point average (GPA) in college admission predictive validity studies. Problems with the use of GPA include those associated with the combination of grades from different courses and disciplines into a single measure,…
Linearity of Quantum Probability Measure and Hardy's Model
NASA Astrophysics Data System (ADS)
Fujikawa, Kazuo; Oh, C. H.; Zhang, Chengjie
2014-01-01
We re-examine d = 4 hidden-variables model for a system of two spin-1/2 particles in view of the concrete model of Hardy, who analyzed the criterion of entanglement without referring to inequality. The basis of our analysis is the linearity of the probability measure related to the Born probability interpretation, which excludes noncontextual hidden-variables model in d≥3. To be specific, we note the inconsistency of the noncontextual hidden-variables model in d = 4 with the linearity of the quantum mechanical probability measure in the sense <ψ|aṡσ ⊗b ṡσ|ψ>+ <ψ|a ṡσ ⊗b‧ ṡσ|ψ> = <ψ|aṡσ⊗(b + b‧)ṡσ|ψ> for noncollinear b and b‧. It is then shown that Hardy's model in d = 4 does not lead to a unique mathematical expression in the demonstration of the discrepancy of local realism (hidden-variables model) with entanglement and thus his proof is incomplete. We identify the origin of this nonuniqueness with the nonuniqueness of translating quantum mechanical expressions into expressions in hidden-variables model, which results from the failure of the above linearity of the probability measure. In contrast, if the linearity of the probability measure is strictly imposed, which tantamounts to asking that the noncontextual hidden-variables model in d = 4 gives the Clauser-Horne-Shimony-Holt (CHSH) inequality ||≤2 uniquely, it is shown that the hidden-variables model can describe only separable quantum mechanical states; this conclusion is in perfect agreement with the so-called Gisin's theorem which states that ||≤2 implies separable states.
Identifiability Results for Several Classes of Linear Compartment Models.
Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa
2015-08-01
Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology. PMID:26337290
STATISTICAL BASED NON-LINEAR MODEL UPDATING USING FEATURE EXTRACTION
Schultz, J.F.; Hemez, F.M.
2000-10-01
This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This is an expansion of the update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the classical linear updating paradigm of utilizing the eigen-parameters or FRFs to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta-model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model, Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric, Finally an investigation of the model to replicate the measured response variation is examined.
Linear parameter varying battery model identification using subspace methods
NASA Astrophysics Data System (ADS)
Hu, Y.; Yurkovich, S.
2011-03-01
The advent of hybrid and plug-in hybrid electric vehicles has created a demand for more precise battery pack management systems (BMS). Among methods used to design various components of a BMS, such as state-of-charge (SoC) estimators, model based approaches offer a good balance between accuracy, calibration effort and implementability. Because models used for these approaches are typically low in order and complexity, the traditional approach is to identify linear (or slightly nonlinear) models that are scheduled based on operating conditions. These models, formally known as linear parameter varying (LPV) models, tend to be difficult to identify because they contain a large amount of coefficients that require calibration. Consequently, the model identification process can be very laborious and time-intensive. This paper describes a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions. Compared with previous methods, this approach is much faster and provides the user with information on the order of the system without placing an a priori structure on the system matrices. The entire process and various nuances are demonstrated using data collected from a lithium ion battery, and the focus is on applications for energy storage in automotive applications.
Linear Sigma Model Toolshed for D-brane Physics
Hellerman, Simeon
2001-08-23
Building on earlier work, we construct linear sigma models for strings on curved spaces in the presence of branes. Our models include an extremely general class of brane-worldvolume gauge field configurations. We explain in an accessible manner the mathematical ideas which suggest appropriate worldsheet interactions for generating a given open string background. This construction provides an explanation for the appearance of the derived category in D-brane physic complementary to that of recent work of Douglas.
Linear Time Invariant Models for Integrated Flight and Rotor Control
NASA Astrophysics Data System (ADS)
Olcer, Fahri Ersel
2011-12-01
Recent developments on individual blade control (IBC) and physics based reduced order models of various on-blade control (OBC) actuation concepts are opening up opportunities to explore innovative rotor control strategies for improved rotor aerodynamic performance, reduced vibration and BVI noise, and improved rotor stability, etc. Further, recent developments in computationally efficient algorithms for the extraction of Linear Time Invariant (LTI) models are providing a convenient framework for exploring integrated flight and rotor control, while accounting for the important couplings that exist between body and low frequency rotor response and high frequency rotor response. Formulation of linear time invariant (LTI) models of a nonlinear system about a periodic equilibrium using the harmonic domain representation of LTI model states has been studied in the literature. This thesis presents an alternative method and a computationally efficient scheme for implementation of the developed method for extraction of linear time invariant (LTI) models from a helicopter nonlinear model in forward flight. The fidelity of the extracted LTI models is evaluated using response comparisons between the extracted LTI models and the nonlinear model in both time and frequency domains. Moreover, the fidelity of stability properties is studied through the eigenvalue and eigenvector comparisons between LTI and LTP models by making use of the Floquet Transition Matrix. For time domain evaluations, individual blade control (IBC) and On-Blade Control (OBC) inputs that have been tried in the literature for vibration and noise control studies are used. For frequency domain evaluations, frequency sweep inputs are used to obtain frequency responses of fixed system hub loads to a single blade IBC input. The evaluation results demonstrate the fidelity of the extracted LTI models, and thus, establish the validity of the LTI model extraction process for use in integrated flight and rotor control
Linear modeling of steady-state behavioral dynamics.
Palya, William L; Walter, Donald; Kessel, Robert; Lucke, Robert
2002-01-01
The observed steady-state behavioral dynamics supported by unsignaled periods of reinforcement within repeating 2,000-s trials were modeled with a linear transfer function. These experiments employed improved schedule forms and analytical methods to improve the precision of the measured transfer function, compared to previous work. The refinements include both the use of multiple reinforcement periods that improve spectral coverage and averaging of independently determined transfer functions. A linear analysis was then used to predict behavior observed for three different test schedules. The fidelity of these predictions was determined. PMID:11831782
Can the Non-linear Ballooning Model describe ELMs?
NASA Astrophysics Data System (ADS)
Henneberg, S. A.; Cowley, S. C.; Wilson, H. R.
2015-11-01
The explosive, filamentary plasma eruptions described by the non-linear ideal MHD ballooning model is tested quantitatively against experimental observations of ELMs in MAST. The equations describing this model were derived by Wilson and Cowley for tokamak-like geometry which includes two differential equations: the linear ballooning equation which describes the spatial distribution along the field lines and the non-linear ballooning mode envelope equation, which is a two-dimensional, non-linear differential equation which can involve fractional temporal-derivatives, but is often second-order in time and space. To employ the second differential equation for a specific geometry one has to evaluate the coefficients of the equation which is non-trivial as it involves field line averaging of slowly converging functions. We have solved this system for MAST, superimposing the solutions of both differential equations and mapping them onto a MAST plasma. Comparisons with the evolution of ELM filaments in MAST will be reported in order to test the model. The support of the EPSRC for the FCDT (Grant EP/K504178/1), of Euratom research and training programme 2014-2018 (No 633053) and of the RCUK Energy Programme [grant number EP/I501045] is gratefully acknowledged.
Models for total dose degradation of linear integrated circuits
Johnston, A.H.; Plaag, R.E.
1987-12-01
Mechanisms for total dose degradation of linear circuits are discussed, including bulk effects, oxide charge buildup and recombination at the Si-SiO/sub 2/ interface. The dependence of damage on bias, dose, particle type and energy is used in conjunction with two-dimensional modeling to identify the failure mechanism in a specific linear device type. The importance of surface recombination is demonstrated along with the absence of bias dependence. Bulk damage is shown to be important for high energy electron irradiation because of wide-base pnp transistors. This causes substantial differences in device failure between electron and cobalt-60 environments that need to be taken into account for test standards and data bases that include commercial bipolar integrated circuits. Valid test methodologies for linear device must consider the energy and particle type present in the actual environment.
Drawbacks of using linear mixture modeling on hyperspectral images
NASA Astrophysics Data System (ADS)
Rodricks, Neena; Kirkland, Laurel E.
2004-10-01
Hyperspectral spectroscopy can be used remotely to measure emitted radiation from minerals and rocks at a series of narrow and continuous wavelength bands resulting in a continuous spectrum for each pixel, thereby providing ample spectral information to identify and distinguish spectrally unique materials. Linear mixture modeling ("spectral unmixing"), a commonly used method, is based on the theory that the radiance in the thermal infrared region (8-12 μm) from a multi-mineral surface can be modeled as a linear combination of the endmembers. A linear mixture model can thus potentially model the minerals present on planetary surfaces. It works by scaling the endmember spectra so that the sum of the scaled endmember spectra matches the measured spectrum with the smallest "error" (difference). But one of the drawbacks of this established method is that mathematically, a fit with an inverted spectrum is valid, which effectively returns a negative abundance of a material. Current models usually address the problem by elimination of endmembers that have negative scale factors. Eliminating the negative abundance problem is not a major issue when the endmembers are known. However, identifying unknown target composition (like on Mars) can be a problem. The goal of this study is to improve the understanding and find a subsequent solution of the negative abundance problem for Mars analog field data obtained from airborne and ground spectrometers. We are using a well-defined library of spectra to test the accuracy of hyperspectral analysis for the identification of minerals on planetary surfaces.
General mirror pairs for gauged linear sigma models
NASA Astrophysics Data System (ADS)
Aspinwall, Paul S.; Plesser, M. Ronen
2015-11-01
We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.
Linear models for joint association and linkage QTL mapping
2009-01-01
Background Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission) average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component) model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward. PMID:19788745
Classifying linearly shielded modified gravity models in effective field theory.
Lombriser, Lucas; Taylor, Andy
2015-01-23
We study the model space generated by the time-dependent operator coefficients in the effective field theory of the cosmological background evolution and perturbations of modified gravity and dark energy models. We identify three classes of modified gravity models that reduce to Newtonian gravity on the small scales of linear theory. These general classes contain enough freedom to simultaneously admit a matching of the concordance model background expansion history. In particular, there exists a large model space that mimics the concordance model on all linear quasistatic subhorizon scales as well as in the background evolution. Such models also exist when restricting the theory space to operators introduced in Horndeski scalar-tensor gravity. We emphasize that whereas the partially shielded scenarios might be of interest to study in connection with tensions between large and small scale data, with conventional cosmological probes, the ability to distinguish the fully shielded scenarios from the concordance model on near-horizon scales will remain limited by cosmic variance. Novel tests of the large-scale structure remedying this deficiency and accounting for the full covariant nature of the alternative gravitational theories, however, might yield further insights on gravity in this regime. PMID:25658988
A non-linear model of economic production processes
NASA Astrophysics Data System (ADS)
Ponzi, A.; Yasutomi, A.; Kaneko, K.
2003-06-01
We present a new two phase model of economic production processes which is a non-linear dynamical version of von Neumann's neoclassical model of production, including a market price-setting phase as well as a production phase. The rate of an economic production process is observed, for the first time, to depend on the minimum of its input supplies. This creates highly non-linear supply and demand dynamics. By numerical simulation, production networks are shown to become unstable when the ratio of different products to total processes increases. This provides some insight into observed stability of competitive capitalist economies in comparison to monopolistic economies. Capitalist economies are also shown to have low unemployment.
Meshless analysis of shear deformable shells: the linear model
NASA Astrophysics Data System (ADS)
Costa, Jorge C.; Tiago, Carlos M.; Pimenta, Paulo M.
2013-10-01
This work develops a kinematically linear shell model departing from a consistent nonlinear theory. The initial geometry is mapped from a flat reference configuration by a stress-free finite deformation, after which, the actual shell motion takes place. The model maintains the features of a complete stress-resultant theory with Reissner-Mindlin kinematics based on an inextensible director. A hybrid displacement variational formulation is presented, where the domain displacements and kinematic boundary reactions are independently approximated. The resort to a flat reference configuration allows the discretization using 2-D Multiple Fixed Least-Squares (MFLS) on the domain. The consistent definition of stress resultants and consequent plane stress assumption led to a neat formulation for the analysis of shells. The consistent linear approximation, combined with MFLS, made possible efficient computations with a desired continuity degree, leading to smooth results for the displacement, strain and stress fields, as shown by several numerical examples.
Flood Nowcasting With Linear Catchment Models, Radar and Kalman Filters
NASA Astrophysics Data System (ADS)
Pegram, Geoff; Sinclair, Scott
A pilot study using real time rainfall data as input to a parsimonious linear distributed flood forecasting model is presented. The aim of the study is to deliver an operational system capable of producing flood forecasts, in real time, for the Mgeni and Mlazi catchments near the city of Durban in South Africa. The forecasts can be made at time steps which are of the order of a fraction of the catchment response time. To this end, the model is formulated in Finite Difference form in an equation similar to an Auto Regressive Moving Average (ARMA) model; it is this formulation which provides the required computational efficiency. The ARMA equation is a discretely coincident form of the State-Space equations that govern the response of an arrangement of linear reservoirs. This results in a functional relationship between the reservoir response con- stants and the ARMA coefficients, which guarantees stationarity of the ARMA model. Input to the model is a combined "Best Estimate" spatial rainfall field, derived from a combination of weather RADAR and Satellite rainfield estimates with point rain- fall given by a network of telemetering raingauges. Several strategies are employed to overcome the uncertainties associated with forecasting. Principle among these are the use of optimal (double Kalman) filtering techniques to update the model states and parameters in response to current streamflow observations and the application of short term forecasting techniques to provide future estimates of the rainfield as input to the model.
LINEAR MODELS FOR MANAGING SOURCES OF GROUNDWATER POLLUTION.
Gorelick, Steven M.; Gustafson, Sven-Ake
1984-01-01
Mathematical models for the problem of maintaining a specified groundwater quality while permitting solute waste disposal at various facilities distributed over space are discussed. The pollutants are assumed to be chemically inert and their concentrations in the groundwater are governed by linear equations for advection and diffusion. The aim is to determine a disposal policy which maximises the total amount of pollutants released during a fixed time T while meeting the condition that the concentration everywhere is below prescribed levels.
Using Quartile-Quartile Lines as Linear Models
ERIC Educational Resources Information Center
Gordon, Sheldon P.
2015-01-01
This article introduces the notion of the quartile-quartile line as an alternative to the regression line and the median-median line to produce a linear model based on a set of data. It is based on using the first and third quartiles of a set of (x, y) data. Dynamic spreadsheets are used as exploratory tools to compare the different approaches and…
Credibility analysis of risk classes by generalized linear model
NASA Astrophysics Data System (ADS)
Erdemir, Ovgucan Karadag; Sucu, Meral
2016-06-01
In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.
Modeling of thermal storage systems in MILP distributed energy resource models
Steen, David; Stadler, Michael; Cardoso, Gonçalo; Groissböck, Markus; DeForest, Nicholas; Marnay, Chris
2014-08-04
Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO_{2} emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations are based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Ultimately,results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.
A general linear model for MEG beamformer imaging.
Brookes, Matthew J; Gibson, Andrew M; Hall, Stephen D; Furlong, Paul L; Barnes, Gareth R; Hillebrand, Arjan; Singh, Krish D; Holliday, Ian E; Francis, Sue T; Morris, Peter G
2004-11-01
A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields). PMID:15528094
ERIC Educational Resources Information Center
Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.
2010-01-01
Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…
Sahin, Rubina; Tapadia, Kavita
2015-01-01
The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area. PMID:26676015
A new pore-scale model for linear and non-linear heterogeneous dissolution and precipitation
NASA Astrophysics Data System (ADS)
Huber, Christian; Shafei, Babak; Parmigiani, Andrea
2014-01-01
Pore-scale processes exert a strong control on the transport of reactants in porous media at the continuum scale. As such, pore-scale numerical models can offer a more quantitative understanding of the coupling between transport and reaction and yield parameterized constitutive equations to introduce pore-scale corrections into macroscopic (continuum) reactive transport models. In the present study, we present a new pore-scale model for the advection and diffusion of reactants in porous media with complex topologies. The model is based on the lattice Boltzmann method and couples a fluid flow solver to an optimal advection-diffusion transport model. Internal solid-fluid boundaries (grain boundaries) are explicitly part of the numerical domain, which allows the algorithm to solve for surface reactions independently from the surface shape and orientation of the grains. Thus, the approach is well suited for the treatment of heterogeneous reactions in complex pore structures. We present single and multispecies reactive transport applications of the model. In the first application we study the permeability change of a porous medium associated with a given porosity change during dissolution and precipitation using linear reaction kinetics. We show that, for a given porous medium, the correlation between porosity and permeability changes depends on the transport regime (the ratio of advective to diffusive transport) and the reaction rate. Finally, we carry out simulations of multispecies reactive transport, focusing on the case of calcium carbonate dissolution/precipitation. Our results highlight the difference between pH dependent and independent reaction rates for heterogeneous reactions in complex geometries at the pore scale.
On the Development of Parameterized Linear Analytical Longitudinal Airship Models
NASA Technical Reports Server (NTRS)
Kulczycki, Eric A.; Johnson, Joseph R.; Bayard, David S.; Elfes, Alberto; Quadrelli, Marco B.
2008-01-01
In order to explore Titan, a moon of Saturn, airships must be able to traverse the atmosphere autonomously. To achieve this, an accurate model and accurate control of the vehicle must be developed so that it is understood how the airship will react to specific sets of control inputs. This paper explains how longitudinal aircraft stability derivatives can be used with airship parameters to create a linear model of the airship solely by combining geometric and aerodynamic airship data. This method does not require system identification of the vehicle. All of the required data can be derived from computational fluid dynamics and wind tunnel testing. This alternate method of developing dynamic airship models will reduce time and cost. Results are compared to other stable airship dynamic models to validate the methods. Future work will address a lateral airship model using the same methods.
Bayesian partial linear model for skewed longitudinal data.
Tang, Yuanyuan; Sinha, Debajyoti; Pati, Debdeep; Lipsitz, Stuart; Lipshultz, Steven
2015-07-01
Unlike majority of current statistical models and methods focusing on mean response for highly skewed longitudinal data, we present a novel model for such data accommodating a partially linear median regression function, a skewed error distribution and within subject association structures. We provide theoretical justifications for our methods including asymptotic properties of the posterior and associated semiparametric Bayesian estimators. We also provide simulation studies to investigate the finite sample properties of our methods. Several advantages of our method compared with existing methods are demonstrated via analysis of a cardiotoxicity study of children of HIV-infected mothers. PMID:25792623
Modelling human balance using switched systems with linear feedback control.
Kowalczyk, Piotr; Glendinning, Paul; Brown, Martin; Medrano-Cerda, Gustavo; Dallali, Houman; Shapiro, Jonathan
2012-02-01
We are interested in understanding the mechanisms behind and the character of the sway motion of healthy human subjects during quiet standing. We assume that a human body can be modelled as a single-link inverted pendulum, and the balance is achieved using linear feedback control. Using these assumptions, we derive a switched model which we then investigate. Stable periodic motions (limit cycles) about an upright position are found. The existence of these limit cycles is studied as a function of system parameters. The exploration of the parameter space leads to the detection of multi-stability and homoclinic bifurcations. PMID:21697168
Linear stability analysis of swirling turbulent flows with turbulence models
NASA Astrophysics Data System (ADS)
Gupta, Vikrant; Juniper, Matthew
2013-11-01
In this paper, we consider the growth of large scale coherent structures in turbulent flows by performing linear stability analysis around a mean flow. Turbulent flows are characterized by fine-scale stochastic perturbations. The momentum transfer caused by these perturbations affects the development of larger structures. Therefore, in a linear stability analysis, it is important to include the perturbations' influence. One way to do this is to include a turbulence model in the stability analysis. This is done in the literature by using eddy viscosity models (EVMs), which are first order turbulence models. We extend this approach by using second order turbulence models, in this case explicit algebraic Reynolds stress models (EARSMs). EARSMs are more versatile than EVMs, in that they can be applied to a wider range of flows, and could also be more accurate. We verify our EARSM-based analysis by applying it to a channel flow and then comparing the results with those from an EVM-based analysis. We then apply the EARSM-based stability analysis to swirling pipe flows and Taylor-Couette flows, which demonstrates the main benefit of EARSM-based analysis. This project is supported by EPSRC and Rolls-Royce through a Dorothy Hodgkin Research Fellowship.
First class models from linear and nonlinear second class constraints
NASA Astrophysics Data System (ADS)
Dehghani, Mehdi; Mardaani, Maryam; Monemzadeh, Majid; Nejad, Salman Abarghouei
2015-10-01
Two models with linear and nonlinear second class constraints are considered and gauged by embedding in an extended phase space. These models are studied by considering a free non-relativistic particle on the hyperplane and hypersphere in the configuration space. The gauged theory of the first model is obtained by converting the very second class system to the first class one directly. In contrast, the first class system related to the free particle on the hypersphere is derived with the help of the infinite Batalin-Fradkin-Tyutin (BFT) embedding procedure. We propose a practical formula, based on the simplified BFT method, which is practical in gauging linear and some nonlinear second class systems. As a result of gauging these two models, we show that in the conversion of second class constraints to the first class ones, the minimum number of phase space degrees of freedom for both systems is a pair of phase space coordinates. This pair is made up of a coordinate and its conjugate momentum for the first model, but the corresponding Poisson structure of the embedded non-relativistic particle on hypersphere is a nontrivial one. We derive infinite correction terms for the Hamiltonian of the nonlinear constraints and an interacting gauged Hamiltonian is constructed by summing over them. At the end, we find an open algebra for three first class objects of the embedded nonlinear system.
Wavefront Sensing for WFIRST with a Linear Optical Model
NASA Technical Reports Server (NTRS)
Jurling, Alden S.; Content, David A.
2012-01-01
In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.
Prediction of mean arterial blood pressure with linear stochastic models.
Genc, Sahika
2011-01-01
A model-based approach that integrates known portion of the cardiovascular system and unknown portion through a parameter estimation to predict evolution of the mean arterial pressure is considered. The unknown portion corresponds to the neural portion that acts like a controller that takes corrective actions to regulate the arterial blood pressure at a constant level. The input to the neural part is the arterial pressure and output is the sympathetic nerve activity. In this model, heart rate is considered a proxy for sympathetic nerve activity. The neural portion is modeled as a linear discrete-time system with random coefficients. The performance of the model is tested on a case study of acute hypotensive episodes (AHEs) on PhysioNet data. TPRs and FPRs improve as more data becomes available during estimation period. PMID:22254409
Marginally specified generalized linear mixed models: a robust approach.
Mills, J E; Field, C A; Dupuis, D J
2002-12-01
Longitudinal data modeling is complicated by the necessity to deal appropriately with the correlation between observations made on the same individual. Building on an earlier nonrobust version proposed by Heagerty (1999, Biometrics 55, 688-698), our robust marginally specified generalized linear mixed model (ROBMS-GLMM) provides an effective method for dealing with such data. This model is one of the first to allow both population-averaged and individual-specific inference. As well, it adopts the flexibility and interpretability of generalized linear mixed models for introducing dependence but builds a regression structure for the marginal mean, allowing valid application with time-dependent (exogenous) and time-independent covariates. These new estimators are obtained as solutions of a robustified likelihood equation involving Huber's least favorable distribution and a collection of weights. Huber's least favorable distribution produces estimates that are resistant to certain deviations from the random effects distributional assumptions. Innovative weighting strategies enable the ROBMS-GLMM to perform well when faced with outlying observations both in the response and covariates. We illustrate the methodology with an analysis of a prospective longitudinal study of laryngoscopic endotracheal intubation, a skill that numerous health-care professionals are expected to acquire. The principal goal of our research is to achieve robust inference in longitudinal analyses. PMID:12495126
Rethinking the linear regression model for spatial ecological data.
Wagner, Helene H
2013-11-01
The linear regression model, with its numerous extensions including multivariate ordination, is fundamental to quantitative research in many disciplines. However, spatial or temporal structure in the data may invalidate the regression assumption of independent residuals. Spatial structure at any spatial scale can be modeled flexibly based on a set of uncorrelated component patterns (e.g., Moran's eigenvector maps, MEM) that is derived from the spatial relationships between sampling locations as defined in a spatial weight matrix. Spatial filtering thus addresses spatial autocorrelation in the residuals by adding such component patterns (spatial eigenvectors) as predictors to the regression model. However, space is not an ecologically meaningful predictor, and commonly used tests for selecting significant component patterns do not take into account the specific nature of these variables. This paper proposes "spatial component regression" (SCR) as a new way of integrating the linear regression model with Moran's eigenvector maps. In its unconditioned form, SCR decomposes the relationship between response and predictors by component patterns, whereas conditioned SCR provides an alternative method of spatial filtering, taking into account the statistical properties of component patterns in the design of statistical hypothesis tests. Application to the well-known multivariate mite data set illustrates how SCR may be used to condition for significant residual spatial structure and to identify additional predictors associated with residual spatial structure. Finally, I argue that all variance is spatially structured, hence spatial independence is best characterized by a lack of excess variance at any spatial scale, i.e., spatial white noise. PMID:24400490
Optimizing the Teaching-Learning Process Through a Linear Programming Model--Stage Increment Model.
ERIC Educational Resources Information Center
Belgard, Maria R.; Min, Leo Yoon-Gee
An operations research method to optimize the teaching-learning process is introduced in this paper. In particular, a linear programing model is proposed which, unlike dynamic or control theory models, allows the computer to react to the responses of a learner in seconds or less. To satisfy the assumptions of linearity, the seemingly complicated…
Monthly pan evaporation modeling using linear genetic programming
NASA Astrophysics Data System (ADS)
Guven, Aytac; Kisi, Ozgur
2013-10-01
This study compares the accuracy of linear genetic programming (LGP), fuzzy genetic (FG), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in modeling pan evaporations. Monthly climatic data including solar radiation, air temperature, relative humidity, wind speed and pan evaporation from Antalya and Mersin stations, in Turkey are used in the study. The study composed of two parts. First part of the study focuses the comparison of LGP models with those of the FG, ANFIS, ANN and SS models in estimating pan evaporations of Antalya and Mersin stations, separately. From the comparison results, the LGP models are found to be better than the other models. Comparison of LGP models with the other models in estimating pan evaporations of the Mersin Station by using both stations' inputs is focused in the second part of the study. The results indicate that the LGP models better accuracy than the FG, ANFIS, ANN and SS models. It is seen that the pan evaporations can be successfully estimated by the LGP method.
ERIC Educational Resources Information Center
Cheong, Yuk Fai; Kamata, Akihito
2013-01-01
In this article, we discuss and illustrate two centering and anchoring options available in differential item functioning (DIF) detection studies based on the hierarchical generalized linear and generalized linear mixed modeling frameworks. We compared and contrasted the assumptions of the two options, and examined the properties of their DIF…
Comparison of linear and nonlinear subgrid scale model for hybrid RANS/LES modelling
NASA Astrophysics Data System (ADS)
Straka, Petr
2016-06-01
The contribution deals with application of the hybrid RANS/LES model for calculation of flow around the circular cylinder. Used hybrid RANS/LES model is based on transport equation for the kinetic energy which is shared in both RANS and LES modes. The linear and the nonlinear closure formulas are described in the paper. Numerical results are compared with the experimental data. Results show that the nonlinear model predicts development of the wake behind the cylinder better than the linear model.
Tommasi, C.; May, C.
2010-09-30
The DKL-optimality criterion has been recently proposed for the dual problem of model discrimination and parameter estimation, for the case of two rival models. A sequential version of the DKL-optimality criterion is herein proposed in order to discriminate and efficiently estimate more than two nested non-linear models. Our sequential method is inspired by the procedure of Biswas and Chaudhuri (2002), which is however useful only in the set up of nested linear models.
Tangent linear analysis of the Mosaic land surface model
NASA Astrophysics Data System (ADS)
Yang, Runhua; Cohn, Stephen E.; da Silva, Arlindo; Joiner, Joanna; Houser, Paul R.
2003-01-01
In this study, a tangent linear eigenanalysis is applied to the Mosaic land surface model (LSM) [, 1992] to examine the impacts of the model internal dynamics and physics on the land surface state variability. The tangent linear model (TLM) of the Mosaic LSM is derived numerically for two sets of basic states and two tile types of land condition, grass and bare soil. An additional TLM, for the soil moisture subsystem of this LSM, is derived analytically for the same cases to obtain explicit expressions for the eigenvalues. An eigenvalue of the TLM determines a characteristic timescale, and the corresponding eigenvector, or mode, describes a particular coupling among the perturbed states. The results show that (1) errors in initial conditions tend to decay with e-folding times given by the characteristic timescales; (2) the LSM exhibits a wide range of internal variability, modes mainly representing surface temperature and surface moisture perturbations exhibit short timescales, whereas modes mainly representing deep soil temperature perturbations and moisture transfer throughout the entire soil column exhibit much longer timescales; (3) the modes of soil moisture tend to be weakly coupled with other perturbed variables, and the mode representing the deep soil temperature perturbation has a consistent e-folding time across the experiments; (4) the key parameters include soil moisture, soil layer depth, and soil hydraulic parameters. The results agree qualitatively with previous findings. However, tangent linear eigenanalysis provides a new approach to the quantitative substantiation of those findings. Also, it reveals the evolution and the coupling of the perturbed land states that are useful for the development of land surface data assimilation schemes. One must be careful when generalizing the quantitative results since they are obtained with respect to two specific basic states and two simple land conditions. Also, the methodology employed here does not apply
Linear system identification via backward-time observer models
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh
1993-01-01
This paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.
Accelerating transient simulation of linear reduced order models.
Thornquist, Heidi K.; Mei, Ting; Keiter, Eric Richard; Bond, Brad
2011-10-01
Model order reduction (MOR) techniques have been used to facilitate the analysis of dynamical systems for many years. Although existing model reduction techniques are capable of providing huge speedups in the frequency domain analysis (i.e. AC response) of linear systems, such speedups are often not obtained when performing transient analysis on the systems, particularly when coupled with other circuit components. Reduced system size, which is the ostensible goal of MOR methods, is often insufficient to improve transient simulation speed on realistic circuit problems. It can be shown that making the correct reduced order model (ROM) implementation choices is crucial to the practical application of MOR methods. In this report we investigate methods for accelerating the simulation of circuits containing ROM blocks using the circuit simulator Xyce.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
NASA Technical Reports Server (NTRS)
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Linear mixing model applied to AVHRR LAC data
NASA Technical Reports Server (NTRS)
Holben, Brent N.; Shimabukuro, Yosio E.
1993-01-01
A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.
A note on a model for quay crane scheduling with non-crossing constraints
NASA Astrophysics Data System (ADS)
Santini, Alberto; Alsing Friberg, Henrik; Ropke, Stefan
2015-06-01
This article studies the quay crane scheduling problem with non-crossing constraints, which is an operational problem that arises in container terminals. An enhancement to a mixed integer programming model for the problem is proposed and a new class of valid inequalities is introduced. Computational results show the effectiveness of these enhancements in solving the problem to optimality.
Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
Almedeij, Jaber
2012-01-01
Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984
Decoding coalescent hidden Markov models in linear time
Harris, Kelley; Sheehan, Sara; Kamm, John A.; Song, Yun S.
2014-01-01
In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent HMMs can quickly become prohibitive. Here we present a new algorithm for reducing the runtime of coalescent HMMs from quadratic in the number of hidden time states to linear, without making any additional approximations. Our algorithm can be incorporated into various coalescent HMMs, including the popular method PSMC for inferring variable effective population sizes. Here we implement this algorithm to speed up our demographic inference method diCal, which is equivalent to PSMC when applied to a sample of two haplotypes. We demonstrate that the linear-time method can reconstruct a population size change history more accurately than the quadratic-time method, given similar computation resources. We also apply the method to data from the 1000 Genomes project, inferring a high-resolution history of size changes in the European population. PMID:25340178
Markov-random-field modeling for linear seismic tomography.
Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro
2014-10-01
We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences. PMID:25375468
The linear interaction model of personality effects in health communication.
Dutta-Bergman, Mohan Jyoti
2003-01-01
The recent growth of research in message tailoring has opened up new avenues for researchers to use personality variables for message delivery. This article builds on research on idiocentrism and self-monitoring to propose a framework for message appeal construction. Based on a scheme for appeal categorization borrowed from commercial marketing, the article suggests that low and high idiocentrics differ from each other in the way they respond to appeal types. Similarly, significant differences are demonstrated between low and high self-monitors in the realm of their response to message appeals. A linear interaction model is proposed to document the combined effects of self-monitoring and idiocentrism. PMID:12553779
Modeling of Linear Gas Tungsten Arc Welding of Stainless Steel
NASA Astrophysics Data System (ADS)
Maran, P.; Sornakumar, T.; Sundararajan, T.
2008-08-01
A heat and fluid flow model has been developed to solve the gas tungsten arc (GTA) linear welding problem for austenitic stainless steel. The moving heat source problem associated with the electrode traverse has been simplified into an equivalent two-dimensional (2-D) transient problem. The torch residence time has been calculated from the arc diameter and torch speed. The mathematical formulation considers buoyancy, electromagnetic induction, and surface tension forces. The governing equations have been solved by the finite volume method. The temperature and velocity fields have been determined. The theoretical predictions for weld bead geometry are in good agreement with experimental measurements.
Sun, Wei; Huang, Guo H; Lv, Ying; Li, Gongchen
2012-06-01
To tackle nonlinear economies-of-scale (EOS) effects in interval-parameter constraints for a representative waste management problem, an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model is developed. In IPFP, interval parameters for waste amounts and transportation/operation costs can be quantified; aspiration levels for net system costs, as well as tolerance intervals for both capacities of waste treatment facilities and waste generation rates can be reflected; and the nonlinear EOS effects transformed from objective function to constraints can be approximated. An interactive algorithm is proposed for solving the IPFP model, which in nature is an interval-parameter mixed-integer quadratically constrained programming model. To demonstrate the IPFP's advantages, two alternative models are developed to compare their performances. One is a conventional linear-regression-based inexact fuzzy programming model (IPFP2) and the other is an IPFP model with all right-hand-sides of fussy constraints being the corresponding interval numbers (IPFP3). The comparison results between IPFP and IPFP2 indicate that the optimized waste amounts would have the similar patterns in both models. However, when dealing with EOS effects in constraints, the IPFP2 may underestimate the net system costs while the IPFP can estimate the costs more accurately. The comparison results between IPFP and IPFP3 indicate that their solutions would be significantly different. The decreased system uncertainties in IPFP's solutions demonstrate its effectiveness for providing more satisfactory interval solutions than IPFP3. Following its first application to waste management, the IPFP can be potentially applied to other environmental problems under multiple complexities. PMID:22370050
Linear and generalized linear models for the detection of QTL effects on within-subject variability
Wittenburg, Dörte; Guiard, Volker; Liese, Friedrich; Reinsch, Norbert
2007-01-01
Summary Quantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H0: No QTL with effect on the within-litter variance is segregating versus HA: There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests. PMID:18208630
Stratospheric ozone time series analysis using dynamical linear models
NASA Astrophysics Data System (ADS)
Laine, Marko; Kyrölä, Erkki
2013-04-01
We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instruments spanning years 1984-2012. The original data sets are combined and gridded monthly using 10 degree latitude bands, and covering 20-60 km with 1 km vertical spacing. Model components include level, trend, seasonal effect with solar activity, and quasi biennial oscillations as proxy variables. A typical feature of an atmospheric time series is that they are not stationary but exhibit both slowly varying and abrupt changes in the distributional properties. These are caused by external forcing such as changes in the solar activity or volcanic eruptions. Further, the data sampling is often nonuniform, there are data gaps, and the uncertainty of the observations can vary. When observations are combined from various sources there will be instrument and retrieval method related biases. The differences in sampling lead also to uncertainties. Standard classical ARIMA type of statistical time series methods are mostly useless for atmospheric data. A more general approach makes use of dynamical linear models and Kalman filter type of sequential algorithms. These state space models assume a linear relationship between the unknown state of the system and the observations and for the process evolution of the hidden states. They are still flexible enough to model both smooth trends and sudden changes. The above mentioned methodological challenges are discussed, together with analysis of change points in trends related to recovery of stratospheric ozone. This work is part of the ESA SPIN and ozone CCI projects.
Non-Linear Slosh Damping Model Development and Validation
NASA Technical Reports Server (NTRS)
Yang, H. Q.; West, Jeff
2015-01-01
Propellant tank slosh dynamics are typically represented by a mechanical model of spring mass damper. This mechanical model is then included in the equation of motion of the entire vehicle for Guidance, Navigation and Control (GN&C) analysis. For a partially-filled smooth wall propellant tank, the critical damping based on classical empirical correlation is as low as 0.05%. Due to this low value of damping, propellant slosh is potential sources of disturbance critical to the stability of launch and space vehicles. It is postulated that the commonly quoted slosh damping is valid only under the linear regime where the slosh amplitude is small. With the increase of slosh amplitude, the critical damping value should also increase. If this nonlinearity can be verified and validated, the slosh stability margin can be significantly improved, and the level of conservatism maintained in the GN&C analysis can be lessened. The purpose of this study is to explore and to quantify the dependence of slosh damping with slosh amplitude. Accurately predicting the extremely low damping value of a smooth wall tank is very challenging for any Computational Fluid Dynamics (CFD) tool. One must resolve thin boundary layers near the wall and limit numerical damping to minimum. This computational study demonstrates that with proper grid resolution, CFD can indeed accurately predict the low damping physics from smooth walls under the linear regime. Comparisons of extracted damping values with experimental data for different tank sizes show very good agreements. Numerical simulations confirm that slosh damping is indeed a function of slosh amplitude. When slosh amplitude is low, the damping ratio is essentially constant, which is consistent with the empirical correlation. Once the amplitude reaches a critical value, the damping ratio becomes a linearly increasing function of the slosh amplitude. A follow-on experiment validated the developed nonlinear damping relationship. This discovery can
Feedbacks, climate sensitivity and the limits of linear models.
Knutti, Reto; Rugenstein, Maria A A
2015-11-13
The term 'feedback' is used ubiquitously in climate research, but implies varied meanings in different contexts. From a specific process that locally affects a quantity, to a formal framework that attempts to determine a global response to a forcing, researchers use this term to separate, simplify and quantify parts of the complex Earth system. We combine new model results with a historical and educational perspective to organize existing ideas around feedbacks and linear models. Our results suggest that the state- and forcing-dependency of feedbacks are probably not appreciated enough, and not considered appropriately in many studies. A non-constant feedback parameter likely explains some of the differences in estimates of equilibrium climate sensitivity from different methods and types of data. Clarifying the value and applicability of the linear forcing feedback framework and a better quantification of feedbacks on various timescales and spatial scales remains a high priority in order to better understand past and predict future changes in the climate system. PMID:26438287
Linear versus quadratic portfolio optimization model with transaction cost
NASA Astrophysics Data System (ADS)
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
Relating Cohesive Zone Model to Linear Elastic Fracture Mechanics
NASA Technical Reports Server (NTRS)
Wang, John T.
2010-01-01
The conditions required for a cohesive zone model (CZM) to predict a failure load of a cracked structure similar to that obtained by a linear elastic fracture mechanics (LEFM) analysis are investigated in this paper. This study clarifies why many different phenomenological cohesive laws can produce similar fracture predictions. Analytical results for five cohesive zone models are obtained, using five different cohesive laws that have the same cohesive work rate (CWR-area under the traction-separation curve) but different maximum tractions. The effect of the maximum traction on the predicted cohesive zone length and the remote applied load at fracture is presented. Similar to the small scale yielding condition for an LEFM analysis to be valid. the cohesive zone length also needs to be much smaller than the crack length. This is a necessary condition for a CZM to obtain a fracture prediction equivalent to an LEFM result.
Pointwise Description for the Linearized Fokker-Planck-Boltzmann Model
NASA Astrophysics Data System (ADS)
Wu, Kung-Chien
2015-09-01
In this paper, we study the pointwise (in the space variable) behavior of the linearized Fokker-Planck-Boltzmann model for nonsmooth initial perturbations. The result reveals both the fluid and kinetic aspects of this model. The fluid-like waves are constructed as the long-wave expansion in the spectrum of the Fourier modes for the space variable, and it has polynomial time decay rate. We design a Picard-type iteration for constructing the increasingly regular kinetic-like waves, which are carried by the transport equations and have exponential time decay rate. The Mixture Lemma plays an important role in constructing the kinetic-like waves, this lemma was originally introduced by Liu-Yu (Commun Pure Appl Math 57:1543-1608, 2004) for Boltzmann equation, but the Fokker-Planck term in this paper creates some technical difficulties.
Linear mixing model applied to coarse resolution satellite data
NASA Technical Reports Server (NTRS)
Holben, Brent N.; Shimabukuro, Yosio E.
1992-01-01
A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.
Application of fractional derivative models in linear viscoelastic problems
NASA Astrophysics Data System (ADS)
Sasso, M.; Palmieri, G.; Amodio, D.
2011-11-01
Appropriate knowledge of viscoelastic properties of polymers and elastomers is of fundamental importance for a correct modelization and analysis of structures where such materials are present, especially when dealing with dynamic and vibration problems. In this paper experimental results of a series of compression and tension tests on specimens of styrene-butadiene rubber and polypropylene plastic are presented; tests consist of creep and relaxation tests, as well as cyclic loading at different frequencies. Experimental data are then used to calibrate some linear viscoelastic models; besides the classical approach based on a combination in series or parallel of standard mechanical elements as springs and dashpots, particular emphasis is given to the application of models whose constitutive equations are based on differential equations of fractional order (Fractional Derivative Model). The two approaches are compared analyzing their capability to reproduce all the experimental data for given materials; also, the main computational issues related with these models are addressed, and the advantage of using a limited number of parameters is demonstrated.
Electroweak corrections and unitarity in linear moose models
Chivukula, R. Sekhar; Simmons, Elizabeth H.; He, H.-J.; Kurachi, Masafumi; Tanabashi, Masaharu
2005-02-01
We calculate the form of the corrections to the electroweak interactions in the class of Higgsless models which can be deconstructed to a chain of SU(2) gauge groups adjacent to a chain of U(1) gauge groups, and with the fermions coupled to any single SU(2) group and to any single U(1) group along the chain. The primary advantage of our technique is that the size of corrections to electroweak processes can be directly related to the spectrum of vector bosons ('KK modes'). In Higgsless models, this spectrum is constrained by unitarity. Our methods also allow for arbitrary background 5D geometry, spatially dependent gauge-couplings, and brane kinetic energy terms. We find that, due to the size of corrections to electroweak processes in any unitary theory, Higgsless models with localized fermions are disfavored by precision electroweak data. Although we stress our results as they apply to continuum Higgsless 5D models, they apply to any linear moose model including those with only a few extra vector bosons. Our calculations of electroweak corrections also apply directly to the electroweak gauge sector of 5D theories with a bulk scalar Higgs boson; the constraints arising from unitarity do not apply in this case.
The Dantzig Selector for Censored Linear Regression Models
Li, Yi; Dicker, Lee; Zhao, Sihai Dave
2013-01-01
The Dantzig variable selector has recently emerged as a powerful tool for fitting regularized regression models. To our knowledge, most work involving the Dantzig selector has been performed with fully-observed response variables. This paper proposes a new class of adaptive Dantzig variable selectors for linear regression models when the response variable is subject to right censoring. This is motivated by a clinical study to identify genes predictive of event-free survival in newly diagnosed multiple myeloma patients. Under some mild conditions, we establish the theoretical properties of our procedures, including consistency in model selection (i.e. the right subset model will be identified with a probability tending to 1) and the optimal efficiency of estimation (i.e. the asymptotic distribution of the estimates is the same as that when the true subset model is known a priori). The practical utility of the proposed adaptive Dantzig selectors is verified via extensive simulations. We apply our new methods to the aforementioned myeloma clinical trial and identify important predictive genes. PMID:24478569
Linearization of the full activated sludge model No 1 for interaction analysis.
Benhalla, Abdelhay; Houssou, Mohamed; Charif, Moussa
2010-08-01
This paper deals with the linearization of the full activated sludge model No 1 (ASM1) in the scope of interaction analysis. For consistency, the linearization procedure is developed and validated within the BSM1 simulation benchmark framework. It is based on reaction rate approximation by linear combinations of states. The linear rate models are identified and incorporated in the mass balance equations, yielding a linear locally equivalent to the ASM1 model. Linear models for anoxic and aerated compartments are proposed. It is observed that the presented models track very closely the nonlinear ASM1 responses to various influent data. The key feature of this linearization strategy is that the gotten linear version of the ASM1 model is linear time invariant (LTI) and that it conserves the states biological interpretation and the original ASM1 dimension. It allows, therefore, application of interaction analysis methods and makes it possible to determine motivated control configurations for the ASM1 model. PMID:20131068
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
ERIC Educational Resources Information Center
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Feedbacks, climate sensitivity, and the limits of linear models
NASA Astrophysics Data System (ADS)
Rugenstein, M.; Knutti, R.
2015-12-01
The term "feedback" is used ubiquitously in climate research, but implies varied meanings in different contexts. From a specific process that locally affects a quantity, to a formal framework that attempts to determine a global response to a forcing, researchers use this term to separate, simplify, and quantify parts of the complex Earth system. We combine large (>120 member) ensemble GCM and EMIC step forcing simulations over a broad range of forcing levels with a historical and educational perspective to organize existing ideas around feedbacks and linear forcing-feedback models. With a new method overcoming internal variability and initial condition problems we quantify the non-constancy of the climate feedback parameter. Our results suggest a strong state- and forcing-dependency of feedbacks, which is not considered appropriately in many studies. A non-constant feedback factor likely explains some of the differences in estimates of equilibrium climate sensitivity from different methods and types of data. We discuss implications for the definition of the forcing term and its various adjustments. Clarifying the value and applicability of the linear forcing feedback framework and a better quantification of feedbacks on various timescales and spatial scales remains a high priority in order to better understand past and predict future changes in the climate system.
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed. PMID:26609741
Fluctuation loops in a noise-driven linear circuit model
NASA Astrophysics Data System (ADS)
Teitsworth, Stephen; Ghanta, Akhil; Neu, John
Understanding the spatio-temporal structure of most probable fluctuation pathways to rarely occurring states is a central problem in the study of noise-driven, non-equilibrium dynamical systems. When the underlying system does not possess detailed balance, the optimal fluctuation pathway to a particular state and relaxation pathway from that state may combine to form a loop-like structure in the system phase space which we call a fluctuation loop. Here, we study fluctuation loops in a linear circuit model consisting of coupled RC elements, where each element is driven by its own noise source and, generally, the effective noise strengths of different elements are not equal. Using a stochastic Hamiltonian approach, we determine the optimal fluctuation pathways, and construct corresponding fluctuation loops. Analytical results agree closely with suitably averaged simulation results based on the associated Langevin equation. To better characterize fluctuation loops, we study the time-dependent area tensor that is swept out by individual stochastic trajectories in the system phase space. At long times, the area tensor scales linearly with time, with a coefficient that precisely vanishes when the system satisfies detailed balance.
A linear geospatial streamflow modeling system for data sparse environments
Asante, Kwabena O.; Arlan, Guleid A.; Pervez, Md Shahriar; Rowland, James
2008-01-01
In many river basins around the world, inaccessibility of flow data is a major obstacle to water resource studies and operational monitoring. This paper describes a geospatial streamflow modeling system which is parameterized with global terrain, soils and land cover data and run operationally with satellite‐derived precipitation and evapotranspiration datasets. Simple linear methods transfer water through the subsurface, overland and river flow phases, and the resulting flows are expressed in terms of standard deviations from mean annual flow. In sample applications, the modeling system was used to simulate flow variations in the Congo, Niger, Nile, Zambezi, Orange and Lake Chad basins between 1998 and 2005, and the resulting flows were compared with mean monthly values from the open‐access Global River Discharge Database. While the uncalibrated model cannot predict the absolute magnitude of flow, it can quantify flow anomalies in terms of relative departures from mean flow. Most of the severe flood events identified in the flow anomalies were independently verified by the Dartmouth Flood Observatory (DFO) and the Emergency Disaster Database (EM‐DAT). Despite its limitations, the modeling system is valuable for rapid characterization of the relative magnitude of flood hazards and seasonal flow changes in data sparse settings.
Forecasting Groundwater Temperature with Linear Regression Models Using Historical Data.
Figura, Simon; Livingstone, David M; Kipfer, Rolf
2015-01-01
Although temperature is an important determinant of many biogeochemical processes in groundwater, very few studies have attempted to forecast the response of groundwater temperature to future climate warming. Using a composite linear regression model based on the lagged relationship between historical groundwater and regional air temperature data, empirical forecasts were made of groundwater temperature in several aquifers in Switzerland up to the end of the current century. The model was fed with regional air temperature projections calculated for greenhouse-gas emissions scenarios A2, A1B, and RCP3PD. Model evaluation revealed that the approach taken is adequate only when the data used to calibrate the models are sufficiently long and contain sufficient variability. These conditions were satisfied for three aquifers, all fed by riverbank infiltration. The forecasts suggest that with respect to the reference period 1980 to 2009, groundwater temperature in these aquifers will most likely increase by 1.1 to 3.8 K by the end of the current century, depending on the greenhouse-gas emissions scenario employed. PMID:25412761
Optimization in generalized linear models: A case study
NASA Astrophysics Data System (ADS)
Silva, Eliana Costa e.; Correia, Aldina; Lopes, Isabel Cristina
2016-06-01
The maximum likelihood method is usually chosen to estimate the regression parameters of Generalized Linear Models (GLM) and also for hypothesis testing and goodness of fit tests. The classical method for estimating GLM parameters is the Fisher scores. In this work we propose to compute the estimates of the parameters with two alternative methods: a derivative-based optimization method, namely the BFGS method which is one of the most popular of the quasi-Newton algorithms, and the PSwarm derivative-free optimization method that combines features of a pattern search optimization method with a global Particle Swarm scheme. As a case study we use a dataset of biological parameters (phytoplankton) and chemical and environmental parameters of the water column of a Portuguese reservoir. The results show that, for this dataset, BFGS and PSwarm methods provided a better fit, than Fisher scores method, and can be good alternatives for finding the estimates for the parameters of a GLM.
Simulation of non-linear coregionalization models by FFTMA
NASA Astrophysics Data System (ADS)
Liang, Min; Marcotte, Denis; Shamsipour, Pejman
2016-04-01
A fast and efficient method to simulate multivariate fields with non-linear models of coregionalization (N-LMC) is described. The method generalizes FFTMA to the multivariate simulation of the N-LMC with symmetric cross-covariances, hence the name GFFTMA. It allows us for example to use an exponential model as the direct covariance for the main variable, a Cauchy model for the secondary variable and a K-Bessel model for the cross-covariance. Each covariance and cross-covariance are Fast Fourier Transformed (FFT) to get the discrete spectral densities. Then the spectral matrix is eigen-decomposed at each frequency separately to provide the square root matrix and to enforce positive-definiteness in cases where small negative eigenvalues are found. Finally the simulated spectrum is obtained as multiplication of the root matrix and the white noise coefficients. The method is particularly fast for covariances having derivatives at the origin and/or for covariances with long range. Hence, two-variables' 2D fields of 100 million pixels with all-Gaussian or all-cubic covariances and cross-covariance are both simulated in less than 200 s. The CPU-time increases only as N log(N) (N, the number of points to simulate). Additional realizations are obtained at a low marginal cost as the eigen-decomposition step needs to be done only once for the first realization. The main limitation of the approach is its rather stringent memory requirement. Synthetic examples illustrate the simulations of N-LMC with two and three variables for different combinations of the seven available models. It shows that the theoretical models are all well reproduced. An illustrative case-study on overburden thickness simulation is provided where the secondary information consists of a latent Gaussian variable identifying the geological domain.
Modeling Electric Vehicle Benefits Connected to Smart Grids
Stadler, Michael; Marnay, Chris; Mendes, Goncalo; Kloess, Maximillian; Cardoso, Goncalo; Mégel, Olivier; Siddiqui, Afzal
2011-07-01
Connecting electric storage technologies to smartgrids will have substantial implications in building energy systems. Local storage will enable demand response. Mobile storage devices in electric vehicles (EVs) are in direct competition with conventional stationary sources at the building. EVs will change the financial as well as environmental attractiveness of on-site generation (e.g. PV, or fuel cells). In order to examine the impact of EVs on building energy costs and CO2 emissions in 2020, a distributed-energy-resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO2 emissions. The mixed-integer linear program is applied to a set of 139 different commercial buildings in California and example results as well as the aggregated economic and environmental benefits are reported. The research shows that considering second life of EV batteries might be very beneficial for commercial buildings.
A Linear City Model with Asymmetric Consumer Distribution
Azar, Ofer H.
2015-01-01
The article analyzes a linear-city model where the consumer distribution can be asymmetric, which is important because in real markets this distribution is often asymmetric. The model yields equilibrium price differences, even though the firms’ costs are equal and their locations are symmetric (at the two endpoints of the city). The equilibrium price difference is proportional to the transportation cost parameter and does not depend on the good's cost. The firms' markups are also proportional to the transportation cost. The two firms’ prices will be equal in equilibrium if and only if half of the consumers are located to the left of the city’s midpoint, even if other characteristics of the consumer distribution are highly asymmetric. An extension analyzes what happens when the firms have different costs and how the two sources of asymmetry – the consumer distribution and the cost per unit – interact together. The model can be useful as a tool for further development by other researchers interested in applying this simple yet flexible framework for the analysis of various topics. PMID:26034984
Blended Linear Models for Reduced Compliant Mechanical Systems.
Andrews, Sheldon; Teichmann, Marek; Kry, Paul G
2016-03-01
We present a method for the simulation of compliant, articulated structures using a plausible approximate model that focuses on modeling endpoint interaction. We approximate the structure's behavior about a reference configuration, resulting in a first order reduced compliant system, or FORK (-1) S. Several levels of approximation are available depending on which parts and surfaces we would like to have interactive contact forces, allowing various levels of detail to be selected. Our approach is fast and computation of the full structure's state may be parallelized. Furthermore, we present a method for reducing error by combining multiple FORK (-1)S models at different linearization points, through twist blending and matrix interpolation. Our approach is suitable for stiff, articulate grippers, such as those used in robotic simulation, or physics-based characters under static proportional derivative control. We demonstrate that simulations with our method can deal with kinematic chains and loops with non-uniform stiffness across joints, and that it produces plausible effects due to stiffness, damping, and inertia. PMID:26829238
Preconditioning the bidomain model with almost linear complexity
NASA Astrophysics Data System (ADS)
Pierre, Charles
2012-01-01
The bidomain model is widely used in electro-cardiology to simulate spreading of excitation in the myocardium and electrocardiograms. It consists of a system of two parabolic reaction diffusion equations coupled with an ODE system. Its discretisation displays an ill-conditioned system matrix to be inverted at each time step: simulations based on the bidomain model therefore are associated with high computational costs. In this paper we propose a preconditioning for the bidomain model either for an isolated heart or in an extended framework including a coupling with the surrounding tissues (the torso). The preconditioning is based on a formulation of the discrete problem that is shown to be symmetric positive semi-definite. A block LU decomposition of the system together with a heuristic approximation (referred to as the monodomain approximation) are the key ingredients for the preconditioning definition. Numerical results are provided for two test cases: a 2D test case on a realistic slice of the thorax based on a segmented heart medical image geometry, a 3D test case involving a small cubic slab of tissue with orthotropic anisotropy. The analysis of the resulting computational cost (both in terms of CPU time and of iteration number) shows an almost linear complexity with the problem size, i.e. of type nlog α( n) (for some constant α) which is optimal complexity for such problems.
Comparison of Linear and Non-Linear Regression Models to Estimate Leaf Area Index of Dryland Shrubs.
NASA Astrophysics Data System (ADS)
Dashti, H.; Glenn, N. F.; Ilangakoon, N. T.; Mitchell, J.; Dhakal, S.; Spaete, L.
2015-12-01
Leaf area index (LAI) is a key parameter in global ecosystem studies. LAI is considered a forcing variable in land surface processing models since ecosystem dynamics are highly correlated to LAI. In response to environmental limitations, plants in semiarid ecosystems have smaller leaf area, making accurate estimation of LAI by remote sensing a challenging issue. Optical remote sensing (400-2500 nm) techniques to estimate LAI are based either on radiative transfer models (RTMs) or statistical approaches. Considering the complex radiation field of dry ecosystems, simple 1-D RTMs lead to poor results, and on the other hand, inversion of more complex 3-D RTMs is a demanding task which requires the specification of many variables. A good alternative to physical approaches is using methods based on statistics. Similar to many natural phenomena, there is a non-linear relationship between LAI and top of canopy electromagnetic waves reflected to optical sensors. Non-linear regression models can better capture this relationship. However, considering the problem of a few numbers of observations in comparison to the feature space (n
models will not necessarily outperform the more simple linear models. In this study linear versus non-linear regression techniques were investigated to estimate LAI. Our study area is located in southwestern Idaho, Great Basin. Sagebrush (Artemisia tridentata spp) serves a critical role in maintaining the structure of this ecosystem. Using a leaf area meter (Accupar LP-80), LAI values were measured in the field. Linear Partial Least Square regression and non-linear, tree based Random Forest regression have been implemented to estimate the LAI of sagebrush from hyperspectral data (AVIRIS-ng) collected in late summer 2014. Cross validation of results indicate that PLS can provide comparable results to Random Forest.
a Linear Model for Meandering Rivers with Arbitrarily Varying Width
NASA Astrophysics Data System (ADS)
Frascati, A.; Lanzoni, S.
2011-12-01
Alluvial rivers usually exhibit quite complex planforms, characterized by a wide variety of alternating bends, that have attracted the interest of a large number of researchers. Much less attention has been paid to another striking feature observed in alluvial rivers, namely the relatively regular spatial variations attained by the channel width. Actively meandering channels, in fact, generally undergo spatial oscillations systematically correlated with channel curvature, with cross sections wider at bends than at crossings. Some other streams have been observed to exhibit irregular width variations. Conversely, rivers flowing in highly vegetated flood plains, i.e. canaliform rivers, may exhibit an opposite behavior, owing to the combined effects of bank erodibility and floodplain depositional processes which, in turn, are strictly linked to vegetation cover. Similarly to streamline curvatures induced by bends, the presence of along channel width variations may have remarkable effects on the flow field and sediment dynamics and, thereby, on the equilibrium river bed configuration. In particular, spatial distribution of channel curvature typically determines the formation of a rhythmic bar-pool pattern in the channel bed strictly associated with the development of river meanders. Channel width variations are on the contrary characterized by a sequence of narrowing, yielding a central scour, alternated to the downstream development of a widening associated with the formation of a central bar. Here we present a morphodynamic model that predict at a linear level the spatial distribution of the flow field and the equilibrium bed configuration of an alluvial river characterized by arbitrary along channel distributions of both the channel axis curvature and the channel width. The mathematical model is averaged over the depth and describes the steady, non-uniform flow and sediment transport in sinuous channels with a noncohesive bed. The governing two-dimensional equations
On the Relation between the Linear Factor Model and the Latent Profile Model
ERIC Educational Resources Information Center
Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul
2011-01-01
The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…
Development and validation of a general purpose linearization program for rigid aircraft models
NASA Technical Reports Server (NTRS)
Duke, E. L.; Antoniewicz, R. F.
1985-01-01
A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.
Performance Models for the Spike Banded Linear System Solver
Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; Grama, Ananth
2011-01-01
With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated
Sensitivity Analysis of Parameters in Linear-Quadratic Radiobiologic Modeling
Fowler, Jack F.
2009-04-01
Purpose: Radiobiologic modeling is increasingly used to estimate the effects of altered treatment plans, especially for dose escalation. The present article shows how much the linear-quadratic (LQ) (calculated biologically equivalent dose [BED] varies when individual parameters of the LQ formula are varied by {+-}20% and by 1%. Methods: Equivalent total doses (EQD2 = normalized total doses (NTD) in 2-Gy fractions for tumor control, acute mucosal reactions, and late complications were calculated using the linear- quadratic formula with overall time: BED = nd (1 + d/ [{alpha}/{beta}]) - log{sub e}2 (T - Tk) / {alpha}Tp, where BED is BED = total dose x relative effectiveness (RE = nd (1 + d/ [{alpha}/{beta}]). Each of the five biologic parameters in turn was altered by {+-}10%, and the altered EQD2s tabulated; the difference was finally divided by 20. EQD2 or NTD is obtained by dividing BED by the RE for 2-Gy fractions, using the appropriate {alpha}/{beta} ratio. Results: Variations in tumor and acute mucosal EQD ranged from 0.1% to 0.45% per 1% change in each parameter for conventional schedules, the largest variation being caused by overall time. Variations in 'late' EQD were 0.4% to 0.6% per 1% change in the only biologic parameter, the {alpha}/{beta} ratio. For stereotactic body radiotherapy schedules, variations were larger, up to 0.6 to 0.9 for tumor and 1.6% to 1.9% for late, per 1% change in parameter. Conclusions: Robustness occurs similar to that of equivalent uniform dose (EUD), for the same reasons. Total dose, dose per fraction, and dose-rate cause their major effects, as well known.
Fourth standard model family neutrino at future linear colliders
Ciftci, A.K.; Ciftci, R.; Sultansoy, S.
2005-09-01
It is known that flavor democracy favors the existence of the fourth standard model (SM) family. In order to give nonzero masses for the first three-family fermions flavor democracy has to be slightly broken. A parametrization for democracy breaking, which gives the correct values for fundamental fermion masses and, at the same time, predicts quark and lepton Cabibbo-Kobayashi-Maskawa (CKM) matrices in a good agreement with the experimental data, is proposed. The pair productions of the fourth SM family Dirac ({nu}{sub 4}) and Majorana (N{sub 1}) neutrinos at future linear colliders with {radical}(s)=500 GeV, 1 TeV, and 3 TeV are considered. The cross section for the process e{sup +}e{sup -}{yields}{nu}{sub 4}{nu}{sub 4}(N{sub 1}N{sub 1}) and the branching ratios for possible decay modes of the both neutrinos are determined. The decays of the fourth family neutrinos into muon channels ({nu}{sub 4}(N{sub 1}){yields}{mu}{sup {+-}}W{sup {+-}}) provide cleanest signature at e{sup +}e{sup -} colliders. Meanwhile, in our parametrization this channel is dominant. W bosons produced in decays of the fourth family neutrinos will be seen in detector as either di-jets or isolated leptons. As an example, we consider the production of 200 GeV mass fourth family neutrinos at {radical}(s)=500 GeV linear colliders by taking into account di-muon plus four jet events as signatures.
Linear Models Based on Noisy Data and the Frisch Scheme*
Ning, Lipeng; Georgiou, Tryphon T.; Tannenbaum, Allen; Boyd, Stephen P.
2016-01-01
We address the problem of identifying linear relations among variables based on noisy measurements. This is a central question in the search for structure in large data sets. Often a key assumption is that measurement errors in each variable are independent. This basic formulation has its roots in the work of Charles Spearman in 1904 and of Ragnar Frisch in the 1930s. Various topics such as errors-in-variables, factor analysis, and instrumental variables all refer to alternative viewpoints on this problem and on ways to account for the anticipated way that noise enters the data. In the present paper we begin by describing certain fundamental contributions by the founders of the field and provide alternative modern proofs to certain key results. We then go on to consider a modern viewpoint and novel numerical techniques to the problem. The central theme is expressed by the Frisch–Kalman dictum, which calls for identifying a noise contribution that allows a maximal number of simultaneous linear relations among the noise-free variables—a rank minimization problem. In the years since Frisch’s original formulation, there have been several insights, including trace minimization as a convenient heuristic to replace rank minimization. We discuss convex relaxations and theoretical bounds on the rank that, when met, provide guarantees for global optimality. A complementary point of view to this minimum-rank dictum is presented in which models are sought leading to a uniformly optimal quadratic estimation error for the error-free variables. Points of contact between these formalisms are discussed, and alternative regularization schemes are presented. PMID:27168672
Estimating population trends with a linear model: technical comments
Sauer, J.R.; Link, W.A.; Royle, J. Andrew
2004-01-01
Controversy has sometimes arisen over whether there is a need to accommodate the limitations of survey design in estimating population change from the count data collected in bird surveys. Analyses of surveys such as the North American Breeding Bird Survey (BBS) can be quite complex; it is natural to ask if the complexity is necessary, or whether the statisticians have run amok. Bart et al. (2003) propose a very simple analysis involving nothing more complicated than simple linear regression, and contrast their approach with model-based procedures. We review the assumptions implicit to their proposed method, and document that these assumptions are unlikely to be valid for surveys such as the BBS. One fundamental limitation of a purely design-based approach is the absence of controls for factors that influence detection of birds at survey sites. We show that failure to model observer effects in survey data leads to substantial bias in estimation of population trends from BBS data for the 20 species that Bart et al. (2003) used as the basis of their simulations. Finally, we note that the simulations presented in Bart et al. (2003) do not provide a useful evaluation of their proposed method, nor do they provide a valid comparison to the estimating- equations alternative they consider.
Linear System Models for Ultrasonic Imaging: Application to Signal Statistics
Zemp, Roger J.; Abbey, Craig K.; Insana, Michael F.
2009-01-01
Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function—defined as a spatial weighting function that determines the scattering volume for a fixed point of time—has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals. PMID:12839176
Understanding cardiac alternans: A piecewise linear modeling framework
NASA Astrophysics Data System (ADS)
Thul, R.; Coombes, S.
2010-12-01
Cardiac alternans is a beat-to-beat alternation in action potential duration (APD) and intracellular calcium (Ca2+) cycling seen in cardiac myocytes under rapid pacing that is believed to be a precursor to fibrillation. The cellular mechanisms of these rhythms and the coupling between cellular Ca2+ and voltage dynamics have been extensively studied leading to the development of a class of physiologically detailed models. These have been shown numerically to reproduce many of the features of myocyte response to pacing, including alternans, and have been analyzed mathematically using various approximation techniques that allow for the formulation of a low dimensional map to describe the evolution of APDs. The seminal work by Shiferaw and Karma is of particular interest in this regard [Shiferaw, Y. and Karma, A., "Turing instability mediated by voltage and calcium diffusion in paced cardiac cells," Proc. Natl. Acad. Sci. U.S.A. 103, 5670-5675 (2006)]. Here, we establish that the key dynamical behaviors of the Shiferaw-Karma model are arranged around a set of switches. These are shown to be the main elements for organizing the nonlinear behavior of the model. Exploiting this observation, we show that a piecewise linear caricature of the Shiferaw-Karma model, with a set of appropriate switching manifolds, can be constructed that preserves the physiological interpretation of the original model while being amenable to a systematic mathematical analysis. In illustration of this point, we formulate the dynamics of Ca2+ cycling (in response to pacing) and compute the properties of periodic orbits in terms of a stroboscopic map that can be constructed without approximation. Using this, we show that alternans emerge via a period-doubling instability and track this bifurcation in terms of physiologically important parameters. We also show that when coupled to a spatially extended model for Ca2+ transport, the model supports spatially varying patterns of alternans. We analyze
Formal modeling and verification of fractional order linear systems.
Zhao, Chunna; Shi, Likun; Guan, Yong; Li, Xiaojuan; Shi, Zhiping
2016-05-01
This paper presents a formalization of a fractional order linear system in a higher-order logic (HOL) theorem proving system. Based on the formalization of the Grünwald-Letnikov (GL) definition, we formally specify and verify the linear and superposition properties of fractional order systems. The proof provides a rigor and solid underpinnings for verifying concrete fractional order linear control systems. Our implementation in HOL demonstrates the effectiveness of our approach in practical applications. PMID:27126601
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. PMID:26774211
Modeling Seismoacoustic Propagation from the Nonlinear to Linear Regimes
NASA Astrophysics Data System (ADS)
Chael, E. P.; Preston, L. A.
2015-12-01
Explosions at shallow depth-of-burial can cause nonlinear material response, such as fracturing and spalling, up to the ground surface above the shot point. These motions at the surface affect the generation of acoustic waves into the atmosphere, as well as the surface-reflected compressional and shear waves. Standard source scaling models for explosions do not account for such nonlinear interactions above the shot, while some recent studies introduce a non-isotropic addition to the moment tensor to represent them (e.g., Patton and Taylor, 2011). We are using Sandia's CTH shock physics code to model the material response in the vicinity of underground explosions, up to the overlying ground surface. Across a boundary where the motions have decayed to nearly linear behavior, we couple the signals from CTH into a linear finite-difference (FD) seismoacoustic code to efficiently propagate the wavefields to greater distances. If we assume only one-way transmission of energy through the boundary, then the particle velocities there suffice as inputs for the FD code, simplifying the specification of the boundary condition. The FD algorithm we use applies the wave equations for velocity in an elastic medium and pressure in an acoustic one, and matches the normal traction and displacement across the interface. Initially we are developing and testing a 2D, axisymmetric seismoacoustic routine; CTH can use this geometry in the source region as well. The Source Physics Experiment (SPE) in Nevada has collected seismic and acoustic data on numerous explosions at different scaled depths, providing an excellent testbed for investigating explosion phenomena (Snelson et al., 2013). We present simulations for shots SPE-4' and SPE-5, illustrating the importance of nonlinear behavior up to the ground surface. Our goal is to develop the capability for accurately predicting the relative signal strengths in the air and ground for a given combination of source yield and depth. Sandia National
Identifying genetically driven clinical phenotypes using linear mixed models.
Mosley, Jonathan D; Witte, John S; Larkin, Emma K; Bastarache, Lisa; Shaffer, Christian M; Karnes, Jason H; Stein, C Michael; Phillips, Elizabeth; Hebbring, Scott J; Brilliant, Murray H; Mayer, John; Ye, Zhan; Roden, Dan M; Denny, Joshua C
2016-01-01
We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1-1.2), P=9.8 × 10(-11)) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3-1.6), P=1.3 × 10(-10)). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations. PMID:27109359
Linear effects models of signaling pathways from combinatorial perturbation data
Szczurek, Ewa; Beerenwinkel, Niko
2016-01-01
Motivation: Perturbations constitute the central means to study signaling pathways. Interrupting components of the pathway and analyzing observed effects of those interruptions can give insight into unknown connections within the signaling pathway itself, as well as the link from the pathway to the effects. Different pathway components may have different individual contributions to the measured perturbation effects, such as gene expression changes. Those effects will be observed in combination when the pathway components are perturbed. Extant approaches focus either on the reconstruction of pathway structure or on resolving how the pathway components control the downstream effects. Results: Here, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturbation data. We use simulated data to demonstrate the accuracy of learning the pathway structure as well as estimation of the individual contributions of pathway components to the perturbation effects. The practical utility of our approach is illustrated by an application to perturbations of the mitogen-activated protein kinase pathway in Saccharomyces cerevisiae. Availability and Implementation: lem is available as a R package at http://www.mimuw.edu.pl/∼szczurek/lem. Contact: szczurek@mimuw.edu.pl; niko.beerenwinkel@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307630
Linear multivariate evaluation models for spatial perception of soundscape.
Deng, Zhiyong; Kang, Jian; Wang, Daiwei; Liu, Aili; Kang, Joe Zhengyu
2015-11-01
Soundscape is a sound environment that emphasizes the awareness of auditory perception and social or cultural understandings. The case of spatial perception is significant to soundscape. However, previous studies on the auditory spatial perception of the soundscape environment have been limited. Based on 21 native binaural-recorded soundscape samples and a set of auditory experiments for subjective spatial perception (SSP), a study of the analysis among semantic parameters, the inter-aural-cross-correlation coefficient (IACC), A-weighted-equal sound-pressure-level (L(eq)), dynamic (D), and SSP is introduced to verify the independent effect of each parameter and to re-determine some of their possible relationships. The results show that the more noisiness the audience perceived, the worse spatial awareness they received, while the closer and more directional the sound source image variations, dynamics, and numbers of sound sources in the soundscape are, the better the spatial awareness would be. Thus, the sensations of roughness, sound intensity, transient dynamic, and the values of Leq and IACC have a suitable range for better spatial perception. A better spatial awareness seems to promote the preference slightly for the audience. Finally, setting SSPs as functions of the semantic parameters and Leq-D-IACC, two linear multivariate evaluation models of subjective spatial perception are proposed. PMID:26627762
Markov Boundary Discovery with Ridge Regularized Linear Models
Visweswaran, Shyam
2016-01-01
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many investigators are reluctant to draw causal interpretations of the selected variables due to the incomplete knowledge of the capabilities of RRLMs in causal inference. Under reasonable assumptions, we show that a modified form of RRLMs can get “very close” to identifying a subset of the Markov boundary by providing a worst-case bound on the space of possible solutions. The results hold for any convex loss, even when the underlying functional relationship is nonlinear, and the solution is not unique. Our approach combines ideas in Markov boundary and sufficient dimension reduction theory. Experimental results show that the modified RRLMs are competitive against state-of-the-art algorithms in discovering part of the Markov boundary from gene expression data. PMID:27170915
Amplitude relations in non-linear sigma model
NASA Astrophysics Data System (ADS)
Chen, Gang; Du, Yi-Jian
2014-01-01
In this paper, we investigate tree-level scattering amplitude relations in U( N) non-linear sigma model. We use Cayley parametrization. As was shown in the recent works [23,24], both on-shell amplitudes and off-shell currents with odd points have to vanish under Cayley parametrization. We prove the off-shell U(1) identity and fundamental BCJ relation for even-point currents. By taking the on-shell limits of the off-shell relations, we show that the color-ordered tree amplitudes with even points satisfy U(1)-decoupling identity and fundamental BCJ relation, which have the same formations within Yang-Mills theory. We further state that all the on-shell general KK, BCJ relations as well as the minimal-basis expansion are also satisfied by color-ordered tree amplitudes. As a consequence of the relations among color-ordered amplitudes, the total 2 m-point tree amplitudes satisfy DDM form of color decomposition as well as KLT relation.
Complex dynamics in the Oregonator model with linear delayed feedback
NASA Astrophysics Data System (ADS)
Sriram, K.; Bernard, S.
2008-06-01
The Belousov-Zhabotinsky (BZ) reaction can display a rich dynamics when a delayed feedback is applied. We used the Oregonator model of the oscillating BZ reaction to explore the dynamics brought about by a linear delayed feedback. The time-delayed feedback can generate a succession of complex dynamics: period-doubling bifurcation route to chaos; amplitude death; fat, wrinkled, fractal, and broken tori; and mixed-mode oscillations. We observed that this dynamics arises due to a delay-driven transition, or toggling of the system between large and small amplitude oscillations, through a canard bifurcation. We used a combination of numerical bifurcation continuation techniques and other numerical methods to explore the dynamics in the strength of feedback-delay space. We observed that the period-doubling and quasiperiodic route to chaos span a low-dimensional subspace, perhaps due to the trapping of the trajectories in the small amplitude regime near the canard; and the trapped chaotic trajectories get ejected from the small amplitude regime due to a crowding effect to generate chaotic-excitable spikes. We also qualitatively explained the observed dynamics by projecting a three-dimensional phase portrait of the delayed dynamics on the two-dimensional nullclines. This is the first instance in which it is shown that the interaction of delay and canard can bring about complex dynamics.
Identifying genetically driven clinical phenotypes using linear mixed models
Mosley, Jonathan D.; Witte, John S.; Larkin, Emma K.; Bastarache, Lisa; Shaffer, Christian M.; Karnes, Jason H.; Stein, C. Michael; Phillips, Elizabeth; Hebbring, Scott J.; Brilliant, Murray H.; Mayer, John; Ye, Zhan; Roden, Dan M.; Denny, Joshua C.
2016-01-01
We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1–1.2), P=9.8 × 10−11) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3–1.6), P=1.3 × 10−10). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations. PMID:27109359
Modelling and Inverse-Modelling: Experiences with O.D.E. Linear Systems in Engineering Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor
2009-01-01
In engineering careers courses, differential equations are widely used to solve problems concerned with modelling. In particular, ordinary differential equations (O.D.E.) linear systems appear regularly in Chemical Engineering, Food Technology Engineering and Environmental Engineering courses, due to the usefulness in modelling chemical kinetics,…
ERIC Educational Resources Information Center
Battauz, Michela; Bellio, Ruggero
2011-01-01
This paper proposes a structural analysis for generalized linear models when some explanatory variables are measured with error and the measurement error variance is a function of the true variables. The focus is on latent variables investigated on the basis of questionnaires and estimated using item response theory models. Latent variable…
NASA Astrophysics Data System (ADS)
Farrugia, Charles; Moestl, Christian; Leitner, Martin; Galvin, Antoinette; Lugaz, Noé; Yu, Wenyuan
2016-07-01
This work is about modeling of those small solar wind transients (STs) which have a flux rope geometry. The two models used are: (i) the linear force free solution of Lundquist in terms of Bessel functions, and (ii) the non-linear Gold-Hoyle solution describing a uniformly-twisted flux tube. The first has been used almost exclusively in modeling of both large and small flux ropes in the solar wind. The second was applied to one small transient. In recent work there have been claims that variant (ii) is more appropriate than (i) for large transients, i.e. magnetic clouds. We select by eye six flux rope STs from STEREO and Wind data, chosen purely on the basis of having a large and smooth rotation. We also choose these during solar maximum activity conditions since our current work shows that only then are these models appropriate.
A log-linear multidimensional Rasch model for capture-recapture.
Pelle, E; Hessen, D J; van der Heijden, P G M
2016-02-20
In this paper, a log-linear multidimensional Rasch model is proposed for capture-recapture analysis of registration data. In the model, heterogeneity of capture probabilities is taken into account, and registrations are viewed as dichotomously scored indicators of one or more latent variables that can account for correlations among registrations. It is shown how the probability of a generic capture profile is expressed under the log-linear multidimensional Rasch model and how the parameters of the traditional log-linear model are derived from those of the log-linear multidimensional Rasch model. Finally, an application of the model to neural tube defects data is presented. PMID:26423044
Models of reduced-noise, probabilistic linear amplifiers
NASA Astrophysics Data System (ADS)
Combes, Joshua; Walk, Nathan; Lund, A. P.; Ralph, T. C.; Caves, Carlton M.
2016-05-01
We construct an amplifier that interpolates between a nondeterministic, immaculate linear amplifier and a deterministic, ideal linear amplifier and beyond to nonideal linear amplifiers. The construction involves cascading an immaculate linear amplifier that has amplitude gain g1 with a (possibly) nonideal linear amplifier that has gain g2. With respect to normally ordered moments, the device has output noise μ2(G2-1 ) where G =g1g2 is the overall amplitude gain and μ2 is a noise parameter. When μ2≥1 , our devices realize ideal (μ2=1 ) and nonideal (μ2>1 ) linear amplifiers. When 0 ≤μ2<1 , these devices work effectively only over a restricted region of phase space and with some subunity success probability p✓. We investigate the performance of our μ2 amplifiers in terms of a gain-corrected probability-fidelity product and the ratio of input to output signal-to-noise ratios corrected for success probability.
Optimization modeling for industrial waste reduction planning
Roberge, H.D.; Baetz, B.W. . Dept. of Civil Engineering)
1994-01-01
A model is developed for planning the implementation of industrial waste reduction and waste management strategies. The model is based on minimizing the overall cost of waste reduction and waste management for an industrial facility over a certain time period. The problem is formulated as a general mixed integer linear programming (MILP) problem, where the objective function includes capital and operating costs and is subject to a number of constraints that define the system under consideration. The information required to use the modeling approach includes the capital and operating costs of the various options being considered, discount rates, escalation factors, the capacity limitations on various options for waste treatment, disposal and management, as well as treatment efficiencies and the potential for waste reduction. The general modeling approach is applied to a case study facility. The MILP formulation was solved using a commercially available software package. The model could be used by an environmental engineer or a planner in an industry that is conserving implementing waste reduction projects. Ideally, the industry would have generated information on modifications that could reduce their waste generation, as well as information on their current waste management practices. In the event that specific waste reduction projects have not been identified, the economic feasibility of potential future projects could be determined.
Modeling of driver's collision avoidance maneuver based on controller switching model.
Kim, Jong-Hae; Hayakawa, Soichiro; Suzuki, Tatsuya; Hayashi, Koji; Okuma, Shigeru; Tsuchida, Nuio; Shimizu, Masayuki; Kido, Shigeyuki
2005-12-01
This paper presents a modeling strategy of human driving behavior based on the controller switching model focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three-dimensional (3-D) driving simulator based on the CAVE Automatic Virtual Environment (CAVE), which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver's sensory information to the operation of the driver such as acceleration, braking, and steering, is expressed by Piecewise Polynomial (PWP) model. Since the PWP model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWP model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the "control law" according to the sensory information. In addition, the driving characteristics of the beginner driver and the expert driver are compared and discussed. These results enable us to capture not only the physical meaning of the driving skill but the decision-making aspect (switching conditions) in the driver's collision avoidance maneuver as well. PMID:16366240
The Simplest Complete Model of Choice Response Time: Linear Ballistic Accumulation
ERIC Educational Resources Information Center
Brown, Scott D.; Heathcote, Andrew
2008-01-01
We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows…
Power and Bias in Hierarchical Linear Growth Models: More Measurements of Fewer People
ERIC Educational Resources Information Center
Haardoerfer, Regine
2010-01-01
Hierarchical Linear Modeling (HLM) sample size recommendations are mostly made with traditional group-design research in mind, as HLM as been used almost exclusively in group-design studies. Single-case research can benefit from utilizing hierarchical linear growth modeling, but sample size recommendations for growth modeling with HLM are scarce…
ATOPS B-737 inner-loop control system linear model construction and verification
NASA Technical Reports Server (NTRS)
Broussard, J. R.
1983-01-01
Nonlinear models and block diagrams of an inner-loop control system for the ATOPS B-737 Research Aircraft are presented. Continuous time linear model representations of the nonlinear inner-loop control systems are derived. Closed-loop aircraft simulations comparing nonlinear and linear dynamic responses to step inputs are used to verify the inner-loop control system models.
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
NASA Technical Reports Server (NTRS)
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
Sun Wei; Huang, Guo H.; Lv Ying; Li Gongchen
2012-06-15
Highlights: Black-Right-Pointing-Pointer Inexact piecewise-linearization-based fuzzy flexible programming is proposed. Black-Right-Pointing-Pointer It's the first application to waste management under multiple complexities. Black-Right-Pointing-Pointer It tackles nonlinear economies-of-scale effects in interval-parameter constraints. Black-Right-Pointing-Pointer It estimates costs more accurately than the linear-regression-based model. Black-Right-Pointing-Pointer Uncertainties are decreased and more satisfactory interval solutions are obtained. - Abstract: To tackle nonlinear economies-of-scale (EOS) effects in interval-parameter constraints for a representative waste management problem, an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model is developed. In IPFP, interval parameters for waste amounts and transportation/operation costs can be quantified; aspiration levels for net system costs, as well as tolerance intervals for both capacities of waste treatment facilities and waste generation rates can be reflected; and the nonlinear EOS effects transformed from objective function to constraints can be approximated. An interactive algorithm is proposed for solving the IPFP model, which in nature is an interval-parameter mixed-integer quadratically constrained programming model. To demonstrate the IPFP's advantages, two alternative models are developed to compare their performances. One is a conventional linear-regression-based inexact fuzzy programming model (IPFP2) and the other is an IPFP model with all right-hand-sides of fussy constraints being the corresponding interval numbers (IPFP3). The comparison results between IPFP and IPFP2 indicate that the optimized waste amounts would have the similar patterns in both models. However, when dealing with EOS effects in constraints, the IPFP2 may underestimate the net system costs while the IPFP can estimate the costs more accurately. The comparison results between IPFP and IPFP3 indicate
Results and Comparison from the SAM Linear Fresnel Technology Performance Model: Preprint
Wagner, M. J.
2012-04-01
This paper presents the new Linear Fresnel technology performance model in NREL's System Advisor Model. The model predicts the financial and technical performance of direct-steam-generation Linear Fresnel power plants, and can be used to analyze a range of system configurations. This paper presents a brief discussion of the model formulation and motivation, and provides extensive discussion of the model performance and financial results. The Linear Fresnel technology is also compared to other concentrating solar power technologies in both qualitative and quantitative measures. The Linear Fresnel model - developed in conjunction with the Electric Power Research Institute - provides users with the ability to model a variety of solar field layouts, fossil backup configurations, thermal receiver designs, and steam generation conditions. This flexibility aims to encompass current market solutions for the DSG Linear Fresnel technology, which is seeing increasing exposure in fossil plant augmentation and stand-alone power generation applications.
Linear relaxation in large two-dimensional Ising models
NASA Astrophysics Data System (ADS)
Lin, Y.; Wang, F.
2016-02-01
Critical dynamics in two-dimension Ising lattices up to 2048 ×2048 is simulated on field-programmable-gate-array- based computing devices. Linear relaxation times are measured from extremely long Monte Carlo simulations. The longest simulation has 7.1 ×1016 spin updates, which would take over 37 years to simulate on a general purpose computer. The linear relaxation time of the Ising lattices is found to follow the dynamic scaling law for correlation lengths as long as 2048. The dynamic exponent z of the system is found to be 2.179(12), which is consistent with previous studies of Ising lattices with shorter correlation lengths. It is also found that Monte Carlo simulations of critical dynamics in Ising lattices larger than 512 ×512 are very sensitive to the statistical correlations between pseudorandom numbers, making it even more difficult to study such large systems.
AN ADA LINEAR ALGEBRA PACKAGE MODELED AFTER HAL/S
NASA Technical Reports Server (NTRS)
Klumpp, A. R.
1994-01-01
This package extends the Ada programming language to include linear algebra capabilities similar to those of the HAL/S programming language. The package is designed for avionics applications such as Space Station flight software. In addition to the HAL/S built-in functions, the package incorporates the quaternion functions used in the Shuttle and Galileo projects, and routines from LINPAK that solve systems of equations involving general square matrices. Language conventions in this package follow those of HAL/S to the maximum extent practical and minimize the effort required for writing new avionics software and translating existent software into Ada. Valid numeric types in this package include scalar, vector, matrix, and quaternion declarations. (Quaternions are fourcomponent vectors used in representing motion between two coordinate frames). Single precision and double precision floating point arithmetic is available in addition to the standard double precision integer manipulation. Infix operators are used instead of function calls to define dot products, cross products, quaternion products, and mixed scalar-vector, scalar-matrix, and vector-matrix products. The package contains two generic programs: one for floating point, and one for integer. The actual component type is passed as a formal parameter to the generic linear algebra package. The procedures for solving systems of linear equations defined by general matrices include GEFA, GECO, GESL, and GIDI. The HAL/S functions include ABVAL, UNIT, TRACE, DET, INVERSE, TRANSPOSE, GET, PUT, FETCH, PLACE, and IDENTITY. This package is written in Ada (Version 1.2) for batch execution and is machine independent. The linear algebra software depends on nothing outside the Ada language except for a call to a square root function for floating point scalars (such as SQRT in the DEC VAX MATHLIB library). This program was developed in 1989, and is a copyrighted work with all copyright vested in NASA.
Wu, Tsan-Pei; Wang, Xiao-Qun; Guo, Guang-Yu; Anders, Frithjof; Chung, Chung-Hou
2016-05-01
The quantum criticality of the two-lead two-channel pseudogap Anderson impurity model is studied. Based on the non-crossing approximation (NCA) and numerical renormalization group (NRG) approaches, we calculate both the linear and nonlinear conductance of the model at finite temperatures with a voltage bias and a power-law vanishing conduction electron density of states, [Formula: see text] (0 < r < 1) near the Fermi energy [Formula: see text]. At a fixed lead-impurity hybridization, a quantum phase transition from the two-channel Kondo (2CK) to the local moment (LM) phase is observed with increasing r from r = 0 to [Formula: see text]. Surprisingly, in the 2CK phase, different power-law scalings from the well-known [Formula: see text] or [Formula: see text] form is found. Moreover, novel power-law scalings in conductances at the 2CK-LM quantum critical point are identified. Clear distinctions are found on the critical exponents between linear and non-linear conductance at criticality. The implications of these two distinct quantum critical properties for the non-equilibrium quantum criticality in general are discussed. PMID:27045815
NASA Astrophysics Data System (ADS)
Wu, Tsan-Pei; Wang, Xiao-Qun; Guo, Guang-Yu; Anders, Frithjof; Chung, Chung-Hou
2016-05-01
The quantum criticality of the two-lead two-channel pseudogap Anderson impurity model is studied. Based on the non-crossing approximation (NCA) and numerical renormalization group (NRG) approaches, we calculate both the linear and nonlinear conductance of the model at finite temperatures with a voltage bias and a power-law vanishing conduction electron density of states, {ρ\\text{c}}(ω )\\propto |ω -{μ\\text{F}}{{|}r} (0 < r < 1) near the Fermi energy {μ\\text{F}} . At a fixed lead-impurity hybridization, a quantum phase transition from the two-channel Kondo (2CK) to the local moment (LM) phase is observed with increasing r from r = 0 to r={{r}\\text{c}}<1 . Surprisingly, in the 2CK phase, different power-law scalings from the well-known \\sqrt{T} or \\sqrt{V} form is found. Moreover, novel power-law scalings in conductances at the 2CK-LM quantum critical point are identified. Clear distinctions are found on the critical exponents between linear and non-linear conductance at criticality. The implications of these two distinct quantum critical properties for the non-equilibrium quantum criticality in general are discussed.
A new mathematical programming model for scheduling flexible manufacturing systems
MacCarthy, B.; Liu, J.
1994-12-31
Flexibility is now a major consideration in the design of many manufacturing systems. Flexible manufacturing systems (FMS) have been developed in the last two decades. The principal elements of an FMS are (1) computer controlled machine tools, (2) a transport system and (3) a host computer system. Such systems may combine high flexibility with high productivity and may allow unsupervised production. However, in order to achieve these benefits, the control system must be capable of exercising intelligent supervisory management. Scheduling is at the heart of the control system and is still a major problem area. This paper describes a new mathematical programming model for a wide class of FMS scheduling problems based on a new classification scheme. A global optimization approach is adopted based on a mixed-integer linear programming model. Many important aspects of operational FMS, omitted form earlier models, are included. Key elements of model structure are highlighted. Computational experience with a comprehensive set of designed experiments is described. The applications of the model are noted and the development of effective heuristic procedures based on the model is highlighted.
Optimal Scaling of Interaction Effects in Generalized Linear Models
ERIC Educational Resources Information Center
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F.
2009-01-01
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
Modeling thermal sensation in a Mediterranean climate—a comparison of linear and ordinal models
NASA Astrophysics Data System (ADS)
Pantavou, Katerina; Lykoudis, Spyridon
2014-08-01
A simple thermo-physiological model of outdoor thermal sensation adjusted with psychological factors is developed aiming to predict thermal sensation in Mediterranean climates. Microclimatic measurements simultaneously with interviews on personal and psychological conditions were carried out in a square, a street canyon and a coastal location of the greater urban area of Athens, Greece. Multiple linear and ordinal regression were applied in order to estimate thermal sensation making allowance for all the recorded parameters or specific, empirically selected, subsets producing so-called extensive and empirical models, respectively. Meteorological, thermo-physiological and overall models - considering psychological factors as well - were developed. Predictions were improved when personal and psychological factors were taken into account as compared to meteorological models. The model based on ordinal regression reproduced extreme values of thermal sensation vote more adequately than the linear regression one, while the empirical model produced satisfactory results in relation to the extensive model. The effects of adaptation and expectation on thermal sensation vote were introduced in the models by means of the exposure time, season and preference related to air temperature and irradiation. The assessment of thermal sensation could be a useful criterion in decision making regarding public health, outdoor spaces planning and tourism.
ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
ERIC Educational Resources Information Center
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2012-01-01
Purpose: The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F[subscript 0]) during anterior-posterior stretching. Method: Three materially linear and 3 materially nonlinear models were…
Item Purification in Differential Item Functioning Using Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Liu, Qian
2011-01-01
For this dissertation, four item purification procedures were implemented onto the generalized linear mixed model for differential item functioning (DIF) analysis, and the performance of these item purification procedures was investigated through a series of simulations. Among the four procedures, forward and generalized linear mixed model (GLMM)…
Linear moose model with pairs of degenerate gauge boson triplets
Casalbuoni, Roberto; Coradeschi, Francesco; De Curtis, Stefania; Dominici, Daniele
2008-05-01
The possibility of a strongly interacting electroweak symmetry breaking sector, as opposed to the weakly interacting light Higgs of the standard model, is not yet ruled out by experiments. In this paper we make an extensive study of a deconstructed model (or ''moose'' model) providing an effective description of such a strong symmetry breaking sector, and show its compatibility with experimental data for a wide portion of the model parameter space. The model is a direct generalization of the previously proposed D-BESS model.
Linear moose model with pairs of degenerate gauge boson triplets
NASA Astrophysics Data System (ADS)
Casalbuoni, Roberto; Coradeschi, Francesco; de Curtis, Stefania; Dominici, Daniele
2008-05-01
The possibility of a strongly interacting electroweak symmetry breaking sector, as opposed to the weakly interacting light Higgs of the standard model, is not yet ruled out by experiments. In this paper we make an extensive study of a deconstructed model (or “moose” model) providing an effective description of such a strong symmetry breaking sector, and show its compatibility with experimental data for a wide portion of the model parameter space. The model is a direct generalization of the previously proposed D-BESS model.
Development of a Linear Stirling System Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
Some computer simulations based on the linear relative risk model
Gilbert, E.S.
1991-10-01
This report presents the results of computer simulations designed to evaluate and compare the performance of the likelihood ratio statistic and the score statistic for making inferences about the linear relative risk mode. The work was motivated by data on workers exposed to low doses of radiation, and the report includes illustration of several procedures for obtaining confidence limits for the excess relative risk coefficient based on data from three studies of nuclear workers. The computer simulations indicate that with small sample sizes and highly skewed dose distributions, asymptotic approximations to the score statistic or to the likelihood ratio statistic may not be adequate. For testing the null hypothesis that the excess relative risk is equal to zero, the asymptotic approximation to the likelihood ratio statistic was adequate, but use of the asymptotic approximation to the score statistic rejected the null hypothesis too often. Frequently the likelihood was maximized at the lower constraint, and when this occurred, the asymptotic approximations for the likelihood ratio and score statistics did not perform well in obtaining upper confidence limits. The score statistic and likelihood ratio statistics were found to perform comparably in terms of power and width of the confidence limits. It is recommended that with modest sample sizes, confidence limits be obtained using computer simulations based on the score statistic. Although nuclear worker studies are emphasized in this report, its results are relevant for any study investigating linear dose-response functions with highly skewed exposure distributions. 22 refs., 14 tabs.
Linear programming model of a meat processing plant
Shah, S.A.; Okos, M.R.; Reklaitis, G.V.
1981-01-01
A multi-period and multi-product production-planning model of an operational meat processing plant is presented. The model input is the time-varying customer demand and the output is the optimum product mix. The model results are interpreted and compared with actual data. Various production strategies are evaluated.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-01-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882
Nonlinearity measure and internal model control based linearization in anti-windup design
Perev, Kamen
2013-12-18
This paper considers the problem of internal model control based linearization in anti-windup design. The nonlinearity measure concept is used for quantifying the control system degree of nonlinearity. The linearizing effect of a modified internal model control structure is presented by comparing the nonlinearity measures of the open-loop and closed-loop systems. It is shown that the linearization properties are improved by increasing the control system local feedback gain. However, it is emphasized that at the same time the stability of the system deteriorates. The conflicting goals of stability and linearization are resolved by solving the design problem in different frequency ranges.
Model Averaging Methods for Weight Trimming in Generalized Linear Regression Models
Elliott, Michael R.
2012-01-01
In sample surveys where units have unequal probabilities of inclusion, associations between the inclusion probability and the statistic of interest can induce bias in unweighted estimates. This is true even in regression models, where the estimates of the population slope may be biased if the underlying mean model is misspecified or the sampling is nonignorable. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have highly variable weights; weight trimming reduces large weights to a maximum value, reducing variability but introducing bias. Most standard approaches are ad hoc in that they do not use the data to optimize bias-variance trade-offs. This article uses Bayesian model averaging to create “data driven” weight trimming estimators. We extend previous results for linear regression models (Elliott 2008) to generalized linear regression models, developing robust models that approximate fully-weighted estimators when bias correction is of greatest importance, and approximate unweighted estimators when variance reduction is critical. PMID:23275683
Dynamic Spring Model of Rubber Bush Based on Linear Viscoelasticity
NASA Astrophysics Data System (ADS)
Fujikawa, Masaki; Sato, Masami; Kobayashi, Takaya
A set of simplified formulae is proposed for estimating the dynamic spring constants of rubber bushes used in suspension systems. These formulae are structured by extending a set of elastic solutions[Editor2] proposed before to calculate the dynamic spring constants according to the associated law (pseudo-elasticity) of the linear viscoelasticity theory. A unique feature of this method is that it helps in the easy and quick evaluation of the dynamic behavior of rubber bushes for all the six degrees of freedom (axial loading, loading normal to an axis in two directions, wrench in two directions, and torsion[Editor3]) with no direct involvement of the FEM. In order to validate this method of calculation, the results obtained for all the degrees of freedom are compared with those obtained using the FEM. It is verified that this approach is capable of qualitatively reproducing the results obtained by using the FEM analysis.
Analysis of Power Model for Linear Plasma Device
NASA Astrophysics Data System (ADS)
Zhang, Weiwei; Deng, Baiquan; Zuo, Haoyi; Zheng, Xianjun; Cao, Xiaogang; Xue, Xiaoyan; Ou, Wei; Cao, Zhi; Gou, Fujun
2016-08-01
A single cathode linear plasma device has been designed and constructed to investigate the interactions between plasma and materials at the Sichuan University. In order to further investigate the Ohmic power of the device, the output heat load on the specimen and electric potential difference (between cathode and anode) have been tested under different discharge currents. This special power distribution in the radial direction of the plasma discharge channel has also been discussed and described by some improved integral equations in this paper; it can be further simplified as P ∝ α‑2 in one-parameter. Besides, we have measured the power loss of the channel under different discharge currents by the calorimetric method, calculated the effective power of the device and evaluated the performances of the plasma device through the power efficiency analysis. supported by International Thermonuclear Experimental Reactor (ITER) Program (No. 2013GB114003) and National Natural Science Foundation of China (Nos. 11275135 and 11475122)
Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming
Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo
2013-05-23
This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.
Time to change from a simple linear model to a complex systems model
2016-01-01
A simple linear model to test the hypothesis based on one-on-one relationship has been used to find the causative factors of diseases. However, we now know that not just one, but many factors from different systems such as chemical exposure, genes, epigenetic changes, and proteins are involved in the pathogenesis of chronic diseases such as diabetes mellitus. So, with availability of modern technologies to understand the intricate nature of relations among complex systems, we need to move forward to the future by taking complex systems model. PMID:27158003
Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2014-01-01
Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874
Semi-physical neural modeling for linear signal restoration.
Bourgois, Laurent; Roussel, Gilles; Benjelloun, Mohammed
2013-02-01
This paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort. PMID:23275139
A deterministic aggregate production planning model considering quality of products
NASA Astrophysics Data System (ADS)
Madadi, Najmeh; Yew Wong, Kuan
2013-06-01
Aggregate Production Planning (APP) is a medium-term planning which is concerned with the lowest-cost method of production planning to meet customers' requirements and to satisfy fluctuating demand over a planning time horizon. APP problem has been studied widely since it was introduced and formulated in 1950s. However, in several conducted studies in the APP area, most of the researchers have concentrated on some common objectives such as minimization of cost, fluctuation in the number of workers, and inventory level. Specifically, maintaining quality at the desirable level as an objective while minimizing cost has not been considered in previous studies. In this study, an attempt has been made to develop a multi-objective mixed integer linear programming model that serves those companies aiming to incur the minimum level of operational cost while maintaining quality at an acceptable level. In order to obtain the solution to the multi-objective model, the Fuzzy Goal Programming approach and max-min operator of Bellman-Zadeh were applied to the model. At the final step, IBM ILOG CPLEX Optimization Studio software was used to obtain the experimental results based on the data collected from an automotive parts manufacturing company. The results show that incorporating quality in the model imposes some costs, however a trade-off should be done between the cost resulting from producing products with higher quality and the cost that the firm may incur due to customer dissatisfaction and sale losses.
NASA Astrophysics Data System (ADS)
Kim, Tae-Jeong; Kim, Ki-Young; Shin, Dong-Hoon; Kwon, Hyun-Han
2015-04-01
It has been widely acknowledged that the appropriate simulation of natural streamflow at ungauged sites is one of the fundamental challenges to hydrology community. In particular, the key to reliable runoff simulation in ungauged basins is a reliable rainfall-runoff model and a parameter estimation. In general, parameter estimation in rainfall-runoff models is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of the continuous rainfall-runoff model in conjunction with Bayesian statistical techniques to facilitate uncertainty analysis. First, this study uses the Bayesian Markov Chain Monte Carlo scheme for the Sacramento rainfall-runoff model that has been widely used around the world. The Sacramento model is calibrated against daily runoff observation, and thirteen parameters of the model are optimized as well as posterior distributor distributions for each parameter are derived. Second, we applied Bayesian generalized linear regression model to set of the parameters with basin characteristics (e.g. area and slope), to obtain a functional relationship between pairs of variables. The proposed model was validated in two gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, coefficient of efficiency, index of agreement and coefficient of correlation. The future study will be further focused on uncertainty analysis to fully incorporate propagation of the uncertainty into the regionalization framework. KEYWORDS: Ungauge, Parameter, Sacramento, Generalized linear model, Regionalization Acknowledgement This research was supported by a Grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA).
NASA Technical Reports Server (NTRS)
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
Linear summation of outputs in a balanced network model of motor cortex
Capaday, Charles; van Vreeswijk, Carl
2015-01-01
Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis. PMID:26097452
Non-linear modelling and optimal control of a hydraulically actuated seismic isolator test rig
NASA Astrophysics Data System (ADS)
Pagano, Stefano; Russo, Riccardo; Strano, Salvatore; Terzo, Mario
2013-02-01
This paper investigates the modelling, parameter identification and control of an unidirectional hydraulically actuated seismic isolator test rig. The plant is characterized by non-linearities such as the valve dead zone and frictions. A non-linear model is derived and then employed for parameter identification. The results concerning the model validation are illustrated and they fully confirm the effectiveness of the proposed model. The testing procedure of the isolation systems is based on the definition of a target displacement time history of the sliding table and, consequently, the precision of the table positioning is of primary importance. In order to minimize the test rig tracking error, a suitable control system has to be adopted. The system non-linearities highly limit the performances of the classical linear control and a non-linear one is therefore adopted. The test rig mathematical model is employed for a non-linear control design that minimizes the error between the target table position and the current one. The controller synthesis is made by taking no specimen into account. The proposed approach consists of a non-linear optimal control based on the state-dependent Riccati equation (SDRE). Numerical simulations have been performed in order to evaluate the soundness of the designed control with and without the specimen under test. The results confirm that the performances of the proposed non-linear controller are not invalidated because of the presence of the specimen.
Cost decomposition of linear systems with application to model reduction
NASA Technical Reports Server (NTRS)
Skelton, R. E.
1980-01-01
A means is provided to assess the value or 'cst' of each component of a large scale system, when the total cost is a quadratic function. Such a 'cost decomposition' of the system has several important uses. When the components represent physical subsystems which can fail, the 'component cost' is useful in failure mode analysis. When the components represent mathematical equations which may be truncated, the 'component cost' becomes a criterion for model truncation. In this latter event component costs provide a mechanism by which the specific control objectives dictate which components should be retained in the model reduction process. This information can be valuable in model reduction and decentralized control problems.
Linear and nonlinear instabilities in unified dark energy models
Avelino, P. P.; Beca, L. M. G.; Martins, C. J. A. P.
2008-03-15
We revisit the paradigm of unified dark energy discussing in detail the averaging problem in this type of scenario, highlighting the need for a full nonlinear treatment. We also address the question of if and how models with one or several dark fluids can be observationally distinguished. Simpler and physically clearer derivations of some key results, most notably on the relation between the generalized Chaplygin gas and the standard ({lambda}CDM) 'concordance' model and on a Jeans-type small-scale instability of some coupled dark energy/dark matter models are presented.
Direct-Steam Linear Fresnel Performance Model for NREL's System Advisor Model
Wagner, M. J.; Zhu, G.
2012-09-01
This paper presents the technical formulation and demonstrated model performance results of a new direct-steam-generation (DSG) model in NREL's System Advisor Model (SAM). The model predicts the annual electricity production of a wide range of system configurations within the DSG Linear Fresnel technology by modeling hourly performance of the plant in detail. The quasi-steady-state formulation allows users to investigate energy and mass flows, operating temperatures, and pressure drops for geometries and solar field configurations of interest. The model includes tools for heat loss calculation using either empirical polynomial heat loss curves as a function of steam temperature, ambient temperature, and wind velocity, or a detailed evacuated tube receiver heat loss model. Thermal losses are evaluated using a computationally efficient nodal approach, where the solar field and headers are discretized into multiple nodes where heat losses, thermal inertia, steam conditions (including pressure, temperature, enthalpy, etc.) are individually evaluated during each time step of the simulation. This paper discusses the mathematical formulation for the solar field model and describes how the solar field is integrated with the other subsystem models, including the power cycle and optional auxiliary fossil system. Model results are also presented to demonstrate plant behavior in the various operating modes.
Modeling results for a linear simulator of a divertor
Hooper, E.B.; Brown, M.D.; Byers, J.A.; Casper, T.A.; Cohen, B.I.; Cohen, R.H.; Jackson, M.C.; Kaiser, T.B.; Molvik, A.W.; Nevins, W.M.; Nilson, D.G.; Pearlstein, L.D.; Rognlien, T.D.
1993-06-23
A divertor simulator, IDEAL, has been proposed by S. Cohen to study the difficult power-handling requirements of the tokamak program in general and the ITER program in particular. Projections of the power density in the ITER divertor reach {approximately} 1 Gw/m{sup 2} along the magnetic fieldlines and > 10 MW/m{sup 2} on a surface inclined at a shallow angle to the fieldlines. These power densities are substantially greater than can be handled reliably on the surface, so new techniques are required to reduce the power density to a reasonable level. Although the divertor physics must be demonstrated in tokamaks, a linear device could contribute to the development because of its flexibility, the easy access to the plasma and to tested components, and long pulse operation (essentially cw). However, a decision to build a simulator requires not just the recognition of its programmatic value, but also confidence that it can meet the required parameters at an affordable cost. Accordingly, as reported here, it was decided to examine the physics of the proposed device, including kinetic effects resulting from the intense heating required to reach the plasma parameters, and to conduct an independent cost estimate. The detailed role of the simulator in a divertor program is not explored in this report.
Location-scale cumulative odds models for ordinal data: a generalized non-linear model approach.
Cox, C
1995-06-15
Proportional odds regression models for multinomial probabilities based on ordered categories have been generalized in two somewhat different directions. Models having scale as well as location parameters for adjustment of boundaries (on an unobservable, underlying continuum) between categories have been employed in the context of ROC analysis. Partial proportional odds models, having different regression adjustments for different multinomial categories, have also been proposed. This paper considers a synthesis and further generalization of these two families. With use of a number of examples, I discuss and illustrate properties of this extended family of models. Emphasis is on the computation of maximum likelihood estimates of parameters, asymptotic standard deviations, and goodness-of-fit statistics with use of non-linear regression programs in standard statistical software such as SAS. PMID:7667560
Numerical modeling of shape memory alloy linear actuator
NASA Astrophysics Data System (ADS)
Jani, Jaronie Mohd; Huang, Sunan; Leary, Martin; Subic, Aleksandar
2015-09-01
The demand for shape memory alloy (SMA) actuators in high-technology applications is increasing; however, there exist technical challenges to the commercial application of SMA actuator technologies, especially associated with actuation duration. Excessive activation duration results in actuator damage due to overheating while excessive deactivation duration is not practical for high-frequency applications. Analytical and finite difference equation models were developed in this work to predict the activation and deactivation durations and associated SMA thermomechanical behavior under variable environmental and design conditions. Relevant factors, including latent heat effect, induced stress and material property variability are accommodated. An existing constitutive model was integrated into the proposed models to generate custom SMA stress-strain curves. Strong agreement was achieved between the proposed numerical models and experimental results; confirming their applicability for predicting the behavior of SMA actuators with variable thermomechanical conditions.
Existence of vortices in a self-dual gauged linear sigma model and its singular limit
NASA Astrophysics Data System (ADS)
Kim, Namkwon
2006-03-01
We study rigorously the static (2 + 1)D gauged linear sigma model introduced by Schroers. Analysing the governing system of partial differential equations, we show the existence of energy finite vortices under the partially broken symmetry on R2 with some conditions consistent with the necessary conditions given by Yang. Also, with a special choice of representation, we show that the gauged O(3) sigma model is a singular limit of the gauged linear sigma model.
Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M; Derocher, Andrew E; Lewis, Mark A; Jonsen, Ian D; Mills Flemming, Joanna
2016-01-01
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results. PMID:27220686
Genetic demixing and evolution in linear stepping stone models
NASA Astrophysics Data System (ADS)
Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.
2010-04-01
Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial
A new approach to modeling linear accelerator systems
Gillespie, G.H.; Hill, B.W.; Jameson, R.A.
1994-07-22
A novel computer code is being developed to generate system level designs of radiofrequency ion accelerators with specific applications to machines of interest to Accelerator Driven Transmutation Technologies (ADTT). The goal of the Accelerator System Model (ASM) code is to create a modeling and analysis tool that is easy to use, automates many of the initial design calculations, supports trade studies used in accessing alternate designs and yet is flexible enough to incorporate new technology concepts as they emerge. Hardware engineering parameters and beam dynamics are to be modeled at comparable levels of fidelity. Existing scaling models of accelerator subsystems were used to produce a prototype of ASM (version 1.0) working within the Shell for Particle Accelerator Related Code (SPARC) graphical user interface. A small user group has been testing and evaluating the prototype for about a year. Several enhancements and improvements are now being developed. The current version of ASM is described and examples of the modeling and analysis capabilities are illustrated. The results of an example study, for an accelerator concept typical of ADTT applications, is presented and sample displays from the computer interface are shown.
A new approach to modeling linear accelerator systems
Gillespie, George H.; Hill, Barrey W.; Jameson, Robert A.
1995-09-15
A novel computer code is being developed to generate system level designs of radiofrequency ion accelerators with specific applications to machines of interest to Accelerator Driven Transmutation Technologies (ADTT). The goal of the Accelerator System Model (ASM) code is to create a modeling and analysis tool that is easy to use, automates many of the initial design calculations, supports trade studies used in assessing alternate designs and yet is flexible enough to incorporate new technology concepts as they emerge. Hardware engineering parameters and beam dynamics are to be modeled at comparable levels of fidelity. Existing scaling models of accelerator subsystems were used to produce a prototype of ASM (version 1.0) working within the Shell for Particle Accelerator Related Code (SPARC) graphical user interface. A small user group has been testing and evaluating the prototype for about a year. Several enhancements and improvements are now being developed. The current version of ASM is described and examples of the modeling and analysis capabilities are illustrated. The results of an example parameter trade study, for an accelerator concept typical of ADTT applications, is presented and sample displays from the computer interface are shown.
Modeling of thermal storage systems in MILP distributed energy resource models
Steen, David; Stadler, Michael; Cardoso, Gonçalo; Groissböck, Markus; DeForest, Nicholas; Marnay, Chris
2014-08-04
Thermal energy storage (TES) and distributed generation technologies, such as combined heat and power (CHP) or photovoltaics (PV), can be used to reduce energy costs and decrease CO2 emissions from buildings by shifting energy consumption to times with less emissions and/or lower energy prices. To determine the feasibility of investing in TES in combination with other distributed energy resources (DER), mixed integer linear programming (MILP) can be used. Such a MILP model is the well-established Distributed Energy Resources Customer Adoption Model (DER-CAM); however, it currently uses only a simplified TES model to guarantee linearity and short run-times. Loss calculations aremore » based only on the energy contained in the storage. This paper presents a new DER-CAM TES model that allows improved tracking of losses based on ambient and storage temperatures, and compares results with the previous version. A multi-layer TES model is introduced that retains linearity and avoids creating an endogenous optimization problem. The improved model increases the accuracy of the estimated storage losses and enables use of heat pumps for low temperature storage charging. Ultimately,results indicate that the previous model overestimates the attractiveness of TES investments for cases without possibility to invest in heat pumps and underestimates it for some locations when heat pumps are allowed. Despite a variation in optimal technology selection between the two models, the objective function value stays quite stable, illustrating the complexity of optimal DER sizing problems in buildings and microgrids.« less
A model of asynchronous iterative algorithms for solving large, sparse, linear systems
NASA Technical Reports Server (NTRS)
Reed, D. A.; Patrick, M. L.
1984-01-01
Solving large, sparse, linear systems of equations is one of the fundamental problems in large scale scientific and engineering computation. A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. This model is then analyzed to determine the expected intertask data transfer and task computational complexity as functions of the number of tasks. Based on the analysis, recommendations for task partitioning are made. These recommendations are a function of the sparseness of the linear system, its structure (i.e., randomly sparse or banded), and dimension.
[Cancer incidence estimates for Germany via log-linear models].
Haberland, J; Bertz, J; Görsch, B; Schön, D
2001-01-01
In Germany presently no nationwide cancer registration exists. To estimate national cancer incidence, Poisson regression models were fitted to incidence/mortality ratios using age and sex specific data of the cancer registry of Saarland, Germany and were then applied to national mortality. The models estimate the absolute number of incident cases at a given point in time and moreover allow the assessment of time trends. Applied to nationwide mortality the models imply a total of 347,000 new cancer cases in Germany for 1998 with 179,000 females and 168,000 males. During the nineties the age-standardised rate (European standard) has slightly decreased for males and slightly increased for females. PMID:11561205
Ma, Rongfei
2015-01-01
In this paper, ammonia quantitative analysis based on miniaturized Al ionization gas sensor and non-linear bistable dynamic model was proposed. Al plate anodic gas-ionization sensor was used to obtain the current-voltage (I-V) data. Measurement data was processed by non-linear bistable dynamics model. Results showed that the proposed method quantitatively determined ammonia concentrations. PMID:25975362
Downscaling of rainfall in Peru using Generalised Linear Models
NASA Astrophysics Data System (ADS)
Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.
2012-04-01
The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.
Response Characteristics of a Linear Rotorcraft Vibration Model
NASA Technical Reports Server (NTRS)
Kunz, Donald L.
1982-01-01
A fully coupled vibration model, consisting of a rotor with only flapping degrees of freedom plus pylon and fuselage pitching motion, was used in a parametric study undertaken to investigate the response characteristics of a simplified helicopter. Among the parameters studied were uncoupled body frequency, blade stiffness, hinge offset, advance ratio, and mast height. Results from the harmonic balance solution of the equations of motion show how each of these quantities affects the response of the model. The results also indicate that there is a potential for reducing vibration response through the judicious definition of the design parameters.
Modeling taper charge with a non-linear equation
NASA Technical Reports Server (NTRS)
Mcdermott, P. P.
1985-01-01
Work aimed at modeling the charge voltage and current characteristics of nickel-cadmium cells subject to taper charge is presented. Work reported at previous NASA Battery Workshops has shown that the voltage of cells subject to constant current charge and discharge can be modeled very accurately with the equation: voltage = A + (B/(C-X)) + De to the -Ex where A, B, D, and E are fit parameters and x is amp-hr of charge removed during discharge or returned during charge. In a constant current regime, x is also equivalent to time on charge or discharge.
Item Response Theory Using Hierarchical Generalized Linear Models
ERIC Educational Resources Information Center
Ravand, Hamdollah
2015-01-01
Multilevel models (MLMs) are flexible in that they can be employed to obtain item and person parameters, test for differential item functioning (DIF) and capture both local item and person dependence. Papers on the MLM analysis of item response data have focused mostly on theoretical issues where applications have been add-ons to simulation…
Numerical analysis of linear buckling of wind turbine blade with different trailing bonding models
NASA Astrophysics Data System (ADS)
Zhang, J. D.; Xu, Y.
2013-12-01
The work focus on the linear buckling analysis of wind turbine blade with different trailing bonding models. Based on finite element model, it has been demonstrated that there are some differences for buckling load factor between different models. Several different models are valid for buckling analysis.
Optimal Selection of Predictor Variables in Statistical Downscaling Models of Precipitation
NASA Astrophysics Data System (ADS)
Goly, A.; Teegavarapu, R. S. V.
2014-12-01
Statistical downscaling models developed for precipitation rely heavily on predictors chosen and on accurate relationships between regional scale predictand and GCM-scale predictor for providing future precipitation projections at different spatial and temporal scales. This study provides two new screening methods for selection of predictor variables for use in downscaling methods based on predictand-predictors relationships. Methods to characterize predictand-predictors relationships via rigid and flexible functional relationships using mixed integer nonlinear programming (MINLP) model with binary variables and artificial neural network (ANN) models respectively are developed and evaluated in this study. In addition to these two methods, a stepwise regression (SWR) and two models that do not use any pre-screening of variables are also evaluated. A two-step process is used to downscale precipitation data with optimal selection of predictors and using them in a statistical downscaling model based on support vector machine (SVM) approach. Experiments with the proposed two new methods and three additional methods based on correlation between predictors and predictand and the other based on principal component analysis are evaluated in this study. Results suggest that optimal selection of variables using MINLP albeit with linear relationship and ANN method provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Of all the three screening methods tested in this study, SWR method selected the least number of variables and also ranked lowest based on several performance measures.
Evaluation of a Linear Mixing Model to Retrieve Soil and Vegetation Temperatures of Land Targets
NASA Astrophysics Data System (ADS)
Yang, Jinxin; Jia, Li; Cui, Yaokui; Zhou, Jie; Menenti, Massimo
2014-03-01
A simple linear mixing model of heterogeneous soil-vegetation system and retrieval of component temperatures from directional remote sensing measurements by inverting this model is evaluated in this paper using observations by a thermal camera. The thermal camera was used to obtain multi-angular TIR (Thermal Infra-Red) images over vegetable and orchard canopies. A whole thermal camera image was treated as a pixel of a satellite image to evaluate the model with the two-component system, i.e. soil and vegetation. The evaluation included two parts: evaluation of the linear mixing model and evaluation of the inversion of the model to retrieve component temperatures. For evaluation of the linear mixing model, the RMSE is 0.2 K between the observed and modelled brightness temperatures, which indicates that the linear mixing model works well under most conditions. For evaluation of the model inversion, the RMSE between the model retrieved and the observed vegetation temperatures is 1.6K, correspondingly, the RMSE between the observed and retrieved soil temperatures is 2.0K. According to the evaluation of the sensitivity of retrieved component temperatures on fractional cover, the linear mixing model gives more accurate retrieval accuracies for both soil and vegetation temperatures under intermediate fractional cover conditions.
Battery Life Estimator Manual Linear Modeling and Simulation
Jon P. Christophersen; Ira Bloom; Ed Thomas; Vince Battaglia
2009-08-01
The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.
Vibration Model Validation for Linear Collider Detector Platforms
Bertsche, Kirk; Amann, J.W.; Markiewicz, T.W.; Oriunno, M.; Weidemann, A.; White, G.; /SLAC
2012-05-16
The ILC and CLIC reference designs incorporate reinforced-concrete platforms underneath the detectors so that the two detectors can each be moved onto and off of the beamline in a Push-Pull configuration. These platforms could potentially amplify ground vibrations, which would reduce luminosity. In this paper we compare vibration models to experimental data on reinforced concrete structures, estimate the impact on luminosity, and summarize implications for the design of a reinforced concrete platform for the ILC or CLIC detectors.
Linear system identification via backward-time observer models
NASA Technical Reports Server (NTRS)
Juang, Jer-Nan; Phan, Minh Q.
1992-01-01
Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works.
Blanco, M; Maspoch, S; Villarroya, I; Peralta, X; González, J M; Torres, J
2001-03-01
The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens. PMID:11284343
NASA Astrophysics Data System (ADS)
Mead, A. J.; Peacock, J. A.; Heymans, C.; Joudaki, S.; Heavens, A. F.
2015-12-01
We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution N-body simulations. For a variety of Λ cold dark matter (ΛCDM) and wCDM models, the halo-model power is accurate to ≃ 5 per cent for k ≤ 10h Mpc-1 and z ≤ 2. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spectrum. We demonstrate this by fitting the halo model to power spectra from the OWLS (OverWhelmingly Large Simulations) hydrodynamical simulation suite via parameters that govern halo internal structure. We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations. Accurate predictions to high k are vital for weak-lensing surveys, and these halo parameters could be considered nuisance parameters to marginalize over in future analyses to mitigate uncertainty regarding the details of feedback. Finally, we investigate how lensing observables predicted by our model compare to those from simulations and from HALOFIT for a range of k-cuts and feedback models and quantify the angular scales at which these effects become important. Code to calculate power spectra from the model presented in this paper can be found at https://github.com/alexander-mead/hmcode.
NASA Astrophysics Data System (ADS)
Rudy, Ashley C. A.; Lamoureux, Scott F.; Treitz, Paul; van Ewijk, Karin Y.
2016-07-01
To effectively assess and mitigate risk of permafrost disturbance, disturbance-prone areas can be predicted through the application of susceptibility models. In this study we developed regional susceptibility models for permafrost disturbances using a field disturbance inventory to test the transferability of the model to a broader region in the Canadian High Arctic. Resulting maps of susceptibility were then used to explore the effect of terrain variables on the occurrence of disturbances within this region. To account for a large range of landscape characteristics, the model was calibrated using two locations: Sabine Peninsula, Melville Island, NU, and Fosheim Peninsula, Ellesmere Island, NU. Spatial patterns of disturbance were predicted with a generalized linear model (GLM) and generalized additive model (GAM), each calibrated using disturbed and randomized undisturbed locations from both locations and GIS-derived terrain predictor variables including slope, potential incoming solar radiation, wetness index, topographic position index, elevation, and distance to water. Each model was validated for the Sabine and Fosheim Peninsulas using independent data sets while the transferability of the model to an independent site was assessed at Cape Bounty, Melville Island, NU. The regional GLM and GAM validated well for both calibration sites (Sabine and Fosheim) with the area under the receiver operating curves (AUROC) > 0.79. Both models were applied directly to Cape Bounty without calibration and validated equally with AUROC's of 0.76; however, each model predicted disturbed and undisturbed samples differently. Additionally, the sensitivity of the transferred model was assessed using data sets with different sample sizes. Results indicated that models based on larger sample sizes transferred more consistently and captured the variability within the terrain attributes in the respective study areas. Terrain attributes associated with the initiation of disturbances were
As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...
Modeling contaminant migration with linear sorption in strongly heterogeneous media
Bai, M.; Roegiers, J.C.; Elsworth, D.; Inyang, H.I.
1997-11-01
A triple-porosity model is presented to evaluate transport behavior in porous media with a structure comprising a spectrum of pore sizes, represented discretely as macro-, meso-, and micropores. Characterizations are completed to provide adequate semianalytical solutions for the validation of codes representing discrete distributions of pore geometry and to adequately describe extended tailing and multicomponent solute front breakthroughs apparent in field and laboratory data. Semianalytical solutions are derived for a one dimensional flow geometry by using Laplace transforms under the assumption that solute transport in the two interactive mobile-transport regions (i.e., macro- and mesopores) is affected by exchange with immobile solutes in the micropore region. Sensitivity analyses are conducted to identify the propensity for extensive tailing in the breakthrough response, over single-porosity approaches, and the development of multiple breakthrough fronts with reverse diffusion. Both behaviors result from the strongly heterogeneous nature of the transport processes, accommodated in the multiporosity model, and are well suited to the representation of real porous and porous-fractured disordered media.
DEM Modelling of Non-linear Viscoelastic Stress Waves
NASA Astrophysics Data System (ADS)
Wang, Wenqiang; Tang, Zhiping; Horie, Yasuyuki
2001-06-01
A DEM(Discrete Element Method) simulation of nonlinear viscoelastic stress wave problems is carried out. The interaction forces among elements are described using a model in which neighbor elements are linked by a nonlinear spring and a certain number of Maxwell components in parallel. By making use of exponential relaxation moduli, it is shown that numerical computation of the convolution integral does not require storing and repeatedly calculating strain history, and can reduce the computational cost dramatically. To validate the viscoelastic DM2 code, stress wave propagation in a Maxwell rod with one end subjected to a constant stress loading is simulated. Results excellently fit those from the characteristics calculation. Satisfactory results are also obtained in the simulation of one-dimensional plane wave in a plastic bonded explosive. The code is then used to investigate the problem of meso-scale damage in this explosive under shock loading. Results not only show "compression damage", but also reveal a complex damage evolution. They demonstrate a unique capability of DEM in modeling heterogeneous materials.
Non-linear sigma-models and string theories
Sen, A.
1986-10-01
The connection between sigma-models and string theories is discussed, as well as how the sigma-models can be used as tools to prove various results in string theories. Closed bosonic string theory in the light cone gauge is very briefly introduced. Then, closed bosonic string theory in the presence of massless background fields is discussed. The light cone gauge is used, and it is shown that in order to obtain a Lorentz invariant theory, the string theory in the presence of background fields must be described by a two-dimensional conformally invariant theory. The resulting constraints on the background fields are found to be the equations of motion of the string theory. The analysis is extended to the case of the heterotic string theory and the superstring theory in the presence of the massless background fields. It is then shown how to use these results to obtain nontrivial solutions to the string field equations. Another application of these results is shown, namely to prove that the effective cosmological constant after compactification vanishes as a consequence of the classical equations of motion of the string theory. 34 refs. (LEW)
Localization of the SFT inspired nonlocal linear models and exact solutions
NASA Astrophysics Data System (ADS)
Vernov, S. Yu.
2011-05-01
A general class of gravitational models driven by a nonlocal scalar field with a linear or quadratic potential is considered. We study the action with an arbitrary analytic function ℱ(□ g ), which has both simple and double roots. The way of localization of nonlocal Einstein equations is generalized on models with linear potentials. Exact solutions in the Friedmann-Robertson-Walker and Bianchi I metrics are presented.
A new adaptive multiple modelling approach for non-linear and non-stationary systems
NASA Astrophysics Data System (ADS)
Chen, Hao; Gong, Yu; Hong, Xia
2016-07-01
This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.
A componential model of human interaction with graphs: 1. Linear regression modeling
NASA Technical Reports Server (NTRS)
Gillan, Douglas J.; Lewis, Robert
1994-01-01
Task analyses served as the basis for developing the Mixed Arithmetic-Perceptual (MA-P) model, which proposes (1) that people interacting with common graphs to answer common questions apply a set of component processes-searching for indicators, encoding the value of indicators, performing arithmetic operations on the values, making spatial comparisons among indicators, and repsonding; and (2) that the type of graph and user's task determine the combination and order of the components applied (i.e., the processing steps). Two experiments investigated the prediction that response time will be linearly related to the number of processing steps according to the MA-P model. Subjects used line graphs, scatter plots, and stacked bar graphs to answer comparison questions and questions requiring arithmetic calculations. A one-parameter version of the model (with equal weights for all components) and a two-parameter version (with different weights for arithmetic and nonarithmetic processes) accounted for 76%-85% of individual subjects' variance in response time and 61%-68% of the variance taken across all subjects. The discussion addresses possible modifications in the MA-P model, alternative models, and design implications from the MA-P model.
Non-linear force-free field modeling: model techniques, boundary conditions, hares, and hounds
NASA Astrophysics Data System (ADS)
Schrijver, C. J.; De Rosa, M. L.; Metcalf, T.
2005-05-01
Understanding the conditions under which solar magnetic fields can destabilize to erupt in flares and coronal mass ejections requires a quantitative understanding of the coronal magnetic field and of the currents that it carries. The increased availability of vector magnetograms, together with EUV and X-ray coronal images, should provide adequate constraints to model the coronal field, and thus to visualize its 3D geometry and to measure the available free energy and helicity. Non-linear force-free fields (NLFFF) are likely a useful model to use when extrapolating the solar surface field upward into the coronal volume. It may even be possible to use the observed trajectories of coronal loops, evident in EUV images of the corona, as a further constraint. We present initial results of a team effort to understand the intricacies of NLFFF modeling: we discuss and evaluate comparisons of NLFFF models computed with different models and applications of boundary conditions, and look ahead to full coronal field modeling for the upcoming Solar-B and SDO missions.
A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets
NASA Technical Reports Server (NTRS)
Chicatelli, Amy; Hartley, Tom T.
1998-01-01
Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.
Predicting musically induced emotions from physiological inputs: linear and neural network models.
Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M
2013-01-01
Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion. PMID:23964250
NASA Astrophysics Data System (ADS)
Cardoso, T.; Oliveira, M. D.; Barbosa-Póvoa, A.; Nickel, S.
2015-05-01
Although the maximization of health is a key objective in health care systems, location-allocation literature has not yet considered this dimension. This study proposes a multi-objective stochastic mathematical programming approach to support the planning of a multi-service network of long-term care (LTC), both in terms of services location and capacity planning. This approach is based on a mixed integer linear programming model with two objectives - the maximization of expected health gains and the minimization of expected costs - with satisficing levels in several dimensions of equity - namely, equity of access, equity of utilization, socioeconomic equity and geographical equity - being imposed as constraints. The augmented ε-constraint method is used to explore the trade-off between these conflicting objectives, with uncertainty in the demand and delivery of care being accounted for. The model is applied to analyze the (re)organization of the LTC network currently operating in the Great Lisbon region in Portugal for the 2014-2016 period. Results show that extending the network of LTC is a cost-effective investment.
ERIC Educational Resources Information Center
Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer
2013-01-01
Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…
Schneider, Uwe
2009-04-15
A simple mechanistic model for predicting cancer induction after fractionated radiotherapy is developed. The model is based upon the linear-quadratic model. The inductions of carcinomas and sarcomas are modeled separately. The linear-quadratic model of cell kill is applied to normal tissues which are unintentionally irradiated during a cancer treatment with radiotherapy. Tumor induction is modeled such that each transformation process results in a tumor cell. The microscopic transformation parameter was chosen such that in the limit of low dose and acute exposure, the parameters of the linear-no-threshold model for tumor induction were approached. The differential equations describing carcinoma and sarcoma inductions can be solved analytically. Cancer induction in this model is a function of treatment dose, the cell kill parameters ({alpha},{beta}), the tumor induction variable ({mu}), and the repopulation parameter ({xi}). Carcinoma induction shows a bell shaped behavior as long as cell repopulation is small. Assuming large cell repopulation rates, a plateaulike function is approached. In contrast, sarcoma induction is negligible for low doses and increases with increasing dose up to a constant value. The proposed model describes carcinoma and sarcoma inductions after fractionated radiotherapy as an analytical function of four parameters. In the limit of low dose and for an instant irradiation it reproduces the results of the linear-no-threshold model. The obtained dose-response curves for cancer induction can be implemented with other models such as the organ-equivalent dose model to predict second cancers after radiotherapy.
ERIC Educational Resources Information Center
Subedi, Bidya Raj; Reese, Nancy; Powell, Randy
2015-01-01
This study explored significant predictors of student's Grade Point Average (GPA) and truancy (days absent), and also determined teacher effectiveness based on proportion of variance explained at teacher level model. We employed a two-level hierarchical linear model (HLM) with student and teacher data at level-1 and level-2 models, respectively.…
ERIC Educational Resources Information Center
Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.
2002-01-01
Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…
A Graphical Method for Assessing the Identification of Linear Structural Equation Models
ERIC Educational Resources Information Center
Eusebi, Paolo
2008-01-01
A graphical method is presented for assessing the state of identifiability of the parameters in a linear structural equation model based on the associated directed graph. We do not restrict attention to recursive models. In the recent literature, methods based on graphical models have been presented as a useful tool for assessing the state of…
Combining and connecting linear, multi-input, multi-output subsystem models
NASA Technical Reports Server (NTRS)
Duke, E. L.
1986-01-01
The mathematical background for combining and connecting linear, multi-input, multi-output subsystem models into an overall system model is provided. Several examples of subsystem configurations are examined in detail. A description of a MATRIX (sub x) command file to aid in the process of combining and connecting these subsystem models is contained.
A Hierarchical Linear Model with Factor Analysis Structure at Level 2
ERIC Educational Resources Information Center
Miyazaki, Yasuo; Frank, Kenneth A.
2006-01-01
In this article the authors develop a model that employs a factor analysis structure at Level 2 of a two-level hierarchical linear model (HLM). The model (HLM2F) imposes a structure on a deficient rank Level 2 covariance matrix [tau], and facilitates estimation of a relatively large [tau] matrix. Maximum likelihood estimators are derived via the…
Tan, Q; Huang, G H; Cai, Y P
2010-09-01
The existing inexact optimization methods based on interval-parameter linear programming can hardly address problems where coefficients in objective functions are subject to dual uncertainties. In this study, a superiority-inferiority-based inexact fuzzy two-stage mixed-integer linear programming (SI-IFTMILP) model was developed for supporting municipal solid waste management under uncertainty. The developed SI-IFTMILP approach is capable of tackling dual uncertainties presented as fuzzy boundary intervals (FuBIs) in not only constraints, but also objective functions. Uncertainties expressed as a combination of intervals and random variables could also be explicitly reflected. An algorithm with high computational efficiency was provided to solve SI-IFTMILP. SI-IFTMILP was then applied to a long-term waste management case to demonstrate its applicability. Useful interval solutions were obtained. SI-IFTMILP could help generate dynamic facility-expansion and waste-allocation plans, as well as provide corrective actions when anticipated waste management plans are violated. It could also greatly reduce system-violation risk and enhance system robustness through examining two sets of penalties resulting from variations in fuzziness and randomness. Moreover, four possible alternative models were formulated to solve the same problem; solutions from them were then compared with those from SI-IFTMILP. The results indicate that SI-IFTMILP could provide more reliable solutions than the alternatives. PMID:20580864
Hemmati, Reza; Saboori, Hedayat
2016-01-01
Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics. PMID:27222741
Hemmati, Reza; Saboori, Hedayat
2016-05-01
Energy storage systems (ESSs) have experienced a very rapid growth in recent years and are expected to be a promising tool in order to improving power system reliability and being economically efficient. The ESSs possess many potential benefits in various areas in the electric power systems. One of the main benefits of an ESS, especially a bulk unit, relies on smoothing the load pattern by decreasing on-peak and increasing off-peak loads, known as load leveling. These devices require new methods and tools in order to model and optimize their effects in the power system studies. In this respect, this paper will model bulk ESSs based on the several technical characteristics, introduce the proposed model in the thermal unit commitment (UC) problem, and analyze it with respect to the various sensitive parameters. The technical limitations of the thermal units and transmission network constraints are also considered in the model. The proposed model is a Mixed Integer Linear Programming (MILP) which can be easily solved by strong commercial solvers (for instance CPLEX) and it is appropriate to be used in the practical large scale networks. The results of implementing the proposed model on a test system reveal that proper load leveling through optimum storage scheduling leads to considerable operation cost reduction with respect to the storage system characteristics. PMID:27222741
Kim, Sung-Phil; Sanchez, Justin C; Erdogmus, Deniz; Rao, Yadunandana N; Wessberg, Johan; Principe, Jose C; Nicolelis, Miguel
2003-01-01
This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network. PMID:12850045
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-01-01
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing-Tianjin-Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study. PMID:27483294
Sun, Yan; Lang, Maoxiang; Wang, Danzhu
2016-01-01
The transportation of hazardous materials is always accompanied by considerable risk that will impact public and environment security. As an efficient and reliable transportation organization, a multimodal service should participate in the transportation of hazardous materials. In this study, we focus on transporting hazardous materials through the multimodal service network and explore the hazardous materials multimodal routing problem from the operational level of network planning. To formulate this problem more practicably, minimizing the total generalized costs of transporting the hazardous materials and the social risk along the planned routes are set as the optimization objectives. Meanwhile, the following formulation characteristics will be comprehensively modelled: (1) specific customer demands; (2) multiple hazardous material flows; (3) capacitated schedule-based rail service and uncapacitated time-flexible road service; and (4) environmental risk constraint. A bi-objective mixed integer nonlinear programming model is first built to formulate the routing problem that combines the formulation characteristics above. Then linear reformations are developed to linearize and improve the initial model so that it can be effectively solved by exact solution algorithms on standard mathematical programming software. By utilizing the normalized weighted sum method, we can generate the Pareto solutions to the bi-objective optimization problem for a specific case. Finally, a large-scale empirical case study from the Beijing–Tianjin–Hebei Region in China is presented to demonstrate the feasibility of the proposed methods in dealing with the practical problem. Various scenarios are also discussed in the case study. PMID:27483294
ERIC Educational Resources Information Center
So, Tak-Shing Harry; Peng, Chao-Ying Joanne
This study compared the accuracy of predicting two-group membership obtained from K-means clustering with those derived from linear probability modeling, linear discriminant function, and logistic regression under various data properties. Multivariate normally distributed populations were simulated based on combinations of population proportions,…
ERIC Educational Resources Information Center
Armey, Michael F.; Crowther, Janis H.
2008-01-01
Research has identified a significant increase in both the incidence and prevalence of non-suicidal self-injury (NSSI). The present study sought to test both linear and non-linear cusp catastrophe models by using aversive self-awareness, which was operationalized as a composite of aversive self-relevant affect and cognitions, and dissociation as…
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. PMID:26850752
Linear and nonlinear quantitative structure-property relationship modelling of skin permeability.
Khajeh, A; Modarress, H
2014-01-01
In this work, quantitative structure-property relationship (QSPR) models were developed to estimate skin permeability based on theoretically derived molecular descriptors and a diverse set of experimental data. The newly developed method combining modified particle swarm optimization (MPSO) and multiple linear regression (MLR) was used to select important descriptors and develop the linear model using a training set of 225 compounds. The adaptive neuro-fuzzy inference system (ANFIS) was used as an efficient nonlinear method to correlate the selected descriptors with experimental skin permeability data (log Kp). The linear and nonlinear models were assessed by internal and external validation. The obtained models with three descriptors show good predictive ability for the test set, with coefficients of determination for the MPSO-MLR and ANFIS models equal to 0.874 and 0.890, respectively. The QSPR study suggests that hydrophobicity (encoded as log P) is the most important factor in transdermal penetration. PMID:24090175
Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.
Khazraee, S Hadi; Sáez-Castillo, Antonio Jose; Geedipally, Srinivas Reddy; Lord, Dominique
2015-05-01
The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model. PMID:25385093
NASA Astrophysics Data System (ADS)
Katzarova, Maria; Desai, Priyanka; Kang, Beomgoo; Hall, Ryan; Huang, Qifan; Lee, Sanghoon; Chang, Taihyun; Venerus, David; Mays, Jimmy; Schieber, Jay; Larson, Ronald
The discrete slip-link model (DSM) is a single-chain mean-field model for entanglement-dominated polymer dynamics. The model has been used successfully to make predictions about the linear and nonlinear rheology of monodisperse homopolymer melts, polydisperse melts, or blends. By using recent advances in coarse-graining, hierarchical modeling, and graphics processors, the model is amenable to predictions of well-entangled branched polymers. Here, the parameters of the most coarse-grained member of the hierarchy are fit to the dynamic modulus of monodisperse linear chains and applied to symmetric 4-arm polybutadiene (PBd) star-linear blends with roughly 20 entanglements per star arm, but with no parameter adjustment. Agreement with data is quantitative. This detailed model is further used to examine assumptions and approximations typically made in tube models for blending, including factorization in the time domain. Failure of these assumptions point towards possible fixes to tube models.
Comparison of Logistic Regression and Linear Regression in Modeling Percentage Data
Zhao, Lihui; Chen, Yuhuan; Schaffner, Donald W.
2001-01-01
Percentage is widely used to describe different results in food microbiology, e.g., probability of microbial growth, percent inactivated, and percent of positive samples. Four sets of percentage data, percent-growth-positive, germination extent, probability for one cell to grow, and maximum fraction of positive tubes, were obtained from our own experiments and the literature. These data were modeled using linear and logistic regression. Five methods were used to compare the goodness of fit of the two models: percentage of predictions closer to observations, range of the differences (predicted value minus observed value), deviation of the model, linear regression between the observed and predicted values, and bias and accuracy factors. Logistic regression was a better predictor of at least 78% of the observations in all four data sets. In all cases, the deviation of logistic models was much smaller. The linear correlation between observations and logistic predictions was always stronger. Validation (accomplished using part of one data set) also demonstrated that the logistic model was more accurate in predicting new data points. Bias and accuracy factors were found to be less informative when evaluating models developed for percentage data, since neither of these indices can compare predictions at zero. Model simplification for the logistic model was demonstrated with one data set. The simplified model was as powerful in making predictions as the full linear model, and it also gave clearer insight in determining the key experimental factors. PMID:11319091
Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model
NASA Technical Reports Server (NTRS)
Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.
1997-01-01
The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface
Lu, H W; Huang, G H; He, L; Zeng, G M
2009-01-01
Municipal solid waste (MSW) should be properly disposed in order to help protect environmental quality and human health, as well as to preserve natural resources. During MSW disposal processes, a large amount of greenhouse gas (GHG) is emitted, leading to a significant impact on climate change. In this study, an inexact dynamic optimization model (IDOM) is developed for MSW-management systems under uncertainty. It grounds upon conventional mixed-integer linear programming (MILP) approaches, and integrates GHG components into the modeling framework. Compared with the existing models, IDOM can not only deal with the complex tradeoff between system cost minimization and GHG-emission mitigation, but also provide optimal allocation strategies under various emission-control standards. A case study is then provided for demonstrating applicability of the developed model. The results indicate that desired waste-flow patterns with a minimized system cost and GHG-emission amount can be obtained. Of more importance, the IDOM solution is associated with over 5.5 million tonnes of TEC reduction, which is of significant economic implication for real implementations. Therefore, the proposed model could be regarded as a useful tool for realizing comprehensive MSW management with regard to mitigating climate-change impacts. PMID:18096299
A three-dimensional network model describing a non-linear composite material
NASA Astrophysics Data System (ADS)
Mårtensson, E.; Gäfvert, U.
2004-01-01
A three-dimensional network model for performing non-linear time-dependent simulations of the electrical characteristics related to a composite material is presented. The considered compounds are represented by a cubic lattice and consist of conducting particles distributed in an insulating matrix. Earlier studies of the non-linear characteristics of silicon carbide (SiC) grains and of the linear frequency-dependent electrical properties of composites are combined and extended. The calculations are compared to measurements on ethylene-propylene-diene monomer rubber filled with angular SiC grains. The field-dependent conductivity measured for the unconsolidated SiC powder is used as input to the simulations. The model can manage the conductivity difference of seven decades between the constituents and the strong exponential non-linearity of the conducting particles. The network calculations replicate the experimental characteristic at high filler concentrations, where direct 'face' contacts between the filler grains dominate the behaviour. At lower concentrations, it is shown that indirect 'edge' contacts involving the polymer control the current transport also in the non-linear high field range. The general effective conductivity describing an edge connection in the linear case is no longer appropriate. Non-linear mechanisms in the polymer and the conducting grains within a field enhanced limited region around the contact need to be represented by an equivalent circuit element with a case-dependent resulting expression.
He, Li; Huang, Guo-He; Zeng, Guang-Ming; Lu, Hong-Wei
2009-01-01
The previous inexact mixed-integer linear programming (IMILP) method can only tackle problems with coefficients of the objective function and constraints being crisp intervals, while the existing inexact mixed-integer semi-infinite programming (IMISIP) method can only deal with single-objective programming problems as it merely allows the number of constraints to be infinite. This study proposes, an inexact mixed-integer bi-infinite programming (IMIBIP) method by incorporating the concept of functional intervals into the programming framework. Different from the existing methods, the IMIBIP can tackle the inexact programming problems that contain both infinite objectives and constraints. The developed method is applied to capacity planning of waste management systems under a variety of uncertainties. Four scenarios are considered for comparing the solutions of IMIBIP with those of IMILP. The results indicate that reasonable solutions can be generated by the IMIBIP method. Compared with IMILP, the system cost from IMIBIP would be relatively high since the fluctuating market factors are considered; however, the IMILP solutions are associated with a raised system reliability level and a reduced constraint violation risk level. PMID:18406594
Liu, Dawei; Lin, Xihong; Ghosh, Debashis
2007-12-01
We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations. PMID:18078480
Equilibrium Phase Behavior of the Square-Well Linear Microphase-Forming Model.
Zhuang, Yuan; Charbonneau, Patrick
2016-07-01
We have recently developed a simulation approach to calculate the equilibrium phase diagram of particle-based microphase formers. Here, this approach is used to calculate the phase behavior of the square-well linear model for different strengths and ranges of the linear long-range repulsive component. The results are compared with various theoretical predictions for microphase formation. The analysis further allows us to better understand the mechanism for microphase formation in colloidal suspensions. PMID:27117230
Vibration Stabilization of a Mechanical Model of a X-Band Linear Collider Final Focus Magnet
Frisch, Josef; Chang, Allison; Decker, Valentin; Doyle, Eric; Eriksson, Leif; Hendrickson, Linda; Himel, Thomas; Markiewicz, Thomas; Partridge, Richard; Seryi, Andrei; /SLAC
2006-09-28
The small beam sizes at the interaction point of a X-band linear collider require mechanical stabilization of the final focus magnets at the nanometer level. While passive systems provide adequate performance at many potential sites, active mechanical stabilization is useful if the natural or cultural ground vibration is higher than expected. A mechanical model of a room temperature linear collider final focus magnet has been constructed and actively stabilized with an accelerometer based system.
Based on linear spectral mixture model (LSMM) unmixing remote sensing image
NASA Astrophysics Data System (ADS)
Liu, Jiaodi; Cao, Weibin
2011-06-01
There are mixed pixels in remote sensing images ordinarily, this is a difficulty of the pixel classification (ie, unmixing) in remote sensing image processing.Linear spectral separation, estimating the value end of Genpo degree, for spatial modeling, through the non-constrained mixed pixel decomposition,with cotton, corn, tomatoes and soil four endmembers to decompose mixed pixels, Got four endmember abundance images and the RMS error image, the planting area of cotton and cotton-growing area of the measurement in the decomposition of mixed pixel block, and obtained unmixing accuracy. Experimental results show that: a simple linear mixed model modeling, and computation is greatly reduced, high precision, strong adaptability.
Analysis of an inventory model for both linearly decreasing demand and holding cost
NASA Astrophysics Data System (ADS)
Malik, A. K.; Singh, Parth Raj; Tomar, Ajay; Kumar, Satish; Yadav, S. K.
2016-03-01
This study proposes the analysis of an inventory model for linearly decreasing demand and holding cost for non-instantaneous deteriorating items. The inventory model focuses on commodities having linearly decreasing demand without shortages. The holding cost doesn't remain uniform with time due to any form of variation in the time value of money. Here we consider that the holding cost decreases with respect to time. The optimal time interval for the total profit and the optimal order quantity are determined. The developed inventory model is pointed up through a numerical example. It also includes the sensitivity analysis.
NASA Technical Reports Server (NTRS)
Taylor, B. K.; Casasent, D. P.
1989-01-01
The use of simplified error models to accurately simulate and evaluate the performance of an optical linear-algebra processor is described. The optical architecture used to perform banded matrix-vector products is reviewed, along with a linear dynamic finite-element case study. The laboratory hardware and ac-modulation technique used are presented. The individual processor error-source models and their simulator implementation are detailed. Several significant simplifications are introduced to ease the computational requirements and complexity of the simulations. The error models are verified with a laboratory implementation of the processor, and are used to evaluate its potential performance.
Zhu, Xiaoyan; Li, Xueping; Yao, Qingzhu; Chen, Yuerong
2011-01-01
This paper analyzed the uniqueness and challenges in designing the logistics system for dedicated biomass-to-bioenergy industry, which differs from the other industries, due to the unique features of dedicated biomass (e.g., switchgrass) including its low bulk density, restrictions on harvesting season and frequency, content variation with time and circumambient conditions, weather effects, scattered distribution over a wide geographical area, and so on. To design it, this paper proposed a mixed integer linear programming model. It covered from planting and harvesting switchgrass to delivering to a biorefinery and included the residue handling, concentrating on integrating strategic decisions on the supply chain design and tactical decisions on the annual operation schedules. The present numerical examples verified the model and demonstrated its use in practice. This paper showed that the operations of the logistics system were significantly different for harvesting and non-harvesting seasons, and that under the well-designed biomass logistics system, the mass production with a steady and sufficient supply of biomass can increase the unit profit of bioenergy. The analytical model and practical methodology proposed in this paper will help realize the commercial production in biomass-to-bioenergy industry. PMID:20863690
A Full Demand Response Model in Co-Optimized Energy and
Liu, Guodong; Tomsovic, Kevin
2014-01-01
It has been widely accepted that demand response will play an important role in reliable and economic operation of future power systems and electricity markets. Demand response can not only influence the prices in the energy market by demand shifting, but also participate in the reserve market. In this paper, we propose a full model of demand response in which demand flexibility is fully utilized by price responsive shiftable demand bids in energy market as well as spinning reserve bids in reserve market. A co-optimized day-ahead energy and spinning reserve market is proposed to minimize the expected net cost under all credible system states, i.e., expected total cost of operation minus total benefit of demand, and solved by mixed integer linear programming. Numerical simulation results on the IEEE Reliability Test System show effectiveness of this model. Compared to conventional demand shifting bids, the proposed full demand response model can further reduce committed capacity from generators, starting up and shutting down of units and the overall system operating costs.
Aboveground biomass and carbon stocks modelling using non-linear regression model
NASA Astrophysics Data System (ADS)
Ain Mohd Zaki, Nurul; Abd Latif, Zulkiflee; Nazip Suratman, Mohd; Zainee Zainal, Mohd
2016-06-01
Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.
Holmes, William R; Trueblood, Jennifer S; Heathcote, Andrew
2016-03-01
In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information. PMID:26760448
NASA Astrophysics Data System (ADS)
Adcock, T. A. A.; Taylor, P. H.
2016-01-01
The non-linear Schrödinger equation and its higher order extensions are routinely used for analysis of extreme ocean waves. This paper compares the evolution of individual wave-packets modelled using non-linear Schrödinger type equations with packets modelled using fully non-linear potential flow models. The modified non-linear Schrödinger Equation accurately models the relatively large scale non-linear changes to the shape of wave-groups, with a dramatic contraction of the group along the mean propagation direction and a corresponding extension of the width of the wave-crests. In addition, as extreme wave form, there is a local non-linear contraction of the wave-group around the crest which leads to a localised broadening of the wave spectrum which the bandwidth limited non-linear Schrödinger Equations struggle to capture. This limitation occurs for waves of moderate steepness and a narrow underlying spectrum.
Siegel, Jeffry A; Welsh, James S
2016-04-01
In the past several years, there has been a great deal of attention from the popular media focusing on the alleged carcinogenicity of low-dose radiation exposures received by patients undergoing medical imaging studies such as X-rays, computed tomography scans, and nuclear medicine scintigraphy. The media has based its reporting on the plethora of articles published in the scientific literature that claim that there is "no safe dose" of ionizing radiation, while essentially ignoring all the literature demonstrating the opposite point of view. But this reported "scientific" literature in turn bases its estimates of cancer induction on the linear no-threshold hypothesis of radiation carcinogenesis. The use of the linear no-threshold model has yielded hundreds of articles, all of which predict a definite carcinogenic effect of any dose of radiation, regardless of how small. Therefore, hospitals and professional societies have begun campaigns and policies aiming to reduce the use of certain medical imaging studies based on perceived risk:benefit ratio assumptions. However, as they are essentially all based on the linear no-threshold model of radiation carcinogenesis, the risk:benefit ratio models used to calculate the hazards of radiological imaging studies may be grossly inaccurate if the linear no-threshold hypothesis is wrong. Here, we review the myriad inadequacies of the linear no-threshold model and cast doubt on the various studies based on this overly simplistic model. PMID:25824269
Missing Data Treatments at the Second Level of Hierarchical Linear Models
ERIC Educational Resources Information Center
St. Clair, Suzanne W.
2011-01-01
The current study evaluated the performance of traditional versus modern MDTs in the estimation of fixed-effects and variance components for data missing at the second level of an hierarchical linear model (HLM) model across 24 different study conditions. Variables manipulated in the analysis included, (a) number of Level-2 variables with missing…
Effects on Predictive Ability of the Linear versus Location Models in Discriminant Analysis.
ERIC Educational Resources Information Center
Steele, Maryann E.
The Mahalanobis distance model was compared with the linear discriminant function model and found to provide very similar results, even when a number of the variables were binary. A group of college freshmen were categorized into two groups: 116 "leavers," students who did not return for the second year, and 269 "returners." Data from the…
Application of wavelet-based multiple linear regression model to rainfall forecasting in Australia
NASA Astrophysics Data System (ADS)
He, X.; Guan, H.; Zhang, X.; Simmons, C.
2013-12-01
In this study, a wavelet-based multiple linear regression model is applied to forecast monthly rainfall in Australia by using monthly historical rainfall data and climate indices as inputs. The wavelet-based model is constructed by incorporating the multi-resolution analysis (MRA) with the discrete wavelet transform and multiple linear regression (MLR) model. The standardized monthly rainfall anomaly and large-scale climate index time series are decomposed using MRA into a certain number of component subseries at different temporal scales. The hierarchical lag relationship between the rainfall anomaly and each potential predictor is identified by cross correlation analysis with a lag time of at least one month at different temporal scales. The components of predictor variables with known lag times are then screened with a stepwise linear regression algorithm to be selectively included into the final forecast model. The MRA-based rainfall forecasting method is examined with 255 stations over Australia, and compared to the traditional multiple linear regression model based on the original time series. The models are trained with data from the 1959-1995 period and then tested in the 1996-2008 period for each station. The performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and correlation coefficient. The results show that the wavelet-based regression model provides considerably more accurate monthly rainfall forecasts for all of the selected stations over Australia than the traditional regression model.
USING LINEAR AND POLYNOMIAL MODELS TO EXAMINE THE ENVIRONMENTAL STABILITY OF VIRUSES
The article presents the development of model equations for describing the fate of viral infectivity in environmental samples. Most of the models were based upon the use of a two-step linear regression approach. The first step employs regression of log base 10 transformed viral t...
Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.
ERIC Educational Resources Information Center
Vidal, Sherry
Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…
Augmenting Visual Analysis in Single-Case Research with Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Davis, Dawn H.; Gagne, Phill; Fredrick, Laura D.; Alberto, Paul A.; Waugh, Rebecca E.; Haardorfer, Regine
2013-01-01
The purpose of this article is to demonstrate how hierarchical linear modeling (HLM) can be used to enhance visual analysis of single-case research (SCR) designs. First, the authors demonstrated the use of growth modeling via HLM to augment visual analysis of a sophisticated single-case study. Data were used from a delayed multiple baseline…
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates
ERIC Educational Resources Information Center
Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.
2012-01-01
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…
NASA Astrophysics Data System (ADS)
Rabieh, Masood; Soukhakian, Mohammad Ali; Mosleh Shirazi, Ali Naghi
2016-03-01
Selecting the best suppliers is crucial for a company's success. Since competition is a determining factor nowadays, reducing cost and increasing quality of products are two key criteria for appropriate supplier selection. In the study, first the inventories of agglomeration plant of Isfahan Steel Company were categorized through VED and ABC methods. Then the models to supply two important kinds of raw materials (inventories) were developed, considering the following items: (1) the optimal consumption composite of the materials, (2) the total cost of logistics, (3) each supplier's terms and conditions, (4) the buyer's limitations and (5) the consumption behavior of the buyers. Among diverse developed and tested models—using the company's actual data within three pervious years—the two new innovative models of mixed-integer non-linear programming type were found to be most suitable. The results of solving two models by lingo software (based on company's data in this particular case) were equaled. Comparing the results of the new models to the actual performance of the company revealed 10.9 and 7.1 % reduction in total procurement costs of the company in two consecutive years.
Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models
Xie, Minge; Simpson, Douglas G.; Carroll, Raymond J.
2009-01-01
This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety. PMID:19444331
A note on probabilistic models over strings: the linear algebra approach.
Bouchard-Côté, Alexandre
2013-12-01
Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems. PMID:24135792
Vestibular coriolis effect differences modeled with three-dimensional linear-angular interactions.
Holly, Jan E
2004-01-01
The vestibular coriolis (or "cross-coupling") effect is traditionally explained by cross-coupled angular vectors, which, however, do not explain the differences in perceptual disturbance under different acceleration conditions. For example, during head roll tilt in a rotating chair, the magnitude of perceptual disturbance is affected by a number of factors, including acceleration or deceleration of the chair rotation or a zero-g environment. Therefore, it has been suggested that linear-angular interactions play a role. The present research investigated whether these perceptual differences and others involving linear coriolis accelerations could be explained under one common framework: the laws of motion in three dimensions, which include all linear-angular interactions among all six components of motion (three angular and three linear). The results show that the three-dimensional laws of motion predict the differences in perceptual disturbance. No special properties of the vestibular system or nervous system are required. In addition, simulations were performed with angular, linear, and tilt time constants inserted into the model, giving the same predictions. Three-dimensional graphics were used to highlight the manner in which linear-angular interaction causes perceptual disturbance, and a crucial component is the Stretch Factor, which measures the "unexpected" linear component. PMID:15735327
Robust estimation for partially linear models with large-dimensional covariates
Zhu, LiPing; Li, RunZe; Cui, HengJian
2014-01-01
We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures. PMID:24955087
Model predictive control of non-linear systems over networks with data quantization and packet loss.
Yu, Jimin; Nan, Liangsheng; Tang, Xiaoming; Wang, Ping
2015-11-01
This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method. PMID:26341070
Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.
2014-01-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281
Impact of using linear optimization models in dose planning for HDR brachytherapy
Holm, Aasa; Larsson, Torbjoern; Carlsson Tedgren, Aasa
2012-02-15
Purpose: Dose plans generated with optimization models hitherto used in high-dose-rate (HDR) brachytherapy have shown a tendency to yield longer dwell times than manually optimized plans. Concern has been raised for the corresponding undesired hot spots, and various methods to mitigate these have been developed. The hypotheses upon this work is based are (a) that one cause for the long dwell times is the use of objective functions comprising simple linear penalties and (b) that alternative penalties, as these are piecewise linear, would lead to reduced length of individual dwell times. Methods: The characteristics of the linear penalties and the piecewise linear penalties are analyzed mathematically. Experimental comparisons between the two types of penalties are carried out retrospectively for a set of prostate cancer patients. Results: When the two types of penalties are compared, significant changes can be seen in the dwell times, while most dose-volume parameters do not differ significantly. On average, total dwell times were reduced by 4.2%, with a reduction of maximum dwell times by 25%, when the alternative penalties were used. Conclusions: The use of linear penalties in optimization models for HDR brachytherapy is one cause for the undesired long dwell times that arise in mathematically optimized plans. By introducing alternative penalties, a significant reduction in dwell times can be achieved for HDR brachytherapy dose plans. Although various measures for mitigating the long dwell times are already available, the observation that linear penalties contribute to their appearance is of fundamental interest.
Three-dimensional finite-difference modeling of non-linear ground notion
Jones, E.M.; Olsen, K.B.
1997-08-01
We present a hybrid finite-difference technique capable of modeling non-linear soil amplification from the 3-D finite-fault radiation pattern for earthquakes in arbitrary earth models. The method is applied to model non-linear effects in the soils of the San Fernando Valley (SFV) from the 17 January 1994 M 6.7 Northridge earthquake. 0-7 Hz particle velocities are computed for an area of 17 km by 19 km immediately above the causative fault and 5 km below the surface where peak strike-parallel, strike-perpendicular, vertical, and total velocities reach values of 71 cm/s, 145 cm/s, 152 cm/s, and 180 cm/s, respectively. Selected Green`s functions and a soil model for the SFV are used to compute the approximate stress level during the earthquake, and comparison to the values for near-surface alluvium at the U.S. Nevada Test Site suggests that the non-linear regime may have been entered. We use selected values from the simulated particle velocity distribution at 5 km depth to compute the non-linear response in a soil column below a site within the Van Norman Complex in SFV, where the strongest ground motion was recorded. Since site-specific non- linear material parameters from the SFV are currently unavailable, values are taken from analyses of observed Test Site ground motions. Preliminary results show significant reduction of spectral velocities at the surface normalized to the peak source velocity due to non-linear effects when the peak velocity increases from 32 cm/s (approximately linear case) to 64 cm/s (30-92%), 93 cm/s (7-83%), and 124 cm/s (2-70%). The largest reduction occurs for frequencies above 1 Hz.
Stability analysis, non-linear pulsations and mass loss of models for 55 Cygni (HD 198478)
NASA Astrophysics Data System (ADS)
Yadav, Abhay Pratap; Glatzel, Wolfgang
2016-04-01
55 Cygni is a variable supergiant. Recent observational studies revealed that this star pulsates in pressure, gravity and strange modes. The pulsations seem to be associated with episodes of mass loss. In this paper we present a theoretical study of stellar models with parameters close to that of 55 Cygni. A linear non-adiabatic stability analysis with respect to radial perturbations is performed and the evolution of instabilities into the non-linear regime is followed by numerical simulation. Our study indicates that the mass of 55 Cygni lies below 28 M⊙. As the final consequence of the instabilities the non-linear simulations revealed finite amplitude pulsations with periods consistent with the observations. The non-linear results also indicate a connection between pulsations and mass loss and allow for an estimate of the mean mass-loss rate. It is consistent with the observed values.
Gene Golub; Kwok Ko
2009-03-30
The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.
Modelling land-fast sea ice using a linear elastic model
NASA Astrophysics Data System (ADS)
Plante, Mathieu; Tremblay, Bruno
2016-04-01
Land-fast ice is an important component of the Arctic system, capping the coastal Arctic waters for most of the year and exerting a large influence on ocean-atmosphere heat exchanges. Yet, the accurate representation of land-fast ice in most large-scale sea ice models remains a challenge, part due to the difficult (and sometimes non-physical) parametrisation of ice fracture. In this study, a linear elastic model is developed to investigate the internal stresses induced by the wind forcing on the land-fast ice, modelled as a 2D elastic plate. The model simulates ice fracture by the implementation of a damage coefficient which causes a local reduction in internal stress. This results in a cascade propagation of damage, simulating the ice fracture that determines the position of the land-fast ice edge. The modelled land-fast ice cover is sensitive to the choice of failure criterion. The parametrised cohesion, tensile and compressive strength and the relationship with the land-fast ice stability is discussed. To estimate the large scale mechanical properties of land-fast ice, these results are compared to a set of land-fast ice break up events and ice bridge formations observed in the Siberian Arctic. These events are identified using brightness temperature imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua satellites, from which the position of the flaw lead is identifiable by the opening of polynyi adjacent to the land-fast ice edge. The shape of the land-fast ice before, during and after these events, along with the characteristic scale of the resulting ice floes, are compared to the model results to extrapolate the stress state that corresponds to these observations. The model setting that best reproduce the scale of the observed break up events is used to provide an estimation of the strength of the ice relative to the wind forcing. These results will then be used to investigate the relationship between the ice thickness and the
Modelling the Non-Linear Viscoelastic and Viscoplastic Behaviour of Aramid Fibre Yarns
NASA Astrophysics Data System (ADS)
Chailleux, E.; Davies, P.
A non-linear viscoelastic viscoplastic model is proposed for the tensile behaviour of aramid fibres, based on an analysis of the deformation mechanisms of these materials. This model uses the macroscopic formulation developed by Schapery together with the plasticity concept of Perzyna. A simple identification procedure for the model parameters has been developed using creep/recovery cycles at different load levels. The identification reveals that two of the four parameters of the viscoelastic model (g1 and aσ) are independent of stress level. This may be due to the simple and regular nature of the fibre structure. The model enables the parameters which characterise the non-linear reversible viscoelasticity to be identified independently from those which characterise the viscoplasticity. The model predictions are compared to experimental data for a more complex load sequence and reasonable correlation is obtained.
Bayesian Model Selection in Complex Linear Systems, as Illustrated in Genetic Association Studies
Wen, Xiaoquan
2013-01-01
Summary Motivated by examples from genetic association studies, this paper considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating context-dependent a priori information through different levels of prior specifications. We also derive analytic Bayes factors and their approximations to facilitate model selection and discuss their theoretical and computational properties. We demonstrate our Bayesian approach based on an implemented Markov Chain Monte Carlo (MCMC) algorithm in simulations and a real data application of mapping tissue-specific eQTLs. Our novel results on Bayes factors provide a general framework to perform efficient model comparisons in complex linear model systems. PMID:24350677
A single-degree-of-freedom model for non-linear soil amplification
Erdik, Mustafa Ozder
1979-01-01
For proper understanding of soil behavior during earthquakes and assessment of a realistic surface motion, studies of the large-strain dynamic response of non-linear hysteretic soil systems are indispensable. Most of the presently available studies are based on the assumption that the response of a soil deposit is mainly due to the upward propagation of horizontally polarized shear waves from the underlying bedrock. Equivalent-linear procedures, currently in common use in non-linear soil response analysis, provide a simple approach and have been favorably compared with the actual recorded motions in some particular cases. Strain compatibility in these equivalent-linear approaches is maintained by selecting values of shear moduli and damping ratios in accordance with the average soil strains, in an iterative manner. Truly non-linear constitutive models with complete strain compatibility have also been employed. The equivalent-linear approaches often raise some doubt as to the reliability of their results concerning the system response in high frequency regions. In these frequency regions the equivalent-linear methods may underestimate the surface motion by as much as a factor of two or more. Although studies are complete in their methods of analysis, they inevitably provide applications pertaining only to a few specific soil systems, and do not lead to general conclusions about soil behavior. This report attempts to provide a general picture of the soil response through the use of a single-degree-of-freedom non-linear-hysteretic model. Although the investigation is based on a specific type of nonlinearity and a set of dynamic soil properties, the method described does not limit itself to these assumptions and is equally applicable to other types of nonlinearity and soil parameters.
Prediction of Nino 3 sea surface temperatures using linear inverse modeling
Penland, C.; Magorian, T. )
1993-06-01
Linear inverse modeling is used to predict sea surface temperatures (SSTs) in the Nino 3 region. Predictors in three geographical locations are used: the tropical Pacific Ocean, the tropical Pacific and Indian oceans, and the global tropical oceans. Predictions did not depend crucially on any of these three domains, and evidence was found to support the assumption that linear dynamics dominates most of the record. The prediction model performs better when SST anomalies are rapidly evolving than during warm events when large anomalies persist. The rms prediction error at a lead time of 9 months is about half a degree Celsius. 31 refs., 9 figs., 1 tab.
Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E.; Downs, Danielle S.; Savage, Jennifer S.
2015-01-01
Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or “just-in-time” behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components. PMID:25635157
Demolition waste generation for development of a regional management chain model.
Bernardo, Miguel; Gomes, Marta Castilho; de Brito, Jorge
2016-03-01
Even though construction and demolition waste (CDW) is the bulkiest waste stream, its estimation and composition in specific regions still faces major difficulties. Therefore new methods are required especially when it comes to make predictions limited to small areas, such as counties. This paper proposes one such method, which makes use of data collected from real demolition works and statistical information on the geographical area under study. Based on a correlation analysis between the demolition waste estimates and indicators such as population density, buildings ageing index, buildings density and land occupation type, relationships are established that can be used to determine demolition waste outputs in a given area. The derived models are presented and explained. This methodology is independent from the specific region with which it is exemplified (the Lisbon Metropolitan Area) and can therefore be applied to any region of the world, from the country to the county level. Generation of demolition waste data at the county level is the basis of the design of a systemic model for CDW management in a region. Future developments proposed include a mixed-integer linear programming formulation of such recycling network. PMID:26838607
Aggregation of LoD 1 building models as an optimization problem
NASA Astrophysics Data System (ADS)
Guercke, R.; Götzelmann, T.; Brenner, C.; Sester, M.
3D city models offered by digital map providers typically consist of several thousands or even millions of individual buildings. Those buildings are usually generated in an automated fashion from high resolution cadastral and remote sensing data and can be very detailed. However, not in every application such a high degree of detail is desirable. One way to remove complexity is to aggregate individual buildings, simplify the ground plan and assign an appropriate average building height. This task is computationally complex because it includes the combinatorial optimization problem of determining which subset of the original set of buildings should best be aggregated to meet the demands of an application. In this article, we introduce approaches to express different aspects of the aggregation of LoD 1 building models in the form of Mixed Integer Programming (MIP) problems. The advantage of this approach is that for linear (and some quadratic) MIP problems, sophisticated software exists to find exact solutions (global optima) with reasonable effort. We also propose two different heuristic approaches based on the region growing strategy and evaluate their potential for optimization by comparing their performance to a MIP-based approach.
NOTE: Estimation of renal scintigraphy parameters using a linear piecewise-continuous model
NASA Astrophysics Data System (ADS)
Zhang, Jeff L.; Zhang, L.; Koh, T. S.; Shuter, B.
2003-06-01
Instead of performing a numerical deconvolution, we propose to use a linear piecewise-continuous model of the renal impulse response function for parametric fitting of renal scintigraphy data, to obtain clinically useful renal parameters. The strengths of the present model are its simplicity and speed of computation, while not compromising on accuracy. Preliminary patient case studies show that the estimated parameters are in good agreement with a more elaborate model.
Finite element modelling of non-linear magnetic circuits using Cosmic NASTRAN
NASA Technical Reports Server (NTRS)
Sheerer, T. J.
1986-01-01
The general purpose Finite Element Program COSMIC NASTRAN currently has the ability to model magnetic circuits with constant permeablilities. An approach was developed which, through small modifications to the program, allows modelling of non-linear magnetic devices including soft magnetic materials, permanent magnets and coils. Use of the NASTRAN code resulted in output which can be used for subsequent mechanical analysis using a variation of the same computer model. Test problems were found to produce theoretically verifiable results.
NASA Astrophysics Data System (ADS)
Wazwaz, Abdul-Majid
2014-03-01
We apply the variational iteration method (VIM) for solving linear and nonlinear ordinary differential equations with variable coefficients. We use distinct Lagrange multiplier for each order of ODE. We emphasize the power of the method by testing a variety of models with distinct orders and variable coefficients. Scientific models, namely, the hybrid selection model, the Thomas-Fermi equation, the Kidder equation of the Unsteady flow of gas through a porous medium, and the Riccati equation, are studied as well.
Development and Application of Non-linear Friction Models for Metal Forming Simulation
NASA Astrophysics Data System (ADS)
Ma, Ninshu; Sugitomo, Nobuhiko
2011-08-01
Friction has a significant effect on the formability of stamping parts. A constant friction coefficient between stamping tools and blank is often employed in the metal forming simulating. In this presented work, several non-linear friction models which considers of the change of friction coefficient with contact pressure, sliding velocity, sliding distance, frictional work, plastic strain and temperature were developed using LS-DYNA customized friction subroutine. The validity was verified by numerical friction testing models and deep drawing model.
Interpreting linear support vector machine models with heat map molecule coloring
2011-01-01
Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031
Forutan, M; Ansari Mahyari, S; Sargolzaei, M
2015-02-01
Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49,583 female calves born during 1998 and 2009 were considered in five age periods as days 1-30, 31-180, 181-365, 366-760 and full period (day 1-760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd-year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82-0.95 and 0.61-0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31-180 and 181-365 (r(g) = 0.59), 31-180 and 366-760 (r(g) = 0.52), and 181-365 and 366-760 (r(g) = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection. PMID:25100295
Performance of a linear robust control strategy on a nonlinear model of spatially developing flows
NASA Astrophysics Data System (ADS)
Lauga, Eric; Bewley, Thomas R.
2004-08-01
This paper investigates the control of self-excited oscillations in spatially developing flow systems such as jets and wakes using {mathcal H}_{infty} control theory on a complex Ginzburg Landau (CGL) model. The coefficients used in this one-dimensional equation, which serves as a simple model of the evolution of hydrodynamic instability waves, are those selected by Roussopoulos & Monkewitz (Physica D 1996, vol. 97, p. 264) to model the behaviour of the near-wake of a circular cylinder. Based on noisy measurements at a point sensor typically located inside the cylinder wake, the compensator uses a linear {mathcal H}_{infty} filter based on the CGL model to construct a state estimate. This estimate is then used to compute linear {mathcal H}_{infty} control feedback at a point actuator location, which is typically located upstream of the sensor. The goal of the control scheme is to stabilize the system by minimizing a weighted average of the ‘system response’ and the ‘control effort’ while rigorously bounding the response of the controlled linear system to external disturbances. The application of such modern control and estimation rules stabilizes the linear CGL system at Reynolds numbers far above the critical Reynolds number Re_c {≈} 47 at which linear global instability appears in the uncontrolled system. In so doing, many unstable modes of the uncontrolled CGL system are linearly stabilized by the single actuator/sensor pair and the model-based feedback control strategy. Further, the linear performance of the closed-loop system, in terms of the relevant transfer function norms quantifying the linear response of the controlled system to external disturbances, is substantially improved beyond that possible with the simple proportional measurement feedback proposed in previous studies. Above Re {≈} 84, the {mathcal H}_{infty} control designs significantly outperform the corresponding {mathcal H}_2 control designs in terms of their ability to stabilize
Generating synthetic wave climates for coastal modelling: a linear mixed modelling approach
NASA Astrophysics Data System (ADS)
Thomas, C.; Lark, R. M.
2013-12-01
Numerical coastline morphological evolution models require wave climate properties to drive morphological change through time. Wave climate properties (typically wave height, period and direction) may be temporally fixed, culled from real wave buoy data, or allowed to vary in some way defined by a Gaussian or other pdf. However, to examine sensitivity of coastline morphologies to wave climate change, it seems desirable to be able to modify wave climate time series from a current to some new state along a trajectory, but in a way consistent with, or initially conditioned by, the properties of existing data, or to generate fully synthetic data sets with realistic time series properties. For example, mean or significant wave height time series may have underlying periodicities, as revealed in numerous analyses of wave data. Our motivation is to develop a simple methodology to generate synthetic wave climate time series that can change in some stochastic way through time. We wish to use such time series in a coastline evolution model to test sensitivities of coastal landforms to changes in wave climate over decadal and centennial scales. We have worked initially on time series of significant wave height, based on data from a Waverider III buoy located off the coast of Yorkshire, England. The statistical framework for the simulation is the linear mixed model. The target variable, perhaps after transformation (Box-Cox), is modelled as a multivariate Gaussian, the mean modelled as a function of a fixed effect, and two random components, one of which is independently and identically distributed (iid) and the second of which is temporally correlated. The model was fitted to the data by likelihood methods. We considered the option of a periodic mean, the period either fixed (e.g. at 12 months) or estimated from the data. We considered two possible correlation structures for the second random effect. In one the correlation decays exponentially with time. In the second
Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor
NASA Technical Reports Server (NTRS)
Metscher, Jonathan F.; Lewandowski, Edward J.
2015-01-01
Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and also compared against each other. Results show both models can be tuned to achieve results within 7 of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.
Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor
NASA Technical Reports Server (NTRS)
Metscher, Jonathan F.; Lewandowski, Edward
2014-01-01
Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and compared against each other. Results show both models can be tuned to achieve results within 7% of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.
Stochastic non-linear oscillator models of EEG: the Alzheimer's disease case
Ghorbanian, Parham; Ramakrishnan, Subramanian; Ashrafiuon, Hashem
2015-01-01
In this article, the Electroencephalography (EEG) signal of the human brain is modeled as the output of stochastic non-linear coupled oscillator networks. It is shown that EEG signals recorded under different brain states in healthy as well as Alzheimer's disease (AD) patients may be understood as distinct, statistically significant realizations of the model. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) resting conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing—van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects by selecting the model physical parameters and noise intensity. The selected signal characteristics are power spectral densities in major brain frequency bands Shannon and sample entropies. These measures allow matching of linear time varying frequency content as well as non-linear signal information content and complexity. The main finding of the work is that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. However, it is also shown that the inclusion of sample entropy in the optimization process, to match the complexity of the EEG signal, enhances the stochastic non-linear oscillator model performance. PMID:25964756
NASA Astrophysics Data System (ADS)
Ripamonti, Francesco; Orsini, Lorenzo; Resta, Ferruccio
2015-04-01
Non-linear behavior is present in many mechanical system operating conditions. In these cases, a common engineering practice is to linearize the equation of motion around a particular operating point, and to design a linear controller. The main disadvantage is that the stability properties and validity of the controller are local. In order to improve the controller performance, non-linear control techniques represent a very attractive solution for many smart structures. The aim of this paper is to compare non-linear model-based and non-model-based control techniques. In particular the model-based sliding-mode-control (SMC) technique is considered because of its easy implementation and the strong robustness of the controller even under heavy model uncertainties. Among the non-model-based control techniques, the fuzzy control (FC), allowing designing the controller according to if-then rules, has been considered. It defines the controller without a system reference model, offering many advantages such as an intrinsic robustness. These techniques have been tested on the pendulum nonlinear system.
Linearized aerodynamic and control law models of the X-29A airplane and comparison with flight data
NASA Technical Reports Server (NTRS)
Bosworth, John T.
1992-01-01
Flight control system design and analysis for aircraft rely on mathematical models of the vehicle dynamics. In addition to a six degree of freedom nonlinear simulation, the X-29A flight controls group developed a set of programs that calculate linear perturbation models throughout the X-29A flight envelope. The models include the aerodynamics as well as flight control system dynamics and were used for stability, controllability, and handling qualities analysis. These linear models were compared to flight test results to help provide a safe flight envelope expansion. A description is given of the linear models at three flight conditions and two flight control system modes. The models are presented with a level of detail that would allow the reader to reproduce the linear results if desired. Comparison between the response of the linear model and flight measured responses are presented to demonstrate the strengths and weaknesses of the linear models' ability to predict flight dynamics.
A self-consistent three-wave coupling model with complex linear frequencies
Kim, J.-H.; Terry, P. W.
2011-09-15
A three-wave coupling model with complex linear frequencies is investigated for the nonlinear interaction in a triad that has linearly unstable and stable modes. Time scales associated with linear and nonlinear physics are identified and compared with features of the frequency spectrum. From appropriate time scales, the frequency spectra are well characterized even in the transition to the steady state. The nonlinear time scales that best match spectral features are the nonlinear frequency of the fixed point and a frequency that depends on the amplitude displacement from the fixed point through the large-amplitude Jacobian elliptic solution. Two limited efforts to model the effect of other triads suggest robustness in the single triad results.
Omaha childhood blood lead and environmental lead: a linear total exposure model
Angle, C.R.; Marcus, A.; Cheng, I.H.; McIntire, M.S.
1984-01-01
The majority of experimental and population studies of blood lead (PbB) and environmental lead, including the Omaha study, have utilized the Goldsmith-Hexter log-log or power function model. Comparison was made of the log-log model and a linear model of total exposure to describe the Omaha Study of 1074 PbBs from children ages 1-18 years as related to air (PbA), soil (PbS), and housedust (PbHD) lead. The data fit of the linear model was statistically equivalent to the power model and the predicted curves were biologically more plausible. The linear model avoids the mathematical limitations of the power model which predicts PbB zero at PbA zero. From the Omaha data, the model, ln PbB = ln (Bo + B1 PbA + B2 PbS + B3 PbHD) predicts that PbB increases 1.92 micrograms/dl as PbA increases 1.0 micrograms/cu. m. Since PbS and PbHD increase with PbA, however, the increases in total exposure predict a PbB increase of 4-5 micrograms/dl as PbA increases 1.0 micrograms/cu. m.
NASA Astrophysics Data System (ADS)
Eghnam, Karam M.; Sheta, Alaa F.
2008-06-01
Development of accurate models is necessary in critical applications such as prediction. In this paper, a solution to the stock prediction problem of the Barents Sea capelin is introduced using Artificial Neural Network (ANN) and Multiple Linear model Regression (MLR) models. The Capelin stock in the Barents Sea is one of the largest in the world. It normally maintained a fishery with annual catches of up to 3 million tons. The Capelin stock problem has an impact in the fish stock development. The proposed prediction model was developed using an ANNs with their weights adapted using Genetic Algorithm (GA). The proposed model was compared to traditional linear model the MLR. The results showed that the ANN-GA model produced an overall accuracy of 21% better than the MLR model.
Examining the Linear Regimes of the Community Earth System Model (CESM)
NASA Astrophysics Data System (ADS)
Sun, S.; Brizius, A.; Du, H.; Foster, I.; Smith, L.
2015-12-01
In ensemble prediction, Gilmour et al. (2001) proposed measures of relative nonlinearity to quantify the duration of the linear regime from "twin" pairs of ensemble members. The duration of the "linear regime" is useful in forming and interpreting ensembles in numerical weather prediction. Here this method is applied to the state-of-the-art climate model CESM, focusing on how its linear durations will change as the perturbations imposed on one location differ spatially and temporally. The spatial and temporal propagations of point perturbations provide insights into model physics and facilitate interpretation of model projections in future climate scenarios. They provide insight into chaos-like behavior on short time scales, and an indication of the sensitivity and saturation (mixing) times of CESM. Starting from the same initial state, we add relatively small "twin" perturbations (that is, positive and negative perturbations of the same magnitudes) to surface variables, with the locations of the perturbations spanning from the tropics to the poles. As the location changes, the model evolves differently in terms of how the point perturbation extends out of its origin and spreads globally, indicating that different physical mechanisms have played roles in different cases. Repeating the same set of experiments by changing only the perturbation magnitudes insures the linear regime is sampled without constructing an adjoint. Further, how uncertainty growth varies with location in the model state space can be explored by repeating the experiment for different initial states. We compare the responses of the linear regime durations in terms of locations, initial states and magnitudes of the perturbations systematically, and the implications for ensemble experiments and sensitivity studies are discussed. This work is a first step towards treating state-of-the-art climate models with the tools of nonlinear dynamics.
Graphing the Model or Modeling the Graph? Not-so-Subtle Problems in Linear IS-LM Analysis.
ERIC Educational Resources Information Center
Alston, Richard M.; Chi, Wan Fu
1989-01-01
Outlines the differences between the traditional and modern theoretical models of demand for money. States that the two models are often used interchangeably in textbooks, causing ambiguity. Argues against the use of linear specifications that imply that income velocity can increase without limit and that autonomous components of aggregate demand…
ERIC Educational Resources Information Center
Kunina-Habenicht, Olga; Rupp, Andre A.; Wilhelm, Oliver
2012-01-01
Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3…
Mixed linear model approach adapted for genome-wide association studies
Technology Transfer Automated Retrieval System (TEKTRAN)
Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called ‘compressed MLM,’ that decrea...
ERIC Educational Resources Information Center
Fung, Karen; ElAtia, Samira
2015-01-01
Using Hierarchical Linear Modelling (HLM), this study aimed to identify factors such as ESL/ELL/EAL status that would predict students' reading performance in an English language arts exam taken across Canada. Using data from the 2007 administration of the Pan-Canadian Assessment Program (PCAP) along with the accompanying surveys for students and…
A Comparison of Robust and Nonparametric Estimators under the Simple Linear Regression Model.
ERIC Educational Resources Information Center
Nevitt, Jonathan; Tam, Hak P.
This study investigates parameter estimation under the simple linear regression model for situations in which the underlying assumptions of ordinary least squares estimation are untenable. Classical nonparametric estimation methods are directly compared against some robust estimation methods for conditions in which varying degrees of outliers are…
ERIC Educational Resources Information Center
Lam, Terence Yuk Ping; Lau, Kwok Chi
2014-01-01
This study uses hierarchical linear modeling to examine the influence of a range of factors on the science performances of Hong Kong students in PISA 2006. Hong Kong has been consistently ranked highly in international science assessments, such as Programme for International Student Assessment and Trends in International Mathematics and Science…
Zhao, Lili; Feng, Dai; Neelon, Brian; Buyse, Marc
2015-05-10
Prostate-specific antigen (PSA) is a widely used marker in clinical trials for patients with prostate cancer. We develop a mixture model to estimate longitudinal PSA trajectory in response to treatment. The model accommodates subjects responding and not responding to therapy through a mixture of two functions. A responder is described by a piecewise linear function, represented by an intercept, a PSA decline rate, a period of PSA decline, and a PSA rising rate; a nonresponder is described by an increasing linear function with an intercept and a PSA rising rate. Each trajectory is classified as a linear or a piecewise linear function with a certain probability, and the weighted average of these two functions sufficiently characterizes a variety of patterns of PSA trajectories. Furthermore, this mixture structure enables us to derive clinically useful endpoints such as a response rate and time-to-progression, as well as biologically meaningful endpoints such as a cancer cell killing fraction and tumor growth delay. We compare our model with the most commonly used dynamic model in the literature and show its advantages. Finally, we illustrate our approach using data from two multicenter prostate cancer trials. The R code used to produce the analyses reported in this paper is available on request. PMID:25630845
Remarks on "Equivalent Linear Logistic Test Models" by Bechger, Verstralen, and Verhelst (2002)
ERIC Educational Resources Information Center
Fischer, Gerhard H.
2004-01-01
This paper discusses a new form of specifying and normalizing a Linear Logistic Test Model (LLTM) as suggested by Bechger, Verstralen, and Verhelst ("Psychometrika," 2002). It is shown that there are infinitely many ways to specify the same normalization. Moreover, the relationship between some of their results and equivalent previous results in…
A linear model fails to predict orientation selectivity of cells in the cat visual cortex.
Volgushev, M; Vidyasagar, T R; Pei, X
1996-01-01
1. Postsynaptic potentials (PSPs) evoked by visual stimulation in simple cells in the cat visual cortex were recorded using in vivo whole-cell technique. Responses to small spots of light presented at different positions over the receptive field and responses to elongated bars of different orientations centred on the receptive field were recorded. 2. To test whether a linear model can account for orientation selectivity of cortical neurones, responses to elongated bars were compared with responses predicted by a linear model from the receptive field map obtained from flashing spots. 3. The linear model faithfully predicted the preferred orientation, but not the degree of orientation selectivity or the sharpness of orientation tuning. The ratio of optimal to non-optimal responses was always underestimated by the model. 4. Thus non-linear mechanisms, which can include suppression of non-optimal responses and/or amplification of optimal responses, are involved in the generation of orientation selectivity in the primary visual cortex. PMID:8930828
NASA Astrophysics Data System (ADS)
Lay, E. H.; Holzworth, R. H.; Cho, M.; Rodger, C. J.; Thomas, J. N.
2008-12-01
We report results on the temporal-spatial modeling of non-linear electron density enhancement due to successive lightning strokes using World Wide Lightning Location Network (WWLLN) data to experimentally describe the rate of large lightning strokes. The WWLLN provides real-time lightning locations globally by measuring the very low frequency (VLF) radiation emanating from lightning discharges. These WWLLN stroke rates are used as input to an axi-symmetric FDTD model that describes the non-linear effect of lightning electromagnetic pulses (EMP) on the ionosphere. This non-linear effect results from accumulating electron density modifications due to the interaction of the EMP from multiple successive lightning strokes with the lower ionosphere. Further studies must be completed to narrow uncertainties in the model, but the qualitative ionospheric response to successive EMPs is presented in two-dimensional, axi-symmetric space. Results from this study show that the non-linear effect of lightning EMP due to successive lightning strokes must be taken into account and varies with altitude. The limiting maximum electron density is reached earlier in time for higher altitudes, and the most significant effect occurs at 88 km. The limiting maximum modeled electron density profile in the 83- to 91-km altitude range does not depend on the initial electron density.
Sampling Schemes and the Selection of Log-Linear Models for Longitudinal Data.
ERIC Educational Resources Information Center
von Eye, Alexander; Schuster, Christof; Kreppner, Kurt
2001-01-01
Discusses the effects of sampling scheme selection on the admissibility of log-linear models for multinomial and product multinomial sampling schemes for prospective and retrospective sampling. Notes that in multinomial sampling, marginal frequencies are not fixed, whereas for product multinomial sampling, uni- or multidimensional frequencies are…
The origins of quantum interference and uncertainty broadening. A linear ribbon model approach
Tang, J.
1996-02-01
As an alternative to the orthodox Schroedinger wave mechanics or Heisenberg matrix mechanics approach, a simple linear ribbon model for quantum theory is presented. A different perspective and better physical insights into the origins of quantum interference and the mechanisms for uncertainty broadening are offered. Quantum interference in the atomic scale and superconducting behaviour in the macroscopic scale are compared.
An improved statistical model for linear antenna input impedance in an electrically large cavity.
Johnson, William Arthur; Warne, Larry Kevin; Jorgenson, Roy Eberhardt; Lee, Kelvin S. H.
2005-03-01
This report presents a modification of a previous model for the statistical distribution of linear antenna impedance. With this modification a simple formula is determined which yields accurate results for all ratios of modal spectral width to spacing. It is shown that the reactance formula approaches the known unit Lorentzian in the lossless limit.
Meta-Analysis in Higher Education: An Illustrative Example Using Hierarchical Linear Modeling
ERIC Educational Resources Information Center
Denson, Nida; Seltzer, Michael H.
2011-01-01
The purpose of this article is to provide higher education researchers with an illustrative example of meta-analysis utilizing hierarchical linear modeling (HLM). This article demonstrates the step-by-step process of meta-analysis using a recently-published study examining the effects of curricular and co-curricular diversity activities on racial…
ERIC Educational Resources Information Center
Henson, Robin K.
In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…
Mathematical Modelling in Engineering: An Alternative Way to Teach Linear Algebra
ERIC Educational Resources Information Center
Domínguez-García, S.; García-Planas, M. I.; Taberna, J.
2016-01-01
Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic…
ERIC Educational Resources Information Center
Choi, In-Hee; Wilson, Mark
2015-01-01
An essential feature of the linear logistic test model (LLTM) is that item difficulties are explained using item design properties. By taking advantage of this explanatory aspect of the LLTM, in a mixture extension of the LLTM, the meaning of latent classes is specified by how item properties affect item difficulties within each class. To improve…
Kaon condensation in the linear sigma model at finite density and temperature
Tran Huu Phat; Nguyen Van Long; Nguyen Tuan Anh; Le Viet Hoa
2008-11-15
Basing on the Cornwall-Jackiw-Tomboulis effective action approach we formulate a theoretical formalism for studying kaon condensation in the linear sigma model at finite density and temperature. We derive the renormalized effective potential in the Hartree-Fock approximation, which preserves the Goldstone theorem. This quantity is then used to consider physical properties of kaon matter.
Estimation of Complex Generalized Linear Mixed Models for Measurement and Growth
ERIC Educational Resources Information Center
Jeon, Minjeong
2012-01-01
Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…
CANFIS: A non-linear regression procedure to produce statistical air-quality forecast models
Burrows, W.R.; Montpetit, J.; Pudykiewicz, J.
1997-12-31
Statistical models for forecasts of environmental variables can provide a good trade-off between significance and precision in return for substantial saving of computer execution time. Recent non-linear regression techniques give significantly increased accuracy compared to traditional linear regression methods. Two are Classification and Regression Trees (CART) and the Neuro-Fuzzy Inference System (NFIS). Both can model predict and distributions, including the tails, with much better accuracy than linear regression. Given a learning data set of matched predict and predictors, CART regression produces a non-linear, tree-based, piecewise-continuous model of the predict and data. Its variance-minimizing procedure optimizes the task of predictor selection, often greatly reducing initial data dimensionality. NFIS reduces dimensionality by a procedure known as subtractive clustering but it does not of itself eliminate predictors. Over-lapping coverage in predictor space is enhanced by NFIS with a Gaussian membership function for each cluster component. Coefficients for a continuous response model based on the fuzzified cluster centers are obtained by a least-squares estimation procedure. CANFIS is a two-stage data-modeling technique that combines the strength of CART to optimize the process of selecting predictors from a large pool of potential predictors with the modeling strength of NFIS. A CANFIS model requires negligible computer time to run. CANFIS models for ground-level O{sub 3}, particulates, and other pollutants will be produced for each of about 100 Canadian sites. The air-quality models will run twice daily using a small number of predictors isolated from a large pool of upstream and local Lagrangian potential predictors.
Spatial temporal disaggregation of daily rainfall from a generalized linear model
NASA Astrophysics Data System (ADS)
Segond, M.-L.; Onof, C.; Wheater, H. S.
2006-12-01
SummaryThis paper describes a methodology for continuous simulation of spatially-distributed hourly rainfall, based on observed data from a daily raingauge network. Generalized linear models (GLMs), which can represent the spatial and temporal non-stationarities of multi-site daily rainfall (Chandler, R.E., Wheater, H.S., 2002. Analysis of rainfall variability using generalised linear models: a case study from the west of Ireland. Water Resources Research, 38 (10), 1192. doi:10.1029/2001WR000906), are combined with a single-site disaggregation model based on Poisson cluster processes (Koutsoyiannis, D., Onof, C., 2001. Rainfall disaggregation using adjusting procedures on a Poisson cluster model. Journal of Hydrology 246, 109-122). The resulting sub-daily temporal profile is then applied linearly to all sites over the catchment to reproduce the spatially-varying daily totals. The method is tested for the River Lee catchment, UK, a tributary of the Thames covering an area of 1400 km 2. Twenty simulations of 12 years of hourly rainfall are generated at 20 sites and compared with the historical series. The proposed model preserves most standard statistics but has some limitations in the representation of extreme rainfall and the correlation structure. The method can be extended to sites within the modelled region not used in the model calibration.
Nonlinear and linear local cartesian relative motion state models for J2 pertubed elliptical orbits
NASA Astrophysics Data System (ADS)
Theron, A.; Kara-Zaitri, M.; Arzelier, D.; Louembet, C.
2009-10-01
Formulating a relative motion model between artificial satellites keeps a very actual interest in order to achieve devices adapted to autonomous rendezvous operating or formation flying control. Scanning the existing models field leads to distinguish them according to their nature -- linear or nonlinear -- the relative state variables -- local cartesian or curvilinear variables, relative orbital elements, classical or not -- the reference state variables -- inertial cartesian or spherical variables, orbital elements -- the pertubations taken into account -- drag, J2, ... -- and other assumptions as eccentricity of the reference satellite. The historical Clohessy-Wiltshire and Tschauner-Hempel models have been outclassed by improved linear models that include J2 pertubation [1, 2, 3] or drag [4] but do not take rigorously into account the perturbed dynamics of the reference local frame. As far as this fundamental point is concerned, Kechichian's nonlinear model [5] provides an interesting but complex formulation because of a non optimal derivation method. More over, it does not take full advantage of the Lagrange conditions [6] implied by orbital elements definition which allows simplifications without loss of generality. These elements are presented in this article to reach an improved relative motion nonlinear model under J2 perturbation assumption which results are validated by comparison with those produced by a nonlinear equinoctial propagator. A linear model is also proposed.
Schultze, J.; Hemez, F.
2000-11-01
This research presents a new method to improve analytical model fidelity for non-linear systems. The approach investigates several mechanisms to assist the analyst in updating an analytical model based on experimental data and statistical analysis of parameter effects. The first is a new approach at data reduction called feature extraction. This is an expansion of the classic update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the familiar linear updating paradigm of utilizing the eigen-parameters or FRF's to include such devices as peak acceleration, time of arrival or standard deviation of model error. The next expansion of the updating process is the inclusion of statistical based parameter analysis to quantify the effects of uncertain or significant effect parameters in the construction of a meta-model. This provides indicators of the statistical variation associated with parameters as well as confidence intervals on the coefficients of the resulting meta-model. Also included in this method is the investigation of linear parameter effect screening using a partial factorial variable array for simulation. This is intended to aid the analyst in eliminating from the investigation the parameters that do not have a significant variation effect on the feature metric. Finally an investigation of the model to replicate the measured response variation is examined.
A linear dispersion relation for the hybrid kinetic-ion/fluid-electron model of plasma physics
NASA Astrophysics Data System (ADS)
Told, D.; Cookmeyer, J.; Astfalk, P.; Jenko, F.
2016-07-01
A dispersion relation for a commonly used hybrid model of plasma physics is developed, which combines fully kinetic ions and a massless-electron fluid description. Although this model and variations of it have been used to describe plasma phenomena for about 40 years, to date there exists no general dispersion relation to describe the linear wave physics contained in the model. Previous efforts along these lines are extended here to retain arbitrary wave propagation angles, temperature anisotropy effects, as well as additional terms in the generalized Ohm’s law which determines the electric field. A numerical solver for the dispersion relation is developed, and linear wave physics is benchmarked against solutions of a full Vlasov–Maxwell dispersion relation solver. This work opens the door to a more accurate interpretation of existing and future wave and turbulence simulations using this type of hybrid model.
Model Order and Identifiability of Non-Linear Biological Systems in Stable Oscillation.
Wigren, Torbjörn
2015-01-01
The paper presents a theoretical result that clarifies when it is at all possible to determine the nonlinear dynamic equations of a biological system in stable oscillation, from measured data. As it turns out the minimal order needed for this is dependent on the minimal dimension in which the stable orbit of the system does not intersect itself. This is illustrated with a simulated fourth order Hodgkin-Huxley spiking neuron model, which is identified using a non-linear second order differential equation model. The simulated result illustrates that the underlying higher order model of the spiking neuron cannot be uniquely determined given only the periodic measured data. The result of the paper is of general validity when the dynamics of biological systems in stable oscillation is identified, and illustrates the need to carefully address non-linear identifiability aspects when validating models based on periodic data. PMID:26671817
A non-linear finite-element model of the newborn ear canal
Qi, Li; Liu, Hengjin; Lutfy, Justyn; Funnell, W. Robert J.; Daniel, Sam J.
2010-01-01
We present a three-dimensional non-linear finite-element model of a 22-day-old newborn ear canal. The geometry is based on a clinical X-ray CT scan. A non-linear hyperelastic constitutive law is applied to model large deformations. The Young’s modulus of the soft tissue is found to have a significant effect on the ear-canal volume change, which ranges from approximately 27% to 75% over the static-pressure range of ±3 kPa. The effects of Poisson’s ratio and of the ratio C10:C01 in the hyperelastic model are found to be small. The volume changes do not reach a plateau at high pressures, which implies that the newborn ear-canal wall would not be rigid in tympanometric measurements. The displacements and volume changes calculated from the model are compared with available experimental data. PMID:17225406
Physical lumping methods for developing linear reduced models for high speed propulsion systems
NASA Technical Reports Server (NTRS)
Immel, S. M.; Hartley, Tom T.; Deabreu-Garcia, J. Alex
1991-01-01
In gasdynamic systems, information travels in one direction for supersonic flow and in both directions for subsonic flow. A shock occurs at the transition from supersonic to subsonic flow. Thus, to simulate these systems, any simulation method implemented for the quasi-one-dimensional Euler equations must have the ability to capture the shock. In this paper, a technique combining both backward and central differencing is presented. The equations are subsequently linearized about an operating point and formulated into a linear state space model. After proper implementation of the boundary conditions, the model order is reduced from 123 to less than 10 using the Schur method of balancing. Simulations comparing frequency and step response of the reduced order model and the original system models are presented.
NASA Technical Reports Server (NTRS)
Scargle, Jeffrey D.
1990-01-01
While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.
Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.
Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu
2016-08-01
This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. PMID:27183335
NASA Astrophysics Data System (ADS)
Cai, Zhenning; Torrilhon, Manuel
2015-08-01
A sequence of approximate linear collision models for hard-sphere and inverse-power-law gases is introduced. These models are obtained by expanding the linearized Boltzmann collision operator into series, and a practical algorithm is proposed for evaluating the coefficients in the series. The sequence is proven to be convergent to the linearized Boltzmann operator, and it established a connection between the Shakhov model and the linearized collision model. The convergence is demonstrated by solving the spatially homogeneous Boltzmann equation. By observing the magnitudes of the coefficients, simpler models are developed through removing small entries in the coefficient matrices.
Yang, Xiaowei; Nie, Kun
2008-03-15
Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data. PMID:17610294
NASA Astrophysics Data System (ADS)
Zhang, Jicai; Lu, Xianqing; Wang, Ping; Wang, Ya Ping
2011-04-01
Data assimilation technique (adjoint method) is applied to study the similarities and the differences between the Ekman (linear) and the Quadratic (nonlinear) bottom friction parameterizations for a two-dimensional tidal model. Two methods are used to treat the bottom friction coefficient (BFC). The first method assumes that the BFC is a constant in the entire computation domain, while the second applies the spatially varying BFCs. The adjoint expressions for the linear and the nonlinear parameterizations and the optimization formulae for the two BFC methods are derived based on the typical Largrangian multiplier method. By assimilating the model-generated 'observations', identical twin experiments are performed to test and validate the inversion ability of the presented methodology. Four experiments, which employ the linear parameterization, the nonlinear parameterizations, the constant BFC and the spatially varying BFC, are carried out to simulate the M 2 tide in the Bohai Sea and the Yellow Sea by assimilating the TOPEX/Poseidon altimetry and tidal gauge data. After the assimilation, the misfit between model-produced and observed data is significantly decreased in the four experiments. The simulation results indicate that the nonlinear Quadratic parameterization is more accurate than the linear Ekman parameterization if the traditional constant BFC is used. However, when the spatially varying BFCs are used, the differences between the Ekman and the Quadratic approaches diminished, the reason of which is analyzed from the viewpoint of dissipation rate caused by bottom friction. Generally speaking, linear bottom friction parameterizations are often used in global tidal models. This study indicates that they are also applicable in regional ocean tidal models with the combination of spatially varying parameters and the adjoint method.
Analytical modeling of a new disc permanent magnet linear synchronous machine for electric vehicles
Liu, C.T.; Chen, J.W.; Su, K.S.
1999-09-01
This paper develops an analytical approach based on a qd0 reference frame model to analyze dynamic and steady state characteristics of disc permanent magnet linear synchronous machines (DPMLSMs). The established compact mathematical model can be more easily employed to analyze the system behavior and to design the controller. Superiority in operational electromagnetic characteristics of the proposed DPMLSM for electric vehicle (EV) applications is verified by both numerical simulations and experimental investigations.
Non-linear dynamic analysis of ancient masonry structures by 3D rigid block models
NASA Astrophysics Data System (ADS)
Orduña, Agustin; Ayala, A. Gustavo
2015-12-01
This work presents a formulation for non-linear dynamic analysis of unreinforced masonry structures using rigid block models. This procedure is akin to the distinct element family of methods, nevertheless, we assume that small displacements occur and, therefore, the formulation does not involve the search for new contacts between blocks. This proposal is also related to the rigid element method, although, in this case we use full three-dimensional models and a more robust interface formulation.
Mathematical models for the control program of the SLAC linear collider
Lee, M.J.; Blocker, C.; Chao, A.W.
1981-02-01
The operation of the SLAC two-mile linear accelerator in the single pass collider mode will be computer controlled. Mathematical models will be used in the control program to set up and restore the beam optics and to correct orbits. Some of the requirements imposed upon the on-line model calculations and the ways to satisfy these requirements will be described in this paper.
A wavelet-linear genetic programming model for sodium (Na+) concentration forecasting in rivers
NASA Astrophysics Data System (ADS)
Ravansalar, Masoud; Rajaee, Taher; Zounemat-Kermani, Mohammad
2016-06-01
The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet-linear genetic programming (WLGP) model for prediction of monthly sodium (Na+) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na+ datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na+ one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash-Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na+ peak values is also presented in this study.
NASA Astrophysics Data System (ADS)
Malik, Aimun; Zhang, Zheming; Agarwal, Ramesh K.
2014-08-01
There is need for a battery model that can accurately describe the battery performance for an electrical system, such as the electric drive train of electric vehicles. In this paper, both linear and non-linear equivalent circuit models (ECM) are employed as a means of extracting the battery parameters that can be used to model the performance of a battery. The linear and non-linear equivalent circuit models differ in the numbers of capacitance and resistance; the non-linear model has an added circuit; however their numerical characteristics are equivalent. A multi-objective genetic algorithm is employed to accurately extract the values of the battery model parameters. The battery model parameters are obtained for several existing industrial batteries as well as for two recently proposed high performance batteries. Once the model parameters are optimally determined, the results demonstrate that both linear and non-linear equivalent circuit models can predict with acceptable accuracy the performance of various batteries of different sizes, characteristics, capacities, and materials. However, the comparisons of results with catalog and experimental data shows that the predictions of results using the non-linear equivalent circuit model are slightly better than those predicted by the linear model, calculating voltages that are closer to the manufacturers' values.
Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W
2014-10-01
All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in
The linearity response of the Planck-LFI flight model receivers
NASA Astrophysics Data System (ADS)
Mennella, A.; Villa, F.; Terenzi, L.; Cuttaia, F.; Battaglia, P.; Bersanelli, M.; Butler, R. C.; D'Arcangelo, O.; Artal, E.; Davis, R.; Frailis, M.; Franceschet, C.; Galeotta, S.; Gregorio, A.; Hughes, N.; Jukkala, P.; Kettle, D.; Kilpiä, V.-H.; Laaninen, M.; Lapolla, P. M.; Leonardi, R.; Leutenegger, P.; Lowe, S.; Mandolesi, N.; Maris, M.; Meinhold, P.; Mendes, L.; Miccolis, M.; Morgante, G.; Roddis, N.; Sandri, M.; Silvestri, R.; Stringhetti, L.; Tomasi, M.; Tuovinen, J.; Valenziano, L.; Zacchei, A.; Varis, J.; Wilkinson, A.; Zonca, A.
2009-12-01
In this paper we discuss the linearity response of the Planck-LFI receivers, with particular reference to signal compression measured on the 30 and 44 GHz channels. In the article we discuss the various sources of compression and present a model that accurately describes data measured during tests performed with individual radiomeric chains. After discussing test results we present the best parameter set representing the receiver response and discuss the impact of non linearity on in-flight calibration, which is shown to be negligible.
Extraction, modelling, and use of linear features for restitution of airborne hyperspectral imagery
NASA Astrophysics Data System (ADS)
Lee, Changno; Bethel, James S.
This paper presents an approach for the restitution of airborne hyperspectral imagery with linear features. The approach consisted of semi-automatic line extraction and mathematical modelling of the linear features. First, the line was approximately determined manually and refined using dynamic programming. The extracted lines could then be used as control data with the ground information of the lines, or as constraints with simple assumption for the ground information of the line. The experimental results are presented numerically in tables of RMS residuals of check points as well as visually in ortho-rectified images.
A sequential algorithm for the non-linear dual-sorption model of percutaneous drug absorption.
Gumel, A B; Kubota, K; Twizell, E H
1998-08-15
A sequential algorithm is developed for the non-linear dual-sorption model developed by Chandrasekaran et al. [1,2] which monitors pharmacokinetic profiles in percutaneous drug absorption. In the experimental study of percutaneous absorption, it is often observed that the lag-time decreases with the increase in the donor concentration when two or more donor concentrations of the same compound are used. The dual-sorption model has sometimes been employed to explain such experimental results. In this paper, it is shown that another feature observed after vehicle removal may also characterize the dual-sorption model. Soon after vehicle removal, the plots of the drug flux versus time become straight lines on a semilogarithmic scale as in the linear model, but the half-life is prolonged thereafter when the dual-sorption model prevails. The initial half-life after vehicle removal with a low donor concentration is longer than that with a higher donor concentration. These features, if observed in experiments, may be used as evidence to confirm that the dual-sorption model gives an explanation to the non-linear kinetic behaviour of a permeant. PMID:9727298
Piecewise-homogeneous model for electron side injection into linear plasma waves
NASA Astrophysics Data System (ADS)
Golovanov, A. A.; Kostyukov, I. Yu.
2016-09-01
An analytical piecewise-homogeneous model for electron side injection into linear plasma waves is developed. The dynamics of transverse betatron oscillations are studied. Based on the characteristics of the transversal motion the longitudinal motion of electrons is described. The electron parameters for which the electron trapping and subsequent acceleration are possible are estimated. The analytical results are verified by numerical simulations in the scope of the piecewise-homogeneous model. The results predicted by this model are also compared to the results given by a more realistic inhomogeneous model.
Derivation of a non-linear ABBM model for the calculation of Barkhausen effect
NASA Astrophysics Data System (ADS)
Lee, S. J.; Zhu, B.; Lo, C. C. H.; Clatterbuck, D. M.; Jiles, D. C.
2001-04-01
The Alessandro, Beatrice, Bertotti, and Montorsi (ABBM) model can describe the dynamics of domain wall motion in a ferromagnetic material which is subjected to a smoothly varying external magnetic field. The assumptions of this model limit its use to experiments where the differential permeability and time derivative of applied field are constant. In this paper, the non-linear permeability of the sample is incorporated into the ABBM model by extracting the differential permeability at different points on the B,H plane from the Jiles-Atherton (J-A) hysteresis model.
A Stochastic Cellular Automaton Model of Non-linear Diffusion and Diffusion with Reaction
NASA Astrophysics Data System (ADS)
Brieger, Leesa M.; Bonomi, Ernesto
1991-06-01
This article presents a stochastic cellular automaton model of diffusion and diffusion with reaction. The master equations for the model are examined, and we assess the difference between the implementation in which a single particle at a time moves (asynchronous dynamics) and one implementation in which all particles move simultaneously (synchronous dynamics). Biasing locally each particle's random walk, we alter the diffusion coefficients of the system. By appropriately choosing the biasing function, we can impose a desired non-linear diffusive behaviour in the model. We present an application of this model, adapted to include two diffusing species, two static species, and a chemical reaction in a prototypical simulation of carbonation in concrete.
NASA Astrophysics Data System (ADS)
Lara, Silvia; Lai, Ying Tong; Love, Cameron; Ramakrishnan, Navneeth; Adam, Shaffique
In recent years, the Effective Medium Theory (EMT) and the Random Resistor Network (RRN) have been separately used to explain disorder induced magnetoresistance that is quadratic at low fields and linear at high fields. We demonstrate that the quadratic and linear coefficients of the magnetoresistance and the transition point from the quadratic to the linear regime depend only on the inhomogeneous carrier density profile. We use this to find a mapping between the two models using dimensionless parameters that determine the magnetoresistance and show numerically that they belong to the same universality class. This work is supported by the Singapore National Research Foundation (NRF-NRFF2012-01) and the Singapore Ministry of Education and Yale-NUS College through Grant Number R-607-265-01312.
Modeling and Simulating of Single Side Short Stator Linear Induction Motor with the End Effect
NASA Astrophysics Data System (ADS)
Hamzehbahmani, Hamed
2011-09-01
Linear induction motors are under development for a variety of demanding applications including high speed ground transportation and specific industrial applications. These applications require machines that can produce large forces, operate at high speeds, and can be controlled precisely to meet performance requirements. The design and implementation of these systems require fast and accurate techniques for performing system simulation and control system design. In this paper, a mathematical model for a single side short stator linear induction motor with a consideration of the end effects is presented; and to study the dynamic performance of this linear motor, MATLAB/SIMULINK based simulations are carried out, and finally, the experimental results are compared to simulation results.
Model Reduction by Balanced Truncation of Linear Systems with a Quadratic Output
NASA Astrophysics Data System (ADS)
Van Beeumen, Roel; Meerbergen, Karl
2010-09-01
Balanced truncation is a widely used and appreciated projection-based model reduction technique for linear systems. This technique has the following two important properties: approximations by balanced truncation preserve the stability and the H∞-norm (the maximum of the frequency response) of the error system is bounded above by twice the sum of the neglected singular values. This paper tries to extend the framework of linear balanced truncation to systems with a quadratic output. For such systems, the controllability Gramian remains the same. The observability Gramian is computed from a linear system with multiple outputs that is derived from the quadratic output of the original system. We give a numerical example for a large-scale system arising from structural analysis.
Hossein-Zadeh, Navid Ghavi
2016-08-01
The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (-2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes. PMID:27600968
ALPS - A LINEAR PROGRAM SOLVER
NASA Technical Reports Server (NTRS)
Viterna, L. A.
1994-01-01
Linear programming is a widely-used engineering and management tool. Scheduling, resource allocation, and production planning are all well-known applications of linear programs (LP's). Most LP's are too large to be solved by hand, so over the decades many computer codes for solving LP's have been developed. ALPS, A Linear Program Solver, is a full-featured LP analysis program. ALPS can solve plain linear programs as well as more complicated mixed integer and pure integer programs. ALPS also contains an efficient solution technique for pure binary (0-1 integer) programs. One of the many weaknesses of LP solvers is the lack of interaction with the user. ALPS is a menu-driven program with no special commands or keywords to learn. In addition, ALPS contains a full-screen editor to enter and maintain the LP formulation. These formulations can be written to and read from plain ASCII files for portability. For those less experienced in LP formulation, ALPS contains a problem "parser" which checks the formulation for errors. ALPS creates fully formatted, readable reports that can be sent to a printer or output file. ALPS is written entirely in IBM's APL2/PC product, Version 1.01. The APL2 workspace containing all the ALPS code can be run on any APL2/PC system (AT or 386). On a 32-bit system, this configuration can take advantage of all extended memory. The user can also examine and modify the ALPS code. The APL2 workspace has also been "packed" to be run on any DOS system (without APL2) as a stand-alone "EXE" file, but has limited memory capacity on a 640K system. A numeric coprocessor (80X87) is optional but recommended. The standard distribution medium for ALPS is a 5.25 inch 360K MS-DOS format diskette. IBM, IBM PC and IBM APL2 are registered trademarks of International Business Machines Corporation. MS-DOS is a registered trademark of Microsoft Corporation.
NASA Astrophysics Data System (ADS)
Marcozzi, M.; Nota, A.
2016-03-01
We consider a test particle moving in a random distribution of obstacles in the plane, under the action of a uniform magnetic field, orthogonal to the plane. We show that, in a weak coupling limit, the particle distribution behaves according to the linear Landau equation with a magnetic transport term. Moreover, we show that, in a low density regime, when each obstacle generates an inverse power law potential, the particle distribution behaves according to the linear Boltzmann equation with a magnetic transport term. We provide an explicit control of the error in the kinetic limit by estimating the contributions of the configurations which prevent the Markovianity. We compare these results with those ones obtained for a system of hard disks in Bobylev et al. (Phys Rev Lett 75:2, 1995), which show instead that the memory effects are not negligible in the Boltzmann-Grad limit.
Xu, Wen-Sheng; Freed, Karl F.
2015-07-14
The lattice cluster theory (LCT) for semiflexible linear telechelic melts, developed in Paper I, is applied to examine the influence of chain stiffness on the average degree of self-assembly and the basic thermodynamic properties of linear telechelic polymer melts. Our calculations imply that chain stiffness promotes self-assembly of linear telechelic polymer melts that assemble on cooling when either polymer volume fraction ϕ or temperature T is high, but opposes self-assembly when both ϕ and T are sufficiently low. This allows us to identify a boundary line in the ϕ-T plane that separates two regions of qualitatively different influence of chain stiffness on self-assembly. The enthalpy and entropy of self-assembly are usually treated as adjustable parameters in classical Flory-Huggins type theories for the equilibrium self-assembly of polymers, but they are demonstrated here to strongly depend on chain stiffness. Moreover, illustrative calculations for the dependence of the entropy density of linear telechelic polymer melts on chain stiffness demonstrate the importance of including semiflexibility within the LCT when exploring the nature of glass formation in models of linear telechelic polymer melts.
NASA Astrophysics Data System (ADS)
Alfi, V.; Cristelli, M.; Pietronero, L.; Zaccaria, A.
2009-02-01
We present a detailed study of the statistical properties of the Agent Based Model introduced in paper I [Eur. Phys. J. B, DOI: 10.1140/epjb/e2009-00028-4] and of its generalization to the multiplicative dynamics. The aim of the model is to consider the minimal elements for the understanding of the origin of the stylized facts and their self-organization. The key elements are fundamentalist agents, chartist agents, herding dynamics and price behavior. The first two elements correspond to the competition between stability and instability tendencies in the market. The herding behavior governs the possibility of the agents to change strategy and it is a crucial element of this class of models. We consider a linear approximation for the price dynamics which permits a simple interpretation of the model dynamics and, for many properties, it is possible to derive analytical results. The generalized non linear dynamics results to be extremely more sensible to the parameter space and much more difficult to analyze and control. The main results for the nature and self-organization of the stylized facts are, however, very similar in the two cases. The main peculiarity of the non linear dynamics is an enhancement of the fluctuations and a more marked evidence of the stylized facts. We will also discuss some modifications of the model to introduce more realistic elements with respect to the real markets.
Simple model for linear and nonlinear mixing at unstable fluid interfaces with variable acceleration
Ramshaw, J D; Rathkopf, J
1998-12-23
A simple model is described for predicting the time evolution of the half-width h of a planar mixing layer between two immiscible incompressible fluids driven by an arbitrary time-dependent variable acceleration history a(l)a (t): The model is based on a heuristic expression for the kinetic energy per unit area of the mixing layer. This expression is based on that for the kinetic energy of a linearly perturbed interface, but with a dynamically renormalized wavelength which becomes proportional to h in the nonlinear regime. An equation of motion for h is then derived by means of Lagrange's equations. This model reproduces the known linear growth rates of the Rayleigh-Taylor (RT) and Richtmyer-Meshkov (RM) instabilities, as well as the quadratic RT and power-law RM growth laws in the nonlinear regime. The time exponent in the RM power law depends on the rate of kinetic energy dissipation. In the case of zero dissipation, this exponent reduces to 2/3 in agreement with elementary scaling arguments. A conservative numerical scheme is proposed to solve the model equations, and is used to perform calculations that agree well with published mixing data from linear electric motor experiments. Considerations involved in implementing the model in hydrodynamics codes are briefly discussed.
Multiphysics modeling of non-linear laser-matter interactions for optically active semiconductors
NASA Astrophysics Data System (ADS)
Kraczek, Brent; Kanp, Jaroslaw
Development of photonic devices for sensors and communications devices has been significantly enhanced by computational modeling. We present a new computational method for modelling laser propagation in optically-active semiconductors within the paraxial wave approximation (PWA). Light propagation is modeled using the Streamline-upwind/Petrov-Galerkin finite element method (FEM). Material response enters through the non-linear polarization, which serves as the right-hand side of the FEM calculation. Maxwell's equations for classical light propagation within the PWA can be written solely in terms of the electric field, producing a wave equation that is a form of the advection-diffusion-reaction equations (ADREs). This allows adaptation of the computational machinery developed for solving ADREs in fluid dynamics to light-propagation modeling. The non-linear polarization is incorporated using a flexible framework to enable the use of multiple methods for carrier-carrier interactions (e.g. relaxation-time-based or Monte Carlo) to enter through the non-linear polarization, as appropriate to the material type. We demonstrate using a simple carrier-carrier model approximating the response of GaN. Supported by ARL Materials Enterprise.
Dynamic Linear Model Analysis of Optical Imaging Data Acquired from the Human Neocortex
Lavine, Michael; Haglund, Michael M.; Hochman, Daryl W.
2011-01-01
The amount of light absorbed and scattered by neocortical tissue is altered by neuronal activity. Imaging of intrinsic optical signals (ImIOS), a technique for mapping these activity-evoked optical changes with an imaging detector, has the potential to be useful for both clinical and experimental investigations of the human neocortex. However, its usefulness for human studies is currently limited because intraoperatively acquired ImIOS data is noisy. To improve the reliability and usefulness of ImIOS for human studies, it is desirable to find appropriate methods for the removal of noise artifacts and its statistical analysis. Here we develop a Bayesian, dynamic linear modeling approach that appears to address these problems. A dynamic linear model (DLM) was constructed that included cyclic components to model the heartbeat and respiration artifacts, and a local linear component to model the activity-evoked response. The robustness of the model was tested on a set of ImIOS data acquired from the exposed cortices of six human subjects illuminated with either 535 nm or 660 nm light. The DLM adequately reduced noise artifacts in these data while reliably preserving their activity-evoked optical responses. To demonstrate how these methods might be used for intraoperative neurosurgical mapping, optical data acquired from a single human subject during direct electrical stimulation of the cortex were quantitatively analyzed. This example showed that the DLM can be used to provide quantitative information about human ImIOS data that is not available through qualitative analysis alone. PMID:21640137
Nonlinear extension of a hemodynamic linear model for coherent hemodynamics spectroscopy.
Sassaroli, Angelo; Kainerstorfer, Jana M; Fantini, Sergio
2016-01-21
In this work, we are proposing an extension of a recent hemodynamic model (Fantini, 2014a), which was developed within the framework of a novel approach to the study of tissue hemodynamics, named coherent hemodynamics spectroscopy (CHS). The previous hemodynamic model, from a signal processing viewpoint, treats the tissue microvasculature as a linear time-invariant system, and considers changes of blood volume, capillary blood flow velocity and the rate of oxygen diffusion as inputs, and the changes of oxy-, deoxy-, and total hemoglobin concentrations (measured in near infrared spectroscopy) as outputs. The model has been used also as a forward solver in an inversion procedure to retrieve quantitative parameters that assess physiological and biological processes such as microcirculation, cerebral autoregulation, tissue metabolic rate of oxygen, and oxygen extraction fraction. Within the assumption of "small" capillary blood flow velocity oscillations the model showed that the capillary and venous compartments "respond" to this input as low pass filters, characterized by two distinct impulse response functions. In this work, we do not make the assumption of "small" perturbations of capillary blood flow velocity by solving without approximations the partial differential equation that governs the spatio-temporal behavior of hemoglobin saturation in capillary and venous blood. Preliminary comparison between the linear time-invariant model and the extended model (here identified as nonlinear model) are shown for the relevant parameters measured in CHS as a function of the oscillation frequency (CHS spectra). We have found that for capillary blood flow velocity oscillations with amplitudes up to 10% of the baseline value (which reflect typical scenarios in CHS), the discrepancies between CHS spectra obtained with the linear and nonlinear models are negligible. For larger oscillations (~50%) the linear and nonlinear models yield CHS spectra with differences within typical
Linear diffusion model dating of cinder cones in Central Anatolia, Turkey
NASA Astrophysics Data System (ADS)
O'Sadnick, L. G.; Reid, M. R.; Cline, M. L.; Cosca, M. A.; Kuscu, G.
2013-12-01
The progressive decrease in slope angle, cone height and cone height/width ratio over time provides the basis for geomorphic dating of cinder cones using linear diffusion models. Previous research using diffusion models to date cinder cones has focused on the cone height/width ratio as the basis for dating cones of unknown age [1,2]. Here we apply linear diffusion models to dating cinder cones. A suite of 16 cinder cones from the Hasandağ volcano area of the Neogene-Quaternary Central Anatolian Volcanic Zone, for which samples are available, were selected for morphologic dating analysis. New 40Ar/39Ar dates for five of these cones range from 62 × 4 to 517 × 9 ka. Linear diffusion models were used to model the erosional degradation of each cone. Diffusion coefficients (κ) for the 5 cinder cones with known ages were constrained by comparing various modeled slope profiles to the current slope profile. The resulting κ is 7.5×0.5 m2kyr-1. Using this κ value, eruption ages were modeled for the remaining 11 cinder cones and range from 53×3 to 455×30 ka. These ages are within the range of ages previously reported for cinder cones in the Hasandağ region. The linear diffusion model-derived ages are being compared to additional new 40Ar/39Ar dates in order to further assess the applicability of morphological dating to constrain the ages of cinder cones. The relatively well-constrained κ value we obtained by applying the linear diffusion model to cinder cones that range in age by nearly 500 ka suggests that this model can be used to date cinder cones. This κ value is higher than the well-established value of κ =3.9 for a cinder cone in a similar climate [3]. Therefore our work confirms the importance of determining appropriate κ values from nearby cones with known ages. References 1. C.A. Wood, J. Volcanol. Geotherm. Res. 8, 137 (1980) 2. D.M. Wood, M.F. Sheridan, J. Volcanol. Geotherm. Res. 83, 241 (1998) 3. J.D. Pelletier, M.L. Cline, Geology 35, 1067 (2007)
A covariance-adaptive approach for regularized inversion in linear models
NASA Astrophysics Data System (ADS)
Kotsakis, Christopher
2007-11-01
The optimal inversion of a linear model under the presence of additive random noise in the input data is a typical problem in many geodetic and geophysical applications. Various methods have been developed and applied for the solution of this problem, ranging from the classic principle of least-squares (LS) estimation to other more complex inversion techniques such as the Tikhonov-Philips regularization, truncated singular value decomposition, generalized ridge regression, numerical iterative methods (Landweber, conjugate gradient) and others. In this paper, a new type of optimal parameter estimator for the inversion of a linear model is presented. The proposed methodology is based on a linear transformation of the classic LS estimator and it satisfies two basic criteria. First, it provides a solution for the model parameters that is optimally fitted (in an average quadratic sense) to the classic LS parameter solution. Second, it complies with an external user-dependent constraint that specifies a priori the error covariance (CV) matrix of the estimated model parameters. The formulation of this constrained estimator offers a unified framework for the description of many regularization techniques that are systematically used in geodetic inverse problems, particularly for those methods that correspond to an eigenvalue filtering of the ill-conditioned normal matrix in the underlying linear model. Our study lies on the fact that it adds an alternative perspective on the statistical properties and the regularization mechanism of many inversion techniques commonly used in geodesy and geophysics, by interpreting them as a family of `CV-adaptive' parameter estimators that obey a common optimal criterion and differ only on the pre-selected form of their error CV matrix under a fixed model design.
González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O
2013-07-01
There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods. PMID:23247520
Modeling and analysis of aircraft non-linear components for harmonics analysis
Karimi, K.J.; Voss, J.
1995-12-31
Modern commercial aircraft Electric Power Systems (EPS) include many nonlinear components which produce harmonics. The addition of all the current harmonics could result in a power system with unacceptable levels of voltage distortion. It is important to be able to predict the levels of voltage distortion at early program stages to correct any potential problems and avoid costly redesigns. In this paper the nature and sources of harmonic producing equipment are described. These sources of harmonics and their effect on aircraft power system operation are described. Models for various aircraft non-linear components are developed in this paper. These component models are used in a model of the Boeing 777 EPS which is used to calculate voltage harmonics for various airplane configurations and flight conditions. A description of this model and the models used for various components are given. Tests performed to validate these models are described. Comparison of experimental results with analytical model predictions are given.
Probability Distribution Functions of freak-waves: nonlinear vs linear model
NASA Astrophysics Data System (ADS)
Kachulin, Dmitriy; Dyachenko, Alexander; Zakharov, Vladimir
2015-04-01
No doubts that estimation of probability of freak-wave appearing at the surface of ocean has practical meaning. Among different mechanisms of this phenomenon linear dispersion and modulational instability are generally recognized. For linear equation of water waves Probability Distribution Functions (PDF) can be calculated analytically and it is nothing but normal Gaussian distribution for surface elevation. Or it is Rayleigh distribution for absolute values of elevations. For nonlinear waves one can expect something different. In this report we consider and compare these two mechanism for various levels of nonlinearity. We present results of numerical experiments on calculation of Probability Distribution Functions for surface elevations of waters waves both for nonlinear and linear models. Both model demonstrates Rayleigh distribution of surface elevations. However dispersion of PDF for nonlinear case is much larger than for linear case. This work was supported by the Grant "Wave turbulence: theory, numerical simulation, experiment" #14-22-00174 of Russian Science Foundation. Numerical simulation was performed on the Informational Computational Center of the Novosibirsk State University.
A new adaptation of linear reservoir models in parallel sets to assess actual hydrological events
NASA Astrophysics Data System (ADS)
Mateo Lázaro, Jesús; Sánchez Navarro, José Ángel; García Gil, Alejandro; Edo Romero, Vanesa
2015-05-01
A methodology based on Parallel Linear Reservoir (PLR) models is presented. To carry it out has been implemented within the software SHEE (Simulation of Hydrological Extreme Events), which is a tool for the analysis of hydrological processes in catchments with the management and display of DEM and datasets. The algorithms of the models pass throughout the cells and drainage network, by means of the Watershed Traversal Algorithm (WTA) that runs the entire drainage network of a basin in both directions, upwards and downwards, which is ideal for incorporating the models of the hydrological processes of the basins into its structure. The WTA methodology is combined with another one based on models of Parallel Linear Reservoirs (PLR) whose main qualities include: (1) the models are defined by observing the recession curves of actual hydrographs, i.e., the watershed actual responses; (2) the models serve as a way to simulate the routing through the watershed and its different reservoirs; and (3) the models allow calculating the water balance, which is essential to the study of actual events in the watershed. A complete hydrometeorological event needs the combination of several models, each one of which represents a hydrological process. The PLR model is a routing model, but it also contributes to the adjustment of other models (e.g., the rainfall-runoff model) and allows establishing a distributed model of effective rainfall for an actual event occurred in a basin. On the other hand, the proposed formulation solves the rainfall distribution problem for each deposit in the reservoir combination models.
A General Linear Relaxometry Model of R1 Using Imaging Data
Callaghan, Martina F; Helms, Gunther; Lutti, Antoine; Mohammadi, Siawoosh; Weiskopf, Nikolaus
2015-01-01
Purpose The longitudinal relaxation rate (R1) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. Methods Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2*) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. Results The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. Conclusion A single set of global coefficients can be used to relate R1, MT, and R2* across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309–1314, 2015. © 2014 Wiley Periodicals, Inc. PMID:24700606
GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate.
Kreidler, Sarah M; Muller, Keith E; Grunwald, Gary K; Ringham, Brandy M; Coker-Dukowitz, Zacchary T; Sakhadeo, Uttara R; Barón, Anna E; Glueck, Deborah H
2013-09-01
GLIMMPSE is a free, web-based software tool that calculates power and sample size for the general linear multivariate model with Gaussian errors (http://glimmpse.SampleSizeShop.org/). GLIMMPSE provides a user-friendly interface for the computation of power and sample size. We consider models with fixed predictors, and models with fixed predictors and a single Gaussian covariate. Validation experiments demonstrate that GLIMMPSE matches the accuracy of previously published results, and performs well against simulations. We provide several online tutorials based on research in head and neck cancer. The tutorials demonstrate the use of GLIMMPSE to calculate power and sample size. PMID:24403868
A non-linear mathematical model for a three species ecosystem: Hippos in Lake Edward.
Bologna, Mauro; Chandía, Kristopher J; Flores, J C
2016-01-21
In this work we study a non-linear mathematical model based on three different interacting species. We apply our model to Lake Edward ecosystem consisting in hippos, tilapia fishes and human inhabitants. In this case, we estimate the values of the key parameters using actual data and show the reliability of the proposed model as a predictive tool. We also show, via numerical calculations and parameter values that the ecosystem associated to the lake is very far from reaching a stable equilibrium. Through our analysis we provide the conditions for a possible coexistence among the three species. PMID:26551152
Reduced-Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses
NASA Technical Reports Server (NTRS)
Silva, Walter A.
1999-01-01
This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.
Reduced Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses
NASA Technical Reports Server (NTRS)
Silva, Walter A.
1999-01-01
This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of an RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.
Lawson, Daniel J; Holtrop, Grietje; Flint, Harry
2011-07-01
Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. PMID:21681780