modeling technique based: Topics by WorldWideScience.org

Sample records for modeling technique based

Model-checking techniques based on cumulative residuals.

Science.gov (United States)

Lin, D Y; Wei, L J; Ying, Z

2002-03-01

Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes tinder the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided.
Line impedance estimation using model based identification technique

DEFF Research Database (Denmark)

Ciobotaru, Mihai; Agelidis, Vassilios; Teodorescu, Remus

2011-01-01

The estimation of the line impedance can be used by the control of numerous grid-connected systems, such as active filters, islanding detection techniques, non-linear current controllers, detection of the on/off grid operation mode. Therefore, estimating the line impedance can add extra functions...... into the operation of the grid-connected power converters. This paper describes a quasi passive method for estimating the line impedance of the distribution electricity network. The method uses the model based identification technique to obtain the resistive and inductive parts of the line impedance. The quasi...
Development Model of Basic Technique Skills Training Shot-Put Obrien Style Based Biomechanics Review

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danang rohmat hidayanto

2018-03-01

Full Text Available The background of this research is the unavailability of learning model of basic technique technique of O'Brien style force that integrated in skill program based on biomechanics study which is used as a reference to build the basic technique skill of the O'Brien style force among students. The purpose of this study is to develop a model of basic-style technique of rejecting the O'Brien-style shot put based on biomechanical studies for beginner levels, including basic prefix technique, glide, final stage, repulsion, further motion and repulsion performance of O'Brien style, all of which arranged in a medium that is easily accessible whenever, by anyone and anywhere, especially in SMK Negeri 1 Kalijambe Sragen . The research method used is "Reasearch and Developement" approach. "Preliminary studies show that 43.0% of respondents considered that the O'Brien style was very important to be developed with a model of skill-based exercise based on biomechanics, as many as 40.0% ressponden stated that it is important to be developed with biomechanics based learning media. Therefore, it is deemed necessary to develop the learning media of the O'Brien style-based training skills based on biomechanical studies. Development of media starts from the design of the storyboard and script form that will be used as media. The design of this model is called the draft model. Draft models that have been prepared are reviewed by the multimedia expert and the O'Brien style expert to get the product's validity. A total of 78.24% of experts declare a viable product with some input. In small groups with n = 6, earned value 72.2% was obtained or valid enough to be tested in large groups. In the large group test with n = 12,values obtained 70.83% or quite feasible to be tested in the field. In the field test, experimental group was prepared with treatment according to media and control group with free treatment. From result of counting of significance test can be
The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

Science.gov (United States)

Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.
Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

CERN Document Server

Ding, Steven X

2013-01-01

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: · new material on fault isolation and identification, and fault detection in feedback control loops; · extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and · enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...
Validation techniques of agent based modelling for geospatial simulations

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M. Darvishi

2014-10-01

Full Text Available One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS, biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI’s ArcGIS, OpenMap, GeoTools, etc for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.
Validation techniques of agent based modelling for geospatial simulations

Science.gov (United States)

Darvishi, M.; Ahmadi, G.

2014-10-01

One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS) is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS), biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI's ArcGIS, OpenMap, GeoTools, etc) for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.
Demand Management Based on Model Predictive Control Techniques

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Yasser A. Davizón

2014-01-01

Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.
Generalized image contrast enhancement technique based on the Heinemann contrast discrimination model

Science.gov (United States)

Liu, Hong; Nodine, Calvin F.

1996-07-01

This paper presents a generalized image contrast enhancement technique, which equalizes the perceived brightness distribution based on the Heinemann contrast discrimination model. It is based on the mathematically proven existence of a unique solution to a nonlinear equation, and is formulated with easily tunable parameters. The model uses a two-step log-log representation of luminance contrast between targets and surround in a luminous background setting. The algorithm consists of two nonlinear gray scale mapping functions that have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of the gray-level distribution of the given image, and can be uniquely determined once the previous three are set. Tests have been carried out to demonstrate the effectiveness of the algorithm for increasing the overall contrast of radiology images. The traditional histogram equalization can be reinterpreted as an image enhancement technique based on the knowledge of human contrast perception. In fact, it is a special case of the proposed algorithm.
A Comparative of business process modelling techniques

Science.gov (United States)

Tangkawarow, I. R. H. T.; Waworuntu, J.

2016-04-01

In this era, there is a lot of business process modeling techniques. This article is the research about differences of business process modeling techniques. For each technique will explain about the definition and the structure. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on 2 criteria: notation and how it works when implemented in Somerleyton Animal Park. Each technique will end with the advantages and disadvantages. The final conclusion will give recommend of business process modeling techniques that easy to use and serve the basis for evaluating further modelling techniques.
Base Oils Biodegradability Prediction with Data Mining Techniques

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Malika Trabelsi

2010-02-01

Full Text Available In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.
Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

Science.gov (United States)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.
Chronology of DIC technique based on the fundamental mathematical modeling and dehydration impact.

Science.gov (United States)

Alias, Norma; Saipol, Hafizah Farhah Saipan; Ghani, Asnida Che Abd

2014-12-01

A chronology of mathematical models for heat and mass transfer equation is proposed for the prediction of moisture and temperature behavior during drying using DIC (Détente Instantanée Contrôlée) or instant controlled pressure drop technique. DIC technique has the potential as most commonly used dehydration method for high impact food value including the nutrition maintenance and the best possible quality for food storage. The model is governed by the regression model, followed by 2D Fick's and Fourier's parabolic equation and 2D elliptic-parabolic equation in a rectangular slice. The models neglect the effect of shrinkage and radiation effects. The simulations of heat and mass transfer equations with parabolic and elliptic-parabolic types through some numerical methods based on finite difference method (FDM) have been illustrated. Intel®Core™2Duo processors with Linux operating system and C programming language have been considered as a computational platform for the simulation. Qualitative and quantitative differences between DIC technique and the conventional drying methods have been shown as a comparative.
Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol.

Science.gov (United States)

O'Connell, Dylan; Shaverdian, Narek; Kishan, Amar U; Thomas, David H; Dou, Tai H; Lewis, John H; Lamb, James M; Cao, Minsong; Tenn, Stephen; Percy, Lee P; Low, Daniel A

2017-11-11

To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods. Copyright © 2017
IMAGE-BASED MODELING TECHNIQUES FOR ARCHITECTURAL HERITAGE 3D DIGITALIZATION: LIMITS AND POTENTIALITIES

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C. Santagati

2013-07-01

Full Text Available 3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS, the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases to large scale buildings for practitioner purpose.
Properties of hypothesis testing techniques and (Bayesian) model selection for exploration-based and theory-based (order-restricted) hypotheses.

Science.gov (United States)

Kuiper, Rebecca M; Nederhoff, Tim; Klugkist, Irene

2015-05-01

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration-based set of hypotheses containing equality constraints on the means, or a theory-based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory-based hypotheses) has advantages over exploration (i.e., examining all possible equality-constrained hypotheses). Furthermore, examining reasonable order-restricted hypotheses has more power to detect the true effect/non-null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory-based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number). © 2014 The British Psychological Society.
A Dynamic Operation Permission Technique Based on an MFM Model and Numerical Simulation

International Nuclear Information System (INIS)

Akio, Gofuku; Masahiro, Yonemura

2011-01-01

It is important to support operator activities to an abnormal plant situation where many counter actions are taken in relatively short time. The authors proposed a technique called dynamic operation permission to decrease human errors without eliminating creative idea of operators to cope with an abnormal plant situation by checking if the counter action taken is consistent with emergency operation procedure. If the counter action is inconsistent, a dynamic operation permission system warns it to operators. It also explains how and why the counter action is inconsistent and what influence will appear on the future plant behavior by a qualitative influence inference technique based on a model by the Mf (Multilevel Flow Modeling). However, the previous dynamic operation permission is not able to explain quantitative effects on plant future behavior. Moreover, many possible influence paths are derived because a qualitative reasoning does not give a solution when positive and negative influences are propagated to the same node. This study extends the dynamic operation permission by combining the qualitative reasoning and the numerical simulation technique. The qualitative reasoning based on an Mf model of plant derives all possible influence propagation paths. Then, a numerical simulation gives a prediction of plant future behavior in the case of taking a counter action. The influence propagation that does not coincide with the simulation results is excluded from possible influence paths. The extended technique is implemented in a dynamic operation permission system for an oil refinery plant. An MFM model and a static numerical simulator are developed. The results of dynamic operation permission for some abnormal plant situations show the improvement of the accuracy of dynamic operation permission and the quality of explanation for the effects of the counter action taken
New techniques for subdivision modelling

OpenAIRE

BEETS, Koen

2006-01-01

In this dissertation, several tools and techniques for modelling with subdivision surfaces are presented. Based on the huge amount of theoretical knowledge about subdivision surfaces, we present techniques to facilitate practical 3D modelling which make subdivision surfaces even more useful. Subdivision surfaces have reclaimed attention several years ago after their application in full-featured 3D animation movies, such as Toy Story. Since then and due to their attractive properties an ever i...
Plasticity models of material variability based on uncertainty quantification techniques

Energy Technology Data Exchange (ETDEWEB)

Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

2017-11-01

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.
Generalized image contrast enhancement technique based on Heinemann contrast discrimination model

Science.gov (United States)

Liu, Hong; Nodine, Calvin F.

1994-03-01

This paper presents a generalized image contrast enhancement technique which equalizes perceived brightness based on the Heinemann contrast discrimination model. This is a modified algorithm which presents an improvement over the previous study by Mokrane in its mathematically proven existence of a unique solution and in its easily tunable parameterization. The model uses a log-log representation of contrast luminosity between targets and the surround in a fixed luminosity background setting. The algorithm consists of two nonlinear gray-scale mapping functions which have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of gray scale distribution of the image, and can be uniquely determined once the previous three are given. Tests have been carried out to examine the effectiveness of the algorithm for increasing the overall contrast of images. It can be demonstrated that the generalized algorithm provides better contrast enhancement than histogram equalization. In fact, the histogram equalization technique is a special case of the proposed mapping.

Mathematical Foundation Based Inter-Connectivity modelling of Thermal Image processing technique for Fire Protection

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Sayantan Nath

2015-09-01

Full Text Available In this paper, integration between multiple functions of image processing and its statistical parameters for intelligent alarming series based fire detection system is presented. The proper inter-connectivity mapping between processing elements of imagery based on classification factor for temperature monitoring and multilevel intelligent alarm sequence is introduced by abstractive canonical approach. The flow of image processing components between core implementation of intelligent alarming system with temperature wise area segmentation as well as boundary detection technique is not yet fully explored in the present era of thermal imaging. In the light of analytical perspective of convolutive functionalism in thermal imaging, the abstract algebra based inter-mapping model between event-calculus supported DAGSVM classification for step-by-step generation of alarm series with gradual monitoring technique and segmentation of regions with its affected boundaries in thermographic image of coal with respect to temperature distinctions is discussed. The connectedness of the multifunctional operations of image processing based compatible fire protection system with proper monitoring sequence is presently investigated here. The mathematical models representing the relation between the temperature affected areas and its boundary in the obtained thermal image defined in partial derivative fashion is the core contribution of this study. The thermal image of coal sample is obtained in real-life scenario by self-assembled thermographic camera in this study. The amalgamation between area segmentation, boundary detection and alarm series are described in abstract algebra. The principal objective of this paper is to understand the dependency pattern and the principles of working of image processing components and structure an inter-connected modelling technique also for those components with the help of mathematical foundation.
Tsunami Hazard Preventing Based Land Use Planning Model Using GIS Techniques in Muang Krabi, Thailand

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Abdul Salam Soomro

2012-10-01

Full Text Available The terrible tsunami disaster, on 26 December 2004 hit Krabi, one of the ecotourist and very fascinating provinces of southern Thailand including its various regions e.g. Phangna and Phuket by devastating the human lives, coastal communications and the financially viable activities. This research study has been aimed to generate the tsunami hazard preventing based lands use planning model using GIS (Geographical Information Systems based on the hazard suitability analysis approach. The different triggering factors e.g. elevation, proximity to shore line, population density, mangrove, forest, stream and road have been used based on the land use zoning criteria. Those criteria have been used by using Saaty scale of importance one, of the mathematical techniques. This model has been classified according to the land suitability classification. The various techniques of GIS, namely subsetting, spatial analysis, map difference and data conversion have been used. The model has been generated with five categories such as high, moderate, low, very low and not suitable regions illustrating with their appropriate definition for the decision makers to redevelop the region.
Tsunami hazard preventing based land use planing model using GIS technique in Muang Krabi, Thailand

International Nuclear Information System (INIS)

Soormo, A.S.

2012-01-01

The terrible tsunami disaster, on 26 December 2004 hit Krabi, one of the ecotourist and very fascinating provinces of southern Thailand including its various regions e.g. Phangna and Phuket by devastating the human lives, coastal communications and the financially viable activities. This research study has been aimed to generate the tsunami hazard preventing based lands use planning model using GIS (Geographical Information Systems) based on the hazard suitability analysis approach. The different triggering factors e.g. elevation, proximity to shore line, population density, mangrove, forest, stream and road have been used based on the land use zoning criteria. Those criteria have been used by using Saaty scale of importance one, of the mathematical techniques. This model has been classified according to the land suitability classification. The various techniques of GIS, namely subsetting, spatial analysis, map difference and data conversion have been used. The model has been generated with five categories such as high, moderate, low, very low and not suitable regions illustrating with their appropriate definition for the decision makers to redevelop the region. (author)
Virtual 3d City Modeling: Techniques and Applications

Science.gov (United States)

Singh, S. P.; Jain, K.; Mandla, V. R.

2013-08-01

3D city model is a digital representation of the Earth's surface and it's related objects such as Building, Tree, Vegetation, and some manmade feature belonging to urban area. There are various terms used for 3D city models such as "Cybertown", "Cybercity", "Virtual City", or "Digital City". 3D city models are basically a computerized or digital model of a city contains the graphic representation of buildings and other objects in 2.5 or 3D. Generally three main Geomatics approach are using for Virtual 3-D City models generation, in first approach, researcher are using Conventional techniques such as Vector Map data, DEM, Aerial images, second approach are based on High resolution satellite images with LASER scanning, In third method, many researcher are using Terrestrial images by using Close Range Photogrammetry with DSM & Texture mapping. We start this paper from the introduction of various Geomatics techniques for 3D City modeling. These techniques divided in to two main categories: one is based on Automation (Automatic, Semi-automatic and Manual methods), and another is Based on Data input techniques (one is Photogrammetry, another is Laser Techniques). After details study of this, finally in short, we are trying to give the conclusions of this study. In the last, we are trying to give the conclusions of this research paper and also giving a short view for justification and analysis, and present trend for 3D City modeling. This paper gives an overview about the Techniques related with "Generation of Virtual 3-D City models using Geomatics Techniques" and the Applications of Virtual 3D City models. Photogrammetry, (Close range, Aerial, Satellite), Lasergrammetry, GPS, or combination of these modern Geomatics techniques play a major role to create a virtual 3-D City model. Each and every techniques and method has some advantages and some drawbacks. Point cloud model is a modern trend for virtual 3-D city model. Photo-realistic, Scalable, Geo-referenced virtual 3
Learning-based computing techniques in geoid modeling for precise height transformation

Science.gov (United States)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.
Model Checking Markov Chains: Techniques and Tools

NARCIS (Netherlands)

Zapreev, I.S.

2008-01-01

This dissertation deals with four important aspects of model checking Markov chains: the development of efficient model-checking tools, the improvement of model-checking algorithms, the efficiency of the state-space reduction techniques, and the development of simulation-based model-checking
The impact of applying product-modelling techniques in configurator projects

DEFF Research Database (Denmark)

Hvam, Lars; Kristjansdottir, Katrin; Shafiee, Sara

2018-01-01

This paper aims to increase understanding of the impact of using product-modelling techniques to structure and formalise knowledge in configurator projects. Companies that provide customised products increasingly apply configurators in support of sales and design activities, reaping benefits...... that include shorter lead times, improved quality of specifications and products, and lower overall product costs. The design and implementation of configurators are a challenging task that calls for scientifically based modelling techniques to support the formal representation of configurator knowledge. Even...... the phenomenon model and information model are considered visually, (2) non-UML-based modelling techniques, in which only the phenomenon model is considered and (3) non-formal modelling techniques. This study analyses the impact to companies from increased availability of product knowledge and improved control...
Advanced Atmospheric Ensemble Modeling Techniques

Energy Technology Data Exchange (ETDEWEB)

Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2017-09-29

Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.
Electromagnetic interference modeling and suppression techniques in variable-frequency drive systems

Science.gov (United States)

Yang, Le; Wang, Shuo; Feng, Jianghua

2017-11-01

Electromagnetic interference (EMI) causes electromechanical damage to the motors and degrades the reliability of variable-frequency drive (VFD) systems. Unlike fundamental frequency components in motor drive systems, high-frequency EMI noise, coupled with the parasitic parameters of the trough system, are difficult to analyze and reduce. In this article, EMI modeling techniques for different function units in a VFD system, including induction motors, motor bearings, and rectifierinverters, are reviewed and evaluated in terms of applied frequency range, model parameterization, and model accuracy. The EMI models for the motors are categorized based on modeling techniques and model topologies. Motor bearing and shaft models are also reviewed, and techniques that are used to eliminate bearing current are evaluated. Modeling techniques for conventional rectifierinverter systems are also summarized. EMI noise suppression techniques, including passive filter, Wheatstone bridge balance, active filter, and optimized modulation, are reviewed and compared based on the VFD system models.
Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

Directory of Open Access Journals (Sweden)

Hediyeh Karimi

2013-01-01

Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.
Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

International Nuclear Information System (INIS)

Yu, P.

2008-01-01

More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.
The role of model-based methods in the development of single scan techniques

International Nuclear Information System (INIS)

Laruelle, Marc

2000-01-01

Single scan techniques are highly desirable for clinical trials involving radiotracers because they increase logistical feasibility, improve patient compliance, and decrease the cost associated with the study. However, the information derived from single scans usually are biased by factors unrelated to the process of interest. Therefore, identification of these factors and evaluation of their impact on the proposed outcome measure is important. In this paper, the impact of confounding factors on single scan measurements is illustrated by discussing the effect of between-subject or between-condition differences in radiotracer plasma clearance on normalized activity ratios (specific to nonspecific ratios) in the tissue of interest. Computer simulation based on kinetic analyses are presented to demonstrate this effect. It is proposed that the presence of this and other confounding factors should not necessarily preclude clinical trials based on single scan techniques. First, knowledge of the distribution of plasma clearance values in a sample of the investigated population allows researchers to assign limits to this potential bias. This information can be integrated in the power analysis. Second, the impact of this problem will vary according to the characteristic of the radiotracer, and this information can be used in the development and selection of the radiotracer. Third, simple modification of the experimental design (such as administration of the radiotracer as a bolus, followed by constant infusion, rather than as a single bolus) might remove this potential confounding factor and allow appropriate quantification within the limits of a single scanning session. In conclusion, model-based kinetic characterization of radiotracer distribution and uptake is critical to the design and interpretation of clinical trials based on single scan techniques
Skin fluorescence model based on the Monte Carlo technique

Science.gov (United States)

Churmakov, Dmitry Y.; Meglinski, Igor V.; Piletsky, Sergey A.; Greenhalgh, Douglas A.

2003-10-01

The novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account spatial distribution of fluorophores following the collagen fibers packing, whereas in epidermis and stratum corneum the distribution of fluorophores assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the NIR spectral region, while fluorescence of sensor layer embedded in epidermis is localized at the adjusted depth. The model is also able to simulate the skin fluorescence spectra.
Comparison between two meshless methods based on collocation technique for the numerical solution of four-species tumor growth model

Science.gov (United States)

Dehghan, Mehdi; Mohammadi, Vahid

2017-03-01

As is said in [27], the tumor-growth model is the incorporation of nutrient within the mixture as opposed to being modeled with an auxiliary reaction-diffusion equation. The formulation involves systems of highly nonlinear partial differential equations of surface effects through diffuse-interface models [27]. Simulations of this practical model using numerical methods can be applied for evaluating it. The present paper investigates the solution of the tumor growth model with meshless techniques. Meshless methods are applied based on the collocation technique which employ multiquadrics (MQ) radial basis function (RBFs) and generalized moving least squares (GMLS) procedures. The main advantages of these choices come back to the natural behavior of meshless approaches. As well as, a method based on meshless approach can be applied easily for finding the solution of partial differential equations in high-dimension using any distributions of points on regular and irregular domains. The present paper involves a time-dependent system of partial differential equations that describes four-species tumor growth model. To overcome the time variable, two procedures will be used. One of them is a semi-implicit finite difference method based on Crank-Nicolson scheme and another one is based on explicit Runge-Kutta time integration. The first case gives a linear system of algebraic equations which will be solved at each time-step. The second case will be efficient but conditionally stable. The obtained numerical results are reported to confirm the ability of these techniques for solving the two and three-dimensional tumor-growth equations.
Modeling and Simulation of Voids in Composite Tape Winding Process Based on Domain Superposition Technique

Science.gov (United States)

Deng, Bo; Shi, Yaoyao

2017-11-01

The tape winding technology is an effective way to fabricate rotationally composite materials. Nevertheless, some inevitable defects will seriously influence the performance of winding products. One of the crucial ways to identify the quality of fiber-reinforced composite material products is examining its void content. Significant improvement in products' mechanical properties can be achieved by minimizing the void defect. Two methods were applied in this study, finite element analysis and experimental testing, respectively, to investigate the mechanism of how void forming in composite tape winding processing. Based on the theories of interlayer intimate contact and Domain Superposition Technique (DST), a three-dimensional model of prepreg tape void with SolidWorks has been modeled in this paper. Whereafter, ABAQUS simulation software was used to simulate the void content change with pressure and temperature. Finally, a series of experiments were performed to determine the accuracy of the model-based predictions. The results showed that the model is effective for predicting the void content in the composite tape winding process.
Solution Procedure for Transport Modeling in Effluent Recharge Based on Operator-Splitting Techniques

Directory of Open Access Journals (Sweden)

Shutang Zhu

2008-01-01

Full Text Available The coupling of groundwater movement and reactive transport during groundwater recharge with wastewater leads to a complicated mathematical model, involving terms to describe convection-dispersion, adsorption/desorption and/or biodegradation, and so forth. It has been found very difficult to solve such a coupled model either analytically or numerically. The present study adopts operator-splitting techniques to decompose the coupled model into two submodels with different intrinsic characteristics. By applying an upwind finite difference scheme to the finite volume integral of the convection flux term, an implicit solution procedure is derived to solve the convection-dominant equation. The dispersion term is discretized in a standard central-difference scheme while the dispersion-dominant equation is solved using either the preconditioned Jacobi conjugate gradient (PJCG method or Thomas method based on local-one-dimensional scheme. The solution method proposed in this study is applied to the demonstration project of groundwater recharge with secondary effluent at Gaobeidian sewage treatment plant (STP successfully.
Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: generation of patient specific models of the human pelvis without segmentation.

Science.gov (United States)

Salo, Zoryana; Beek, Maarten; Wright, David; Whyne, Cari Marisa

2015-04-13

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R(2)=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.
Power system stabilizers based on modern control techniques

Energy Technology Data Exchange (ETDEWEB)

Malik, O P; Chen, G P; Zhang, Y; El-Metwally, K [Calgary Univ., AB (Canada). Dept. of Electrical and Computer Engineering

1994-12-31

Developments in digital technology have made it feasible to develop and implement improved controllers based on sophisticated control techniques. Power system stabilizers based on adaptive control, fuzzy logic and artificial networks are being developed. Each of these control techniques possesses unique features and strengths. In this paper, the relative performance of power systems stabilizers based on adaptive control, fuzzy logic and neural network, both in simulation studies and real time tests on a physical model of a power system, is presented and compared to that of a fixed parameter conventional power system stabilizer. (author) 16 refs., 45 figs., 3 tabs.
A Strategy Modelling Technique for Financial Services

OpenAIRE

Heinrich, Bernd; Winter, Robert

2004-01-01

Strategy planning processes often suffer from a lack of conceptual models that can be used to represent business strategies in a structured and standardized form. If natural language is replaced by an at least semi-formal model, the completeness, consistency, and clarity of strategy descriptions can be drastically improved. A strategy modelling technique is proposed that is based on an analysis of modelling requirements, a discussion of related work and a critical analysis of generic approach...
Constraint-based Student Modelling in Probability Story Problems with Scaffolding Techniques

Directory of Open Access Journals (Sweden)

Nabila Khodeir

2018-01-01

Full Text Available Constraint-based student modelling (CBM is an important technique employed in intelligent tutoring systems to model student knowledge to provide relevant assistance. This paper introduces the Math Story Problem Tutor (MAST, a Web-based intelligent tutoring system for probability story problems, which is able to generate problems of different contexts, types and difficulty levels for self-paced learning. Constraints in MAST are specified at a low-level of granularity to allow fine-grained diagnosis of the student error. Furthermore, MAST extends CBM to address errors due to misunderstanding of the narrative story. It can locate and highlight keywords that may have been overlooked or misunderstood leading to an error. This is achieved by utilizing the role of sentences and keywords that are defined through the Natural Language Generation (NLG methods deployed in the story problem generation. MAST also integrates CBM with scaffolding questions and feedback to provide various forms of help and guidance to the student. This allows the student to discover and correct any errors in his/her solution. MAST has been preliminary evaluated empirically and the results show the potential effectiveness in tutoring students with a decrease in the percentage of violated constraints along the learning curve. Additionally, there is a significant improvement in the results of the post–test exam in comparison to the pre-test exam of the students using MAST in comparison to those relying on the textbook

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

Science.gov (United States)

Wentworth, Mami Tonoe

techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide
IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

Institute of Scientific and Technical Information of China (English)

纳瑟; 刘重庆

2002-01-01

A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.
Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method

International Nuclear Information System (INIS)

Andrei, Petru; Oniciuc, Liviu; Stancu, Alexandru; Stoleriu, Laurentiu

2007-01-01

An identification technique for the parameters of phenomenological models of hysteresis is presented. The basic idea of our technique is to set up a system of equations for the parameters of the model as a function of known quantities on the major or minor hysteresis loops (e.g. coercive force, susceptibilities at various points, remanence), or other magnetization curves. This system of equations can be either over or underspecified and is solved by using the conjugate gradient method. Numerical results related to the identification of parameters in the Energetic, Jiles-Atherton, and Preisach models are presented
Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.
Model-based Sensor Data Acquisition and Management

OpenAIRE

Aggarwal, Charu C.; Sathe, Saket; Papaioannou, Thanasis G.; Jeung, Ho Young; Aberer, Karl

2012-01-01

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition...
Structural Modeling Using "Scanning and Mapping" Technique

Science.gov (United States)

Amos, Courtney L.; Dash, Gerald S.; Shen, J. Y.; Ferguson, Frederick; Noga, Donald F. (Technical Monitor)

2000-01-01

Supported by NASA Glenn Center, we are in the process developing a structural damage diagnostic and monitoring system for rocket engines, which consists of five modules: Structural Modeling, Measurement Data Pre-Processor, Structural System Identification, Damage Detection Criterion, and Computer Visualization. The function of the system is to detect damage as it is incurred by the engine structures. The scientific principle to identify damage is to utilize the changes in the vibrational properties between the pre-damaged and post-damaged structures. The vibrational properties of the pre-damaged structure can be obtained based on an analytic computer model of the structure. Thus, as the first stage of the whole research plan, we currently focus on the first module - Structural Modeling. Three computer software packages are selected, and will be integrated for this purpose. They are PhotoModeler-Pro, AutoCAD-R14, and MSC/NASTRAN. AutoCAD is the most popular PC-CAD system currently available in the market. For our purpose, it plays like an interface to generate structural models of any particular engine parts or assembly, which is then passed to MSC/NASTRAN for extracting structural dynamic properties. Although AutoCAD is a powerful structural modeling tool, the complexity of engine components requires a further improvement in structural modeling techniques. We are working on a so-called "scanning and mapping" technique, which is a relatively new technique. The basic idea is to producing a full and accurate 3D structural model by tracing on multiple overlapping photographs taken from different angles. There is no need to input point positions, angles, distances or axes. Photographs can be taken by any types of cameras with different lenses. With the integration of such a modeling technique, the capability of structural modeling will be enhanced. The prototypes of any complex structural components will be produced by PhotoModeler first based on existing similar
Laser-based direct-write techniques for cell printing

Energy Technology Data Exchange (ETDEWEB)

Schiele, Nathan R; Corr, David T [Biomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY (United States); Huang Yong [Department of Mechanical Engineering, Clemson University, Clemson, SC (United States); Raof, Nurazhani Abdul; Xie Yubing [College of Nanoscale Science and Engineering, University at Albany, SUNY, Albany, NY (United States); Chrisey, Douglas B, E-mail: schien@rpi.ed, E-mail: chrisd@rpi.ed [Material Science and Engineering Department, Rensselaer Polytechnic Institute, Troy, NY (United States)

2010-09-15

Fabrication of cellular constructs with spatial control of cell location ({+-}5 {mu}m) is essential to the advancement of a wide range of applications including tissue engineering, stem cell and cancer research. Precise cell placement, especially of multiple cell types in co- or multi-cultures and in three dimensions, can enable research possibilities otherwise impossible, such as the cell-by-cell assembly of complex cellular constructs. Laser-based direct writing, a printing technique first utilized in electronics applications, has been adapted to transfer living cells and other biological materials (e.g., enzymes, proteins and bioceramics). Many different cell types have been printed using laser-based direct writing, and this technique offers significant improvements when compared to conventional cell patterning techniques. The predominance of work to date has not been in application of the technique, but rather focused on demonstrating the ability of direct writing to pattern living cells, in a spatially precise manner, while maintaining cellular viability. This paper reviews laser-based additive direct-write techniques for cell printing, and the various cell types successfully laser direct-written that have applications in tissue engineering, stem cell and cancer research are highlighted. A particular focus is paid to process dynamics modeling and process-induced cell injury during laser-based cell direct writing. (topical review)
Laser-based direct-write techniques for cell printing

International Nuclear Information System (INIS)

Schiele, Nathan R; Corr, David T; Huang Yong; Raof, Nurazhani Abdul; Xie Yubing; Chrisey, Douglas B

2010-01-01

Fabrication of cellular constructs with spatial control of cell location (±5 μm) is essential to the advancement of a wide range of applications including tissue engineering, stem cell and cancer research. Precise cell placement, especially of multiple cell types in co- or multi-cultures and in three dimensions, can enable research possibilities otherwise impossible, such as the cell-by-cell assembly of complex cellular constructs. Laser-based direct writing, a printing technique first utilized in electronics applications, has been adapted to transfer living cells and other biological materials (e.g., enzymes, proteins and bioceramics). Many different cell types have been printed using laser-based direct writing, and this technique offers significant improvements when compared to conventional cell patterning techniques. The predominance of work to date has not been in application of the technique, but rather focused on demonstrating the ability of direct writing to pattern living cells, in a spatially precise manner, while maintaining cellular viability. This paper reviews laser-based additive direct-write techniques for cell printing, and the various cell types successfully laser direct-written that have applications in tissue engineering, stem cell and cancer research are highlighted. A particular focus is paid to process dynamics modeling and process-induced cell injury during laser-based cell direct writing. (topical review)
Simulation-based optimization parametric optimization techniques and reinforcement learning

CERN Document Server

Gosavi, Abhijit

2003-01-01

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...
Dynamic model reduction: An overview of available techniques with application to power systems

Directory of Open Access Journals (Sweden)

Đukić Savo D.

2012-01-01

Full Text Available This paper summarises the model reduction techniques used for the reduction of large-scale linear and nonlinear dynamic models, described by the differential and algebraic equations that are commonly used in control theory. The groups of methods discussed in this paper for reduction of the linear dynamic model are based on singular perturbation analysis, modal analysis, singular value decomposition, moment matching and methods based on a combination of singular value decomposition and moment matching. Among the nonlinear dynamic model reduction methods, proper orthogonal decomposition, the trajectory piecewise linear method, balancing-based methods, reduction by optimising system matrices and projection from a linearised model, are described. Part of the paper is devoted to the techniques commonly used for reduction (equivalencing of large-scale power systems, which are based on coherency, synchrony, singular perturbation analysis, modal analysis and identification. Two (most interesting of the described techniques are applied to the reduction of the commonly used New England 10-generator, 39-bus test power system.
Analysis of Multipath Mitigation Techniques with Land Mobile Satellite Channel Model

Directory of Open Access Journals (Sweden)

M. Z. H. Bhuiyan J. Zhang

2012-12-01

Full Text Available Multipath is undesirable for Global Navigation Satellite System (GNSS receivers, since the reception of multipath can create a significant distortion to the shape of the correlation function leading to an error in the receivers’ position estimate. Many multipath mitigation techniques exist in the literature to deal with the multipath propagation problem in the context of GNSS. The multipath studies in the literature are often based on optimistic assumptions, for example, assuming a static two-path channel or a fading channel with a Rayleigh or a Nakagami distribution. But, in reality, there are a lot of channel modeling issues, for example, satellite-to-user geometry, variable number of paths, variable path delays and gains, Non Line-Of-Sight (NLOS path condition, receiver movements, etc. that are kept out of consideration when analyzing the performance of these techniques. Therefore, this is of utmost importance to analyze the performance of different multipath mitigation techniques in some realistic measurement-based channel models, for example, the Land Multipath is undesirable for Global Navigation Satellite System (GNSS receivers, since the reception of multipath can create a significant distortion to the shape of the correlation function leading to an error in the receivers’ position estimate. Many multipath mitigation techniques exist in the literature to deal with the multipath propagation problem in the context of GNSS. The multipath studies in the literature are often based on optimistic assumptions, for example, assuming a static two-path channel or a fading channel with a Rayleigh or a Nakagami distribution. But, in reality, there are a lot of channel modeling issues, for example, satellite-to-user geometry, variable number of paths, variable path delays and gains, Non Line-Of-Sight (NLOS path condition, receiver movements, etc. that are kept out of consideration when analyzing the performance of these techniques. Therefore, this
Modeling techniques for quantum cascade lasers

Energy Technology Data Exchange (ETDEWEB)

Jirauschek, Christian [Institute for Nanoelectronics, Technische Universität München, D-80333 Munich (Germany); Kubis, Tillmann [Network for Computational Nanotechnology, Purdue University, 207 S Martin Jischke Drive, West Lafayette, Indiana 47907 (United States)

2014-03-15

Quantum cascade lasers are unipolar semiconductor lasers covering a wide range of the infrared and terahertz spectrum. Lasing action is achieved by using optical intersubband transitions between quantized states in specifically designed multiple-quantum-well heterostructures. A systematic improvement of quantum cascade lasers with respect to operating temperature, efficiency, and spectral range requires detailed modeling of the underlying physical processes in these structures. Moreover, the quantum cascade laser constitutes a versatile model device for the development and improvement of simulation techniques in nano- and optoelectronics. This review provides a comprehensive survey and discussion of the modeling techniques used for the simulation of quantum cascade lasers. The main focus is on the modeling of carrier transport in the nanostructured gain medium, while the simulation of the optical cavity is covered at a more basic level. Specifically, the transfer matrix and finite difference methods for solving the one-dimensional Schrödinger equation and Schrödinger-Poisson system are discussed, providing the quantized states in the multiple-quantum-well active region. The modeling of the optical cavity is covered with a focus on basic waveguide resonator structures. Furthermore, various carrier transport simulation methods are discussed, ranging from basic empirical approaches to advanced self-consistent techniques. The methods include empirical rate equation and related Maxwell-Bloch equation approaches, self-consistent rate equation and ensemble Monte Carlo methods, as well as quantum transport approaches, in particular the density matrix and non-equilibrium Green's function formalism. The derived scattering rates and self-energies are generally valid for n-type devices based on one-dimensional quantum confinement, such as quantum well structures.
Modeling techniques for quantum cascade lasers

Science.gov (United States)

Jirauschek, Christian; Kubis, Tillmann

2014-03-01

Quantum cascade lasers are unipolar semiconductor lasers covering a wide range of the infrared and terahertz spectrum. Lasing action is achieved by using optical intersubband transitions between quantized states in specifically designed multiple-quantum-well heterostructures. A systematic improvement of quantum cascade lasers with respect to operating temperature, efficiency, and spectral range requires detailed modeling of the underlying physical processes in these structures. Moreover, the quantum cascade laser constitutes a versatile model device for the development and improvement of simulation techniques in nano- and optoelectronics. This review provides a comprehensive survey and discussion of the modeling techniques used for the simulation of quantum cascade lasers. The main focus is on the modeling of carrier transport in the nanostructured gain medium, while the simulation of the optical cavity is covered at a more basic level. Specifically, the transfer matrix and finite difference methods for solving the one-dimensional Schrödinger equation and Schrödinger-Poisson system are discussed, providing the quantized states in the multiple-quantum-well active region. The modeling of the optical cavity is covered with a focus on basic waveguide resonator structures. Furthermore, various carrier transport simulation methods are discussed, ranging from basic empirical approaches to advanced self-consistent techniques. The methods include empirical rate equation and related Maxwell-Bloch equation approaches, self-consistent rate equation and ensemble Monte Carlo methods, as well as quantum transport approaches, in particular the density matrix and non-equilibrium Green's function formalism. The derived scattering rates and self-energies are generally valid for n-type devices based on one-dimensional quantum confinement, such as quantum well structures.
Development of acoustic model-based iterative reconstruction technique for thick-concrete imaging

Science.gov (United States)

Almansouri, Hani; Clayton, Dwight; Kisner, Roger; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2016-02-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.1
Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2016-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.
Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Energy Technology Data Exchange (ETDEWEB)

Almansouri, Hani [Purdue University; Clayton, Dwight A [ORNL; Kisner, Roger A [ORNL; Polsky, Yarom [ORNL; Bouman, Charlie [Purdue University; Santos-Villalobos, Hector J [ORNL

2015-01-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well s health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.
Modelling Technique for Demonstrating Gravity Collapse Structures in Jointed Rock.

Science.gov (United States)

Stimpson, B.

1979-01-01

Described is a base-friction modeling technique for studying the development of collapse structures in jointed rocks. A moving belt beneath weak material is designed to simulate gravity. A description is given of the model frame construction. (Author/SA)
Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements

Science.gov (United States)

Kosmopoulos, Panagiotis G.; Kazadzis, Stelios; Taylor, Michael; Raptis, Panagiotis I.; Keramitsoglou, Iphigenia; Kiranoudis, Chris; Bais, Alkiviadis F.

2018-02-01

This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05°, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range -15 to 15 % for the NN that produces spectral irradiances (NNS), 5-6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from -100 to 40 and -20 to 20 W m-2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.
A New Profile Learning Model for Recommendation System based on Machine Learning Technique

Directory of Open Access Journals (Sweden)

Shereen H. Ali

2016-03-01

Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.
Ultrabroadband Phased-Array Receivers Based on Optical Techniques

Science.gov (United States)

2016-02-26

bandwidths, and with it receiver noise floors , are unavoidable. Figure 1. SNR of a thermally limited receiver based on Friis equation showing the...techniques for RF and photonic integration based on liquid crystal polymer substrates were pursued that would aid in the realization of potential imaging...These models assumed that sufficient LNA gain was used on the antenna to set the noise floor of the imaging receiver, which necessitated physical

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

Science.gov (United States)

Haste, Deepak; Ghoshal, Sudipto; Johnson, Stephen B.; Moore, Craig

2018-01-01

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.
Coronary artery plaques: Cardiac CT with model-based and adaptive-statistical iterative reconstruction technique

International Nuclear Information System (INIS)

Scheffel, Hans; Stolzmann, Paul; Schlett, Christopher L.; Engel, Leif-Christopher; Major, Gyöngi Petra; Károlyi, Mihály; Do, Synho; Maurovich-Horvat, Pál; Hoffmann, Udo

2012-01-01

Objectives: To compare image quality of coronary artery plaque visualization at CT angiography with images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model based iterative reconstruction (MBIR) techniques. Methods: The coronary arteries of three ex vivo human hearts were imaged by CT and reconstructed with FBP, ASIR and MBIR. Coronary cross-sectional images were co-registered between the different reconstruction techniques and assessed for qualitative and quantitative image quality parameters. Readers were blinded to the reconstruction algorithm. Results: A total of 375 triplets of coronary cross-sectional images were co-registered. Using MBIR, 26% of the images were rated as having excellent overall image quality, which was significantly better as compared to ASIR and FBP (4% and 13%, respectively, all p < 0.001). Qualitative assessment of image noise demonstrated a noise reduction by using ASIR as compared to FBP (p < 0.01) and further noise reduction by using MBIR (p < 0.001). The contrast-to-noise-ratio (CNR) using MBIR was better as compared to ASIR and FBP (44 ± 19, 29 ± 15, 26 ± 9, respectively; all p < 0.001). Conclusions: Using MBIR improved image quality, reduced image noise and increased CNR as compared to the other available reconstruction techniques. This may further improve the visualization of coronary artery plaque and allow radiation reduction.
A New ABCD Technique to Analyze Business Models & Concepts

OpenAIRE

Aithal P. S.; Shailasri V. T.; Suresh Kumar P. M.

2015-01-01

Various techniques are used to analyze individual characteristics or organizational effectiveness like SWOT analysis, SWOC analysis, PEST analysis etc. These techniques provide an easy and systematic way of identifying various issues affecting a system and provides an opportunity for further development. Whereas these provide a broad-based assessment of individual institutions and systems, it suffers limitations while applying to business context. The success of any business model depends on ...
Towards Model Validation and Verification with SAT Techniques

OpenAIRE

Gogolla, Martin

2010-01-01

After sketching how system development and the UML (Unified Modeling Language) and the OCL (Object Constraint Language) are related, validation and verification with the tool USE (UML-based Specification Environment) is demonstrated. As a more efficient alternative for verification tasks, two approaches using SAT-based techniques are put forward: First, a direct encoding of UML and OCL with Boolean variables and propositional formulas, and second, an encoding employing an...
Experimental models of brain ischemia: a review of techniques, magnetic resonance imaging and investigational cell-based therapies

Directory of Open Access Journals (Sweden)

Alessandra eCanazza

2014-02-01

Full Text Available Stroke continues to be a significant cause of death and disability worldwide. Although major advances have been made in the past decades in prevention, treatment and rehabilitation, enormous challenges remain in the way of translating new therapeutic approaches from bench to bedside. Thrombolysis, while routinely used for ischemic stroke, is only a viable option within a narrow time window. Recently, progress in stem cell biology has opened up avenues to therapeutic strategies aimed at supporting and replacing neural cells in infarcted areas. Realistic experimental animal models are crucial to understand the mechanisms of neuronal survival following ischemic brain injury and to develop therapeutic interventions. Current studies on experimental stroke therapies evaluate the efficiency of neuroprotective agents and cell-based approaches using primarily rodent models of permanent or transient focal cerebral ischemia. In parallel, advancements in imaging techniques permit better mapping of the spatial-temporal evolution of the lesioned cortex and its functional responses. This review provides a condensed conceptual review of the state of the art of this field, from models and magnetic resonance imaging techniques through to stem cell therapies.
Using Web-Based Knowledge Extraction Techniques to Support Cultural Modeling

Science.gov (United States)

Smart, Paul R.; Sieck, Winston R.; Shadbolt, Nigel R.

The World Wide Web is a potentially valuable source of information about the cognitive characteristics of cultural groups. However, attempts to use the Web in the context of cultural modeling activities are hampered by the large-scale nature of the Web and the current dominance of natural language formats. In this paper, we outline an approach to support the exploitation of the Web for cultural modeling activities. The approach begins with the development of qualitative cultural models (which describe the beliefs, concepts and values of cultural groups), and these models are subsequently used to develop an ontology-based information extraction capability. Our approach represents an attempt to combine conventional approaches to information extraction with epidemiological perspectives of culture and network-based approaches to cultural analysis. The approach can be used, we suggest, to support the development of models providing a better understanding of the cognitive characteristics of particular cultural groups.
Experience with the Large Eddy Simulation (LES) Technique for the Modelling of Premixed and Non-premixed Combustion

OpenAIRE

Malalasekera, W; Ibrahim, SS; Masri, AR; Gubba, SR; Sadasivuni, SK

2013-01-01

Compared to RANS based combustion modelling, the Large Eddy Simulation (LES) technique has recently emerged as a more accurate and very adaptable technique in terms of handling complex turbulent interactions in combustion modelling problems. In this paper application of LES based combustion modelling technique and the validation of models in non-premixed and premixed situations are considered. Two well defined experimental configurations where high quality data are available for validation is...
Replacement Value - Representation of Fair Value in Accounting. Techniques and Modeling Suitable for the Income Based Approach

OpenAIRE

Manea Marinela – Daniela

2011-01-01

The term fair value is spread within the sphere of international standards without reference to any detailed guidance on how to apply. However, specialized tangible assets, which are rarely sold, the rule IAS 16 "Intangible assets " makes it possible to estimate fair value using an income approach or a replacement cost or depreciation. The following material is intended to identify potential modeling of fair value as an income-based approach, appealing to techniques used by professional evalu...
Model-Based Methods for Fault Diagnosis: Some Guide-Lines

DEFF Research Database (Denmark)

Patton, R.J.; Chen, J.; Nielsen, S.B.

1995-01-01

This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....
Properties of hypothesis testing techniques and (Bayesian) model selection for exploration-based and theory-based (order-restricted) hypotheses

NARCIS (Netherlands)

Kuiper, Rebecca M.; Nederhoff, Tim; Klugkist, Irene

2015-01-01

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is
Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

DEFF Research Database (Denmark)

Dotto, C. B.; Mannina, G.; Kleidorfer, M.

2012-01-01

-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multiobjective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty...... techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside...... the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper...
Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques

Science.gov (United States)

Jain, Ashu; Srinivasulu, Sanaga

2006-02-01

This paper presents the findings of a study aimed at decomposing a flow hydrograph into different segments based on physical concepts in a catchment, and modelling different segments using different technique viz. conceptual and artificial neural networks (ANNs). An integrated modelling framework is proposed capable of modelling infiltration, base flow, evapotranspiration, soil moisture accounting, and certain segments of the decomposed flow hydrograph using conceptual techniques and the complex, non-linear, and dynamic rainfall-runoff process using ANN technique. Specifically, five different multi-layer perceptron (MLP) and two self-organizing map (SOM) models have been developed. The rainfall and streamflow data derived from the Kentucky River catchment were employed to test the proposed methodology and develop all the models. The performance of all the models was evaluated using seven different standard statistical measures. The results obtained in this study indicate that (a) the rainfall-runoff relationship in a large catchment consists of at least three or four different mappings corresponding to different dynamics of the underlying physical processes, (b) an integrated approach that models the different segments of the decomposed flow hydrograph using different techniques is better than a single ANN in modelling the complex, dynamic, non-linear, and fragmented rainfall runoff process, (c) a simple model based on the concept of flow recession is better than an ANN to model the falling limb of a flow hydrograph, and (d) decomposing a flow hydrograph into the different segments corresponding to the different dynamics based on the physical concepts is better than using the soft decomposition employed using SOM.
System health monitoring using multiple-model adaptive estimation techniques

Science.gov (United States)

Sifford, Stanley Ryan

Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary
Automated Techniques for the Qualitative Analysis of Ecological Models: Continuous Models

Directory of Open Access Journals (Sweden)

Lynn van Coller

1997-06-01

Full Text Available The mathematics required for a detailed analysis of the behavior of a model can be formidable. In this paper, I demonstrate how various computer packages can aid qualitative analyses by implementing techniques from dynamical systems theory. Because computer software is used to obtain the results, the techniques can be used by nonmathematicians as well as mathematicians. In-depth analyses of complicated models that were previously very difficult to study can now be done. Because the paper is intended as an introduction to applying the techniques to ecological models, I have included an appendix describing some of the ideas and terminology. A second appendix shows how the techniques can be applied to a fairly simple predator-prey model and establishes the reliability of the computer software. The main body of the paper discusses a ratio-dependent model. The new techniques highlight some limitations of isocline analyses in this three-dimensional setting and show that the model is structurally unstable. Another appendix describes a larger model of a sheep-pasture-hyrax-lynx system. Dynamical systems techniques are compared with a traditional sensitivity analysis and are found to give more information. As a result, an incomplete relationship in the model is highlighted. I also discuss the resilience of these models to both parameter and population perturbations.
Survey of semantic modeling techniques

Energy Technology Data Exchange (ETDEWEB)

Smith, C.L.

1975-07-01

The analysis of the semantics of programing languages was attempted with numerous modeling techniques. By providing a brief survey of these techniques together with an analysis of their applicability for answering semantic issues, this report attempts to illuminate the state-of-the-art in this area. The intent is to be illustrative rather than thorough in the coverage of semantic models. A bibliography is included for the reader who is interested in pursuing this area of research in more detail.
Optimization-based human motion prediction using an inverse-inverse dynamics technique implemented in the AnyBody Modeling System

DEFF Research Database (Denmark)

Farahani, Saeed Davoudabadi; Andersen, Michael Skipper; de Zee, Mark

2012-01-01

derived from the detailed musculoskeletal analysis. The technique is demonstrated on a human model pedaling a bicycle. We use a physiology-based cost function expressing the mean square of all muscle activities over the cycle to predict a realistic motion pattern. Posture and motion prediction...... on a physics model including dynamic effects and a high level of anatomical realism. First, a musculoskeletal model comprising several hundred muscles is built in AMS. The movement is then parameterized by means of time functions controlling selected degrees of freedom of the model. Subsequently......, the parameters of these functions are optimized to produce an optimum posture or movement according to a user-defined cost function and constraints. The cost function and the constraints are typically express performance, comfort, injury risk, fatigue, muscle load, joint forces and other physiological properties...
Failure Mechanism of Rock Bridge Based on Acoustic Emission Technique

Directory of Open Access Journals (Sweden)

Guoqing Chen

2015-01-01

Full Text Available Acoustic emission (AE technique is widely used in various fields as a reliable nondestructive examination technology. Two experimental tests were carried out in a rock mechanics laboratory, which include (1 small scale direct shear tests of rock bridge with different lengths and (2 large scale landslide model with locked section. The relationship of AE event count and record time was analyzed during the tests. The AE source location technology and comparative analysis with its actual failure model were done. It can be found that whether it is small scale test or large scale landslide model test, AE technique accurately located the AE source point, which reflected the failure generation and expansion of internal cracks in rock samples. Large scale landslide model with locked section test showed that rock bridge in rocky slope has typical brittle failure behavior. The two tests based on AE technique well revealed the rock failure mechanism in rocky slope and clarified the cause of high speed and long distance sliding of rocky slope.
A Survey of Model-based Sensor Data Acquisition and Management

OpenAIRE

Aggarwal, Charu C.; Sathe, Saket; Papaioannou, Thanasis; Jeung, Hoyoung; Aberer, Karl

2013-01-01

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition...
SEM-based characterization techniques

International Nuclear Information System (INIS)

Russell, P.E.

1986-01-01

The scanning electron microscope is now a common instrument in materials characterization laboratories. The basic role of the SEM as a topographic imaging system has steadily been expanding to include a variety of SEM-based analytical techniques. These techniques cover the range of basic semiconductor materials characterization to live-time device characterization of operating LSI or VLSI devices. This paper introduces many of the more commonly used techniques, describes the modifications or additions to a conventional SEM required to utilize the techniques, and gives examples of the use of such techniques. First, the types of signals available from a sample being irradiated by an electron beam are reviewed. Then, where applicable, the type of spectroscopy or microscopy which has evolved to utilize the various signal types are described. This is followed by specific examples of the use of such techniques to solve problems related to semiconductor technology. Techniques emphasized include: x-ray fluorescence spectroscopy, electron beam induced current (EBIC), stroboscopic voltage analysis, cathodoluminescnece and electron beam IC metrology. Current and future trends of some of the these techniques, as related to the semiconductor industry are discussed
Modeling of an Aged Porous Silicon Humidity Sensor Using ANN Technique

Directory of Open Access Journals (Sweden)

Tarikul ISLAM

2006-10-01

Full Text Available Porous silicon (PS sensor based on capacitive technique used for measuring relative humidity has the advantages of low cost, ease of fabrication with controlled structure and CMOS compatibility. But the response of the sensor is nonlinear function of humidity and suffers from errors due to aging and stability. One adaptive linear (ADALINE ANN model has been developed to model the behavior of the sensor with a view to estimate these errors and compensate them. The response of the sensor is represented by third order polynomial basis function whose coefficients are determined by the ANN technique. The drift in sensor output due to aging of PS layer is also modeled by adapting the weights of the polynomial function. ANN based modeling is found to be more suitable than conventional physical modeling of PS humidity sensor in changing environment and drift due to aging. It helps online estimation of nonlinearity as well as monitoring of the fault of the PS humidity sensor using the coefficients of the model.

Agent-Based Modeling in Systems Pharmacology.

Science.gov (United States)

Cosgrove, J; Butler, J; Alden, K; Read, M; Kumar, V; Cucurull-Sanchez, L; Timmis, J; Coles, M

2015-11-01

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.
Repositioning the knee joint in human body FE models using a graphics-based technique.

Science.gov (United States)

Jani, Dhaval; Chawla, Anoop; Mukherjee, Sudipto; Goyal, Rahul; Vusirikala, Nataraju; Jayaraman, Suresh

2012-01-01

Human body finite element models (FE-HBMs) are available in standard occupant or pedestrian postures. There is a need to have FE-HBMs in the same posture as a crash victim or to be configured in varying postures. Developing FE models for all possible positions is not practically viable. The current work aims at obtaining a posture-specific human lower extremity model by reconfiguring an existing one. A graphics-based technique was developed to reposition the lower extremity of an FE-HBM by specifying the flexion-extension angle. Elements of the model were segregated into rigid (bones) and deformable components (soft tissues). The bones were rotated about the flexion-extension axis followed by rotation about the longitudinal axis to capture the twisting of the tibia. The desired knee joint movement was thus achieved. Geometric heuristics were then used to reposition the skin. A mapping defined over the space between bones and the skin was used to regenerate the soft tissues. Mesh smoothing was then done to augment mesh quality. The developed method permits control over the kinematics of the joint and maintains the initial mesh quality of the model. For some critical areas (in the joint vicinity) where element distortion is large, mesh smoothing is done to improve mesh quality. A method to reposition the knee joint of a human body FE model was developed. Repositions of a model from 9 degrees of flexion to 90 degrees of flexion in just a few seconds without subjective interventions was demonstrated. Because the mesh quality of the repositioned model was maintained to a predefined level (typically to the level of a well-made model in the initial configuration), the model was suitable for subsequent simulations.
Least-squares model-based halftoning

Science.gov (United States)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach
FDTD technique based crosstalk analysis of bundled SWCNT interconnects

International Nuclear Information System (INIS)

Duksh, Yograj Singh; Kaushik, Brajesh Kumar; Agarwal, Rajendra P.

2015-01-01

The equivalent electrical circuit model of a bundled single-walled carbon nanotube based distributed RLC interconnects is employed for the crosstalk analysis. The accurate time domain analysis and crosstalk effect in the VLSI interconnect has emerged as an essential design criteria. This paper presents a brief description of the numerical method based finite difference time domain (FDTD) technique that is intended for estimation of voltages and currents on coupled transmission lines. For the FDTD implementation, the stability of the proposed model is strictly restricted by the Courant condition. This method is used for the estimation of crosstalk induced propagation delay and peak voltage in lossy RLC interconnects. Both functional and dynamic crosstalk effects are analyzed in the coupled transmission line. The effect of line resistance on crosstalk induced delay, and peak voltage under dynamic and functional crosstalk is also evaluated. The FDTD analysis and the SPICE simulations are carried out at 32 nm technology node for the global interconnects. It is observed that the analytical results obtained using the FDTD technique are in good agreement with the SPICE simulation results. The crosstalk induced delay, propagation delay, and peak voltage obtained using the FDTD technique shows average errors of 4.9%, 3.4% and 0.46%, respectively, in comparison to SPICE. (paper)
Efficient techniques for wave-based sound propagation in interactive applications

Science.gov (United States)

Mehra, Ravish

Sound propagation techniques model the effect of the environment on sound waves and predict their behavior from point of emission at the source to the final point of arrival at the listener. Sound is a pressure wave produced by mechanical vibration of a surface that propagates through a medium such as air or water, and the problem of sound propagation can be formulated mathematically as a second-order partial differential equation called the wave equation. Accurate techniques based on solving the wave equation, also called the wave-based techniques, are too expensive computationally and memory-wise. Therefore, these techniques face many challenges in terms of their applicability in interactive applications including sound propagation in large environments, time-varying source and listener directivity, and high simulation cost for mid-frequencies. In this dissertation, we propose a set of efficient wave-based sound propagation techniques that solve these three challenges and enable the use of wave-based sound propagation in interactive applications. Firstly, we propose a novel equivalent source technique for interactive wave-based sound propagation in large scenes spanning hundreds of meters. It is based on the equivalent source theory used for solving radiation and scattering problems in acoustics and electromagnetics. Instead of using a volumetric or surface-based approach, this technique takes an object-centric approach to sound propagation. The proposed equivalent source technique generates realistic acoustic effects and takes orders of magnitude less runtime memory compared to prior wave-based techniques. Secondly, we present an efficient framework for handling time-varying source and listener directivity for interactive wave-based sound propagation. The source directivity is represented as a linear combination of elementary spherical harmonic sources. This spherical harmonic-based representation of source directivity can support analytical, data
Model-based Clustering of High-Dimensional Data in Astrophysics

Science.gov (United States)

Bouveyron, C.

2016-05-01

The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model-based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.
Modelling of ground penetrating radar data in stratified media using the reflectivity technique

International Nuclear Information System (INIS)

Sena, Armando R; Sen, Mrinal K; Stoffa, Paul L

2008-01-01

Horizontally layered media are often encountered in shallow exploration geophysics. Ground penetrating radar (GPR) data in these environments can be modelled by techniques that are more efficient than finite difference (FD) or finite element (FE) schemes because the lateral homogeneity of the media allows us to reduce the dependence on the horizontal spatial variables through Fourier transforms on these coordinates. We adapt and implement the invariant embedding or reflectivity technique used to model elastic waves in layered media to model GPR data. The results obtained with the reflectivity and FDTD modelling techniques are in excellent agreement and the effects of the air–soil interface on the radiation pattern are correctly taken into account by the reflectivity technique. Comparison with real wide-angle GPR data shows that the reflectivity technique can satisfactorily reproduce the real GPR data. These results and the computationally efficient characteristics of the reflectivity technique (compared to FD or FE) demonstrate its usefulness in interpretation and possible model-based inversion schemes of GPR data in stratified media
DEVELOPMENT MODEL OF PATISSERIE PROJECT-BASED LEARNING

OpenAIRE

Ana Ana; Lutfhiyah Nurlaela

2013-01-01

The study aims to find a model of patisserie project-based learning with production approach that can improve effectiveness of patisserie learning. Delphi Technique, Cohen's Kappa and percentages of agreements were used to assess model of patisserie project based learning. Data collection techniques employed in the study were questionnaire, check list worksheet, observation, and interview sheets. Subjects were 13 lectures of expertise food and nutrition and 91 students of Food and Nutrition ...
Traceability in Model-Based Testing

Directory of Open Access Journals (Sweden)

Mathew George

2012-11-01

Full Text Available The growing complexities of software and the demand for shorter time to market are two important challenges that face today’s IT industry. These challenges demand the increase of both productivity and quality of software. Model-based testing is a promising technique for meeting these challenges. Traceability modeling is a key issue and challenge in model-based testing. Relationships between the different models will help to navigate from one model to another, and trace back to the respective requirements and the design model when the test fails. In this paper, we present an approach for bridging the gaps between the different models in model-based testing. We propose relation definition markup language (RDML for defining the relationships between models.
Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

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José R. Casar

2011-09-01

Full Text Available The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network. The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.
New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique

International Nuclear Information System (INIS)

Demirhan, Haydar; Kayhan Atilgan, Yasemin

2015-01-01

Highlights: • Precise horizontal global solar radiation estimation models are proposed for Turkey. • Genetic programming technique is used to construct the models. • Robust coplot analysis is applied to reduce the impact of outlier observations. • Better estimation and prediction properties are observed for the models. - Abstract: Renewable energy sources have been attracting more and more attention of researchers due to the diminishing and harmful nature of fossil energy sources. Because of the importance of solar energy as a renewable energy source, an accurate determination of significant covariates and their relationships with the amount of global solar radiation reaching the Earth is a critical research problem. There are numerous meteorological and terrestrial covariates that can be used in the analysis of horizontal global solar radiation. Some of these covariates are highly correlated with each other. It is possible to find a large variety of linear or non-linear models to explain the amount of horizontal global solar radiation. However, models that explain the amount of global solar radiation with the smallest set of covariates should be obtained. In this study, use of the robust coplot technique to reduce the number of covariates before going forward with advanced modelling techniques is considered. After reducing the dimensionality of model space, yearly and monthly mean daily horizontal global solar radiation estimation models for Turkey are built by using the genetic programming technique. It is observed that application of robust coplot analysis is helpful for building precise models that explain the amount of global solar radiation with the minimum number of covariates without suffering from outlier observations and the multicollinearity problem. Consequently, over a dataset of Turkey, precise yearly and monthly mean daily global solar radiation estimation models are introduced using the model spaces obtained by robust coplot technique and
Case-based reasoning diagnostic technique based on multi-attribute similarity

Energy Technology Data Exchange (ETDEWEB)

Makoto, Takahashi [Tohoku University, Miyagi (Japan); Akio, Gofuku [Okayama University, Okayamaa (Japan)

2014-08-15

Case-based diagnostic technique has been developed based on the multi-attribute similarity. Specific feature of the developed system is to use multiple attributes of process signals for similarity evaluation to retrieve a similar case stored in a case base. The present technique has been applied to the measurement data from Monju with some simulated anomalies. The results of numerical experiments showed that the present technique can be utilizes as one of the methods for a hybrid-type diagnosis system.
A fermionic molecular dynamics technique to model nuclear matter

International Nuclear Information System (INIS)

Vantournhout, K.; Jachowicz, N.; Ryckebusch, J.

2009-01-01

Full text: At sub-nuclear densities of about 10 14 g/cm 3 , nuclear matter arranges itself in a variety of complex shapes. This can be the case in the crust of neutron stars and in core-collapse supernovae. These slab like and rod like structures, designated as nuclear pasta, have been modelled with classical molecular dynamics techniques. We present a technique, based on fermionic molecular dynamics, to model nuclear matter at sub-nuclear densities in a semi classical framework. The dynamical evolution of an antisymmetric ground state is described making the assumption of periodic boundary conditions. Adding the concepts of antisymmetry, spin and probability distributions to classical molecular dynamics, brings the dynamical description of nuclear matter to a quantum mechanical level. Applications of this model vary from investigation of macroscopic observables and the equation of state to the study of fundamental interactions on the microscopic structure of the matter. (author)
Quantification of intervertebral displacement with a novel MRI-based modeling technique: Assessing measurement bias and reliability with a porcine spine model.

Science.gov (United States)

Mahato, Niladri K; Montuelle, Stephane; Goubeaux, Craig; Cotton, John; Williams, Susan; Thomas, James; Clark, Brian C

2017-05-01

The purpose of this study was to develop a novel magnetic resonance imaging (MRI)-based modeling technique for measuring intervertebral displacements. Here, we present the measurement bias and reliability of the developmental work using a porcine spine model. Porcine lumbar vertebral segments were fitted in a custom-built apparatus placed within an externally calibrated imaging volume of an open-MRI scanner. The apparatus allowed movement of the vertebrae through pre-assigned magnitudes of sagittal and coronal translation and rotation. The induced displacements were imaged with static (T 1 ) and fast dynamic (2D HYCE S) pulse sequences. These images were imported into animation software, in which these images formed a background 'scene'. Three-dimensional models of vertebrae were created using static axial scans from the specimen and then transferred into the animation environment. In the animation environment, the user manually moved the models (rotoscoping) to perform model-to-'scene' matching to fit the models to their image silhouettes and assigned anatomical joint axes to the motion-segments. The animation protocol quantified the experimental translation and rotation displacements between the vertebral models. Accuracy of the technique was calculated as 'bias' using a linear mixed effects model, average percentage error and root mean square errors. Between-session reliability was examined by computing intra-class correlation coefficients (ICC) and the coefficient of variations (CV). For translation trials, a constant bias (β 0 ) of 0.35 (±0.11) mm was detected for the 2D HYCE S sequence (p=0.01). The model did not demonstrate significant additional bias with each mm increase in experimental translation (β 1 Displacement=0.01mm; p=0.69). Using the T 1 sequence for the same assessments did not significantly change the bias (p>0.05). ICC values for the T 1 and 2D HYCE S pulse sequences were 0.98 and 0.97, respectively. For rotation trials, a constant bias (�
Experimental evaluation of a quasi-modal parameter based rotor foundation identification technique

Science.gov (United States)

Yu, Minli; Liu, Jike; Feng, Ningsheng; Hahn, Eric J.

2017-12-01

Correct modelling of the foundation of rotating machinery is an invaluable asset in model-based rotor dynamic study. One attractive approach for such purpose is to identify the relevant modal parameters of an equivalent foundation using the motion measurements of rotor and foundation at the bearing supports. Previous research showed that, a complex quasi-modal parameter based system identification technique could be feasible for this purpose; however, the technique was only validated by identifying simple structures under harmonic excitation. In this paper, such identification technique is further extended and evaluated by identifying the foundation of a numerical rotor-bearing-foundation system and an experimental rotor rig respectively. In the identification of rotor foundation with multiple bearing supports, all application points of excitation forces transmitted through bearings need to be included; however the assumed vibration modes far outside the rotor operating speed cannot or not necessary to be identified. The extended identification technique allows one to identify correctly an equivalent foundation with fewer modes than the assumed number of degrees of freedom, essentially by generalising the technique to be able to handle rectangular complex modal matrices. The extended technique is robust in numerical and experimental validation and is therefore likely to be applicable in the field.
GRAVTool, a Package to Compute Geoid Model by Remove-Compute-Restore Technique

Science.gov (United States)

Marotta, G. S.; Blitzkow, D.; Vidotti, R. M.

2015-12-01

Currently, there are several methods to determine geoid models. They can be based on terrestrial gravity data, geopotential coefficients, astro-geodetic data or a combination of them. Among the techniques to compute a precise geoid model, the Remove-Compute-Restore (RCR) has been widely applied. It considers short, medium and long wavelengths derived from altitude data provided by Digital Terrain Models (DTM), terrestrial gravity data and global geopotential coefficients, respectively. In order to apply this technique, it is necessary to create procedures that compute gravity anomalies and geoid models, by the integration of different wavelengths, and that adjust these models to one local vertical datum. This research presents a developed package called GRAVTool based on MATLAB software to compute local geoid models by RCR technique and its application in a study area. The studied area comprehends the federal district of Brazil, with ~6000 km², wavy relief, heights varying from 600 m to 1340 m, located between the coordinates 48.25ºW, 15.45ºS and 47.33ºW, 16.06ºS. The results of the numerical example on the studied area show the local geoid model computed by the GRAVTool package (Figure), using 1377 terrestrial gravity data, SRTM data with 3 arc second of resolution, and geopotential coefficients of the EIGEN-6C4 model to degree 360. The accuracy of the computed model (σ = ± 0.071 m, RMS = 0.069 m, maximum = 0.178 m and minimum = -0.123 m) matches the uncertainty (σ =± 0.073) of 21 points randomly spaced where the geoid was computed by geometrical leveling technique supported by positioning GNSS. The results were also better than those achieved by Brazilian official regional geoid model (σ = ± 0.099 m, RMS = 0.208 m, maximum = 0.419 m and minimum = -0.040 m).
A probabilistic graphical model based stochastic input model construction

International Nuclear Information System (INIS)

Wan, Jiang; Zabaras, Nicholas

2014-01-01

Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media
Gravity Matching Aided Inertial Navigation Technique Based on Marginal Robust Unscented Kalman Filter

Directory of Open Access Journals (Sweden)

Ming Liu

2015-01-01

Full Text Available This paper is concerned with the topic of gravity matching aided inertial navigation technology using Kalman filter. The dynamic state space model for Kalman filter is constructed as follows: the error equation of the inertial navigation system is employed as the process equation while the local gravity model based on 9-point surface interpolation is employed as the observation equation. The unscented Kalman filter is employed to address the nonlinearity of the observation equation. The filter is refined in two ways as follows. The marginalization technique is employed to explore the conditionally linear substructure to reduce the computational load; specifically, the number of the needed sigma points is reduced from 15 to 5 after this technique is used. A robust technique based on Chi-square test is employed to make the filter insensitive to the uncertainties in the above constructed observation model. Numerical simulation is carried out, and the efficacy of the proposed method is validated by the simulation results.
Model-based monitoring techniques for leakage localization in distribution water networks

OpenAIRE

Meseguer Amela, Jordi; Mirats Tur, Josep Maria; Cembrano Gennari, Gabriela; Puig Cayuela, Vicenç

2015-01-01

This is an open access article under the CC BY-NC-ND license This paper describes an integrated model-based monitoring framework for leakage localization in district-metered areas (DMA) of water distribution networks, which takes advantage of the availability of a hydraulic model of the network. The leakage localization methodology is based on the use of flow and pressure sensors at the DMA inlets and a limited number of pressure sensors deployed inside the DMA. The placement of these sens...
Composite Techniques Based Color Image Compression

Directory of Open Access Journals (Sweden)

Zainab Ibrahim Abood

2017-03-01

Full Text Available Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S, composite wavelet technique (W and composite multi-wavelet technique (M. For the high energy sub-band of the 3rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet transform (MMM in M technique which has the highest values of energy (En and compression ratio (CR and least values of bit per pixel (bpp, time (T and rate distortion R(D. Also the values of the compression parameters of the color image are nearly the same as the average values of the compression parameters of the three bands of the same image.

Under-Frequency Load Shedding Technique Considering Event-Based for an Islanded Distribution Network

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Hasmaini Mohamad

2016-06-01

Full Text Available One of the biggest challenge for an islanding operation is to sustain the frequency stability. A large power imbalance following islanding would cause under-frequency, hence an appropriate control is required to shed certain amount of load. The main objective of this research is to develop an adaptive under-frequency load shedding (UFLS technique for an islanding system. The technique is designed considering an event-based which includes the moment system is islanded and a tripping of any DG unit during islanding operation. A disturbance magnitude is calculated to determine the amount of load to be shed. The technique is modeled by using PSCAD simulation tool. A simulation studies on a distribution network with mini hydro generation is carried out to evaluate the UFLS model. It is performed under different load condition: peak and base load. Results show that the load shedding technique have successfully shed certain amount of load and stabilized the system frequency.
Model-Based Security Testing

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Ina Schieferdecker

2012-02-01

Full Text Available Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.
Enhancing photogrammetric 3d city models with procedural modeling techniques for urban planning support

International Nuclear Information System (INIS)

Schubiger-Banz, S; Arisona, S M; Zhong, C

2014-01-01

This paper presents a workflow to increase the level of detail of reality-based 3D urban models. It combines the established workflows from photogrammetry and procedural modeling in order to exploit distinct advantages of both approaches. The combination has advantages over purely automatic acquisition in terms of visual quality, accuracy and model semantics. Compared to manual modeling, procedural techniques can be much more time effective while maintaining the qualitative properties of the modeled environment. In addition, our method includes processes for procedurally adding additional features such as road and rail networks. The resulting models meet the increasing needs in urban environments for planning, inventory, and analysis
Identifying and prioritizing the tools/techniques of knowledge management based on the Asian Productivity Organization Model (APO) to use in hospitals.

Science.gov (United States)

Khajouei, Hamid; Khajouei, Reza

2017-12-01

Appropriate knowledge, correct information, and relevant data are vital in medical diagnosis and treatment systems. Knowledge Management (KM) through its tools/techniques provides a pertinent framework for decision-making in healthcare systems. The objective of this study was to identify and prioritize the KM tools/techniques that apply to hospital setting. This is a descriptive-survey study. Data were collected using a -researcher-made questionnaire that was developed based on experts' opinions to select the appropriate tools/techniques from 26 tools/techniques of the Asian Productivity Organization (APO) model. Questions were categorized into five steps of KM (identifying, creating, storing, sharing, and applying the knowledge) according to this model. The study population consisted of middle and senior managers of hospitals and managing directors of Vice-Chancellor for Curative Affairs in Kerman University of Medical Sciences in Kerman, Iran. The data were analyzed in SPSS v.19 using one-sample t-test. Twelve out of 26 tools/techniques of the APO model were identified as the tools applicable in hospitals. "Knowledge café" and "APO knowledge management assessment tool" with respective means of 4.23 and 3.7 were the most and the least applicable tools in the knowledge identification step. "Mentor-mentee scheme", as well as "voice and Voice over Internet Protocol (VOIP)" with respective means of 4.20 and 3.52 were the most and the least applicable tools/techniques in the knowledge creation step. "Knowledge café" and "voice and VOIP" with respective means of 3.85 and 3.42 were the most and the least applicable tools/techniques in the knowledge storage step. "Peer assist and 'voice and VOIP' with respective means of 4.14 and 3.38 were the most and the least applicable tools/techniques in the knowledge sharing step. Finally, "knowledge worker competency plan" and "knowledge portal" with respective means of 4.38 and 3.85 were the most and the least applicable tools/techniques
Image based 3D city modeling : Comparative study

Directory of Open Access Journals (Sweden)

S. P. Singh

2014-06-01

Full Text Available 3D city model is a digital representation of the Earth’s surface and it’s related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing rapidly for various engineering and non-engineering applications. Generally four main image based approaches were used for virtual 3D city models generation. In first approach, researchers were used Sketch based modeling, second method is Procedural grammar based modeling, third approach is Close range photogrammetry based modeling and fourth approach is mainly based on Computer Vision techniques. SketchUp, CityEngine, Photomodeler and Agisoft Photoscan are the main softwares to represent these approaches respectively. These softwares have different approaches & methods suitable for image based 3D city modeling. Literature study shows that till date, there is no complete such type of comparative study available to create complete 3D city model by using images. This paper gives a comparative assessment of these four image based 3D modeling approaches. This comparative study is mainly based on data acquisition methods, data processing techniques and output 3D model products. For this research work, study area is the campus of civil engineering department, Indian Institute of Technology, Roorkee (India. This 3D campus acts as a prototype for city. This study also explains various governing parameters, factors and work experiences. This research work also gives a brief introduction, strengths and weakness of these four image based techniques. Some personal comment is also given as what can do or what can’t do from these softwares. At the last, this study shows; it concluded that, each and every software has some advantages and limitations. Choice of software depends on user requirements of 3D project. For normal visualization project, SketchUp software is a good option. For 3D documentation record, Photomodeler gives good
Model-based recognition of 3-D objects by geometric hashing technique

International Nuclear Information System (INIS)

Severcan, M.; Uzunalioglu, H.

1992-09-01

A model-based object recognition system is developed for recognition of polyhedral objects. The system consists of feature extraction, modelling and matching stages. Linear features are used for object descriptions. Lines are obtained from edges using rotation transform. For modelling and recognition process, geometric hashing method is utilized. Each object is modelled using 2-D views taken from the viewpoints on the viewing sphere. A hidden line elimination algorithm is used to find these views from the wire frame model of the objects. The recognition experiments yielded satisfactory results. (author). 8 refs, 5 figs
Array-based techniques for fingerprinting medicinal herbs

Directory of Open Access Journals (Sweden)

Xue Charlie

2011-05-01

Full Text Available Abstract Poor quality control of medicinal herbs has led to instances of toxicity, poisoning and even deaths. The fundamental step in quality control of herbal medicine is accurate identification of herbs. Array-based techniques have recently been adapted to authenticate or identify herbal plants. This article reviews the current array-based techniques, eg oligonucleotides microarrays, gene-based probe microarrays, Suppression Subtractive Hybridization (SSH-based arrays, Diversity Array Technology (DArT and Subtracted Diversity Array (SDA. We further compare these techniques according to important parameters such as markers, polymorphism rates, restriction enzymes and sample type. The applicability of the array-based methods for fingerprinting depends on the availability of genomics and genetics of the species to be fingerprinted. For the species with few genome sequence information but high polymorphism rates, SDA techniques are particularly recommended because they require less labour and lower material cost.
The Integrated Use of Enterprise and System Dynamics Modelling Techniques in Support of Business Decisions

Directory of Open Access Journals (Sweden)

K. Agyapong-Kodua

2012-01-01

Full Text Available Enterprise modelling techniques support business process (reengineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulation modelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink.
IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

Institute of Scientific and Technical Information of China (English)

纳瑟; 刘重庆

2002-01-01

This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K-means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.
IoT Security Techniques Based on Machine Learning

OpenAIRE

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

2018-01-01

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...
Model-Based Reconstructive Elasticity Imaging Using Ultrasound

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Salavat R. Aglyamov

2007-01-01

Full Text Available Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.
A new frequency matching technique for FRF-based model updating

Science.gov (United States)

Yang, Xiuming; Guo, Xinglin; Ouyang, Huajiang; Li, Dongsheng

2017-05-01

Frequency Response Function (FRF) residues have been widely used to update Finite Element models. They are a kind of original measurement information and have the advantages of rich data and no extraction errors, etc. However, like other sensitivity-based methods, an FRF-based identification method also needs to face the ill-conditioning problem which is even more serious since the sensitivity of the FRF in the vicinity of a resonance is much greater than elsewhere. Furthermore, for a given frequency measurement, directly using a theoretical FRF at a frequency may lead to a huge difference between the theoretical FRF and the corresponding experimental FRF which finally results in larger effects of measurement errors and damping. Hence in the solution process, correct selection of the appropriate frequency to get the theoretical FRF in every iteration in the sensitivity-based approach is an effective way to improve the robustness of an FRF-based algorithm. A primary tool for right frequency selection based on the correlation of FRFs is the Frequency Domain Assurance Criterion. This paper presents a new frequency selection method which directly finds the frequency that minimizes the difference of the order of magnitude between the theoretical and experimental FRFs. A simulated truss structure is used to compare the performance of different frequency selection methods. For the sake of reality, it is assumed that not all the degrees of freedom (DoFs) are available for measurement. The minimum number of DoFs required in each approach to correctly update the analytical model is regarded as the right identification standard.
HIGHLY-ACCURATE MODEL ORDER REDUCTION TECHNIQUE ON A DISCRETE DOMAIN

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L. D. Ribeiro

2015-09-01

Full Text Available AbstractIn this work, we present a highly-accurate technique of model order reduction applied to staged processes. The proposed method reduces the dimension of the original system based on null values of moment-weighted sums of heat and mass balance residuals on real stages. To compute these sums of weighted residuals, a discrete form of Gauss-Lobatto quadrature was developed, allowing a high degree of accuracy in these calculations. The locations where the residuals are cancelled vary with time and operating conditions, characterizing a desirable adaptive nature of this technique. Balances related to upstream and downstream devices (such as condenser, reboiler, and feed tray of a distillation column are considered as boundary conditions of the corresponding difference-differential equations system. The chosen number of moments is the dimension of the reduced model being much lower than the dimension of the complete model and does not depend on the size of the original model. Scaling of the discrete independent variable related with the stages was crucial for the computational implementation of the proposed method, avoiding accumulation of round-off errors present even in low-degree polynomial approximations in the original discrete variable. Dynamical simulations of distillation columns were carried out to check the performance of the proposed model order reduction technique. The obtained results show the superiority of the proposed procedure in comparison with the orthogonal collocation method.
Using ecosystem modelling techniques in exposure assessments of radionuclides - an overview

International Nuclear Information System (INIS)

Kumblad, L.

2005-01-01

The risk to humans from potential releases from nuclear facilities is evaluated in safety assessments. Essential components of these assessments are exposure models, which estimate the transport of radionuclides in the environment, the uptake in biota, and transfer to humans. Recently, there has been a growing concern for radiological protection of the whole environment, not only humans, and a first attempt has been to employ model approaches based on stylized environments and transfer functions to biota based exclusively on bioconcentration factors (BCF). They are generally of a non-mechanistic nature and involve no knowledge of the actual processes involved, which is a severe limitation when assessing real ecosystems. in this paper, the possibility of using an ecological modelling approach as a complement or an alternative to the use of BCF-based models is discussed. The paper gives an overview of ecological and ecosystem modelling and examples of studies where ecosystem models have been used in association to ecological risk assessment studies for other pollutants than radionuclides. It also discusses the potential to use this technique in exposure assessments of radionuclides with a few examples from the safety assessment work performed by the Swedish nuclear fuel and waste management company (SKB). Finally there is a comparison of the characteristics of ecosystem models and traditionally exposure models for radionuclides used to estimate the radionuclide exposure of biota. The evaluation of ecosystem models already applied in safety assessments has shown that the ecosystem approach is possible to use to assess exposure to biota, and that it can handle many of the modelling problems identified related to BCF-models. The findings in this paper suggest that both national and international assessment frameworks for protection of the environment from ionising radiation would benefit from striving to adopt methodologies based on ecologically sound principles and
Verification of Orthogrid Finite Element Modeling Techniques

Science.gov (United States)

Steeve, B. E.

1996-01-01

The stress analysis of orthogrid structures, specifically with I-beam sections, is regularly performed using finite elements. Various modeling techniques are often used to simplify the modeling process but still adequately capture the actual hardware behavior. The accuracy of such 'Oshort cutso' is sometimes in question. This report compares three modeling techniques to actual test results from a loaded orthogrid panel. The finite element models include a beam, shell, and mixed beam and shell element model. Results show that the shell element model performs the best, but that the simpler beam and beam and shell element models provide reasonable to conservative results for a stress analysis. When deflection and stiffness is critical, it is important to capture the effect of the orthogrid nodes in the model.
Intelligent Transportation and Evacuation Planning A Modeling-Based Approach

CERN Document Server

Naser, Arab

2012-01-01

Intelligent Transportation and Evacuation Planning: A Modeling-Based Approach provides a new paradigm for evacuation planning strategies and techniques. Recently, evacuation planning and modeling have increasingly attracted interest among researchers as well as government officials. This interest stems from the recent catastrophic hurricanes and weather-related events that occurred in the southeastern United States (Hurricane Katrina and Rita). The evacuation methods that were in place before and during the hurricanes did not work well and resulted in thousands of deaths. This book offers insights into the methods and techniques that allow for implementing mathematical-based, simulation-based, and integrated optimization and simulation-based engineering approaches for evacuation planning. This book also: Comprehensively discusses the application of mathematical models for evacuation and intelligent transportation modeling Covers advanced methodologies in evacuation modeling and planning Discusses principles a...
PERFORMANCE ANALYSIS OF PILOT BASED CHANNEL ESTIMATION TECHNIQUES IN MB OFDM SYSTEMS

Directory of Open Access Journals (Sweden)

M. Madheswaran

2011-12-01

Full Text Available Ultra wideband (UWB communication is mainly used for short range of communication in wireless personal area networks. Orthogonal Frequency Division Multiplexing (OFDM is being used as a key physical layer technology for Fourth Generation (4G wireless communication. OFDM based communication gives high spectral efficiency and mitigates Inter-symbol Interference (ISI in a wireless medium. In this paper the IEEE 802.15.3a based Multiband OFDM (MB OFDM system is considered. The pilot based channel estimation techniques are considered to analyze the performance of MB OFDM systems over Liner Time Invariant (LTI Channel models. In this paper, pilot based Least Square (LS and Least Minimum Mean Square Error (LMMSE channel estimation technique has been considered for UWB OFDM system. In the proposed method, the estimated Channel Impulse Responses (CIRs are filtered in the time domain for the consideration of the channel delay spread. Also the performance of proposed system has been analyzed for different modulation techniques for various pilot density patterns.
AN OVERVIEW OF REDUCED ORDER MODELING TECHNIQUES FOR SAFETY APPLICATIONS

Energy Technology Data Exchange (ETDEWEB)

Mandelli, D.; Alfonsi, A.; Talbot, P.; Wang, C.; Maljovec, D.; Smith, C.; Rabiti, C.; Cogliati, J.

2016-10-01

The RISMC project is developing new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of the reactors primary and secondary systems, but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem being addressed, but also a multi-scale problem (both spatial, µm-mm-m, and temporal, seconds-hours-years). As part of the RISMC CRA approach, a large amount of computationally-expensive simulation runs may be required. An important aspect is that even though computational power is growing, the overall computational cost of a RISMC analysis using brute-force methods may be not viable for certain cases. A solution that is being evaluated to assist the computational issue is the use of reduced order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RISMC analysis computational cost by decreasing the number of simulation runs; for this analysis improvement we used surrogate models instead of the actual simulation codes. This article focuses on the use of reduced order modeling techniques that can be applied to RISMC analyses in order to generate, analyze, and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (microseconds instead of hours/days).
Integrating model checking with HiP-HOPS in model-based safety analysis

International Nuclear Information System (INIS)

Sharvia, Septavera; Papadopoulos, Yiannis

2015-01-01

The ability to perform an effective and robust safety analysis on the design of modern safety–critical systems is crucial. Model-based safety analysis (MBSA) has been introduced in recent years to support the assessment of complex system design by focusing on the system model as the central artefact, and by automating the synthesis and analysis of failure-extended models. Model checking and failure logic synthesis and analysis (FLSA) are two prominent MBSA paradigms. Extensive research has placed emphasis on the development of these techniques, but discussion on their integration remains limited. In this paper, we propose a technique in which model checking and Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) – an advanced FLSA technique – can be applied synergistically with benefit for the MBSA process. The application of the technique is illustrated through an example of a brake-by-wire system. - Highlights: • We propose technique to integrate HiP-HOPS and model checking. • State machines can be systematically constructed from HiP-HOPS. • The strengths of different MBSA techniques are combined. • Demonstrated through modeling and analysis of brake-by-wire system. • Root cause analysis is automated and system dynamic behaviors analyzed and verified
A Method to Test Model Calibration Techniques: Preprint

Energy Technology Data Exchange (ETDEWEB)

Judkoff, Ron; Polly, Ben; Neymark, Joel

2016-09-01

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then the calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.

Coronary stent on coronary CT angiography: Assessment with model-based iterative reconstruction technique

Energy Technology Data Exchange (ETDEWEB)

Lee, Eun Chae; Kim, Yeo Koon; Chun, Eun Ju; Choi, Sang IL [Dept. of of Radiology, Seoul National University Bundang Hospital, Seongnam (Korea, Republic of)

2016-05-15

To assess the performance of model-based iterative reconstruction (MBIR) technique for evaluation of coronary artery stents on coronary CT angiography (CCTA). Twenty-two patients with coronary stent implantation who underwent CCTA were retrospectively enrolled for comparison of image quality between filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR) and MBIR. In each data set, image noise was measured as the standard deviation of the measured attenuation units within circular regions of interest in the ascending aorta (AA) and left main coronary artery (LM). To objectively assess the noise and blooming artifacts in coronary stent, we additionally measured the standard deviation of the measured attenuation and intra-luminal stent diameters of total 35 stents with dedicated software. All image noise measured in the AA (all p < 0.001), LM (p < 0.001, p = 0.001) and coronary stent (all p < 0.001) were significantly lower with MBIR in comparison to those with FBP or ASIR. Intraluminal stent diameter was significantly higher with MBIR, as compared with ASIR or FBP (p < 0.001, p = 0.001). MBIR can reduce image noise and blooming artifact from the stent, leading to better in-stent assessment in patients with coronary artery stent.
TL Dating Technique Based on a Trap Model and its Application as a Geochronometer for Granitic Quartz

International Nuclear Information System (INIS)

Han, Y.; Li, H.; Tso, Y.W.

1999-01-01

A trap model is introduced to describe the behaviours of both thermally sensitive and radiation sensitive TL traps. The former are relatively shallow traps. The latter are deep traps, in which population increases with exposure to alpha dose. Thermal decay of both types of traps at ambient temperature is dependent on the trap lifetimes. A trap's population can be measured as TL sensitivity to a laboratory test dose. The trap model has been supported by observations of age dependent TL signals from granitic quartz samples with different crystallisation ages. The trap lifetimes are from 1.98 x 10 9 to 5.36 x 10 15 years estimated using the isothermal decay experiment with the assumption of first order kinetics. Dating techniques are proposed based on the trap model. For old granites (>400 Ma), ages can be obtained by measuring the total exposed alpha dose using the additive alpha dose method, whereas for young granites (<400 Ma), ages can also be obtained by interpolating the TL sensitivity to a curve of TL sensitivities for known ages. (author)
Mobile Augmented Reality Support for Architects based on feature Tracking Techniques

DEFF Research Database (Denmark)

Grønbæk, Kaj; Nielsen, Mikkel Bang; Kramp, Gunnar

2004-01-01

This paper presents a mobile Augmented Reality (AR) system called the SitePack supporting architects in visualizing 3D models in real-time on site. We describe how vision based feature tracking techniques can help architects making decisions on site concerning visual impact assessment. The AR sys...
Agent-based Modeling Methodology for Analyzing Weapons Systems

Science.gov (United States)

2015-03-26

technique involve model structure, system representation and the degree of validity, coupled with the simplicity, of the overall model. ABM is best suited... system representation of the air combat system . We feel that a simulation model that combines ABM with equation-based representation of weapons and...AGENT-BASED MODELING METHODOLOGY FOR ANALYZING WEAPONS SYSTEMS THESIS Casey D. Connors, Major, USA
Fuzzy modeling and control of rotary inverted pendulum system using LQR technique

International Nuclear Information System (INIS)

Fairus, M A; Mohamed, Z; Ahmad, M N

2013-01-01

Rotary inverted pendulum (RIP) system is a nonlinear, non-minimum phase, unstable and underactuated system. Controlling such system can be a challenge and is considered a benchmark in control theory problem. Prior to designing a controller, equations that represent the behaviour of the RIP system must be developed as accurately as possible without compromising the complexity of the equations. Through Takagi-Sugeno (T-S) fuzzy modeling technique, the nonlinear system model is then transformed into several local linear time-invariant models which are then blended together to reproduce, or approximate, the nonlinear system model within local region. A parallel distributed compensation (PDC) based fuzzy controller using linear quadratic regulator (LQR) technique is designed to control the RIP system. The results show that the designed controller able to balance the RIP system
A pilot modeling technique for handling-qualities research

Science.gov (United States)

Hess, R. A.

1980-01-01

A brief survey of the more dominant analysis techniques used in closed-loop handling-qualities research is presented. These techniques are shown to rely on so-called classical and modern analytical models of the human pilot which have their foundation in the analysis and design principles of feedback control. The optimal control model of the human pilot is discussed in some detail and a novel approach to the a priori selection of pertinent model parameters is discussed. Frequency domain and tracking performance data from 10 pilot-in-the-loop simulation experiments involving 3 different tasks are used to demonstrate the parameter selection technique. Finally, the utility of this modeling approach in handling-qualities research is discussed.
Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique

Science.gov (United States)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.
Applications of soft computing in time series forecasting simulation and modeling techniques

CERN Document Server

Singh, Pritpal

2016-01-01

This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...
Probabilistic method/techniques of evaluation/modeling that permits to optimize/reduce the necessary resources

International Nuclear Information System (INIS)

Florescu, G.; Apostol, M.; Farcasiu, M.; Luminita Bedreaga, M.; Nitoi, M.; Turcu, I.

2004-01-01

Fault tree/event tree modeling approach is widely used in modeling and behavior simulation of nuclear structures, systems and components (NSSCs), during different condition of operation. Evaluation of NSSCs reliability, availability, risk or safety, during operation, by using probabilistic techniques, is also largely used. Development of computer capabilities offered new possibilities for large NSSCs models designing, processing and using. There are situations where large, complex and correct NSSC models are desired to be associated with rapid results/solutions/decisions or with multiple processing in order to obtain specific results. Large fault/event trees are hardly to be developed, reviewed and processed. During operation of NSSCs, the time, especially, is an important factor in taking decision. The paper presents a probabilistic method that permits evaluation/modeling of NSSCs and intents to solve the above problems by adopting appropriate techniques. The method is stated for special applications and is based on specific PSA analysis steps, information, algorithms, criteria and relations, in correspondence with the fault tree/event tree modeling and similar techniques, in order to obtain appropriate results for NSSC model analysis. Special classification of NSSCs is stated in order to reflect aspects of use of the method. Also the common reliability databases are part of information necessary to complete the analysis process. Special data and information bases contribute to state the proposed method/techniques. The paper also presents the specific steps of the method, its applicability, the main advantages and problems to be furthermore studied. The method permits optimization/reducing of resources used to perform the PSA activities. (author)
Model-based accelerator controls: What, why and how

International Nuclear Information System (INIS)

Sidhu, S.S.

1987-01-01

Model-based control is defined as a gamut of techniques whose aim is to improve the reliability of an accelerator and enhance the capabilities of the operator, and therefore of the whole control system. The aim of model-based control is seen as gradually moving the function of model-reference from the operator to the computer. The role of the operator in accelerator control and the need for and application of model-based control are briefly summarized
Performability Modelling Tools, Evaluation Techniques and Applications

NARCIS (Netherlands)

Haverkort, Boudewijn R.H.M.

1990-01-01

This thesis deals with three aspects of quantitative evaluation of fault-tolerant and distributed computer and communication systems: performability evaluation techniques, performability modelling tools, and performability modelling applications. Performability modelling is a relatively new
A characteristic study of CCF modeling techniques and optimization of CCF defense strategies

International Nuclear Information System (INIS)

Kim, Min Chull

2000-02-01

Common Cause Failures (CCFs ) are among the major contributors to risk and core damage frequency (CDF ) from operating nuclear power plants (NPPs ). Our study on CCF focused on the following aspects : 1) a characteristic study on the CCF modeling techniques and 2) development of the optimal CCF defense strategy. Firstly, the characteristics of CCF modeling techniques were studied through sensitivity study of CCF occurrence probability upon system redundancy. The modeling techniques considered in this study include those most widely used worldwide, i.e., beta factor, MGL, alpha factor, and binomial failure rate models. We found that MGL and alpha factor models are essentially identical in terms of the CCF probability. Secondly, in the study for CCF defense, the various methods identified in the previous studies for defending against CCF were classified into five different categories. Based on these categories, we developed a generic method by which the optimal CCF defense strategy can be selected. The method is not only qualitative but also quantitative in nature: the selection of the optimal strategy among candidates is based on the use of analytic hierarchical process (AHP). We applied this method to two motor-driven valves for containment sump isolation in Ulchin 3 and 4 nuclear power plants. The result indicates that the method for developing an optimal CCF defense strategy is effective
Multi-disciplinary techniques for understanding time-varying space-based imagery

Science.gov (United States)

Casasent, D.; Sanderson, A.; Kanade, T.

1984-06-01

A multidisciplinary program for space-based image processing is reported. This project combines optical and digital processing techniques and pattern recognition, image understanding and artificial intelligence methodologies. Time change image processing was recognized as the key issue to be addressed. Three time change scenarios were defined based on the frame rate of the data change. This report details the recent research on: various statistical and deterministic image features, recognition of sub-pixel targets in time varying imagery, and 3-D object modeling and recognition.
A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

Science.gov (United States)

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.
Wind Turbine Rotor Simulation via CFD Based Actuator Disc Technique Compared to Detailed Measurement

Directory of Open Access Journals (Sweden)

Esmail Mahmoodi

2015-10-01

Full Text Available In this paper, a generalized Actuator Disc (AD is used to model the wind turbine rotor of the MEXICO experiment, a collaborative European wind turbine project. The AD model as a combination of CFD technique and User Defined Functions codes (UDF, so-called UDF/AD model is used to simulate loads and performance of the rotor in three different wind speed tests. Distributed force on the blade, thrust and power production of the rotor as important designing parameters of wind turbine rotors are focused to model. A developed Blade Element Momentum (BEM theory as a code based numerical technique as well as a full rotor simulation both from the literature are included into the results to compare and discuss. The output of all techniques is compared to detailed measurements for validation, which led us to final conclusions.
A Search Technique for Weak and Long-Duration Gamma-Ray Bursts from Background Model Residuals

Science.gov (United States)

Skelton, R. T.; Mahoney, W. A.

1993-01-01

We report on a planned search technique for Gamma-Ray Bursts too weak to trigger the on-board threshold. The technique is to search residuals from a physically based background model used for analysis of point sources by the Earth occultation method.
Probabilistic evaluation of process model matching techniques

NARCIS (Netherlands)

Kuss, Elena; Leopold, Henrik; van der Aa, Han; Stuckenschmidt, Heiner; Reijers, Hajo A.

2016-01-01

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to
Use of advanced modeling techniques to optimize thermal packaging designs.

Science.gov (United States)

Formato, Richard M; Potami, Raffaele; Ahmed, Iftekhar

2010-01-01

Through a detailed case study the authors demonstrate, for the first time, the capability of using advanced modeling techniques to correctly simulate the transient temperature response of a convective flow-based thermal shipper design. The objective of this case study was to demonstrate that simulation could be utilized to design a 2-inch-wall polyurethane (PUR) shipper to hold its product box temperature between 2 and 8 °C over the prescribed 96-h summer profile (product box is the portion of the shipper that is occupied by the payload). Results obtained from numerical simulation are in excellent agreement with empirical chamber data (within ±1 °C at all times), and geometrical locations of simulation maximum and minimum temperature match well with the corresponding chamber temperature measurements. Furthermore, a control simulation test case was run (results taken from identical product box locations) to compare the coupled conduction-convection model with a conduction-only model, which to date has been the state-of-the-art method. For the conduction-only simulation, all fluid elements were replaced with "solid" elements of identical size and assigned thermal properties of air. While results from the coupled thermal/fluid model closely correlated with the empirical data (±1 °C), the conduction-only model was unable to correctly capture the payload temperature trends, showing a sizeable error compared to empirical values (ΔT > 6 °C). A modeling technique capable of correctly capturing the thermal behavior of passively refrigerated shippers can be used to quickly evaluate and optimize new packaging designs. Such a capability provides a means to reduce the cost and required design time of shippers while simultaneously improving their performance. Another advantage comes from using thermal modeling (assuming a validated model is available) to predict the temperature distribution in a shipper that is exposed to ambient temperatures which were not bracketed
Search-based model identification of smart-structure damage

Science.gov (United States)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.
Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

Science.gov (United States)

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

Assess and Predict Automatic Generation Control Performances for Thermal Power Generation Units Based on Modeling Techniques

Science.gov (United States)

Zhao, Yan; Yang, Zijiang; Gao, Song; Liu, Jinbiao

2018-02-01

Automatic generation control(AGC) is a key technology to maintain real time power generation and load balance, and to ensure the quality of power supply. Power grids require each power generation unit to have a satisfactory AGC performance, being specified in two detailed rules. The two rules provide a set of indices to measure the AGC performance of power generation unit. However, the commonly-used method to calculate these indices is based on particular data samples from AGC responses and will lead to incorrect results in practice. This paper proposes a new method to estimate the AGC performance indices via system identification techniques. In addition, a nonlinear regression model between performance indices and load command is built in order to predict the AGC performance indices. The effectiveness of the proposed method is validated through industrial case studies.
Models and Techniques for Proving Data Structure Lower Bounds

DEFF Research Database (Denmark)

Larsen, Kasper Green

In this dissertation, we present a number of new techniques and tools for proving lower bounds on the operational time of data structures. These techniques provide new lines of attack for proving lower bounds in both the cell probe model, the group model, the pointer machine model and the I...... bound of tutq = (lgd􀀀1 n). For ball range searching, we get a lower bound of tutq = (n1􀀀1=d). The highest previous lower bound proved in the group model does not exceed ((lg n= lg lg n)2) on the maximum of tu and tq. Finally, we present a new technique for proving lower bounds....../O-model. In all cases, we push the frontiers further by proving lower bounds higher than what could possibly be proved using previously known techniques. For the cell probe model, our results have the following consequences: The rst (lg n) query time lower bound for linear space static data structures...
A DIFFERENT WEB-BASED GEOCODING SERVICE USING FUZZY TECHNIQUES

Directory of Open Access Journals (Sweden)

P. Pahlavani

2015-12-01

Full Text Available Geocoding – the process of finding position based on descriptive data such as address or postal code - is considered as one of the most commonly used spatial analyses. Many online map providers such as Google Maps, Bing Maps and Yahoo Maps present geocoding as one of their basic capabilities. Despite the diversity of geocoding services, users usually face some limitations when they use available online geocoding services. In existing geocoding services, proximity and nearness concept is not modelled appropriately as well as these services search address only by address matching based on descriptive data. In addition there are also some limitations in display searching results. Resolving these limitations can enhance efficiency of the existing geocoding services. This paper proposes the idea of integrating fuzzy technique with geocoding process to resolve these limitations. In order to implement the proposed method, a web-based system is designed. In proposed method, nearness to places is defined by fuzzy membership functions and multiple fuzzy distance maps are created. Then these fuzzy distance maps are integrated using fuzzy overlay technique for obtain the results. Proposed methods provides different capabilities for users such as ability to search multi-part addresses, searching places based on their location, non-point representation of results as well as displaying search results based on their priority.
Comparing modelling techniques when designing VPH gratings for BigBOSS

Science.gov (United States)

Poppett, Claire; Edelstein, Jerry; Lampton, Michael; Jelinsky, Patrick; Arns, James

2012-09-01

BigBOSS is a Stage IV Dark Energy instrument based on the Baryon Acoustic Oscillations (BAO) and Red Shift Distortions (RSD) techniques using spectroscopic data of 20 million ELG and LRG galaxies at 0.5VPH) gratings have been identified as a key technology which will enable the efficiency requirement to be met, however it is important to be able to accurately predict their performance. In this paper we quantitatively compare different modelling techniques in order to assess the parameter space over which they are more capable of accurately predicting measured performance. Finally we present baseline parameters for grating designs that are most suitable for the BigBOSS instrument.
A Comparative Analysis of Transmission Control Protocol Improvement Techniques over Space-Based Transmission Media

National Research Council Canada - National Science Library

Lawson, Joseph M

2006-01-01

... justification for the implementation of a given enhancement technique. The research questions were answered through model and simulation of a satellite transmission system via a Linux-based network topology...
Level-set techniques for facies identification in reservoir modeling

Science.gov (United States)

Iglesias, Marco A.; McLaughlin, Dennis

2011-03-01

In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil-water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301-29 2004 Inverse Problems 20 259-82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg-Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush-Kuhn-Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies.
Level-set techniques for facies identification in reservoir modeling

International Nuclear Information System (INIS)

Iglesias, Marco A; McLaughlin, Dennis

2011-01-01

In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil–water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301–29; 2004 Inverse Problems 20 259–82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg–Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush–Kuhn–Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies
Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

Science.gov (United States)

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.
Néron Models and Base Change

DEFF Research Database (Denmark)

Halle, Lars Halvard; Nicaise, Johannes

Presenting the first systematic treatment of the behavior of Néron models under ramified base change, this book can be read as an introduction to various subtle invariants and constructions related to Néron models of semi-abelian varieties, motivated by concrete research problems and complemented...... on Néron component groups, Edixhoven’s filtration and the base change conductor of Chai and Yu, and we study these invariants using various techniques such as models of curves, sheaves on Grothendieck sites and non-archimedean uniformization. We then apply our results to the study of motivic zeta functions...
Increasing the Intelligence of Virtual Sales Assistants through Knowledge Modeling Techniques

OpenAIRE

Molina, Martin

2001-01-01

Shopping agents are web-based applications that help consumers to find appropriate products in the context of e-commerce. In this paper we argue about the utility of advanced model-based techniques that recently have been proposed in the fields of Artificial Intelligence and Knowledge Engineering, in order to increase the level of support provided by this type of applications. We illustrate this approach with a virtual sales assistant that dynamically configures a product according to the nee...
Model-based reasoning technology for the power industry

International Nuclear Information System (INIS)

Touchton, R.A.; Subramanyan, N.S.; Naser, J.A.

1991-01-01

This paper reports on model-based reasoning which refers to an expert system implementation methodology that uses a model of the system which is being reasoned about. Model-based representation and reasoning techniques offer many advantages and are highly suitable for domains where the individual components, their interconnection, and their behavior is well-known. Technology Applications, Inc. (TAI), under contract to the Electric Power Research Institute (EPRI), investigated the use of model-based reasoning in the power industry including the nuclear power industry. During this project, a model-based monitoring and diagnostic tool, called ProSys, was developed. Also, an alarm prioritization system was developed as a demonstration prototype
Model-Based Motion Tracking of Infants

DEFF Research Database (Denmark)

Olsen, Mikkel Damgaard; Herskind, Anna; Nielsen, Jens Bo

2014-01-01

Even though motion tracking is a widely used technique to analyze and measure human movements, only a few studies focus on motion tracking of infants. In recent years, a number of studies have emerged focusing on analyzing the motion pattern of infants, using computer vision. Most of these studies...... are based on 2D images, but few are based on 3D information. In this paper, we present a model-based approach for tracking infants in 3D. The study extends a novel study on graph-based motion tracking of infants and we show that the extension improves the tracking results. A 3D model is constructed...
Model-based fault diagnosis techniques design schemes, algorithms, and tools

CERN Document Server

Ding, Steven

2008-01-01

The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms, and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers. This is a textbook with extensive examples and references. Most methods are given in the form of an algorithm that enables a direct implementation in a programme. Comparisons among different methods are included when possible.
Experimental evaluation of optimal Vehicle Dynamic Control based on the State Dependent Riccati Equation technique

NARCIS (Netherlands)

Alirezaei, M.; Kanarachos, S.A.; Scheepers, B.T.M.; Maurice, J.P.

2013-01-01

Development and experimentally evaluation of an optimal Vehicle Dynamic Control (VDC) strategy based on the State Dependent Riccati Equation (SDRE) control technique is presented. The proposed nonlinear controller is based on a nonlinear vehicle model with nonlinear tire characteristics. A novel
Model-based security testing

OpenAIRE

Schieferdecker, Ina; Großmann, Jürgen; Schneider, Martin

2012-01-01

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security...
Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-06

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Surrogate based approaches to parameter inference in ocean models

KAUST Repository

Knio, Omar

2016-01-01

This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.
Clone Detection for Graph-Based Model Transformation Languages

DEFF Research Database (Denmark)

Strüber, Daniel; Plöger, Jennifer; Acretoaie, Vlad

2016-01-01

and analytical quality assurance. From these use cases, we derive a set of key requirements. We describe our customization of existing model clone detection techniques allowing us to address these requirements. Finally, we provide an experimental evaluation, indicating that our customization of ConQAT, one......Cloning is a convenient mechanism to enable reuse across and within software artifacts. On the downside, it is also a practice related to significant long-term maintainability impediments, thus generating a need to identify clones in affected artifacts. A large variety of clone detection techniques...... has been proposed for programming and modeling languages; yet no specific ones have emerged for model transformation languages. In this paper, we explore clone detection for graph-based model transformation languages. We introduce potential use cases for such techniques in the context of constructive...
Development of a morphology-based modeling technique for tracking solid-body displacements: examining the reliability of a potential MRI-only approach for joint kinematics assessment

International Nuclear Information System (INIS)

Mahato, Niladri K.; Montuelle, Stephane; Cotton, John; Williams, Susan; Thomas, James; Clark, Brian

2016-01-01

Single or biplanar video radiography and Roentgen stereophotogrammetry (RSA) techniques used for the assessment of in-vivo joint kinematics involves application of ionizing radiation, which is a limitation for clinical research involving human subjects. To overcome this limitation, our long-term goal is to develop a magnetic resonance imaging (MRI)-only, three dimensional (3-D) modeling technique that permits dynamic imaging of joint motion in humans. Here, we present our initial findings, as well as reliability data, for an MRI-only protocol and modeling technique. We developed a morphology-based motion-analysis technique that uses MRI of custom-built solid-body objects to animate and quantify experimental displacements between them. The technique involved four major steps. First, the imaging volume was calibrated using a custom-built grid. Second, 3-D models were segmented from axial scans of two custom-built solid-body cubes. Third, these cubes were positioned at pre-determined relative displacements (translation and rotation) in the magnetic resonance coil and scanned with a T 1 and a fast contrast-enhanced pulse sequences. The digital imaging and communications in medicine (DICOM) images were then processed for animation. The fourth step involved importing these processed images into an animation software, where they were displayed as background scenes. In the same step, 3-D models of the cubes were imported into the animation software, where the user manipulated the models to match their outlines in the scene (rotoscoping) and registered the models into an anatomical joint system. Measurements of displacements obtained from two different rotoscoping sessions were tested for reliability using coefficient of variations (CV), intraclass correlation coefficients (ICC), Bland-Altman plots, and Limits of Agreement analyses. Between-session reliability was high for both the T 1 and the contrast-enhanced sequences. Specifically, the average CVs for translation were 4
Development of a morphology-based modeling technique for tracking solid-body displacements: examining the reliability of a potential MRI-only approach for joint kinematics assessment.

Science.gov (United States)

Mahato, Niladri K; Montuelle, Stephane; Cotton, John; Williams, Susan; Thomas, James; Clark, Brian

2016-05-18

Single or biplanar video radiography and Roentgen stereophotogrammetry (RSA) techniques used for the assessment of in-vivo joint kinematics involves application of ionizing radiation, which is a limitation for clinical research involving human subjects. To overcome this limitation, our long-term goal is to develop a magnetic resonance imaging (MRI)-only, three dimensional (3-D) modeling technique that permits dynamic imaging of joint motion in humans. Here, we present our initial findings, as well as reliability data, for an MRI-only protocol and modeling technique. We developed a morphology-based motion-analysis technique that uses MRI of custom-built solid-body objects to animate and quantify experimental displacements between them. The technique involved four major steps. First, the imaging volume was calibrated using a custom-built grid. Second, 3-D models were segmented from axial scans of two custom-built solid-body cubes. Third, these cubes were positioned at pre-determined relative displacements (translation and rotation) in the magnetic resonance coil and scanned with a T1 and a fast contrast-enhanced pulse sequences. The digital imaging and communications in medicine (DICOM) images were then processed for animation. The fourth step involved importing these processed images into an animation software, where they were displayed as background scenes. In the same step, 3-D models of the cubes were imported into the animation software, where the user manipulated the models to match their outlines in the scene (rotoscoping) and registered the models into an anatomical joint system. Measurements of displacements obtained from two different rotoscoping sessions were tested for reliability using coefficient of variations (CV), intraclass correlation coefficients (ICC), Bland-Altman plots, and Limits of Agreement analyses. Between-session reliability was high for both the T1 and the contrast-enhanced sequences. Specifically, the average CVs for translation were 4

Agent Based Modeling Applications for Geosciences

Science.gov (United States)

Stein, J. S.

2004-12-01

Agent-based modeling techniques have successfully been applied to systems in which complex behaviors or outcomes arise from varied interactions between individuals in the system. Each individual interacts with its environment, as well as with other individuals, by following a set of relatively simple rules. Traditionally this "bottom-up" modeling approach has been applied to problems in the fields of economics and sociology, but more recently has been introduced to various disciplines in the geosciences. This technique can help explain the origin of complex processes from a relatively simple set of rules, incorporate large and detailed datasets when they exist, and simulate the effects of extreme events on system-wide behavior. Some of the challenges associated with this modeling method include: significant computational requirements in order to keep track of thousands to millions of agents, methods and strategies of model validation are lacking, as is a formal methodology for evaluating model uncertainty. Challenges specific to the geosciences, include how to define agents that control water, contaminant fluxes, climate forcing and other physical processes and how to link these "geo-agents" into larger agent-based simulations that include social systems such as demographics economics and regulations. Effective management of limited natural resources (such as water, hydrocarbons, or land) requires an understanding of what factors influence the demand for these resources on a regional and temporal scale. Agent-based models can be used to simulate this demand across a variety of sectors under a range of conditions and determine effective and robust management policies and monitoring strategies. The recent focus on the role of biological processes in the geosciences is another example of an area that could benefit from agent-based applications. A typical approach to modeling the effect of biological processes in geologic media has been to represent these processes in
EXCHANGE-RATES FORECASTING: EXPONENTIAL SMOOTHING TECHNIQUES AND ARIMA MODELS

Directory of Open Access Journals (Sweden)

Dezsi Eva

2011-07-01

Full Text Available Exchange rates forecasting is, and has been a challenging task in finance. Statistical and econometrical models are widely used in analysis and forecasting of foreign exchange rates. This paper investigates the behavior of daily exchange rates of the Romanian Leu against the Euro, United States Dollar, British Pound, Japanese Yen, Chinese Renminbi and the Russian Ruble. Smoothing techniques are generated and compared with each other. These models include the Simple Exponential Smoothing technique, as the Double Exponential Smoothing technique, the Simple Holt-Winters, the Additive Holt-Winters, namely the Autoregressive Integrated Moving Average model.
A survey on OFDM channel estimation techniques based on denoising strategies

Directory of Open Access Journals (Sweden)

Pallaviram Sure

2017-04-01

Full Text Available Channel estimation forms the heart of any orthogonal frequency division multiplexing (OFDM based wireless communication receiver. Frequency domain pilot aided channel estimation techniques are either least squares (LS based or minimum mean square error (MMSE based. LS based techniques are computationally less complex. Unlike MMSE ones, they do not require a priori knowledge of channel statistics (KCS. However, the mean square error (MSE performance of the channel estimator incorporating MMSE based techniques is better compared to that obtained with the incorporation of LS based techniques. To enhance the MSE performance using LS based techniques, a variety of denoising strategies have been developed in the literature, which are applied on the LS estimated channel impulse response (CIR. The advantage of denoising threshold based LS techniques is that, they do not require KCS but still render near optimal MMSE performance similar to MMSE based techniques. In this paper, a detailed survey on various existing denoising strategies, with a comparative discussion of these strategies is presented.
Automated extraction of knowledge for model-based diagnostics

Science.gov (United States)

Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

1990-01-01

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.
Exact and Direct Modeling Technique for Rotor-Bearing Systems with Arbitrary Selected Degrees-of-Freedom

Directory of Open Access Journals (Sweden)

Shilin Chen

1994-01-01

Full Text Available An exact and direct modeling technique is proposed for modeling of rotor-bearing systems with arbitrary selected degrees-of-freedom. This technique is based on the combination of the transfer and dynamic stiffness matrices. The technique differs from the usual combination methods in that the global dynamic stiffness matrix for the system or the subsystem is obtained directly by rearranging the corresponding global transfer matrix. Therefore, the dimension of the global dynamic stiffness matrix is independent of the number of the elements or the substructures. In order to show the simplicity and efficiency of the method, two numerical examples are given.
Introduction to Information Visualization (InfoVis) Techniques for Model-Based Systems Engineering

Science.gov (United States)

Sindiy, Oleg; Litomisky, Krystof; Davidoff, Scott; Dekens, Frank

2013-01-01

This paper presents insights that conform to numerous system modeling languages/representation standards. The insights are drawn from best practices of Information Visualization as applied to aerospace-based applications.
Review of the systems biology of the immune system using agent-based models.

Science.gov (United States)

Shinde, Snehal B; Kurhekar, Manish P

2018-06-01

The immune system is an inherent protection system in vertebrate animals including human beings that exhibit properties such as self-organisation, self-adaptation, learning, and recognition. It interacts with the other allied systems such as the gut and lymph nodes. There is a need for immune system modelling to know about its complex internal mechanism, to understand how it maintains the homoeostasis, and how it interacts with the other systems. There are two types of modelling techniques used for the simulation of features of the immune system: equation-based modelling (EBM) and agent-based modelling. Owing to certain shortcomings of the EBM, agent-based modelling techniques are being widely used. This technique provides various predictions for disease causes and treatments; it also helps in hypothesis verification. This study presents a review of agent-based modelling of the immune system and its interactions with the gut and lymph nodes. The authors also review the modelling of immune system interactions during tuberculosis and cancer. In addition, they also outline the future research directions for the immune system simulation through agent-based techniques such as the effects of stress on the immune system, evolution of the immune system, and identification of the parameters for a healthy immune system.
Model–Based Techniques for Virtual Sensing of Longitudinal Flight Parameters

Directory of Open Access Journals (Sweden)

Seren Cédric

2015-03-01

Full Text Available Introduction of fly-by-wire and increasing levels of automation significantly improve the safety of civil aircraft, and result in advanced capabilities for detecting, protecting and optimizing A/C guidance and control. However, this higher complexity requires the availability of some key flight parameters to be extended. Hence, the monitoring and consolidation of those signals is a significant issue, usually achieved via many functionally redundant sensors to extend the way those parameters are measured. This solution penalizes the overall system performance in terms of weight, maintenance, and so on. Other alternatives rely on signal processing or model-based techniques that make a global use of all or part of the sensor data available, supplemented by a model-based simulation of the flight mechanics. That processing achieves real-time estimates of the critical parameters and yields dissimilar signals. Filtered and consolidated information is delivered in unfaulty conditions by estimating an extended state vector, including wind components, and can replace failed signals in degraded conditions. Accordingly, this paper describes two model-based approaches allowing the longitudinal flight parameters of a civil A/C to be estimated on-line. Results are displayed to evaluate the performances in different simulated and real flight conditions, including realistic external disturbances and modeling errors.
Characterising and modelling regolith stratigraphy using multiple geophysical techniques

Science.gov (United States)

Thomas, M.; Cremasco, D.; Fotheringham, T.; Hatch, M. A.; Triantifillis, J.; Wilford, J.

2013-12-01

Regolith is the weathered, typically mineral-rich layer from fresh bedrock to land surface. It encompasses soil (A, E and B horizons) that has undergone pedogenesis. Below is the weathered C horizon that retains at least some of the original rocky fabric and structure. At the base of this is the lower regolith boundary of continuous hard bedrock (the R horizon). Regolith may be absent, e.g. at rocky outcrops, or may be many 10's of metres deep. Comparatively little is known about regolith, and critical questions remain regarding composition and characteristics - especially deeper where the challenge of collecting reliable data increases with depth. In Australia research is underway to characterise and map regolith using consistent methods at scales ranging from local (e.g. hillslope) to continental scales. These efforts are driven by many research needs, including Critical Zone modelling and simulation. Pilot research in South Australia using digitally-based environmental correlation techniques modelled the depth to bedrock to 9 m for an upland area of 128 000 ha. One finding was the inability to reliably model local scale depth variations over horizontal distances of 2 - 3 m and vertical distances of 1 - 2 m. The need to better characterise variations in regolith to strengthen models at these fine scales was discussed. Addressing this need, we describe high intensity, ground-based multi-sensor geophysical profiling of three hillslope transects in different regolith-landscape settings to characterise fine resolution (i.e. a number of frequencies; multiple frequency, multiple coil electromagnetic induction; and high resolution resistivity. These were accompanied by georeferenced, closely spaced deep cores to 9 m - or to core refusal. The intact cores were sub-sampled to standard depths and analysed for regolith properties to compile core datasets consisting of: water content; texture; electrical conductivity; and weathered state. After preprocessing (filtering, geo
Advanced structural equation modeling issues and techniques

CERN Document Server

Marcoulides, George A

2013-01-01

By focusing primarily on the application of structural equation modeling (SEM) techniques in example cases and situations, this book provides an understanding and working knowledge of advanced SEM techniques with a minimum of mathematical derivations. The book was written for a broad audience crossing many disciplines, assumes an understanding of graduate level multivariate statistics, including an introduction to SEM.
CEAI: CCM based Email Authorship Identification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah

2013-01-01

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...
Rapid analysis of steels using laser-based techniques

International Nuclear Information System (INIS)

Cremers, D.A.; Archuleta, F.L.; Dilworth, H.C.

1985-01-01

Based on the data obtained by this study, we conclude that laser-based techniques can be used to provide at least semi-quantitative information about the elemental composition of molten steel. Of the two techniques investigated here, the Sample-Only method appears preferable to the LIBS (laser-induced breakdown spectroscopy) method because of its superior analytical performance. In addition, the Sample-Only method would probably be easier to incorporate into a steel plant environment. However, before either technique can be applied to steel monitoring, additional research is needed
Controller Design of DFIG Based Wind Turbine by Using Evolutionary Soft Computational Techniques

Directory of Open Access Journals (Sweden)

O. P. Bharti

2017-06-01

Full Text Available This manuscript illustrates the controller design for a doubly fed induction generator based variable speed wind turbine by using a bioinspired scheme. This methodology is based on exploiting two proficient swarm intelligence based evolutionary soft computational procedures. The particle swarm optimization (PSO and bacterial foraging optimization (BFO techniques are employed to design the controller intended for small damping plant of the DFIG. Wind energy overview and DFIG operating principle along with the equivalent circuit model is adequately discussed in this paper. The controller design for DFIG based WECS using PSO and BFO are described comparatively in detail. The responses of the DFIG system regarding terminal voltage, current, active-reactive power, and DC-Link voltage have slightly improved with the evolutionary soft computational procedure. Lastly, the obtained output is equated with a standard technique for performance improvement of DFIG based wind energy conversion system.
Integration of computational modeling and experimental techniques to design fuel surrogates

DEFF Research Database (Denmark)

Choudhury, H.A.; Intikhab, S.; Kalakul, Sawitree

2017-01-01

performance. A simplified alternative is to develop surrogate fuels that have fewer compounds and emulate certain important desired physical properties of the target fuels. Six gasoline blends were formulated through a computer aided model based technique “Mixed Integer Non-Linear Programming” (MINLP...... Virtual Process-Product Design Laboratory (VPPD-Lab) are applied onto the defined compositions of the surrogate gasoline. The aim is to primarily verify the defined composition of gasoline by means of VPPD-Lab. ρ, η and RVP are calculated with more accuracy and constraints such as distillation curve...... and flash point on the blend design are also considered. A post-design experiment-based verification step is proposed to further improve and fine-tune the “best” selected gasoline blends following the computation work. Here, advanced experimental techniques are used to measure the RVP, ρ, η, RON...
Study of factors affecting the productivity of nurses based on the ACHIEVE model and prioritizing them using analytic hierarchy process technique, 2012

Directory of Open Access Journals (Sweden)

Payam Farhadi

2013-01-01

Full Text Available Objective: Improving productivity is one of the most important strategies for social-economic development. Human resources are known as the most important resources in the organizations′ survival and success. Aims: To determine the factors affecting the human resource productivity using the ACHIEVEa model from the nurses′ perspective and then prioritize them from the perspective of head nurses using Analytic Hierarchy Process (AHP technique. Settings and Design: Iran, Shiraz University of Medical Sciences teaching hospitals in 2012. Materials and Methods: This was an applied, cross-sectional and analytical-descriptive study conducted in two phases. In the first phase, to determine the factors affecting the human resource productivity from nurses′ perspective, 110 nurses were selected using a two-stage cluster sampling method. Required data were collected using the Persian version of Hersey and Goldsmith′s Human Resource Productivity Questionnaire. In the second phase, in order to prioritize the factors affecting human resource productivity based on the ACHIEVE model using AHP technique, pairwise comparisons matrices were given to the 19 randomly selected head nurses to express their opinions about those factors relative priorities or importance. Statistical Analysis Used: Collected data and matrices in two mentioned phases were analyzed using SPSS 15.0 and some statistical tests including Independent-Samples T-Test and Pearson Correlation coefficient, as well as, Super Decisions software (Latest Beta. Results: The human resource productivity had significant relationships with nurses′ sex (P = 0.008, marital status (P < 0.001, education level (P < 0.001, and all questionnaire factors (P < 0.05. Nurses′ productivity from their perspective was below average (44.97 ΁ 7.43. Also, the priorities of factors affecting the productivity of nurses based on the ACHIEVE model from the head nurses′ perspective using AHP technique, from the
Reliability Assessment of Wind Farm Electrical System Based on a Probability Transfer Technique

Directory of Open Access Journals (Sweden)

Hejun Yang

2018-03-01

Full Text Available The electrical system of a wind farm has a significant influence on the wind farm reliability and electrical energy yield. The disconnect switch installed in an electrical system cannot only improve the operating flexibility, but also enhance the reliability for a wind farm. Therefore, this paper develops a probabilistic transfer technique for integrating the electrical topology structure, the isolation operation of disconnect switch, and stochastic failure of electrical equipment into the reliability assessment of wind farm electrical system. Firstly, as the traditional two-state reliability model of electrical equipment cannot consider the isolation operation, so the paper develops a three-state reliability model to replace the two-state model for incorporating the isolation operation. In addition, a proportion apportion technique is presented to evaluate the state probability. Secondly, this paper develops a probabilistic transfer technique based on the thoughts that through transfer the unreliability of electrical system to the energy transmission interruption of wind turbine generators (WTGs. Finally, some novel indices for describing the reliability of wind farm electrical system are designed, and the variance coefficient of the designed indices is used as a convergence criterion to determine the termination of the assessment process. The proposed technique is applied to the reliability assessment of a wind farm with the different topologies. The simulation results show that the proposed techniques are effective in practical applications.
Developing a hybrid dictionary-based bio-entity recognition technique

Science.gov (United States)

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907
Developing a hybrid dictionary-based bio-entity recognition technique.

Science.gov (United States)

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

Science.gov (United States)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
Finite-element-model updating using computational intelligence techniques applications to structural dynamics

CERN Document Server

Marwala, Tshilidzi

2010-01-01

Finite element models (FEMs) are widely used to understand the dynamic behaviour of various systems. FEM updating allows FEMs to be tuned better to reflect measured data and may be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. Finite Element Model Updating Using Computational Intelligence Techniques applies both strategies to the field of structural mechanics, an area vital for aerospace, civil and mechanical engineering. Vibration data is used for the updating process. Following an introduction a number of computational intelligence techniques to facilitate the updating process are proposed; they include: • multi-layer perceptron neural networks for real-time FEM updating; • particle swarm and genetic-algorithm-based optimization methods to accommodate the demands of global versus local optimization models; • simulated annealing to put the methodologies into a sound statistical basis; and • response surface methods and expectation m...

Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils

Directory of Open Access Journals (Sweden)

Fatimah Khaleel Ibrahim

2017-08-01

Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.
Integration of supervisory control synthesis in model-based systems engineering

NARCIS (Netherlands)

Baeten, J.C.M.; van de Mortel - Fronczak, J.M.; Rooda, J.E.

2016-01-01

Increasing system complexity, time to market and development costs reduction place higher demands on engineering processes. Formal models play an important role here because they enable the use of various model-based analyses and early integration techniques and tools. Engineering processes based on
Fuzzy model-based control of a nuclear reactor

International Nuclear Information System (INIS)

Van Den Durpel, L.; Ruan, D.

1994-01-01

The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project
Spectral-based features ranking for gamelan instruments identification using filter techniques

Directory of Open Access Journals (Sweden)

Diah P Wulandari

2013-03-01

Full Text Available In this paper, we describe an approach of spectral-based features ranking for Javanese gamelaninstruments identification using filter techniques. The model extracted spectral-based features set of thesignal using Short Time Fourier Transform (STFT. The rank of the features was determined using the fivealgorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then,we tested the ranked features by cross validation using Support Vector Machine (SVM. The experimentshowed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%.
A stream-based mathematical model for distributed information processing systems - SysLab system model

OpenAIRE

Klein, Cornel; Rumpe, Bernhard; Broy, Manfred

2014-01-01

In the SysLab project we develop a software engineering method based on a mathematical foundation. The SysLab system model serves as an abstract mathematical model for information systems and their components. It is used to formalize the semantics of all used description techniques such as object diagrams state automata sequence charts or data-flow diagrams. Based on the requirements for such a reference model, we define the system model including its different views and their relationships.
Hamming Code Based Watermarking Scheme for 3D Model Verification

Directory of Open Access Journals (Sweden)

Jen-Tse Wang

2014-01-01

Full Text Available Due to the explosive growth of the Internet and maturing of 3D hardware techniques, protecting 3D objects becomes a more and more important issue. In this paper, a public hamming code based fragile watermarking technique is proposed for 3D objects verification. An adaptive watermark is generated from each cover model by using the hamming code technique. A simple least significant bit (LSB substitution technique is employed for watermark embedding. In the extraction stage, the hamming code based watermark can be verified by using the hamming code checking without embedding any verification information. Experimental results shows that 100% vertices of the cover model can be watermarked, extracted, and verified. It also shows that the proposed method can improve security and achieve low distortion of stego object.
Selection of productivity improvement techniques via mathematical modeling

Directory of Open Access Journals (Sweden)

Mahassan M. Khater

2011-07-01

Full Text Available This paper presents a new mathematical model to select an optimal combination of productivity improvement techniques. The proposed model of this paper considers four-stage cycle productivity and the productivity is assumed to be a linear function of fifty four improvement techniques. The proposed model of this paper is implemented for a real-world case study of manufacturing plant. The resulted problem is formulated as a mixed integer programming which can be solved for optimality using traditional methods. The preliminary results of the implementation of the proposed model of this paper indicate that the productivity can be improved through a change on equipments and it can be easily applied for both manufacturing and service industries.
Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

Science.gov (United States)

Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

2014-06-01

This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.
In-cylinder pressure-based direct techniques and time frequency analysis for combustion diagnostics in IC engines

International Nuclear Information System (INIS)

D’Ambrosio, S.; Ferrari, A.; Galleani, L.

2015-01-01

Highlights: • Direct pressure-based techniques have been applied successfully to spark-ignition engines. • The burned mass fraction of pressure-based techniques has been compared with that of 2- and 3-zone combustion models. • The time frequency analysis has been employed to simulate complex diesel combustion events. - Abstract: In-cylinder pressure measurement and analysis has historically been a key tool for off-line combustion diagnosis in internal combustion engines, but online applications for real-time condition monitoring and combustion management have recently become popular. The present investigation presents and compares different low computing-cost in-cylinder pressure based methods for the analyses of the main features of combustion, that is, the start of combustion, the end of combustion and the crankshaft angle that responds to half of the overall burned mass. The instantaneous pressure in the combustion chamber has been used as an input datum for the described analytical procedures and it has been measured by means of a standard piezoelectric transducer. Traditional pressure-based techniques have been shown to be able to predict the burned mass fraction time history more accurately in spark ignition engines than in diesel engines. The most suitable pressure-based techniques for both spark ignition and compression ignition engines have been chosen on the basis of the available experimental data. Time–frequency analysis has also been applied to the analysis of diesel combustion, which is richer in events than spark ignited combustion. Time frequency algorithms for the calculation of the mean instantaneous frequency are computationally efficient, allow the main events of the diesel combustion to be identified and provide the greatest benefits in the presence of multiple injection events. These algorithms can be optimized and applied to onboard diagnostics tools designed for real control, but can also be used as an advanced validation tool for
Adaptive subdomain modeling: A multi-analysis technique for ocean circulation models

Science.gov (United States)

Altuntas, Alper; Baugh, John

2017-07-01

Many coastal and ocean processes of interest operate over large temporal and geographical scales and require a substantial amount of computational resources, particularly when engineering design and failure scenarios are also considered. This study presents an adaptive multi-analysis technique that improves the efficiency of these computations when multiple alternatives are being simulated. The technique, called adaptive subdomain modeling, concurrently analyzes any number of child domains, with each instance corresponding to a unique design or failure scenario, in addition to a full-scale parent domain providing the boundary conditions for its children. To contain the altered hydrodynamics originating from the modifications, the spatial extent of each child domain is adaptively adjusted during runtime depending on the response of the model. The technique is incorporated in ADCIRC++, a re-implementation of the popular ADCIRC ocean circulation model with an updated software architecture designed to facilitate this adaptive behavior and to utilize concurrent executions of multiple domains. The results of our case studies confirm that the method substantially reduces computational effort while maintaining accuracy.
Viable Techniques, Leontief’s Closed Model, and Sraffa’s Subsistence Economies

Directory of Open Access Journals (Sweden)

Alberto Benítez

2014-11-01

Full Text Available This paper studies the production techniques employed in economies that reproduce themselves. Special attention is paid to the distinction usually made between those that do not produce a surplus and those that do, which are referred to as first and second class economies, respectively. Based on this, we present a new definition of viable economies and show that every viable economy of the second class can be represented as a viable economy of the first class under two different forms, Leontief‘s closed model and Sraffa’s subsistence economies. This allows us to present some remarks concerning the economic interpretation of the two models. On the one hand, we argue that the participation of each good in the production of every good can be considered as a normal characteristic of the first model and, on the other hand, we provide a justification for the same condition to be considered a characteristic of the second model. Furthermore, we discuss three definitions of viable techniques advanced by other authors and show that they differ from ours because they admit economies that do not reproduce themselves completely.
Huffman-based code compression techniques for embedded processors

KAUST Repository

Bonny, Mohamed Talal

2010-09-01

The size of embedded software is increasing at a rapid pace. It is often challenging and time consuming to fit an amount of required software functionality within a given hardware resource budget. Code compression is a means to alleviate the problem by providing substantial savings in terms of code size. In this article we introduce a novel and efficient hardware-supported compression technique that is based on Huffman Coding. Our technique reduces the size of the generated decoding table, which takes a large portion of the memory. It combines our previous techniques, Instruction Splitting Technique and Instruction Re-encoding Technique into new one called Combined Compression Technique to improve the final compression ratio by taking advantage of both previous techniques. The instruction Splitting Technique is instruction set architecture (ISA)-independent. It splits the instructions into portions of varying size (called patterns) before Huffman coding is applied. This technique improves the final compression ratio by more than 20% compared to other known schemes based on Huffman Coding. The average compression ratios achieved using this technique are 48% and 50% for ARM and MIPS, respectively. The Instruction Re-encoding Technique is ISA-dependent. It investigates the benefits of reencoding unused bits (we call them reencodable bits) in the instruction format for a specific application to improve the compression ratio. Reencoding those bits can reduce the size of decoding tables by up to 40%. Using this technique, we improve the final compression ratios in comparison to the first technique to 46% and 45% for ARM and MIPS, respectively (including all overhead that incurs). The Combined Compression Technique improves the compression ratio to 45% and 42% for ARM and MIPS, respectively. In our compression technique, we have conducted evaluations using a representative set of applications and we have applied each technique to two major embedded processor architectures
PELE: Protein Energy Landscape Exploration. A Novel Monte Carlo Based Technique.

Science.gov (United States)

Borrelli, Kenneth W; Vitalis, Andreas; Alcantara, Raul; Guallar, Victor

2005-11-01

Combining protein structure prediction algorithms and Metropolis Monte Carlo techniques, we provide a novel method to explore all-atom energy landscapes. The core of the technique is based on a steered localized perturbation followed by side-chain sampling as well as minimization cycles. The algorithm and its application to ligand diffusion are presented here. Ligand exit pathways are successfully modeled for different systems containing ligands of various sizes: carbon monoxide in myoglobin, camphor in cytochrome P450cam, and palmitic acid in the intestinal fatty-acid-binding protein. These initial applications reveal the potential of this new technique in mapping millisecond-time-scale processes. The computational cost associated with the exploration is significantly less than that of conventional MD simulations.
Model techniques for testing heated concrete structures

International Nuclear Information System (INIS)

Stefanou, G.D.

1983-01-01

Experimental techniques are described which may be used in the laboratory to measure strains of model concrete structures representing to scale actual structures of any shape or geometry, operating at elevated temperatures, for which time-dependent creep and shrinkage strains are dominant. These strains could be used to assess the distribution of stress in the scaled structure and hence to predict the actual behaviour of concrete structures used in nuclear power stations. Similar techniques have been employed in an investigation to measure elastic, thermal, creep and shrinkage strains in heated concrete models representing to scale parts of prestressed concrete pressure vessels for nuclear reactors. (author)
Numerical model updating technique for structures using firefly algorithm

Science.gov (United States)

Sai Kubair, K.; Mohan, S. C.

2018-03-01

Numerical model updating is a technique used for updating the existing experimental models for any structures related to civil, mechanical, automobiles, marine, aerospace engineering, etc. The basic concept behind this technique is updating the numerical models to closely match with experimental data obtained from real or prototype test structures. The present work involves the development of numerical model using MATLAB as a computational tool and with mathematical equations that define the experimental model. Firefly algorithm is used as an optimization tool in this study. In this updating process a response parameter of the structure has to be chosen, which helps to correlate the numerical model developed with the experimental results obtained. The variables for the updating can be either material or geometrical properties of the model or both. In this study, to verify the proposed technique, a cantilever beam is analyzed for its tip deflection and a space frame has been analyzed for its natural frequencies. Both the models are updated with their respective response values obtained from experimental results. The numerical results after updating show that there is a close relationship that can be brought between the experimental and the numerical models.
Evaluation of 3-dimensional superimposition techniques on various skeletal structures of the head using surface models.

Science.gov (United States)

Gkantidis, Nikolaos; Schauseil, Michael; Pazera, Pawel; Zorkun, Berna; Katsaros, Christos; Ludwig, Björn

2015-01-01

To test the applicability, accuracy, precision, and reproducibility of various 3D superimposition techniques for radiographic data, transformed to triangulated surface data. Five superimposition techniques (3P: three-point registration; AC: anterior cranial base; AC + F: anterior cranial base + foramen magnum; BZ: both zygomatic arches; 1Z: one zygomatic arch) were tested using eight pairs of pre-existing CT data (pre- and post-treatment). These were obtained from non-growing orthodontic patients treated with rapid maxillary expansion. All datasets were superimposed by three operators independently, who repeated the whole procedure one month later. Accuracy was assessed by the distance (D) between superimposed datasets on three form-stable anatomical areas, located on the anterior cranial base and the foramen magnum. Precision and reproducibility were assessed using the distances between models at four specific landmarks. Non parametric multivariate models and Bland-Altman difference plots were used for analyses. There was no difference among operators or between time points on the accuracy of each superimposition technique (p>0.05). The AC + F technique was the most accurate (D0.05), the detected structural changes differed significantly between different techniques (p<0.05). Bland-Altman difference plots showed that BZ superimposition was comparable to AC, though it presented slightly higher random error. Superimposition of 3D datasets using surface models created from voxel data can provide accurate, precise, and reproducible results, offering also high efficiency and increased post-processing capabilities. In the present study population, the BZ superimposition was comparable to AC, with the added advantage of being applicable to scans with a smaller field of view.
Diffusion of a Sustainable Farming Technique in Sri Lanka: An Agent-Based Modeling Approach

Science.gov (United States)

Jacobi, J. H.; Gilligan, J. M.; Carrico, A. R.; Truelove, H. B.; Hornberger, G.

2012-12-01

We live in a changing world - anthropogenic climate change is disrupting historic climate patterns and social structures are shifting as large scale population growth and massive migrations place unprecedented strain on natural and social resources. Agriculture in many countries is affected by these changes in the social and natural environments. In Sri Lanka, rice farmers in the Mahaweli River watershed have seen increases in temperature and decreases in precipitation. In addition, a government led resettlement project has altered the demographics and social practices in villages throughout the watershed. These changes have the potential to impact rice yields in a country where self-sufficiency in rice production is a point of national pride. Studies of the climate can elucidate physical effects on rice production, while research on social behaviors can illuminate the influence of community dynamics on agricultural practices. Only an integrated approach, however, can capture the combined and interactive impacts of these global changes on Sri Lankan agricultural. As part of an interdisciplinary team, we present an agent-based modeling (ABM) approach to studying the effects of physical and social changes on farmers in Sri Lanka. In our research, the diffusion of a sustainable farming technique, the system of rice intensification (SRI), throughout a farming community is modeled to identify factors that either inhibit or promote the spread of a more sustainable approach to rice farming. Inputs into the ABM are both physical and social and include temperature, precipitation, the Palmer Drought Severity Index (PDSI), community trust, and social networks. Outputs from the ABM demonstrate the importance of meteorology and social structure on the diffusion of SRI throughout a farming community.
Synchrotron radiation based analytical techniques (XAS and XRF)

International Nuclear Information System (INIS)

Jha, Shambhu Nath

2014-01-01

A brief description of the principles of X-ray absorption spectroscopy (XAS) and X-ray fluorescence (XRF) techniques is given in this article with emphasis on the advantages of using synchrotron radiation-based instrumentation/beamline. XAS technique is described in more detail to emphasize the strength of the technique as a local structural probe. (author)
EPR-based material modelling of soils

Science.gov (United States)

Faramarzi, Asaad; Alani, Amir M.

2013-04-01

In the past few decades, as a result of the rapid developments in computational software and hardware, alternative computer aided pattern recognition approaches have been introduced to modelling many engineering problems, including constitutive modelling of materials. The main idea behind pattern recognition systems is that they learn adaptively from experience and extract various discriminants, each appropriate for its purpose. In this work an approach is presented for developing material models for soils based on evolutionary polynomial regression (EPR). EPR is a recently developed hybrid data mining technique that searches for structured mathematical equations (representing the behaviour of a system) using genetic algorithm and the least squares method. Stress-strain data from triaxial tests are used to train and develop EPR-based material models for soil. The developed models are compared with some of the well-known conventional material models and it is shown that EPR-based models can provide a better prediction for the behaviour of soils. The main benefits of using EPR-based material models are that it provides a unified approach to constitutive modelling of all materials (i.e., all aspects of material behaviour can be implemented within a unified environment of an EPR model); it does not require any arbitrary choice of constitutive (mathematical) models. In EPR-based material models there are no material parameters to be identified. As the model is trained directly from experimental data therefore, EPR-based material models are the shortest route from experimental research (data) to numerical modelling. Another advantage of EPR-based constitutive model is that as more experimental data become available, the quality of the EPR prediction can be improved by learning from the additional data, and therefore, the EPR model can become more effective and robust. The developed EPR-based material models can be incorporated in finite element (FE) analysis.
Comparing Four Touch-Based Interaction Techniques for an Image-Based Audience Response System

NARCIS (Netherlands)

Jorritsma, Wiard; Prins, Jonatan T.; van Ooijen, Peter M. A.

2015-01-01

This study aimed to determine the most appropriate touch-based interaction technique for I2Vote, an image-based audience response system for radiology education in which users need to accurately mark a target on a medical image. Four plausible techniques were identified: land-on, take-off,

Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique

Directory of Open Access Journals (Sweden)

M. Sridevi

2010-10-01

Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.
A cavitation model based on Eulerian stochastic fields

Science.gov (United States)

Magagnato, F.; Dumond, J.

2013-12-01

Non-linear phenomena can often be described using probability density functions (pdf) and pdf transport models. Traditionally the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian "particles" or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and in particular to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. Firstly, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations.
Integration of supervisory control synthesis in model-based systems engineering

NARCIS (Netherlands)

Baeten, J.C.M.; Mortel - Fronczak, van de J.M.; Rooda, J.E.

2011-01-01

Due to increasing system complexity, time-to-market and development costs reduction, there are higher demands on engineering processes. Model-based engineering can play a role here because it supports system development by enabling the use of various model-based analysis techniques and tools. As a
Structural design systems using knowledge-based techniques

International Nuclear Information System (INIS)

Orsborn, K.

1993-01-01

Engineering information management and the corresponding information systems are of a strategic importance for industrial enterprises. This thesis treats the interdisciplinary field of designing computing systems for structural design and analysis using knowledge-based techniques. Specific conceptual models have been designed for representing the structure and the process of objects and activities in a structural design and analysis domain. In this thesis, it is shown how domain knowledge can be structured along several classification principles in order to reduce complexity and increase flexibility. By increasing the conceptual level of the problem description and representation of the domain knowledge in a declarative form, it is possible to enhance the development, maintenance and use of software for mechanical engineering. This will result in a corresponding increase of the efficiency of the mechanical engineering design process. These ideas together with the rule-based control point out the leverage of declarative knowledge representation within this domain. Used appropriately, a declarative knowledge representation preserves information better, is more problem-oriented and change-tolerant than procedural representations. 74 refs
A Technique for Estimating Intensity of Emotional Expressions and Speaking Styles in Speech Based on Multiple-Regression HSMM

Science.gov (United States)

Nose, Takashi; Kobayashi, Takao

In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.
Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model

International Nuclear Information System (INIS)

Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao

2013-01-01

Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests
An improved visualization-based force-measurement technique for short-duration hypersonic facilities

Energy Technology Data Exchange (ETDEWEB)

Laurence, Stuart J.; Karl, Sebastian [Institute of Aerodynamics and Flow Technology, Spacecraft Section, German Aerospace Center (DLR), Goettingen (Germany)

2010-06-15

This article is concerned with describing and exploring the limitations of an improved version of a recently proposed visualization-based technique for the measurement of forces and moments in short-duration hypersonic wind tunnels. The technique is based on tracking the motion of a free-flying body over a sequence of high-speed visualizations; while this idea is not new in itself, the use of high-speed digital cinematography combined with a highly accurate least-squares tracking algorithm allows improved results over what have been previously possible with such techniques. The technique precision is estimated through the analysis of artificially constructed and experimental test images, and the resulting error in acceleration measurements is characterized. For wind-tunnel scale models, position measurements to within a few microns are shown to be readily attainable. Image data from two previous experimental studies in the T5 hypervelocity shock tunnel are then reanalyzed with the improved technique: the uncertainty in the mean drag acceleration is shown to be reduced to the order of the flow unsteadiness, 2-3%, and time-resolved acceleration measurements are also shown to be possible. The response time of the technique for the configurations studied is estimated to be {proportional_to}0.5 ms. Comparisons with computations using the DLR TAU code also yield agreement to within the overall experimental uncertainty. Measurement of the pitching moment for blunt geometries still appears challenging, however. (orig.)
Cluster-based analysis of multi-model climate ensembles

Science.gov (United States)

Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

2018-06-01

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and
Model order reduction techniques with applications in finite element analysis

CERN Document Server

Qu, Zu-Qing

2004-01-01

Despite the continued rapid advance in computing speed and memory the increase in the complexity of models used by engineers persists in outpacing them. Even where there is access to the latest hardware, simulations are often extremely computationally intensive and time-consuming when full-blown models are under consideration. The need to reduce the computational cost involved when dealing with high-order/many-degree-of-freedom models can be offset by adroit computation. In this light, model-reduction methods have become a major goal of simulation and modeling research. Model reduction can also ameliorate problems in the correlation of widely used finite-element analyses and test analysis models produced by excessive system complexity. Model Order Reduction Techniques explains and compares such methods focusing mainly on recent work in dynamic condensation techniques: - Compares the effectiveness of static, exact, dynamic, SEREP and iterative-dynamic condensation techniques in producing valid reduced-order mo...
Radiation synthesized protein-based nanoparticles: A technique overview

International Nuclear Information System (INIS)

Varca, Gustavo H.C.; Perossi, Gabriela G.; Grasselli, Mariano; Lugão, Ademar B.

2014-01-01

Seeking for alternative routes for protein engineering a novel technique – radiation induced synthesis of protein nanoparticles – to achieve size controlled particles with preserved bioactivity has been recently reported. This work aimed to evaluate different process conditions to optimize and provide an overview of the technique using γ-irradiation. Papain was used as model protease and the samples were irradiated in a gamma cell irradiator in phosphate buffer (pH=7.0) containing ethanol (0–35%). The dose effect was evaluated by exposure to distinct γ-irradiation doses (2.5, 5, 7.5 and 10 kGy) and scale up experiments involving distinct protein concentrations (12.5–50 mg mL −1 ) were also performed. Characterization involved size monitoring using dynamic light scattering. Bityrosine detection was performed using fluorescence measurements in order to provide experimental evidence of the mechanism involved. Best dose effects were achieved at 10 kGy with regard to size and no relevant changes were observed as a function of papain concentration, highlighting very broad operational concentration range. Bityrosine changes were identified for the samples as a function of the process confirming that such linkages play an important role in the nanoparticle formation. - Highlights: • Synthesis of protein-based nanoparticles by γ-irradiation. • Optimization of the technique. • Overview of mechanism involved in the nanoparticle formation. • Engineered papain nanoparticles for biomedical applications
A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm

International Nuclear Information System (INIS)

Guo, Zhenhai; Chi, Dezhong; Wu, Jie; Zhang, Wenyu

2014-01-01

Highlights: • Impact of meteorological factors on wind speed forecasting is taken into account. • Forecasted wind speed results are corrected by the associated rules. • Forecasting accuracy is improved by the new wind speed forecasting strategy. • Robust of the proposed model is validated by data sampled from different sites. - Abstract: Wind energy has been the fastest growing renewable energy resource in recent years. Because of the intermittent nature of wind, wind power is a fluctuating source of electrical energy. Therefore, to minimize the impact of wind power on the electrical grid, accurate and reliable wind power forecasting is mandatory. In this paper, a new wind speed forecasting approach based on based on the chaotic time series modelling technique and the Apriori algorithm has been developed. The new approach consists of four procedures: (I) Clustering by using the k-means clustering approach; (II) Employing the Apriori algorithm to discover the association rules; (III) Forecasting the wind speed according to the chaotic time series forecasting model; and (IV) Correcting the forecasted wind speed data using the associated rules discovered previously. This procedure has been verified by 31-day-ahead daily average wind speed forecasting case studies, which employed the wind speed and other meteorological data collected from four meteorological stations located in the Hexi Corridor area of China. The results of these case studies reveal that the chaotic forecasting model can efficiently improve the accuracy of the wind speed forecasting, and the Apriori algorithm can effectively discover the association rules between the wind speed and other meteorological factors. In addition, the correction results demonstrate that the association rules discovered by the Apriori algorithm have powerful capacities in handling the forecasted wind speed values correction when the forecasted values do not match the classification discovered by the association rules
Multiscale agent-based cancer modeling.

Science.gov (United States)

Zhang, Le; Wang, Zhihui; Sagotsky, Jonathan A; Deisboeck, Thomas S

2009-04-01

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.
A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

Science.gov (United States)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-01

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

Science.gov (United States)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-05

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

2006-05-15

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.
Identifying and quantifying energy savings on fired plant using low cost modelling techniques

International Nuclear Information System (INIS)

Tucker, Robert; Ward, John

2012-01-01

Research highlights: → Furnace models based on the zone method for radiation calculation are described. → Validated steady-state and transient models have been developed. → We show how these simple models can identify the best options for saving energy. → High emissivity coatings predicted to give performance enhancement on a fired heater. → Optimal heat recovery strategies on a steel reheating furnace are predicted. -- Abstract: Combustion in fired heaters, boilers and furnaces often accounts for the major energy consumption on industrial processes. Small improvements in efficiency can result in large reductions in energy consumption, CO 2 emissions, and operating costs. This paper will describe some useful low cost modelling techniques based on the zone method to help identify energy saving opportunities on high temperature fuel-fired process plant. The zone method has for many decades, been successfully applied to small batch furnaces through to large steel-reheating furnaces, glass tanks, boilers and fired heaters on petrochemical plant. Zone models can simulate both steady-state furnace operation and more complex transient operation typical of a production environment. These models can be used to predict thermal efficiency and performance, and more importantly, to assist in identifying and predicting energy saving opportunities from such measures as: ·Improving air/fuel ratio and temperature controls. ·Improved insulation. ·Use of oxygen or oxygen enrichment. ·Air preheating via flue gas heat recovery. ·Modification to furnace geometry and hearth loading. There is also increasing interest in the application of refractory coatings for increasing surface radiation in fired plant. All of the techniques can yield savings ranging from a few percent upwards and can deliver rapid financial payback, but their evaluation often requires robust and reliable models in order to increase confidence in making financial investment decisions. This paper gives
Evaluation of 3-dimensional superimposition techniques on various skeletal structures of the head using surface models.

Directory of Open Access Journals (Sweden)

Nikolaos Gkantidis

Full Text Available To test the applicability, accuracy, precision, and reproducibility of various 3D superimposition techniques for radiographic data, transformed to triangulated surface data.Five superimposition techniques (3P: three-point registration; AC: anterior cranial base; AC + F: anterior cranial base + foramen magnum; BZ: both zygomatic arches; 1Z: one zygomatic arch were tested using eight pairs of pre-existing CT data (pre- and post-treatment. These were obtained from non-growing orthodontic patients treated with rapid maxillary expansion. All datasets were superimposed by three operators independently, who repeated the whole procedure one month later. Accuracy was assessed by the distance (D between superimposed datasets on three form-stable anatomical areas, located on the anterior cranial base and the foramen magnum. Precision and reproducibility were assessed using the distances between models at four specific landmarks. Non parametric multivariate models and Bland-Altman difference plots were used for analyses.There was no difference among operators or between time points on the accuracy of each superimposition technique (p>0.05. The AC + F technique was the most accurate (D0.05, the detected structural changes differed significantly between different techniques (p<0.05. Bland-Altman difference plots showed that BZ superimposition was comparable to AC, though it presented slightly higher random error.Superimposition of 3D datasets using surface models created from voxel data can provide accurate, precise, and reproducible results, offering also high efficiency and increased post-processing capabilities. In the present study population, the BZ superimposition was comparable to AC, with the added advantage of being applicable to scans with a smaller field of view.
Current control design for three-phase grid-connected inverters using a pole placement technique based on numerical models

OpenAIRE

Citro, Costantino; Gavriluta, Catalin; Nizak Md, H. K.; Beltran, H.

2012-01-01

This paper presents a design procedure for linear current controllers of three-phase grid-connected inverters. The proposed method consists in deriving a numerical model of the converter by using software simulations and applying the pole placement technique to design the controller with the desired performances. A clear example on how to apply the technique is provided. The effectiveness of the proposed design procedure has been verified through the experimental results obtained with ...
Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Science.gov (United States)

Robins, Marthony; Solomon, Justin; Sahbaee, Pooyan; Sedlmair, Martin; Choudhury, Kingshuk Roy; Pezeshk, Aria; Sahiner, Berkman; Samei, Ehsan

2017-09-01

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (Mea{{n}RHD} , ST{{D}RHD} and C{{V}RHD}{) }~ of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and
Model-based wear measurements in total knee arthroplasty : development and validation of novel radiographic techniques

NARCIS (Netherlands)

IJsseldijk, van E.A.

2016-01-01

The primary aim of this work was to develop novel model-based mJSW measurement methods using a 3D reconstruction and compare the accuracy and precision of these methods to conventional mJSW measurement. This thesis contributed to the development, validation and clinical application of model-based

Segment-based acoustic models for continuous speech recognition

Science.gov (United States)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.
A Multiagent Based Model for Tactical Planning

Science.gov (United States)

2002-10-01

Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such
Evaluation of mesh morphing and mapping techniques in patient specific modeling of the human pelvis.

Science.gov (United States)

Salo, Zoryana; Beek, Maarten; Whyne, Cari Marisa

2013-01-01

Robust generation of pelvic finite element models is necessary to understand the variation in mechanical behaviour resulting from differences in gender, aging, disease and injury. The objective of this study was to apply and evaluate mesh morphing and mapping techniques to facilitate the creation and structural analysis of specimen-specific finite element (FE) models of the pelvis. A specimen-specific pelvic FE model (source mesh) was generated following a traditional user-intensive meshing scheme. The source mesh was morphed onto a computed tomography scan generated target surface of a second pelvis using a landmarked-based approach, in which exterior source nodes were shifted to target surface vertices, while constrained along a normal. A second copy of the morphed model was further refined through mesh mapping, in which surface nodes of the initial morphed model were selected in patches and remapped onto the surfaces of the target model. Computed tomography intensity based material properties were assigned to each model. The source, target, morphed and mapped models were analyzed under axial compression using linear static FE analysis and their strain distributions evaluated. Morphing and mapping techniques were effectively applied to generate good quality geometrically complex specimen-specific pelvic FE models. Mapping significantly improved strain concurrence with the target pelvis FE model. Copyright © 2012 John Wiley & Sons, Ltd.
Evaluation of mesh morphing and mapping techniques in patient specific modelling of the human pelvis.

Science.gov (United States)

Salo, Zoryana; Beek, Maarten; Whyne, Cari Marisa

2012-08-01

Robust generation of pelvic finite element models is necessary to understand variation in mechanical behaviour resulting from differences in gender, aging, disease and injury. The objective of this study was to apply and evaluate mesh morphing and mapping techniques to facilitate the creation and structural analysis of specimen-specific finite element (FE) models of the pelvis. A specimen-specific pelvic FE model (source mesh) was generated following a traditional user-intensive meshing scheme. The source mesh was morphed onto a computed tomography scan generated target surface of a second pelvis using a landmarked-based approach, in which exterior source nodes were shifted to target surface vertices, while constrained along a normal. A second copy of the morphed model was further refined through mesh mapping, in which surface nodes of the initial morphed model were selected in patches and remapped onto the surfaces of the target model. Computed tomography intensity-based material properties were assigned to each model. The source, target, morphed and mapped models were analyzed under axial compression using linear static FE analysis, and their strain distributions were evaluated. Morphing and mapping techniques were effectively applied to generate good quality and geometrically complex specimen-specific pelvic FE models. Mapping significantly improved strain concurrence with the target pelvis FE model. Copyright © 2012 John Wiley & Sons, Ltd.
A new slit lamp-based technique for anterior chamber angle estimation.

Science.gov (United States)

Gispets, Joan; Cardona, Genís; Tomàs, Núria; Fusté, Cèlia; Binns, Alison; Fortes, Miguel A

2014-06-01

To design and test a new noninvasive method for anterior chamber angle (ACA) estimation based on the slit lamp that is accessible to all eye-care professionals. A new technique (slit lamp anterior chamber estimation [SLACE]) that aims to overcome some of the limitations of the van Herick procedure was designed. The technique, which only requires a slit lamp, was applied to estimate the ACA of 50 participants (100 eyes) using two different slit lamp models, and results were compared with gonioscopy as the clinical standard. The Spearman nonparametric correlation between ACA values as determined by gonioscopy and SLACE were 0.81 (p gonioscopy (Spaeth classification). The SLACE technique, when compared with gonioscopy, displayed good accuracy in the detection of narrow angles, and it may be useful for eye-care clinicians without access to expensive alternative equipment or those who cannot perform gonioscopy because of legal constraints regarding the use of diagnostic drugs.
Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation

DEFF Research Database (Denmark)

Saari, Pasi; Fazekas, György; Eerola, Tuomas

2016-01-01

This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are prop......This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling...... related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements......-based genre representation for genre-adaptive music mood analysis....
An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models

NARCIS (Netherlands)

Tax, N.; van Zelst, S.J.; Teinemaa, I.; Gulden, Jens; Reinhartz-Berger, Iris; Schmidt, Rainer; Guerreiro, Sérgio; Guédria, Wided; Bera, Palash

2018-01-01

A plethora of automated process discovery techniques have been developed which aim to discover a process model based on event data originating from the execution of business processes. The aim of the discovered process models is to describe the control-flow of the underlying business process. At the
Contests versus Norms: Implications of Contest-Based and Norm-Based Intervention Techniques.

Science.gov (United States)

Bergquist, Magnus; Nilsson, Andreas; Hansla, André

2017-01-01

Interventions using either contests or norms can promote environmental behavioral change. Yet research on the implications of contest-based and norm-based interventions is lacking. Based on Goal-framing theory, we suggest that a contest-based intervention frames a gain goal promoting intensive but instrumental behavioral engagement. In contrast, the norm-based intervention was expected to frame a normative goal activating normative obligations for targeted and non-targeted behavior and motivation to engage in pro-environmental behaviors in the future. In two studies participants ( n = 347) were randomly assigned to either a contest- or a norm-based intervention technique. Participants in the contest showed more intensive engagement in both studies. Participants in the norm-based intervention tended to report higher intentions for future energy conservation (Study 1) and higher personal norms for non-targeted pro-environmental behaviors (Study 2). These findings suggest that contest-based intervention technique frames a gain goal, while norm-based intervention frames a normative goal.
Contests versus Norms: Implications of Contest-Based and Norm-Based Intervention Techniques

Directory of Open Access Journals (Sweden)

Magnus Bergquist

2017-11-01

Full Text Available Interventions using either contests or norms can promote environmental behavioral change. Yet research on the implications of contest-based and norm-based interventions is lacking. Based on Goal-framing theory, we suggest that a contest-based intervention frames a gain goal promoting intensive but instrumental behavioral engagement. In contrast, the norm-based intervention was expected to frame a normative goal activating normative obligations for targeted and non-targeted behavior and motivation to engage in pro-environmental behaviors in the future. In two studies participants (n = 347 were randomly assigned to either a contest- or a norm-based intervention technique. Participants in the contest showed more intensive engagement in both studies. Participants in the norm-based intervention tended to report higher intentions for future energy conservation (Study 1 and higher personal norms for non-targeted pro-environmental behaviors (Study 2. These findings suggest that contest-based intervention technique frames a gain goal, while norm-based intervention frames a normative goal.
Acceleration techniques for the direct use of CAD-based geometry in fusion neutronics analysis

International Nuclear Information System (INIS)

Wilson, Paul P.H.; Tautges, Timothy J.; Kraftcheck, Jason A.; Smith, Brandon M.; Henderson, Douglass L.

2010-01-01

The Direct Accelerated Geometry Monte Carlo (DAGMC) software library offers a unique approach to performing neutronics analysis on CAD-based geometries of fusion systems. By employing a number of acceleration techniques, the ray-tracing operations that are fundamental to Monte Carlo radiation transport are implemented efficiently for direct use on the CAD-based solid model, eliminating the need to translate to the native Monte Carlo input language. By forming hierarchical trees of oriented bounding boxes, one for each facet that results from a high-fidelity tessellation of the model, the ray-tracing performance is adequate to permit detailed analysis of large complex systems. In addition to the reduction in human effort and improvement in quality assurance that is found in the translation approaches, the DAGMC approach also permits the analysis of geometries with higher order surfaces that cannot be represented by many native Monte Carlo radiation transport tools. The paper describes the various acceleration techniques and demonstrates the resulting capability in a real fusion neutronics analysis.
On a Graphical Technique for Evaluating Some Rational Expectations Models

DEFF Research Database (Denmark)

Johansen, Søren; Swensen, Anders R.

2011-01-01

Campbell and Shiller (1987) proposed a graphical technique for the present value model, which consists of plotting estimates of the spread and theoretical spread as calculated from the cointegrated vector autoregressive model without imposing the restrictions implied by the present value model....... In addition to getting a visual impression of the fit of the model, the purpose is to see if the two spreads are nevertheless similar as measured by correlation, variance ratio, and noise ratio. We extend these techniques to a number of rational expectation models and give a general definition of spread...
A systematic fault tree analysis based on multi-level flow modeling

International Nuclear Information System (INIS)

Gofuku, Akio; Ohara, Ai

2010-01-01

The fault tree analysis (FTA) is widely applied for the safety evaluation of a large-scale and mission-critical system. Because the potential of the FTA, however, strongly depends on human skill of analyzers, problems are pointed out in (1) education and training, (2) unreliable quality, (3) necessity of expertise knowledge, and (4) update of FTA results after the reconstruction of a target system. To get rid of these problems, many techniques to systematize FTA activities by applying computer technologies have been proposed. However, these techniques only use structural information of a target system and do not use functional information that is one of important properties of an artifact. The principle of FTA is to trace comprehensively cause-effect relations from a top undesirable effect to anomaly causes. The tracing is similar to the causality estimation technique that the authors proposed to find plausible counter actions to prevent or to mitigate the undesirable behavior of plants based on the model by a functional modeling technique, Multilevel Flow Modeling (MFM). The authors have extended this systematic technique to construct a fault tree (FT). This paper presents an algorithm of systematic construction of FT based on MFM models and demonstrates the applicability of the extended technique by the FT construction result of a cooling plant of nitric acid. (author)
New approaches in agent-based modeling of complex financial systems

Science.gov (United States)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.
3D Modeling Techniques for Print and Digital Media

Science.gov (United States)

Stephens, Megan Ashley

In developing my thesis, I looked to gain skills using ZBrush to create 3D models, 3D scanning, and 3D printing. The models created compared the hearts of several vertebrates and were intended for students attending Comparative Vertebrate Anatomy. I used several resources to create a model of the human heart and was able to work from life while creating heart models from other vertebrates. I successfully learned ZBrush and 3D scanning, and successfully printed 3D heart models. ZBrush allowed me to create several intricate models for use in both animation and print media. The 3D scanning technique did not fit my needs for the project, but may be of use for later projects. I was able to 3D print using two different techniques as well.
Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

CERN Document Server

Si, Xiao-Sheng; Hu, Chang-Hua

2017-01-01

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...
Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea

International Nuclear Information System (INIS)

Palit, Mousumi; Tudu, Bipan; Bhattacharyya, Nabarun; Dutta, Ankur; Dutta, Pallab Kumar; Jana, Arun; Bandyopadhyay, Rajib; Chatterjee, Anutosh

2010-01-01

In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.
Nasal base narrowing: the combined alar base excision technique.

Science.gov (United States)

Foda, Hossam M T

2007-01-01

To evaluate the role of the combined alar base excision technique in narrowing the nasal base and correcting excessive alar flare. The study included 60 cases presenting with a wide nasal base and excessive alar flaring. The surgical procedure combined an external alar wedge resection with an internal vestibular floor excision. All cases were followed up for a mean of 32 (range, 12-144) months. Nasal tip modification and correction of any preexisting caudal septal deformities were always completed before the nasal base narrowing. The mean width of the external alar wedge excised was 7.2 (range, 4-11) mm, whereas the mean width of the sill excision was 3.1 (range, 2-7) mm. Completing the internal excision first resulted in a more conservative external resection, thus avoiding any blunting of the alar-facial crease. No cases of postoperative bleeding, infection, or keloid formation were encountered, and the external alar wedge excision healed with an inconspicuous scar that was well hidden in the depth of the alar-facial crease. Finally, the risk of notching of the alar rim, which can occur at the junction of the external and internal excisions, was significantly reduced by adopting a 2-layered closure of the vestibular floor (P = .01). The combined alar base excision resulted in effective narrowing of the nasal base with elimination of excessive alar flare. Commonly feared complications, such as blunting of the alar-facial crease or notching of the alar rim, were avoided by using simple modifications in the technique of excision and closure.
Hybrid Modeling Method for a DEP Based Particle Manipulation

Directory of Open Access Journals (Sweden)

Mohamad Sawan

2013-01-01

Full Text Available In this paper, a new modeling approach for Dielectrophoresis (DEP based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results.
Propositions for a PDF model based on fluid particle acceleration

International Nuclear Information System (INIS)

Minier, J.P.; Pozorski, J.

1997-05-01

This paper describes theoretical propositions to model the acceleration of a fluid particle in a turbulent flow. Such a model is useful for the PDF approach to turbulent reactive flows as well as for the Lagrangian modelling of two-phase flows. The model developed here draws from ideas already put forward by Sawford but which are generalized to the case of non-homogeneous flows. The model is built so as to revert continuously to Pope's model, which uses a Langevin equation for particle velocities, when the Reynolds number becomes very high. The derivation is based on the technique of fast variable elimination. This technique allow a careful analysis of the relations between different levels of modelling. It also allows to address certain problems in a more rigorous way. In particular, application of this technique shows that models presently used can in principle simulate bubbly flows including the pressure-gradient and added-mass forces. (author)
Towards elicitation of users requirements for hospital information system: from a care process modelling technique to a web based collaborative tool.

Science.gov (United States)

Staccini, Pascal M; Joubert, Michel; Quaranta, Jean-Francois; Fieschi, Marius

2002-01-01

Growing attention is being given to the use of process modeling methodology for user requirements elicitation. In the analysis phase of hospital information systems, the usefulness of care-process models has been investigated to evaluate the conceptual applicability and practical understandability by clinical staff and members of users teams. Nevertheless, there still remains a gap between users and analysts in their mutual ability to share conceptual views and vocabulary, keeping the meaning of clinical context while providing elements for analysis. One of the solutions for filling this gap is to consider the process model itself in the role of a hub as a centralized means of facilitating communication between team members. Starting with a robust and descriptive technique for process modeling called IDEF0/SADT, we refined the basic data model by extracting concepts from ISO 9000 process analysis and from enterprise ontology. We defined a web-based architecture to serve as a collaborative tool and implemented it using an object-oriented database. The prospects of such a tool are discussed notably regarding to its ability to generate data dictionaries and to be used as a navigation tool through the medium of hospital-wide documentation.

Combining variational and model-based techniques to register PET and MR images in hand osteoarthritis

International Nuclear Information System (INIS)

Magee, Derek; Tanner, Steven F; Jeavons, Alan P; Waller, Michael; Tan, Ai Lyn; McGonagle, Dennis

2010-01-01

Co-registration of clinical images acquired using different imaging modalities and equipment is finding increasing use in patient studies. Here we present a method for registering high-resolution positron emission tomography (PET) data of the hand acquired using high-density avalanche chambers with magnetic resonance (MR) images of the finger obtained using a 'microscopy coil'. This allows the identification of the anatomical location of the PET radiotracer and thereby locates areas of active bone metabolism/'turnover'. Image fusion involving data acquired from the hand is demanding because rigid-body transformations cannot be employed to accurately register the images. The non-rigid registration technique that has been implemented in this study uses a variational approach to maximize the mutual information between images acquired using these different imaging modalities. A piecewise model of the fingers is employed to ensure that the methodology is robust and that it generates an accurate registration. Evaluation of the accuracy of the technique is tested using both synthetic data and PET and MR images acquired from patients with osteoarthritis. The method outperforms some established non-rigid registration techniques and results in a mean registration error that is less than approximately 1.5 mm in the vicinity of the finger joints.
The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

Directory of Open Access Journals (Sweden)

Astiti Kade kAyu

2018-01-01

Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.
A comparison of approximation techniques for variance-based sensitivity analysis of biochemical reaction systems

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Goutsias John

2010-05-01

Full Text Available Abstract Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of biochemical reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large biochemical reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by biochemical reaction systems that consist of a large number of reactions and molecular species. Results We present four techniques, derivative approximation (DA, polynomial approximation (PA, Gauss-Hermite integration (GHI, and orthonormal Hermite approximation (OHA, for analytically approximating the variance-based sensitivity indices associated with a biochemical reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the
Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

Science.gov (United States)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.
A probabilistic evaluation procedure for process model matching techniques

NARCIS (Netherlands)

Kuss, Elena; Leopold, Henrik; van der Aa, Han; Stuckenschmidt, Heiner; Reijers, Hajo A.

2018-01-01

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to
Application of neural network technique to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt

Science.gov (United States)

Elshambaky, Hossam Talaat

2018-01-01

Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.
Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

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Irina-Steliana STAN

2014-09-01

Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.
GPU-accelerated 3-D model-based tracking

International Nuclear Information System (INIS)

Brown, J Anthony; Capson, David W

2010-01-01

Model-based approaches to tracking the pose of a 3-D object in video are effective but computationally demanding. While statistical estimation techniques, such as the particle filter, are often employed to minimize the search space, real-time performance remains unachievable on current generation CPUs. Recent advances in graphics processing units (GPUs) have brought massively parallel computational power to the desktop environment and powerful developer tools, such as NVIDIA Compute Unified Device Architecture (CUDA), have provided programmers with a mechanism to exploit it. NVIDIA GPUs' single-instruction multiple-thread (SIMT) programming model is well-suited to many computer vision tasks, particularly model-based tracking, which requires several hundred 3-D model poses to be dynamically configured, rendered, and evaluated against each frame in the video sequence. Using 6 degree-of-freedom (DOF) rigid hand tracking as an example application, this work harnesses consumer-grade GPUs to achieve real-time, 3-D model-based, markerless object tracking in monocular video.
Short review of runoff and erosion physically based models

Directory of Open Access Journals (Sweden)

Gabrić Ognjen

2015-01-01

Full Text Available Processes of runoff and erosion are one of the main research subjects in hydrological science. Based on the field and laboratory measurements, and analogous with development of computational techniques, runoff and erosion models based on equations which describe the physics of the process are also developed. Several models of runoff and erosion which describes entire process of genesis and sediment transport on the catchment are described and compared.
PD/PID controller tuning based on model approximations: Model reduction of some unstable and higher order nonlinear models

Directory of Open Access Journals (Sweden)

Christer Dalen

2017-10-01

Full Text Available A model reduction technique based on optimization theory is presented, where a possible higher order system/model is approximated with an unstable DIPTD model by using only step response data. The DIPTD model is used to tune PD/PID controllers for the underlying possible higher order system. Numerous examples are used to illustrate the theory, i.e. both linear and nonlinear models. The Pareto Optimal controller is used as a reference controller.
Temperature Control of Gas Chromatograph Based on Switched Delayed System Techniques

Directory of Open Access Journals (Sweden)

Xiao-Liang Wang

2014-01-01

Full Text Available We address the temperature control problem of the gas chromatograph. We model the temperature control system of the gas chromatograph into a switched delayed system and analyze the stability by common Lyapunov functional technique. The PI controller parameters can be given based on the proposed linear matrix inequalities (LMIs condition and the designed controller can make the temperature of gas chromatograph track the reference signal asymptotically. An experiment is given to illustrate the effectiveness of the stability criterion.
Range and Image Based Modelling: a way for Frescoed Vault Texturing Optimization

Science.gov (United States)

Caroti, G.; Martínez-Espejo Zaragoza, I.; Piemonte, A.

2015-02-01

In the restoration of the frescoed vaults it is not only important to know the geometric shape of the painted surface, but it is essential to document its chromatic characterization and conservation status. The new techniques of range-based and image-based modelling, each with its limitations and advantages, offer a wide range of methods to obtain the geometric shape. In fact, several studies widely document that laser scanning enable obtaining three-dimensional models with high morphological precision. However, the quality level of the colour obtained with built-in laser scanner cameras is not comparable to that obtained for the shape. It is possible to improve the texture quality by means of a dedicated photographic campaign. This procedure, however, requires to calculate the external orientation of each image identifying the control points on it and on the model through a costly step of post processing. With image-based modelling techniques it is possible to obtain models that maintain the colour quality of the original images, but with variable geometric precision, locally lower than the laser scanning model. This paper presents a methodology that uses the camera external orientation parameters calculated by image based modelling techniques to project the same image on the model obtained from the laser scan. This methodology is tested on an Italian mirror (a schifo) frescoed vault. In the paper the different models, the analysis of precision and the efficiency evaluation of proposed methodology are presented.
Néron Models and Base Change

DEFF Research Database (Denmark)

Halle, Lars Halvard; Nicaise, Johannes

Presenting the first systematic treatment of the behavior of Néron models under ramified base change, this book can be read as an introduction to various subtle invariants and constructions related to Néron models of semi-abelian varieties, motivated by concrete research problems and complemented...... with explicit examples. Néron models of abelian and semi-abelian varieties have become an indispensable tool in algebraic and arithmetic geometry since Néron introduced them in his seminal 1964 paper. Applications range from the theory of heights in Diophantine geometry to Hodge theory. We focus specifically...... on Néron component groups, Edixhoven’s filtration and the base change conductor of Chai and Yu, and we study these invariants using various techniques such as models of curves, sheaves on Grothendieck sites and non-archimedean uniformization. We then apply our results to the study of motivic zeta functions...
Creation of voxel-based models for paediatric dosimetry from automatic segmentation methods

International Nuclear Information System (INIS)

Acosta, O.; Li, R.; Ourselin, S.; Caon, M.

2006-01-01

Full text: The first computational models representing human anatomy were mathematical phantoms, but still far from accurate representations of human body. These models have been used with radiation transport codes (Monte Carlo) to estimate organ doses from radiological procedures. Although new medical imaging techniques have recently allowed the construction of voxel-based models based on the real anatomy, few children models from individual CT or MRI data have been reported [1,3]. For pediatric dosimetry purposes, a large range of voxel models by ages is required since scaling the anatomy from existing models is not sufficiently accurate. The small number of models available arises from the small number of CT or MRI data sets of children available and the long amount of time required to segment the data sets. The existing models have been constructed by manual segmentation slice by slice and using simple thresholding techniques. In medical image segmentation, considerable difficulties appear when applying classical techniques like thresholding or simple edge detection. Until now, any evidence of more accurate or near-automatic methods used in construction of child voxel models exists. We aim to construct a range of pediatric voxel models, integrating automatic or semi-automatic 3D segmentation techniques. In this paper we present the first stage of this work using pediatric CT data.
Modelling, analysis and validation of microwave techniques for the characterisation of metallic nanoparticles

Science.gov (United States)

Sulaimalebbe, Aslam

High Frequency Structure Simulator (HFSS), followed by the electrical characterisation of synthesised Pt NP films using the novel miniature fabricated OCP technique. The results obtained from this technique provided the inspiration to synthesise and evaluate the microwave properties of Au NPs. The findings from this technique provided the motivation to characterise both the Pt and Au NP films using the DR technique. Unlike the OCP technique, the DR method is highly sensitive but the achievable measurement accuracy is limited since this technique does not have broadband frequency capability like the OCP method. The results obtained from the DR technique show a good agreement with the theoretical prediction. In the last phase of this research, a further validation of the aperture admittance models on different types OCP (i.e. RG-405 and RG-402 cables and SMA connector) have been carried out on the developed 3D full wave models using HFSS software, followed by the development of universal models for the aforementioned OCPs based on the same 3D full wave models.
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

Science.gov (United States)

Sharma, Anuj; Kumar Panigrahi, Prabin

2012-02-01

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
Application of nonliner reduction techniques in chemical process modeling: a review

International Nuclear Information System (INIS)

Muhaimin, Z; Aziz, N.; Abd Shukor, S.R.

2006-01-01

Model reduction techniques have been used widely in engineering fields for electrical, mechanical as well as chemical engineering. The basic idea of reduction technique is to replace the original system by an approximating system with much smaller state-space dimension. A reduced order model is more beneficial to process and industrial field in terms of control purposes. This paper is to provide a review on application of nonlinear reduction techniques in chemical processes. The advantages and disadvantages of each technique reviewed are also highlighted
Summary on several key techniques in 3D geological modeling.

Science.gov (United States)

Mei, Gang

2014-01-01

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.
Rabbit tissue model (RTM) harvesting technique.

Science.gov (United States)

Medina, Marelyn

2002-01-01

A method for creating a tissue model using a female rabbit for laparoscopic simulation exercises is described. The specimen is called a Rabbit Tissue Model (RTM). Dissection techniques are described for transforming the rabbit carcass into a small, compact unit that can be used for multiple training sessions. Preservation is accomplished by using saline and refrigeration. Only the animal trunk is used, with the rest of the animal carcass being discarded. Practice exercises are provided for using the preserved organs. Basic surgical skills, such as dissection, suturing, and knot tying, can be practiced on this model. In addition, the RTM can be used with any pelvic trainer that permits placement of larger practice specimens within its confines.
Sensitivity analysis technique for application to deterministic models

International Nuclear Information System (INIS)

Ishigami, T.; Cazzoli, E.; Khatib-Rahbar, M.; Unwin, S.D.

1987-01-01

The characterization of sever accident source terms for light water reactors should include consideration of uncertainties. An important element of any uncertainty analysis is an evaluation of the sensitivity of the output probability distributions reflecting source term uncertainties to assumptions regarding the input probability distributions. Historically, response surface methods (RSMs) were developed to replace physical models using, for example, regression techniques, with simplified models for example, regression techniques, with simplified models for extensive calculations. The purpose of this paper is to present a new method for sensitivity analysis that does not utilize RSM, but instead relies directly on the results obtained from the original computer code calculations. The merits of this approach are demonstrated by application of the proposed method to the suppression pool aerosol removal code (SPARC), and the results are compared with those obtained by sensitivity analysis with (a) the code itself, (b) a regression model, and (c) Iman's method

Multi-factor models and signal processing techniques application to quantitative finance

CERN Document Server

Darolles, Serges; Jay, Emmanuelle

2013-01-01

With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages "embedded" quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented "risk assessment-based" practices.This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an intere
Constructing canine carotid artery stenosis model by endovascular technique

International Nuclear Information System (INIS)

Cheng Guangsen; Liu Yizhi

2005-01-01

Objective: To establish a carotid artery stenosis model by endovascular technique suitable for neuro-interventional therapy. Methods: Twelve dogs were anesthetized, the unilateral segments of the carotid arteries' tunica media and intima were damaged by a corneous guiding wire of home made. Twenty-four carotid artery stenosis models were thus created. DSA examination was performed on postprocedural weeks 2, 4, 8, 10 to estimate the changes of those stenotic carotid arteries. Results: Twenty-four carotid artery stenosis models were successfully created in twelve dogs. Conclusions: Canine carotid artery stenosis models can be created with the endovascular method having variation of pathologic characters and hemodynamic changes similar to human being. It is useful for further research involving the new technique and new material for interventional treatment. (authors)
Towards model-based testing of electronic funds transfer systems

OpenAIRE

Asaadi, H.R.; Khosravi, R.; Mousavi, M.R.; Noroozi, N.

2010-01-01

We report on our first experience with applying model-based testing techniques to an operational Electronic Funds Transfer (EFT) switch. The goal is to test the conformance of the EFT switch to the standard flows described by the ISO 8583 standard. To this end, we first make a formalization of the transaction flows specified in the ISO 8583 standard in terms of a Labeled Transition System (LTS). This formalization paves the way for model-based testing based on the formal notion of Input-Outpu...
Current STR-based techniques in forensic science

Directory of Open Access Journals (Sweden)

Phuvadol Thanakiatkrai

2013-01-01

Full Text Available DNA analysis in forensic science is mainly based on short tandem repeat (STR genotyping. The conventional analysis is a three-step process of DNA extraction, amplification and detection. An overview of various techniques that are currently in use and are being actively researched for STR typing is presented. The techniques are separated into STR amplification and detection. New techniques for forensic STR analysis focus on increasing sensitivity, resolution and discrimination power for suboptimal samples. These are achieved by shifting primer-binding sites, using high-fidelity and tolerant polymerases and applying novel methods to STR detection. Examples in which STRs are used in criminal investigations are provided and future research directions are discussed.
Nonlinear modelling of polymer electrolyte membrane fuel cell stack using nonlinear cancellation technique

Energy Technology Data Exchange (ETDEWEB)

Barus, R. P. P., E-mail: rismawan.ppb@gmail.com [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung and Centre for Material and Technical Product, Jalan Sangkuriang No. 14 Bandung (Indonesia); Tjokronegoro, H. A.; Leksono, E. [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia); Ismunandar [Chemistry Study, Faculty of Mathematics and Science, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia)

2014-09-25

Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range.
Nonlinear modelling of polymer electrolyte membrane fuel cell stack using nonlinear cancellation technique

International Nuclear Information System (INIS)

Barus, R. P. P.; Tjokronegoro, H. A.; Leksono, E.; Ismunandar

2014-01-01

Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range
[Intestinal lengthening techniques: an experimental model in dogs].

Science.gov (United States)

Garibay González, Francisco; Díaz Martínez, Daniel Alberto; Valencia Flores, Alejandro; González Hernández, Miguel Angel

2005-01-01

To compare two intestinal lengthening procedures in an experimental dog model. Intestinal lengthening is one of the methods for gastrointestinal reconstruction used for treatment of short bowel syndrome. The modification to the Bianchi's technique is an alternative. The modified technique decreases the number of anastomoses to a single one, thus reducing the risk of leaks and strictures. To our knowledge there is not any clinical or experimental report that studied both techniques, so we realized the present report. Twelve creole dogs were operated with the Bianchi technique for intestinal lengthening (group A) and other 12 creole dogs from the same race and weight were operated by the modified technique (Group B). Both groups were compared in relation to operating time, difficulties in technique, cost, intestinal lengthening and anastomoses diameter. There were no statistical difference in the anastomoses diameter (A = 9.0 mm vs. B = 8.5 mm, p = 0.3846). Operating time (142 min vs. 63 min) cost and technique difficulties were lower in group B (p anastomoses (of Group B) and intestinal segments had good blood supply and were patent along their full length. Bianchi technique and the modified technique offer two good reliable alternatives for the treatment of short bowel syndrome. The modified technique improved operating time, cost and technical issues.
A technology path to tactical agent-based modeling

Science.gov (United States)

James, Alex; Hanratty, Timothy P.

2017-05-01

Wargaming is a process of thinking through and visualizing events that could occur during a possible course of action. Over the past 200 years, wargaming has matured into a set of formalized processes. One area of growing interest is the application of agent-based modeling. Agent-based modeling and its additional supporting technologies has potential to introduce a third-generation wargaming capability to the Army, creating a positive overmatch decision-making capability. In its simplest form, agent-based modeling is a computational technique that helps the modeler understand and simulate how the "whole of a system" responds to change over time. It provides a decentralized method of looking at situations where individual agents are instantiated within an environment, interact with each other, and empowered to make their own decisions. However, this technology is not without its own risks and limitations. This paper explores a technology roadmap, identifying research topics that could realize agent-based modeling within a tactical wargaming context.
Knowledge-based geometric modeling in construction

DEFF Research Database (Denmark)

Bonev, Martin; Hvam, Lars

2012-01-01

a considerably high amount of their recourses is required for designing and specifying the majority of their product assortment. As design decisions are hereby based on knowledge and experience about behaviour and applicability of construction techniques and materials for a predefined design situation, smart......A wider application of IT-based solutions, such as configuration systems and the implementation of modeling standards, has facilitated the trend to produce mass customized products to support inter alia the specification process of the increasing product variety. However, not all industries have...
Is the Linear Modeling Technique Good Enough for Optimal Form Design? A Comparison of Quantitative Analysis Models

Science.gov (United States)

Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

2012-01-01

How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process. PMID:23258961
Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques

Directory of Open Access Journals (Sweden)

H. T. R. Kurmasha

2017-12-01

Full Text Available An Edge-based image quality measure (IQM technique for the assessment of histogram equalization (HE-based contrast enhancement techniques has been proposed that outperforms the Absolute Mean Brightness Error (AMBE and Entropy which are the most commonly used IQMs to evaluate Histogram Equalization based techniques, and also the two prominent fidelity-based IQMs which are Multi-Scale Structural Similarity (MSSIM and Information Fidelity Criterion-based (IFC measures. The statistical evaluation results show that the Edge-based IQM, which was designed for detecting noise artifacts distortion, has a Person Correlation Coefficient (PCC > 0.86 while the others have poor or fair correlation to human opinion, considering the Human Visual Perception (HVP. Based on HVP, this paper propose an enhancement to classic Edge-based IQM by taking into account the brightness saturation distortion which is the most prominent distortion in HE-based contrast enhancement techniques. It is tested and found to have significantly well correlation (PCC > 0.87, Spearman rank order correlation coefficient (SROCC > 0.92, Root Mean Squared Error (RMSE < 0.1054, and Outlier Ratio (OR = 0%.
Floating Node Method and Virtual Crack Closure Technique for Modeling Matrix Cracking-Delamination Migration

Science.gov (United States)

DeCarvalho, Nelson V.; Chen, B. Y.; Pinho, Silvestre T.; Baiz, P. M.; Ratcliffe, James G.; Tay, T. E.

2013-01-01

A novel approach is proposed for high-fidelity modeling of progressive damage and failure in composite materials that combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. In this study, the approach is applied to the modeling of delamination migration in cross-ply tape laminates. Delamination, matrix cracking, and migration are all modeled using fracture mechanics based failure and migration criteria. The methodology proposed shows very good qualitative and quantitative agreement with experiments.
Improving head and neck CTA with hybrid and model-based iterative reconstruction techniques

NARCIS (Netherlands)

Niesten, J. M.; van der Schaaf, I. C.; Vos, P. C.; Willemink, MJ; Velthuis, B. K.

2015-01-01

AIM: To compare image quality of head and neck computed tomography angiography (CTA) reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MIR) algorithms. MATERIALS AND METHODS: The raw data of 34 studies were
Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

KAUST Repository

Khaki, M.

2017-07-06

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.
An Observed Voting System Based On Biometric Technique

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B. Devikiruba

2015-08-01

Full Text Available ABSTRACT This article describes a computational framework which can run almost on every computer connected to an IP based network to study biometric techniques. This paper discusses with a system protecting confidential information puts strong security demands on the identification. Biometry provides us with a user-friendly method for this identification and is becoming a competitor for current identification mechanisms. The experimentation section focuses on biometric verification specifically based on fingerprints. This article should be read as a warning to those thinking of using methods of identification without first examine the technical opportunities for compromising mechanisms and the associated legal consequences. The development is based on the java language that easily improves software packages that is useful to test new control techniques.
Using of Structural Equation Modeling Techniques in Cognitive Levels Validation

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Natalija Curkovic

2012-10-01

Full Text Available When constructing knowledge tests, cognitive level is usually one of the dimensions comprising the test specifications with each item assigned to measure a particular level. Recently used taxonomies of the cognitive levels most often represent some modification of the original Bloom’s taxonomy. There are many concerns in current literature about existence of predefined cognitive levels. The aim of this article is to investigate can structural equation modeling techniques confirm existence of different cognitive levels. For the purpose of the research, a Croatian final high-school Mathematics exam was used (N = 9626. Confirmatory factor analysis and structural regression modeling were used to test three different models. Structural equation modeling techniques did not support existence of different cognitive levels in this case. There is more than one possible explanation for that finding. Some other techniques that take into account nonlinear behaviour of the items as well as qualitative techniques might be more useful for the purpose of the cognitive levels validation. Furthermore, it seems that cognitive levels were not efficient descriptors of the items and so improvements are needed in describing the cognitive skills measured by items.
Knowledge-Based Environmental Context Modeling

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Pukite, P. R.; Challou, D. J.

2017-12-01

As we move from the oil-age to an energy infrastructure based on renewables, the need arises for new educational tools to support the analysis of geophysical phenomena and their behavior and properties. Our objective is to present models of these phenomena to make them amenable for incorporation into more comprehensive analysis contexts. Starting at the level of a college-level computer science course, the intent is to keep the models tractable and therefore practical for student use. Based on research performed via an open-source investigation managed by DARPA and funded by the Department of Interior [1], we have adapted a variety of physics-based environmental models for a computer-science curriculum. The original research described a semantic web architecture based on patterns and logical archetypal building-blocks (see figure) well suited for a comprehensive environmental modeling framework. The patterns span a range of features that cover specific land, atmospheric and aquatic domains intended for engineering modeling within a virtual environment. The modeling engine contained within the server relied on knowledge-based inferencing capable of supporting formal terminology (through NASA JPL's Semantic Web for Earth and Environmental Technology (SWEET) ontology and a domain-specific language) and levels of abstraction via integrated reasoning modules. One of the key goals of the research was to simplify models that were ordinarily computationally intensive to keep them lightweight enough for interactive or virtual environment contexts. The breadth of the elements incorporated is well-suited for learning as the trend toward ontologies and applying semantic information is vital for advancing an open knowledge infrastructure. As examples of modeling, we have covered such geophysics topics as fossil-fuel depletion, wind statistics, tidal analysis, and terrain modeling, among others. Techniques from the world of computer science will be necessary to promote efficient
Néron models and base change

CERN Document Server

Halle, Lars Halvard

2016-01-01

Presenting the first systematic treatment of the behavior of Néron models under ramified base change, this book can be read as an introduction to various subtle invariants and constructions related to Néron models of semi-abelian varieties, motivated by concrete research problems and complemented with explicit examples. Néron models of abelian and semi-abelian varieties have become an indispensable tool in algebraic and arithmetic geometry since Néron introduced them in his seminal 1964 paper. Applications range from the theory of heights in Diophantine geometry to Hodge theory. We focus specifically on Néron component groups, Edixhoven’s filtration and the base change conductor of Chai and Yu, and we study these invariants using various techniques such as models of curves, sheaves on Grothendieck sites and non-archimedean uniformization. We then apply our results to the study of motivic zeta functions of abelian varieties. The final chapter contains a list of challenging open questions. This book is a...
Towards a new technique to construct a 3D shear-wave velocity model based on converted waves

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Hetényi, G.; Colavitti, L.

2017-12-01

A 3D model is essential in all branches of solid Earth sciences because geological structures can be heterogeneous and change significantly in their lateral dimension. The main target of this research is to build a crustal S-wave velocity structure in 3D. The currently popular methodologies to construct 3D shear-wave velocity models are Ambient Noise Tomography (ANT) and Local Earthquake Tomography (LET). Here we propose a new technique to map Earth discontinuities and velocities at depth based on the analysis of receiver functions. The 3D model is obtained by simultaneously inverting P-to-S converted waveforms recorded at a dense array. The individual velocity models corresponding to each trace are extracted from the 3D initial model along ray paths that are calculated using the shooting method, and the velocity model is updated during the inversion. We consider a spherical approximation of ray propagation using a global velocity model (iasp91, Kennett and Engdahl, 1991) for the teleseismic part, while we adopt Cartesian coordinates and a local velocity model for the crust. During the inversion process we work with a multi-layer crustal model for shear-wave velocity, with a flexible mesh for the depth of the interfaces. The RFs inversion represents a complex problem because the amplitude and the arrival time of different phases depend in a non-linear way on the depth of interfaces and the characteristics of the velocity structure. The solution we envisage to manage the inversion problem is the stochastic Neighbourhood Algorithm (NA, Sambridge, 1999), whose goal is to find an ensemble of models that sample the good data-fitting regions of a multidimensional parameter space. Depending on the studied area, this method can accommodate possible independent and complementary geophysical data (gravity, active seismics, LET, ANT, etc.), helping to reduce the non-linearity of the inversion. Our first focus of application is the Central Alps, where a 20-year long dataset of
A multivariate quadrature based moment method for LES based modeling of supersonic combustion

Science.gov (United States)

Donde, Pratik; Koo, Heeseok; Raman, Venkat

2012-07-01

The transported probability density function (PDF) approach is a powerful technique for large eddy simulation (LES) based modeling of scramjet combustors. In this approach, a high-dimensional transport equation for the joint composition-enthalpy PDF needs to be solved. Quadrature based approaches provide deterministic Eulerian methods for solving the joint-PDF transport equation. In this work, it is first demonstrated that the numerical errors associated with LES require special care in the development of PDF solution algorithms. The direct quadrature method of moments (DQMOM) is one quadrature-based approach developed for supersonic combustion modeling. This approach is shown to generate inconsistent evolution of the scalar moments. Further, gradient-based source terms that appear in the DQMOM transport equations are severely underpredicted in LES leading to artificial mixing of fuel and oxidizer. To overcome these numerical issues, a semi-discrete quadrature method of moments (SeQMOM) is formulated. The performance of the new technique is compared with the DQMOM approach in canonical flow configurations as well as a three-dimensional supersonic cavity stabilized flame configuration. The SeQMOM approach is shown to predict subfilter statistics accurately compared to the DQMOM approach.

Modeling, Control and Analyze of Multi-Machine Drive Systems using Bond Graph Technique

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J. Belhadj

2006-03-01

Full Text Available In this paper, a system viewpoint method has been investigated to study and analyze complex systems using Bond Graph technique. These systems are multimachine multi-inverter based on Induction Machine (IM, well used in industries like rolling mills, textile, and railway traction. These systems are multi-domains, multi-scales time and present very strong internal and external couplings, with non-linearity characterized by a high model order. The classical study with analytic model is difficult to manipulate and it is limited to some performances. In this study, a “systemic approach” is presented to design these kinds of systems, using an energetic representation based on Bond Graph formalism. Three types of multimachine are studied with their control strategies. The modeling is carried out by Bond Graph and results are discussed to show the performances of this methodology
Power system dynamic state estimation using prediction based evolutionary technique

International Nuclear Information System (INIS)

Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

2016-01-01

In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.
Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

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Munahefi, D. N.; Waluya, S. B.; Rochmad

2018-03-01

The purpose of this research identified the effectiveness of Problem Based Learning (PBL) models based on Self Regulation Leaning (SRL) on the ability of mathematical creative thinking and analyzed the ability of mathematical creative thinking of high school students in solving mathematical problems. The population of this study was students of grade X SMA N 3 Klaten. The research method used in this research was sequential explanatory. Quantitative stages with simple random sampling technique, where two classes were selected randomly as experimental class was taught with the PBL model based on SRL and control class was taught with expository model. The selection of samples at the qualitative stage was non-probability sampling technique in which each selected 3 students were high, medium, and low academic levels. PBL model with SRL approach effectived to students’ mathematical creative thinking ability. The ability of mathematical creative thinking of low academic level students with PBL model approach of SRL were achieving the aspect of fluency and flexibility. Students of academic level were achieving fluency and flexibility aspects well. But the originality of students at the academic level was not yet well structured. Students of high academic level could reach the aspect of originality.
Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

Energy Technology Data Exchange (ETDEWEB)

Zheng Guoyan [Institute for Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, CH-3014 Bern (Switzerland)

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction. The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based
ONTOLOGY BASED MEANINGFUL SEARCH USING SEMANTIC WEB AND NATURAL LANGUAGE PROCESSING TECHNIQUES

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K. Palaniammal

2013-10-01

Full Text Available The semantic web extends the current World Wide Web by adding facilities for the machine understood description of meaning. The ontology based search model is used to enhance efficiency and accuracy of information retrieval. Ontology is the core technology for the semantic web and this mechanism for representing formal and shared domain descriptions. In this paper, we proposed ontology based meaningful search using semantic web and Natural Language Processing (NLP techniques in the educational domain. First we build the educational ontology then we present the semantic search system. The search model consisting three parts which are embedding spell-check, finding synonyms using WordNet API and querying ontology using SPARQL language. The results are both sensitive to spell check and synonymous context. This paper provides more accurate results and the complete details for the selected field in a single page.
A novel technique for extracting clouds base height using ground based imaging

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E. Hirsch

2011-01-01

Full Text Available The height of a cloud in the atmospheric column is a key parameter in its characterization. Several remote sensing techniques (passive and active, either ground-based or on space-borne platforms and in-situ measurements are routinely used in order to estimate top and base heights of clouds. In this article we present a novel method that combines thermal imaging from the ground and sounded wind profile in order to derive the cloud base height. This method is independent of cloud types, making it efficient for both low boundary layer and high clouds. In addition, using thermal imaging ensures extraction of clouds' features during daytime as well as at nighttime. The proposed technique was validated by comparison to active sounding by ceilometers (which is a standard ground based method, to lifted condensation level (LCL calculations, and to MODIS products obtained from space. As all passive remote sensing techniques, the proposed method extracts only the height of the lowest cloud layer, thus upper cloud layers are not detected. Nevertheless, the information derived from this method can be complementary to space-borne cloud top measurements when deep-convective clouds are present. Unlike techniques such as LCL, this method is not limited to boundary layer clouds, and can extract the cloud base height at any level, as long as sufficient thermal contrast exists between the radiative temperatures of the cloud and its surrounding air parcel. Another advantage of the proposed method is its simplicity and modest power needs, making it particularly suitable for field measurements and deployment at remote locations. Our method can be further simplified for use with visible CCD or CMOS camera (although nighttime clouds will not be observed.
Analytical system availability techniques

NARCIS (Netherlands)

Brouwers, J.J.H.; Verbeek, P.H.J.; Thomson, W.R.

1987-01-01

Analytical techniques are presented to assess the probability distributions and related statistical parameters of loss of production from equipment networks subject to random failures and repairs. The techniques are based on a theoretical model for system availability, which was further developed
MTK: An AI tool for model-based reasoning

Science.gov (United States)

Erickson, William K.; Schwartz, Mary R.

1987-01-01

A 1988 goal for the Systems Autonomy Demonstration Project Office of the NASA Ames Research Center is to apply model-based representation and reasoning techniques in a knowledge-based system that will provide monitoring, fault diagnosis, control and trend analysis of the space station Thermal Management System (TMS). A number of issues raised during the development of the first prototype system inspired the design and construction of a model-based reasoning tool called MTK, which was used in the building of the second prototype. These issues are outlined, along with examples from the thermal system to highlight the motivating factors behind them. An overview of the capabilities of MTK is given.
High frequency magnetic field technique: mathematical modelling and development of a full scale water fraction meter

Energy Technology Data Exchange (ETDEWEB)

Cimpan, Emil

2004-09-15

This work is concerned with the development of a new on-line measuring technique to be used in measurements of the water concentration in a two component oil/water or three component (i.e. multiphase) oil/water/gas flow. The technique is based on using non-intrusive coil detectors and experiments were performed both statically (medium at rest) and dynamically (medium flowing through a flow rig). The various coil detectors were constructed with either one or two coils and specially designed electronics were used. The medium was composed by air, machine oil, and water having different conductivity values, i.e. seawater and salt water with various conductivities (salt concentrations) such as 1 S/m, 4.9 S/m and 9.3 S/m. The experimental measurements done with the different mixtures were further used to mathematically model the physical principle used in the technique. This new technique is based on measuring the coil impedance and signal frequency at the self-resonance frequency of the coil to determine the water concentration in the mix. By using numerous coils it was found, experimentally, that generally both the coil impedance and the self-resonance frequency of the coil decreased as the medium conductivity increased. Both the impedance and the self-resonance frequency of the coil depended on the medium loss due to the induced eddy currents within the conductive media in the mixture, i.e. water. In order to detect relatively low values of the medium loss, the self-resonance frequency of the coil and also of the magnetic field penetrating the media should be relatively high (within the MHz range and higher). Therefore, the technique was called and referred to throughout the entire work as the high frequency magnetic field technique (HFMFT). To practically use the HFMFT, it was necessary to circumscribe an analytical frame to this technique. This was done by working out a mathematical model that relates the impedance and the self-resonance frequency of the coil to the
Machine Learning Techniques for Modelling Short Term Land-Use Change

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Mileva Samardžić-Petrović

2017-11-01

Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.
Empirical agent-based modelling challenges and solutions

CERN Document Server

Barreteau, Olivier

2014-01-01

This instructional book showcases techniques to parameterise human agents in empirical agent-based models (ABM). In doing so, it provides a timely overview of key ABM methodologies and the most innovative approaches through a variety of empirical applications. It features cutting-edge research from leading academics and practitioners, and will provide a guide for characterising and parameterising human agents in empirical ABM. In order to facilitate learning, this text shares the valuable experiences of other modellers in particular modelling situations. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as topics related to sustainability. In a similar vein to the instruction found in a cookbook, this text provides the empirical modeller with a set of 'recipes' ready to be implemented. Agent-based modeling (AB...
Increasing the reliability of ecological models using modern software engineering techniques

Science.gov (United States)

Robert M. Scheller; Brian R. Sturtevant; Eric J. Gustafson; Brendan C. Ward; David J. Mladenoff

2009-01-01

Modern software development techniques are largely unknown to ecologists. Typically, ecological models and other software tools are developed for limited research purposes, and additional capabilities are added later, usually in an ad hoc manner. Modern software engineering techniques can substantially increase scientific rigor and confidence in ecological models and...
Comparison of acrylamide intake from Western and guideline based diets using probabilistic techniques and linear programming.

Science.gov (United States)

Katz, Josh M; Winter, Carl K; Buttrey, Samuel E; Fadel, James G

2012-03-01

Western and guideline based diets were compared to determine if dietary improvements resulting from following dietary guidelines reduce acrylamide intake. Acrylamide forms in heat treated foods and is a human neurotoxin and animal carcinogen. Acrylamide intake from the Western diet was estimated with probabilistic techniques using teenage (13-19 years) National Health and Nutrition Examination Survey (NHANES) food consumption estimates combined with FDA data on the levels of acrylamide in a large number of foods. Guideline based diets were derived from NHANES data using linear programming techniques to comport to recommendations from the Dietary Guidelines for Americans, 2005. Whereas the guideline based diets were more properly balanced and rich in consumption of fruits, vegetables, and other dietary components than the Western diets, acrylamide intake (mean±SE) was significantly greater (Plinear programming and results demonstrate that linear programming techniques can be used to model specific diets for the assessment of toxicological and nutritional dietary components. Copyright Â© 2011 Elsevier Ltd. All rights reserved.
Bases of technique of sprinting

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Valeriy Druz

2015-06-01

Full Text Available Purpose: to determine the biomechanical consistent patterns of a movement of a body providing the highest speed of sprinting. Material and Methods: the analysis of scientific and methodical literature on the considered problem, the anthropometrical characteristics of the surveyed contingent of sportsmen, the analysis of high-speed shootings of the leading runners of the world. Results: the biomechanical bases of technique of sprinting make dispersal and movement of the general center of body weight of the sportsman on a parabolic curve in a start phase taking into account the initial height of its stay in a pose of a low start. Its further movement happens on a cycloidal trajectory which is formed due to a pendulum movement of the extremities creating the lifting power which provides flight duration more in a running step, than duration of a basic phase. Conclusions: the received biomechanical regularities of technique of sprinting allow increasing the efficiency of training of sportsmen in sprinting.
Lab-Based Measurement of Remediation Techniques for Radiation Portal Monitors (Initial Report)

International Nuclear Information System (INIS)

Livesay, Jake

2012-01-01

Radiation Portal Monitors (RPM) deployed by the Second Line of Defense (SLD) are known to be sensitive to the natural environmental radioactive background. There are several techniques used to mitigate the effects of background on the monitors, but since the installation environments can vary significantly from one another the need for a standardized, systematic, study of remediation techniques was proposed and carried out. This study is not meant to serve as the absolute last word on the subject. The data collected are, however, intelligible and useful. Some compromises were made, each of which will be described in detail. The hope of this initial report is to familiarize the SLD science teams with ORNL's effort to model the effect of various remediation techniques on simple, static backgrounds. This study provides a good start toward benchmarking the model, and each additional increment of data will serve to make the model more robust. The scope of this initial study is limited to a few basic cases. Its purpose is to prove the utility of lab-based study of remediation techniques and serve as a standard data set for future use. This importance of this first step of standardization will become obvious when science teams are working in parallel on issues of remediation; having a common starting point will do away with one category of difference, thereby making easier the task of determining the sources of disagreement. Further measurements will augment this data set, allowing for further constraint of the universe of possible situations. As will be discussed in the 'Going Forward' section, more data will be included in the final report of this work. Of particular interest will be the data taken with the official TSA lead collimators, which will provide more direct results for comparison with installation data.
Personalized models of bones based on radiographic photogrammetry.

Science.gov (United States)

Berthonnaud, E; Hilmi, R; Dimnet, J

2009-07-01

The radiographic photogrammetry is applied, for locating anatomical landmarks in space, from their two projected images. The goal of this paper is to define a personalized geometric model of bones, based uniquely on photogrammetric reconstructions. The personalized models of bones are obtained from two successive steps: their functional frameworks are first determined experimentally, then, the 3D bone representation results from modeling techniques. Each bone functional framework is issued from direct measurements upon two radiographic images. These images may be obtained using either perpendicular (spine and sacrum) or oblique incidences (pelvis and lower limb). Frameworks link together their functional axes and punctual landmarks. Each global bone volume is decomposed in several elementary components. Each volumic component is represented by simple geometric shapes. Volumic shapes are articulated to the patient's bone structure. The volumic personalization is obtained by best fitting the geometric model projections to their real images, using adjustable articulations. Examples are presented to illustrating the technique of personalization of bone volumes, directly issued from the treatment of only two radiographic images. The chosen techniques for treating data are then discussed. The 3D representation of bones completes, for clinical users, the information brought by radiographic images.
A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods

International Nuclear Information System (INIS)

Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei

2014-01-01

As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate O(1/k 2 ). In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques. (paper)
A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods.

Science.gov (United States)

Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei

2014-06-21

As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.
Orientation of student entrepreneurial practices based on administrative techniques

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Héctor Horacio Murcia Cabra

2005-07-01

Full Text Available As part of the second phase of the research project «Application of a creativity model to update the teaching of the administration in Colombian agricultural entrepreneurial systems» it was decided to re-enforce student planning and execution of the students of the Agricultural business Administration Faculty of La Salle University. Those finishing their studies were given special attention. The plan of action was initiated in the second semester of 2003. It was initially defined as a model of entrepreneurial strengthening based on a coherent methodology that included the most recent administration and management techniques. Later, the applicability of this model was tested in some organizations of the agricultural sector that had asked for support in their planning processes. Through an investigation-action process the methodology was redefined in order to arrive at a final model that could be used by faculty students and graduates. The results obtained were applied to the teaching of Entrepreneurial Laboratory of ninth semester students with the hope of improving administrative support to agricultural enterprises. Following this procedure more than 100 students and 200 agricultural producers have applied this procedure between June 2003 and July 2005. The methodology used and the results obtained are presented in this article.
Optimizing Availability of a Framework in Series Configuration Utilizing Markov Model and Monte Carlo Simulation Techniques

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Mansoor Ahmed Siddiqui

2017-06-01

Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

Directory of Open Access Journals (Sweden)

A. Elshorbagy

2010-10-01

Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

Science.gov (United States)

Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

2010-10-01

In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should
Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

International Nuclear Information System (INIS)

Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

1996-01-01

The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)
Construct canine intracranial aneurysm model by endovascular technique

International Nuclear Information System (INIS)

Liang Xiaodong; Liu Yizhi; Ni Caifang; Ding Yi

2004-01-01

Objective: To construct canine bifurcation aneurysms suitable for evaluating the exploration of endovascular devices for interventional therapy by endovascular technique. Methods: The right common carotid artery of six dogs was expanded with a pliable balloon by means of endovascular technique, then embolization with detached balloon was taken at their originations DAS examination were performed on 1, 2, 3 d after the procedurse. Results: 6 aneurysm models were created in six dogs successfully with the mean width and height of the aneurysms decreasing in 3 days. Conclusions: This canine aneurysm model presents the virtue in the size and shape of human cerebral bifurcation saccular aneurysms on DSA image, suitable for developing the exploration of endovascular devices for aneurismal therapy. The procedure is quick, reliable and reproducible. (authors)
Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

International Nuclear Information System (INIS)

Kapur, G.S.; Sastry, M.I.S.; Jaiswal, A.K.; Sarpal, A.S.

2004-01-01

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their 1 H NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the 'measured' and 'estimated' RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only 1 H NMR data) also showed high correlation between the 'measured' and 'estimated' RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets
Skull base tumours part I: Imaging technique, anatomy and anterior skull base tumours

Energy Technology Data Exchange (ETDEWEB)

Borges, Alexandra [Instituto Portugues de Oncologia Francisco Gentil, Centro de Lisboa, Servico de Radiologia, Rua Professor Lima Basto, 1093 Lisboa Codex (Portugal)], E-mail: borgesalexandra@clix.pt

2008-06-15

Advances in cross-sectional imaging, surgical technique and adjuvant treatment have largely contributed to ameliorate the prognosis, lessen the morbidity and mortality of patients with skull base tumours and to the growing medical investment in the management of these patients. Because clinical assessment of the skull base is limited, cross-sectional imaging became indispensable in the diagnosis, treatment planning and follow-up of patients with suspected skull base pathology and the radiologist is increasingly responsible for the fate of these patients. This review will focus on the advances in imaging technique; contribution to patient's management and on the imaging features of the most common tumours affecting the anterior skull base. Emphasis is given to a systematic approach to skull base pathology based upon an anatomic division taking into account the major tissue constituents in each skull base compartment. The most relevant information that should be conveyed to surgeons and radiation oncologists involved in patient's management will be discussed.
Skull base tumours part I: Imaging technique, anatomy and anterior skull base tumours

International Nuclear Information System (INIS)

Borges, Alexandra

2008-01-01

Advances in cross-sectional imaging, surgical technique and adjuvant treatment have largely contributed to ameliorate the prognosis, lessen the morbidity and mortality of patients with skull base tumours and to the growing medical investment in the management of these patients. Because clinical assessment of the skull base is limited, cross-sectional imaging became indispensable in the diagnosis, treatment planning and follow-up of patients with suspected skull base pathology and the radiologist is increasingly responsible for the fate of these patients. This review will focus on the advances in imaging technique; contribution to patient's management and on the imaging features of the most common tumours affecting the anterior skull base. Emphasis is given to a systematic approach to skull base pathology based upon an anatomic division taking into account the major tissue constituents in each skull base compartment. The most relevant information that should be conveyed to surgeons and radiation oncologists involved in patient's management will be discussed
Constraint-Based Abstraction of a Model Checker for Inﬁnite State Systems

DEFF Research Database (Denmark)

Banda, Gourinath; Gallagher, John Patrick

Abstract interpretation-based model checking provides an approach to verifying properties of inﬁnite-state systems. In practice, most previous work on abstract model checking is either restricted to verifying universal properties, or develops special techniques for temporal logics such as modal t...... to implementation of abstract model checking algorithms for abstract domains based on constraints, making use of an SMT solver....
AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES

Directory of Open Access Journals (Sweden)

F. Alidoost

2015-12-01

Full Text Available Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment.
Circuit oriented electromagnetic modeling using the PEEC techniques

CERN Document Server

Ruehli, Albert; Jiang, Lijun

2017-01-01

This book provides intuitive solutions to electromagnetic problems by using the Partial Eelement Eequivalent Ccircuit (PEEC) method. This book begins with an introduction to circuit analysis techniques, laws, and frequency and time domain analyses. The authors also treat Maxwell's equations, capacitance computations, and inductance computations through the lens of the PEEC method. Next, readers learn to build PEEC models in various forms: equivalent circuit models, non orthogonal PEEC models, skin-effect models, PEEC models for dielectrics, incident and radiate field models, and scattering PEEC models. The book concludes by considering issues like such as stability and passivity, and includes five appendices some with formulas for partial elements.
A Range-Based Multivariate Model for Exchange Rate Volatility

OpenAIRE

Tims, Ben; Mahieu, Ronald

2003-01-01

textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...
A local isotropic/global orthotropic finite element technique for modeling the crush of wood in impact limiters

International Nuclear Information System (INIS)

Attaway, S.W.; Yoshimura, H.R.

1989-01-01

Wood is often used as the energy absorbing material in impact limiters, because it begins to crush at low strains, then maintains a near constant crush stress up to nearly 60% volume reduction, and then locks up. Hill (Hill and Joseph, 1974) has performed tests that show that wood is an excellent absorber. However, wood's orthotropic behavior for large crush is difficult to model. In the past, analysts have used isotropic foam-like material models for modeling wood. A new finite element technique is presented in this paper that gives a better model of wood crush than the model currently in use. The orthotropic technique is based on locally isotropic, but globally orthotropic (LIGO) (Attaway, 1988) assumptions in which alternating layers of hard and soft crushable material are used. Each layer is isotropic; however, by alternating hard and soft thin layers, the resulting global behavior is orthotropic. In the remainder of this paper, the new technique for modeling orthotropic wood crush will be presented. The model is used to predict the crush behavior for different grain orientations of balsa wood. As an example problem, an impact limiter containing balsa wood as the crushable material is analyzed using both an isotropic model and the LIGO model
Nasal base narrowing of the caucasian nose through the cerclage technique

Directory of Open Access Journals (Sweden)

Mocellin, Marcos

2010-06-01

Full Text Available Introduction: Several techniques can be performed to reduce the nasal base (narrowing, as skin resection vestibular columellar skin resection, resection of skin in elliptical lip narinary, sloughing of skin and advancements (VY technique of Bernstein and the use of cerclage sutures in the nasal base. Objective: To evaluate the technique of cerclage performed in the nasal base, through endonasal rhinoplasty without delivery of basic technique, in the Caucasian nose, reducing the distance inter-alar flare and correcting the wing with consequent improvement in nasal harmony in the whole face. Methods: A retrospective analysis by analysis of clinical documents and photos of 43 patients in whom cerclage was made of the nasal base by resecting skin ellipse in the region of the vestibule and the nasal base (modified technique of Weir using colorless mononylon® 4 "0" with a straight cutting needle. The study was conducted in 2008 and 2009 at Hospital of Paraná Institute of Otolaryngology - IPO in Curitiba, Parana - Brazil. Patients had a follow up ranging 7-12 months. Results: In 100% of cases was achieved an improvement in nasal harmony, by decreasing the inter-alar distance. Conclusion: The encircling with minimal resection of vestibular skin and the nasal base is an effective method for the narrowing of the nasal base in the Caucasian nose, with predictable results and easy to perform.
Nitrous oxide-based techniques versus nitrous oxide-free techniques for general anaesthesia.

Science.gov (United States)

Sun, Rao; Jia, Wen Qin; Zhang, Peng; Yang, KeHu; Tian, Jin Hui; Ma, Bin; Liu, Yali; Jia, Run H; Luo, Xiao F; Kuriyama, Akira

2015-11-06

anaesthesia (or both) with any general anaesthesia using a volatile anaesthetic or propofol-based maintenance of anaesthesia but no nitrous oxide for adults undergoing surgery. Our primary outcome was inhospital case fatality rate. Secondary outcomes were complications and length of stay. Two review authors independently assessed trial quality and extracted the outcome data. We used meta-analysis for data synthesis. Heterogeneity was examined with the Chi² test and by calculating the I² statistic. We used a fixed-effect model if the measure of inconsistency was low for all comparisons (I² statistic nitrous oxide-based techniques increased the incidence of pulmonary atelectasis (odds ratio (OR) 1.57, 95% confidence interval (CI) 1.18 to 2.10, P = 0.002), but had no effects on the inhospital case fatality rate, the incidence of pneumonia, myocardial infarction, stroke, severe nausea and vomiting, venous thromboembolism, wound infection, or the length of hospital stay. The sensitivity analyses suggested that the results of the meta-analyses were all robust except for the outcomes of pneumonia, and severe nausea and vomiting. Two trials reported length of intensive care unit (ICU) stay but the data were skewed so were not pooled. Both trials reported that nitrous oxide-based techniques had no effects on the length of ICU stay. We rated the quality of evidence for two outcomes (pulmonary atelectasis, myocardial infarction) as high, four outcomes (inhospital case fatality rate, stroke, venous thromboembolism, length of hospital stay) as moderate, and three (pneumonia, severe nausea and vomiting, wound infection rate) as low. Given the evidence from this Cochrane review, the avoidance of nitrous oxide may be reasonable in participants with pre-existing poor pulmonary function or at high risk of postoperative nausea and vomiting. Since there are eight studies awaiting classification, selection bias may exist in our systematic review.
Agent-Based Model of Price Competition and Product Differentiation on Congested Networks

OpenAIRE

Lei Zhang; David Levinson; Shanjiang Zhu

2007-01-01

Using consistent agent-based techniques, this research models the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Rep...
blockcluster: An R Package for Model-Based Co-Clustering

Directory of Open Access Journals (Sweden)

Parmeet Singh Bhatia

2017-02-01

Full Text Available Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003 which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.
Active Vibration damping of Smart composite beams based on system identification technique

Science.gov (United States)

Bendine, Kouider; Satla, Zouaoui; Boukhoulda, Farouk Benallel; Nouari, Mohammed

2018-03-01

In the present paper, the active vibration control of a composite beam using piezoelectric actuator is investigated. The space state equation is determined using system identification technique based on the structure input output response provided by ANSYS APDL finite element package. The Linear Quadratic (LQG) control law is designed and integrated into ANSYS APDL to perform closed loop simulations. Numerical examples for different types of excitation loads are presented to test the efficiency and the accuracy of the proposed model.
A correlation-based pulse detection technique for gamma-ray/neutron detectors

International Nuclear Information System (INIS)

Faisal, Muhammad; Schiffer, Randolph T.; Flaska, Marek; Pozzi, Sara A.; Wentzloff, David D.

2011-01-01

We present a correlation-based detection technique that significantly improves the probability of detection for low energy pulses. We propose performing a normalized cross-correlation of the incoming pulse data to a predefined pulse template, and using a threshold correlation value to trigger the detection of a pulse. This technique improves the detector sensitivity by amplifying the signal component of incoming pulse data and rejecting noise. Simulation results for various different templates are presented. Finally, the performance of the correlation-based detection technique is compared to the current state-of-the-art techniques.
Data-driven techniques to estimate parameters in a rate-dependent ferromagnetic hysteresis model

International Nuclear Information System (INIS)

Hu Zhengzheng; Smith, Ralph C.; Ernstberger, Jon M.

2012-01-01

The quantification of rate-dependent ferromagnetic hysteresis is important in a range of applications including high speed milling using Terfenol-D actuators. There exist a variety of frameworks for characterizing rate-dependent hysteresis including the magnetic model in Ref. , the homogenized energy framework, Preisach formulations that accommodate after-effects, and Prandtl-Ishlinskii models. A critical issue when using any of these models to characterize physical devices concerns the efficient estimation of model parameters through least squares data fits. A crux of this issue is the determination of initial parameter estimates based on easily measured attributes of the data. In this paper, we present data-driven techniques to efficiently and robustly estimate parameters in the homogenized energy model. This framework was chosen due to its physical basis and its applicability to ferroelectric, ferromagnetic and ferroelastic materials.
Memory Based Machine Intelligence Techniques in VLSI hardware

OpenAIRE

James, Alex Pappachen

2012-01-01

We briefly introduce the memory based approaches to emulate machine intelligence in VLSI hardware, describing the challenges and advantages. Implementation of artificial intelligence techniques in VLSI hardware is a practical and difficult problem. Deep architectures, hierarchical temporal memories and memory networks are some of the contemporary approaches in this area of research. The techniques attempt to emulate low level intelligence tasks and aim at providing scalable solutions to high ...

Feasibility Study on Tension Estimation Technique for Hanger Cables Using the FE Model-Based System Identification Method

Directory of Open Access Journals (Sweden)

Kyu-Sik Park

2015-01-01

Full Text Available Hanger cables in suspension bridges are partly constrained by horizontal clamps. So, existing tension estimation methods based on a single cable model are prone to higher errors as the cable gets shorter, making it more sensitive to flexural rigidity. Therefore, inverse analysis and system identification methods based on finite element models are suggested recently. In this paper, the applicability of system identification methods is investigated using the hanger cables of Gwang-An bridge. The test results show that the inverse analysis and systemic identification methods based on finite element models are more reliable than the existing string theory and linear regression method for calculating the tension in terms of natural frequency errors. However, the estimation error of tension can be varied according to the accuracy of finite element model in model based methods. In particular, the boundary conditions affect the results more profoundly when the cable gets shorter. Therefore, it is important to identify the boundary conditions through experiment if it is possible. The FE model-based tension estimation method using system identification method can take various boundary conditions into account. Also, since it is not sensitive to the number of natural frequency inputs, the availability of this system is high.
Numerical modeling techniques for flood analysis

Science.gov (United States)

Anees, Mohd Talha; Abdullah, K.; Nawawi, M. N. M.; Ab Rahman, Nik Norulaini Nik; Piah, Abd. Rahni Mt.; Zakaria, Nor Azazi; Syakir, M. I.; Mohd. Omar, A. K.

2016-12-01

Topographic and climatic changes are the main causes of abrupt flooding in tropical areas. It is the need to find out exact causes and effects of these changes. Numerical modeling techniques plays a vital role for such studies due to their use of hydrological parameters which are strongly linked with topographic changes. In this review, some of the widely used models utilizing hydrological and river modeling parameters and their estimation in data sparse region are discussed. Shortcomings of 1D and 2D numerical models and the possible improvements over these models through 3D modeling are also discussed. It is found that the HEC-RAS and FLO 2D model are best in terms of economical and accurate flood analysis for river and floodplain modeling respectively. Limitations of FLO 2D in floodplain modeling mainly such as floodplain elevation differences and its vertical roughness in grids were found which can be improve through 3D model. Therefore, 3D model was found to be more suitable than 1D and 2D models in terms of vertical accuracy in grid cells. It was also found that 3D models for open channel flows already developed recently but not for floodplain. Hence, it was suggested that a 3D model for floodplain should be developed by considering all hydrological and high resolution topographic parameter's models, discussed in this review, to enhance the findings of causes and effects of flooding.
Plants status monitor: Modelling techniques and inherent benefits

International Nuclear Information System (INIS)

Breeding, R.J.; Lainoff, S.M.; Rees, D.C.; Prather, W.A.; Fickiessen, K.O.E.

1987-01-01

The Plant Status Monitor (PSM) is designed to provide plant personnel with information on the operational status of the plant and compliance with the plant technical specifications. The PSM software evaluates system models using a 'distributed processing' technique in which detailed models of individual systems are processed rather than by evaluating a single, plant-level model. In addition, development of the system models for PSM provides inherent benefits to the plant by forcing detailed reviews of the technical specifications, system design and operating procedures, and plant documentation. (orig.)
Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

Energy Technology Data Exchange (ETDEWEB)

Quan, Wei; Lv, Lin, E-mail: lvlinlch1990@163.com; Liu, Baiqi [School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191 (China)

2014-11-15

In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.
Advanced Techniques for Reservoir Simulation and Modeling of Non-Conventional Wells

Energy Technology Data Exchange (ETDEWEB)

Durlofsky, Louis J.

2000-08-28

This project targets the development of (1) advanced reservoir simulation techniques for modeling non-conventional wells; (2) improved techniques for computing well productivity (for use in reservoir engineering calculations) and well index (for use in simulation models), including the effects of wellbore flow; and (3) accurate approaches to account for heterogeneity in the near-well region.
CEAI: CCM-based email authorship identification model

Directory of Open Access Journals (Sweden)

Sarwat Nizamani

2013-11-01

Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].
Characterization techniques for graphene-based materials in catalysis

Directory of Open Access Journals (Sweden)

Maocong Hu

2017-06-01

Full Text Available Graphene-based materials have been studied in a wide range of applications including catalysis due to the outstanding electronic, thermal, and mechanical properties. The unprecedented features of graphene-based catalysts, which are believed to be responsible for their superior performance, have been characterized by many techniques. In this article, we comprehensively summarized the characterization methods covering bulk and surface structure analysis, chemisorption ability determination, and reaction mechanism investigation. We reviewed the advantages/disadvantages of different techniques including Raman spectroscopy, X-ray photoelectron spectroscopy (XPS, Fourier transform infrared spectroscopy (FTIR and Diffuse Reflectance Fourier Transform Infrared Spectroscopy (DRIFTS, X-Ray diffraction (XRD, X-ray absorption near edge structure (XANES and X-ray absorption fine structure (XAFS, atomic force microscopy (AFM, scanning electron microscopy (SEM, transmission electron microscopy (TEM, high-resolution transmission electron microscopy (HRTEM, ultraviolet-visible spectroscopy (UV-vis, X-ray fluorescence (XRF, inductively coupled plasma mass spectrometry (ICP, thermogravimetric analysis (TGA, Brunauer–Emmett–Teller (BET, and scanning tunneling microscopy (STM. The application of temperature-programmed reduction (TPR, CO chemisorption, and NH3/CO2-temperature-programmed desorption (TPD was also briefly introduced. Finally, we discussed the challenges and provided possible suggestions on choosing characterization techniques. This review provides key information to catalysis community to adopt suitable characterization techniques for their research.
Non-Destructive Techniques Based on Eddy Current Testing

Science.gov (United States)

García-Martín, Javier; Gómez-Gil, Jaime; Vázquez-Sánchez, Ernesto

2011-01-01

Non-destructive techniques are used widely in the metal industry in order to control the quality of materials. Eddy current testing is one of the most extensively used non-destructive techniques for inspecting electrically conductive materials at very high speeds that does not require any contact between the test piece and the sensor. This paper includes an overview of the fundamentals and main variables of eddy current testing. It also describes the state-of-the-art sensors and modern techniques such as multi-frequency and pulsed systems. Recent advances in complex models towards solving crack-sensor interaction, developments in instrumentation due to advances in electronic devices, and the evolution of data processing suggest that eddy current testing systems will be increasingly used in the future. PMID:22163754
Proposal of a congestion control technique in LAN networks using an econometric model ARIMA

Directory of Open Access Journals (Sweden)

Joaquín F Sánchez

2017-01-01

Full Text Available Hasty software development can produce immediate implementations with source code unnecessarily complex and hardly readable. These small kinds of software decay generate a technical debt that could be big enough to seriously affect future maintenance activities. This work presents an analysis technique for identifying architectural technical debt related to non-uniformity of naming patterns; the technique is based on term frequency over package hierarchies. The proposal has been evaluated on projects of two popular organizations, Apache and Eclipse. The results have shown that most of the projects have frequent occurrences of the proposed naming patterns, and using a graph model and aggregated data could enable the elaboration of simple queries for debt identification. The technique has features that favor its applicability on emergent architectures and agile software development.
Hybrid perturbation methods based on statistical time series models

Science.gov (United States)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.
Modeling with data tools and techniques for scientific computing

CERN Document Server

Klemens, Ben

2009-01-01

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods
EVE: Explainable Vector Based Embedding Technique Using Wikipedia

OpenAIRE

Qureshi, M. Atif; Greene, Derek

2017-01-01

We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a word using human-readable labels, thereby it readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed word embedding model, we consider its usefulne...
Intelligent model-based diagnostics for vehicle health management

Science.gov (United States)

Luo, Jianhui; Tu, Fang; Azam, Mohammad S.; Pattipati, Krishna R.; Willett, Peter K.; Qiao, Liu; Kawamoto, Masayuki

2003-08-01

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.
Experimental Study on Damage Detection in Timber Specimens Based on an Electromechanical Impedance Technique and RMSD-Based Mahalanobis Distance

Directory of Open Access Journals (Sweden)

Dansheng Wang

2016-10-01

Full Text Available In the electromechanical impedance (EMI method, the PZT patch performs the functions of both sensor and exciter. Due to the high frequency actuation and non-model based characteristics, the EMI method can be utilized to detect incipient structural damage. In recent years EMI techniques have been widely applied to monitor the health status of concrete and steel materials, however, studies on application to timber are limited. This paper will explore the feasibility of using the EMI technique for damage detection in timber specimens. In addition, the conventional damage index, namely root mean square deviation (RMSD is employed to evaluate the level of damage. On that basis, a new damage index, Mahalanobis distance based on RMSD, is proposed to evaluate the damage severity of timber specimens. Experimental studies are implemented to detect notch and hole damage in the timber specimens. Experimental results verify the availability and robustness of the proposed damage index and its superiority over the RMSD indexes.
Review of the phenomenon of fluidization and its numerical modelling techniques

Directory of Open Access Journals (Sweden)

H Khawaja

2016-10-01

Full Text Available The paper introduces the phenomenon of fluidization as a process. Fluidization occurs when a fluid (liquid or gas is pushed upwards through a bed of granular material. This may make the granular material to behave like a liquid and, for example, keep a level meniscus on a tilted container, or make a lighter object float on top and a heavier object sink to the bottom. The behavior of the granular material, when fluidized, depends on the superficial gas velocity, particle size, particle density, and fluid properties resulting in various regimes of fluidization. These regimes are discussed in detail in the paper. This paper also discusses the application of fluidized beds from its early usage in the Winkler coal gasifier to more recent applications for manufacturing of carbon nano-tubes. In addition, Geldart grouping based on the range of particle sizes is discussed. The minimum fluidization condition is defined and it is demonstrated that it may be registered slightly different when particles are being fluidized or de-fluidized. The paper presents discussion on three numerical modelling techniques: the two fluid model, unresolved fluid-particle model and resolved fluid particle model. The two fluid model is often referred to Eulerian-Eulerian method of solution and assumes particles as well as fluid as continuum. The unresolved and resolved fluid-particle models are based on Eulerian-Lagrangian method of solution. The key difference between them is the whether to use a drag correlation or solve the boundary layer around the particles. The paper ends with the discussion on the applicability of these models.
Development of Energy Management System Based on Internet of Things Technique

OpenAIRE

Wen-Jye Shyr; Chia-Ming Lin and Hung-Yun Feng

2017-01-01

The purpose of this study was to develop an energy management system for university campuses based on the Internet of Things (IoT) technique. The proposed IoT technique based on WebAccess is used via network browser Internet Explore and applies TCP/IP protocol. The case study of IoT for lighting energy usage management system was proposed. Structure of proposed IoT technique included perception layer, equipment layer, control layer, application layer and network layer.
Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Directory of Open Access Journals (Sweden)

Jun Zhang

2017-06-01

Full Text Available Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H2/H∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.
Surrogate-Based Optimization of Biogeochemical Transport Models

Science.gov (United States)

Prieß, Malte; Slawig, Thomas

2010-09-01

First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.
Biosensor-based microRNA detection: techniques, design, performance, and challenges.

Science.gov (United States)

Johnson, Blake N; Mutharasan, Raj

2014-04-07

The current state of biosensor-based techniques for amplification-free microRNA (miRNA) detection is critically reviewed. Comparison with non-sensor and amplification-based molecular techniques (MTs), such as polymerase-based methods, is made in terms of transduction mechanism, associated protocol, and sensitivity. Challenges associated with miRNA hybridization thermodynamics which affect assay selectivity and amplification bias are briefly discussed. Electrochemical, electromechanical, and optical classes of miRNA biosensors are reviewed in terms of transduction mechanism, limit of detection (LOD), time-to-results (TTR), multiplexing potential, and measurement robustness. Current trends suggest that biosensor-based techniques (BTs) for miRNA assay will complement MTs due to the advantages of amplification-free detection, LOD being femtomolar (fM)-attomolar (aM), short TTR, multiplexing capability, and minimal sample preparation requirement. Areas of future importance in miRNA BT development are presented which include focus on achieving high measurement confidence and multiplexing capabilities.
Agent-Based Modeling for Testing and Designing Novel Decentralized Command and Control System Paradigms

National Research Council Canada - National Science Library

Bonabeau, Eric; Hunt, Carl W; Gaudiano, Paolo

2003-01-01

Agent-based modeling (ABM) is a recent simulation modeling technique that consists of modeling a system from the bottom up, capturing the interactions taking place between the system's constituent units...

A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

Directory of Open Access Journals (Sweden)

Wu Jianhua

2014-03-01

Full Text Available River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.
On a Numerical and Graphical Technique for Evaluating some Models Involving Rational Expectations

DEFF Research Database (Denmark)

Johansen, Søren; Swensen, Anders Rygh

Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
On a numerical and graphical technique for evaluating some models involving rational expectations

DEFF Research Database (Denmark)

Johansen, Søren; Swensen, Anders Rygh

Campbell and Shiller (1987) proposed a graphical technique for the present value model which consists of plotting the spread and theoretical spread as calculated from the cointegrated vector autoregressive model. We extend these techniques to a number of rational expectation models and give...
Model-Based Dependability Analysis of Physical Systems with Modelica

Directory of Open Access Journals (Sweden)

Andrea Tundis

2017-01-01

Full Text Available Modelica is an innovative, equation-based, and acausal language that allows modeling complex physical systems, which are made of mechanical, electrical, and electrotechnical components, and evaluates their design through simulation techniques. Unfortunately, the increasing complexity and accuracy of such physical systems require new, more powerful, and flexible tools and techniques for evaluating important system properties and, in particular, the dependability ones such as reliability, safety, and maintainability. In this context, the paper describes some extensions of the Modelica language to support the modeling of system requirements and their relationships. Such extensions enable the requirement verification analysis through native constructs in the Modelica language. Furthermore, they allow exporting a Modelica-based system design as a Bayesian Network in order to analyze its dependability by employing a probabilistic approach. The proposal is exemplified through a case study concerning the dependability analysis of a Tank System.
Testing photogrammetry-based techniques for three-dimensional surface documentation in forensic pathology.

Science.gov (United States)

Urbanová, Petra; Hejna, Petr; Jurda, Mikoláš

2015-05-01

Three-dimensional surface technologies particularly close range photogrammetry and optical surface scanning have recently advanced into affordable, flexible and accurate techniques. Forensic postmortem investigation as performed on a daily basis, however, has not yet fully benefited from their potentials. In the present paper, we tested two approaches to 3D external body documentation - digital camera-based photogrammetry combined with commercial Agisoft PhotoScan(®) software and stereophotogrammetry-based Vectra H1(®), a portable handheld surface scanner. In order to conduct the study three human subjects were selected, a living person, a 25-year-old female, and two forensic cases admitted for postmortem examination at the Department of Forensic Medicine, Hradec Králové, Czech Republic (both 63-year-old males), one dead to traumatic, self-inflicted, injuries (suicide by hanging), the other diagnosed with the heart failure. All three cases were photographed in 360° manner with a Nikon 7000 digital camera and simultaneously documented with the handheld scanner. In addition to having recorded the pre-autopsy phase of the forensic cases, both techniques were employed in various stages of autopsy. The sets of collected digital images (approximately 100 per case) were further processed to generate point clouds and 3D meshes. Final 3D models (a pair per individual) were counted for numbers of points and polygons, then assessed visually and compared quantitatively using ICP alignment algorithm and a cloud point comparison technique based on closest point to point distances. Both techniques were proven to be easy to handle and equally laborious. While collecting the images at autopsy took around 20min, the post-processing was much more time-demanding and required up to 10h of computation time. Moreover, for the full-body scanning the post-processing of the handheld scanner required rather time-consuming manual image alignment. In all instances the applied approaches
Experience gained with the application of the MODIS diffusion model compared with the ATMOS Gauss-function-based model

International Nuclear Information System (INIS)

Mueller, A.

1985-01-01

The advantage of the Gauss-function-based models doubtlessly consists in their proven propagation parameter sets and empirical stack plume rise formulas and in their easy matchability and handability. However, grid models based on trace matter transport equation are more convincing concerning their fundamental principle. Grid models of the MODIS type are to acquire a practical applicability comparable to Gauss models by developing techniques allowing to consider the vertical self-movement of the plumes in grid models and to secure improved diffusion co-efficient determination. (orig./PW) [de
Model-Based Trade Space Exploration for Near-Earth Space Missions

Science.gov (United States)

Cohen, Ronald H.; Boncyk, Wayne; Brutocao, James; Beveridge, Iain

2005-01-01

We developed a capability for model-based trade space exploration to be used in the conceptual design of Earth-orbiting space missions. We have created a set of reusable software components to model various subsystems and aspects of space missions. Several example mission models were created to test the tools and process. This technique and toolset has demonstrated itself to be valuable for space mission architectural design.
Model-based engineering for medical-device software.

Science.gov (United States)

Ray, Arnab; Jetley, Raoul; Jones, Paul L; Zhang, Yi

2010-01-01

This paper demonstrates the benefits of adopting model-based design techniques for engineering medical device software. By using a patient-controlled analgesic (PCA) infusion pump as a candidate medical device, the authors show how using models to capture design information allows for i) fast and efficient construction of executable device prototypes ii) creation of a standard, reusable baseline software architecture for a particular device family, iii) formal verification of the design against safety requirements, and iv) creation of a safety framework that reduces verification costs for future versions of the device software. 1.
Using neuro-fuzzy based method to develop nuclear turbine cycle model

International Nuclear Information System (INIS)

Chan Yeakuang; Chang Chinjang

2009-01-01

The purpose of this study is to describe a hybrid soft-computing modeling technique used to develop the steam turbine cycle model for nuclear power plants. The technique uses neuro-fuzzy model to predict the generator output. Firstly, the plant past three fuel cycles operating data above 95% load were collected and validated as the baseline performance data set. Then the signal errors for new operating data were detected by comparison with the baseline data set and their allowable range of variations. Finally, the most important parameters were selected as an input of the neuro-fuzzy based steam turbine cycle model. After training and testing with key parameters (i.e. throttle pressure, condenser backpressure, feedwater flow rate, and final feedwater temperature), the proposed model can be used to predict the generator output. The analysis results show this neuro-fuzzy based turbine cycle model can be used to predict the generator output with a good agreement. Moreover, the achievement of this study provides an alternative approach in thermal performance evaluation for nuclear power plants. (author)
Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

Science.gov (United States)

Wang, Jiqiang; Ye, Zhifeng; Hu, Zhongzhi; Wu, Xin; Dimirovsky, Georgi; Yue, Hong

2017-05-01

Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.
Polynomial fuzzy model-based control systems stability analysis and control synthesis using membership function dependent techniques

CERN Document Server

Lam, Hak-Keung

2016-01-01

This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function dependent analysis are considered. The membership-function-dependent analysis offers a new research direction for fuzzy-model-based control systems by taking into account the characteristic and information of the membership functions in the stability analysis. The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for read...
Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest

Science.gov (United States)

Kumar, Shashi; Khati, Unmesh G.; Chandola, Shreya; Agrawal, Shefali; Kushwaha, Satya P. S.

2017-08-01

The regulation of the carbon cycle is a critical ecosystem service provided by forests globally. It is, therefore, necessary to have robust techniques for speedy assessment of forest biophysical parameters at the landscape level. It is arduous and time taking to monitor the status of vast forest landscapes using traditional field methods. Remote sensing and GIS techniques are efficient tools that can monitor the health of forests regularly. Biomass estimation is a key parameter in the assessment of forest health. Polarimetric SAR (PolSAR) remote sensing has already shown its potential for forest biophysical parameter retrieval. The current research work focuses on the retrieval of forest biophysical parameters of tropical deciduous forest, using fully polarimetric spaceborne C-band data with Polarimetric SAR Interferometry (PolInSAR) techniques. PolSAR based Interferometric Water Cloud Model (IWCM) has been used to estimate aboveground biomass (AGB). Input parameters to the IWCM have been extracted from the decomposition modeling of SAR data as well as PolInSAR coherence estimation. The technique of forest tree height retrieval utilized PolInSAR coherence based modeling approach. Two techniques - Coherence Amplitude Inversion (CAI) and Three Stage Inversion (TSI) - for forest height estimation are discussed, compared and validated. These techniques allow estimation of forest stand height and true ground topography. The accuracy of the forest height estimated is assessed using ground-based measurements. PolInSAR based forest height models showed enervation in the identification of forest vegetation and as a result height values were obtained in river channels and plain areas. Overestimation in forest height was also noticed at several patches of the forest. To overcome this problem, coherence and backscatter based threshold technique is introduced for forest area identification and accurate height estimation in non-forested regions. IWCM based modeling for forest
Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling

International Nuclear Information System (INIS)

Oguma, Ritsuo; Matsubara, Kunihiko

1980-01-01

The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)
ADJUSTMENT OF MORPHOMETRIC PARAMETERS OF WATER BASINS BASED ON DIGITAL TERRAIN MODELS

Directory of Open Access Journals (Sweden)

Krasil'nikov Vitaliy Mikhaylovich

2012-10-01

Full Text Available The authors argue that effective use of water resources requires accurate morphometric characteristics of water basins. Accurate parameters are needed to analyze their condition, and to assure their appropriate control and operation. Today multiple water basins need their morphometric characteristics to be adjusted and properly stored. The procedure employed so far is based on plane geometric horizontals depicted onto topographic maps. It is described in the procedural guidelines issued in respect of the «Application of water resource regulations governing the operation of waterworks facilities of power plants». The technology described there is obsolete due to the availability of specialized software. The computer technique is based on a digital terrain model. The authors provide an overview of the technique implemented at Rybinsk and Gorkiy water basins in this article. Thus, the digital terrain model generated on the basis of the field data is used at Gorkiy water basin, while the model based on maps and charts is applied at Rybinsk water basin. The authors believe that the software technique can be applied to any other water basin on the basis of the analysis and comparison of morphometric characteristics of the two water basins.
In silico modeling techniques for predicting the tertiary structure of human H4 receptor.

Science.gov (United States)

Zaid, Hilal; Raiyn, Jamal; Osman, Midhat; Falah, Mizied; Srouji, Samer; Rayan, Anwar

2016-01-01

First cloned in 2000, the human Histamine H4 Receptor (hH4R) is the last member of the histamine receptors family discovered so far, it belongs to the GPCR super-family and is involved in a wide variety of immunological and inflammatory responses. Potential hH4R antagonists are proposed to have therapeutic potential for the treatment of allergies, inflammation, asthma and colitis. So far, no hH4R ligands have been successfully introduced to the pharmaceutical market, which creates a strong demand for new selective ligands to be developed. in silico techniques and structural based modeling are likely to facilitate the achievement of this goal. In this review paper we attempt to cover the fundamental concepts of hH4R structure modeling and its implementations in drug discovery and development, especially those that have been experimentally tested and to highlight some ideas that are currently being discussed on the dynamic nature of hH4R and GPCRs, in regards to computerized techniques for 3-D structure modeling.
Identifying content-based and relational techniques to change behaviour in motivational interviewing.

Science.gov (United States)

Hardcastle, Sarah J; Fortier, Michelle; Blake, Nicola; Hagger, Martin S

2017-03-01

Motivational interviewing (MI) is a complex intervention comprising multiple techniques aimed at changing health-related motivation and behaviour. However, MI techniques have not been systematically isolated and classified. This study aimed to identify the techniques unique to MI, classify them as content-related or relational, and evaluate the extent to which they overlap with techniques from the behaviour change technique taxonomy version 1 [BCTTv1; Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., … Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine, 46, 81-95]. Behaviour change experts (n = 3) content-analysed MI techniques based on Miller and Rollnick's [(2013). Motivational interviewing: Preparing people for change (3rd ed.). New York: Guildford Press] conceptualisation. Each technique was then coded for independence and uniqueness by independent experts (n = 10). The experts also compared each MI technique to those from the BCTTv1. Experts identified 38 distinct MI techniques with high agreement on clarity, uniqueness, preciseness, and distinctiveness ratings. Of the identified techniques, 16 were classified as relational techniques. The remaining 22 techniques were classified as content based. Sixteen of the MI techniques were identified as having substantial overlap with techniques from the BCTTv1. The isolation and classification of MI techniques will provide researchers with the necessary tools to clearly specify MI interventions and test the main and interactive effects of the techniques on health behaviour. The distinction between relational and content-based techniques within MI is also an important advance, recognising that changes in motivation and behaviour in MI is a function of both intervention content and the interpersonal style
Microgrids Real-Time Pricing Based on Clustering Techniques

Directory of Open Access Journals (Sweden)

Hao Liu

2018-05-01

Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.
A quantitative comparison of noise reduction across five commercial (hybrid and model-based) iterative reconstruction techniques: an anthropomorphic phantom study.

Science.gov (United States)

Patino, Manuel; Fuentes, Jorge M; Hayano, Koichi; Kambadakone, Avinash R; Uyeda, Jennifer W; Sahani, Dushyant V

2015-02-01

OBJECTIVE. The objective of our study was to compare the performance of three hybrid iterative reconstruction techniques (IRTs) (ASiR, iDose4, SAFIRE) and their respective strengths for image noise reduction on low-dose CT examinations using filtered back projection (FBP) as the standard reference. Also, we compared the performance of these three hybrid IRTs with two model-based IRTs (Veo and IMR) for image noise reduction on low-dose examinations. MATERIALS AND METHODS. An anthropomorphic abdomen phantom was scanned at 100 and 120 kVp and different tube current-exposure time products (25-100 mAs) on three CT systems (for ASiR and Veo, Discovery CT750 HD; for iDose4 and IMR, Brilliance iCT; and for SAFIRE, Somatom Definition Flash). Images were reconstructed using FBP and using IRTs at various strengths. Nine noise measurements (mean ROI size, 423 mm(2)) on extracolonic fat for the different strengths of IRTs were recorded and compared with FBP using ANOVA. Radiation dose, which was measured as the volume CT dose index and dose-length product, was also compared. RESULTS. There were no significant differences in radiation dose and image noise among the scanners when FBP was used (p > 0.05). Gradual image noise reduction was observed with each increasing increment of hybrid IRT strength, with a maximum noise suppression of approximately 50% (48.2-53.9%). Similar noise reduction was achieved on the scanners by applying specific hybrid IRT strengths. Maximum noise reduction was higher on model-based IRTs (68.3-81.1%) than hybrid IRTs (48.2-53.9%) (p < 0.05). CONCLUSION. When constant scanning parameters are used, radiation dose and image noise on FBP are similar for CT scanners made by different manufacturers. Significant image noise reduction is achieved on low-dose CT examinations rendered with IRTs. The image noise on various scanners can be matched by applying specific hybrid IRT strengths. Model-based IRTs attain substantially higher noise reduction than hybrid
Model technique for aerodynamic study of boiler furnace

Energy Technology Data Exchange (ETDEWEB)

1966-02-01

The help of the Division was recently sought to improve the heat transfer and reduce the exit gas temperature in a pulverized-fuel-fired boiler at an Australian power station. One approach adopted was to construct from Perspex a 1:20 scale cold-air model of the boiler furnace and to use a flow-visualization technique to study the aerodynamic patterns established when air was introduced through the p.f. burners of the model. The work established good correlations between the behaviour of the model and of the boiler furnace.
Going Multi-viral: Synthedemic Modelling of Internet-based Spreading Phenomena

Directory of Open Access Journals (Sweden)

Marily Nika

2015-02-01

Full Text Available Epidemics of a biological and technological nature pervade modern life. For centuries, scientific research focused on biological epidemics, with simple compartmental epidemiological models emerging as the dominant explanatory paradigm. Yet there has been limited translation of this effort to explain internet-based spreading phenomena. Indeed, single-epidemic models are inadequate to explain the multimodal nature of complex phenomena. In this paper we propose a novel paradigm for modelling internet-based spreading phenomena based on the composition of multiple compartmental epidemiological models. Our approach is inspired by Fourier analysis, but rather than trigonometric wave forms, our components are compartmental epidemiological models. We show results on simulated multiple epidemic data, swine flu data and BitTorrent downloads of a popular music artist. Our technique can characterise these multimodal data sets utilising a parsimonous number of subepidemic models.

New Temperature-based Models for Predicting Global Solar Radiation

International Nuclear Information System (INIS)

Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.

2016-01-01

Highlights: • New temperature-based models for estimating solar radiation are investigated. • The models are validated against 20-years measured data of global solar radiation. • The new temperature-based model shows the best performance for coastal sites. • The new temperature-based model is more accurate than the sunshine-based models. • The new model is highly applicable with weather temperature forecast techniques. - Abstract: This study presents new ambient-temperature-based models for estimating global solar radiation as alternatives to the widely used sunshine-based models owing to the unavailability of sunshine data at all locations around the world. Seventeen new temperature-based models are established, validated and compared with other three models proposed in the literature (the Annandale, Allen and Goodin models) to estimate the monthly average daily global solar radiation on a horizontal surface. These models are developed using a 20-year measured dataset of global solar radiation for the case study location (Lat. 30°51′N and long. 29°34′E), and then, the general formulae of the newly suggested models are examined for ten different locations around Egypt. Moreover, the local formulae for the models are established and validated for two coastal locations where the general formulae give inaccurate predictions. Mostly common statistical errors are utilized to evaluate the performance of these models and identify the most accurate model. The obtained results show that the local formula for the most accurate new model provides good predictions for global solar radiation at different locations, especially at coastal sites. Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature. The quick and accurate estimations of the global solar radiation using this approach can be employed in the design and evaluation of performance for
MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

International Nuclear Information System (INIS)

Chen, G; Pan, X; Stayman, J; Samei, E

2014-01-01

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical
Polynomial fuzzy model-based approach for underactuated surface vessels

DEFF Research Database (Denmark)

Khooban, Mohammad Hassan; Vafamand, Navid; Dragicevic, Tomislav

2018-01-01

The main goal of this study is to introduce a new polynomial fuzzy model-based structure for a class of marine systems with non-linear and polynomial dynamics. The suggested technique relies on a polynomial Takagi–Sugeno (T–S) fuzzy modelling, a polynomial dynamic parallel distributed compensation...... surface vessel (USV). Additionally, in order to overcome the USV control challenges, including the USV un-modelled dynamics, complex nonlinear dynamics, external disturbances and parameter uncertainties, the polynomial fuzzy model representation is adopted. Moreover, the USV-based control structure...... and a sum-of-squares (SOS) decomposition. The new proposed approach is a generalisation of the standard T–S fuzzy models and linear matrix inequality which indicated its effectiveness in decreasing the tracking time and increasing the efficiency of the robust tracking control problem for an underactuated...
Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

Science.gov (United States)

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.
Analytical research using synchrotron radiation based techniques

International Nuclear Information System (INIS)

Jha, Shambhu Nath

2015-01-01

There are many Synchrotron Radiation (SR) based techniques such as X-ray Absorption Spectroscopy (XAS), X-ray Fluorescence Analysis (XRF), SR-Fourier-transform Infrared (SRFTIR), Hard X-ray Photoelectron Spectroscopy (HAXPS) etc. which are increasingly being employed worldwide in analytical research. With advent of modern synchrotron sources these analytical techniques have been further revitalized and paved ways for new techniques such as microprobe XRF and XAS, FTIR microscopy, Hard X-ray Photoelectron Spectroscopy (HAXPS) etc. The talk will cover mainly two techniques illustrating its capability in analytical research namely XRF and XAS. XRF spectroscopy: XRF spectroscopy is an analytical technique which involves the detection of emitted characteristic X-rays following excitation of the elements within the sample. While electron, particle (protons or alpha particles), or X-ray beams can be employed as the exciting source for this analysis, the use of X-ray beams from a synchrotron source has been instrumental in the advancement of the technique in the area of microprobe XRF imaging and trace level compositional characterisation of any sample. Synchrotron radiation induced X-ray emission spectroscopy, has become competitive with the earlier microprobe and nanoprobe techniques following the advancements in manipulating and detecting these X-rays. There are two important features that contribute to the superb elemental sensitivities of microprobe SR induced XRF: (i) the absence of the continuum (Bremsstrahlung) background radiation that is a feature of spectra obtained from charged particle beams, and (ii) the increased X-ray flux on the sample associated with the use of tunable third generation synchrotron facilities. Detection sensitivities have been reported in the ppb range, with values of 10 -17 g - 10 -14 g (depending on the particular element and matrix). Keeping in mind its demand, a microprobe XRF beamline has been setup by RRCAT at Indus-2 synchrotron
Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

Science.gov (United States)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future
Teaching Floating and Sinking Concepts with Different Methods and Techniques Based on the 5E Instructional Model

Science.gov (United States)

Cepni, Salih; Sahin, Cigdem; Ipek, Hava

2010-01-01

The purpose of this study was to test the influences of prepared instructional material based on the 5E instructional model combined with CCT, CC, animations, worksheets and POE on conceptual changes about floating and sinking concepts. The experimental group was taught with teaching material based on the 5E instructional model enriched with…
Determination of rock fragmentation based on a photographic technique

International Nuclear Information System (INIS)

Dehgan Banadaki, M.M.; Majdi, A.; Raessi Gahrooei, D.

2002-01-01

The paper represents a physical blasting model in laboratory scale along with a photographic approach to describe the distribution of blasted rock materials. For this purpose, based on wobble probability distribution function, eight samples each weighted 100 kg,were obtained. Four pictures from four different section of each sample were taken. Then, pictures were converted into graphic files with characterizing boundary of each piece of rocks in the samples. Error caused due to perspective were eliminated. Volume of each piece of the blasted rock materials and hence the required sieve size, each piece of rock to pass through, were calculated. Finally, original blasted rock size distribution was compared with that obtained from the photographic method. The paper concludes with presenting an approach to convert the results of photographic technique into size distribution obtained by seine analysis with sufficient verification
Knowledge based systems advanced concepts, techniques and applications

CERN Document Server

1997-01-01

The field of knowledge-based systems (KBS) has expanded enormously during the last years, and many important techniques and tools are currently available. Applications of KBS range from medicine to engineering and aerospace.This book provides a selected set of state-of-the-art contributions that present advanced techniques, tools and applications. These contributions have been prepared by a group of eminent researchers and professionals in the field.The theoretical topics covered include: knowledge acquisition, machine learning, genetic algorithms, knowledge management and processing under unc
Vibration measurement-based simple technique for damage detection of truss bridges: A case study

Directory of Open Access Journals (Sweden)

Sudath C. Siriwardane

2015-10-01

Full Text Available The bridges experience increasing traffic volume and weight, deteriorating of components and large number of stress cycles. Therefore, assessment of the current condition of steel railway bridges becomes necessary. Most of the commonly available approaches for structural health monitoring are based on visual inspection and non-destructive testing methods. The visual inspection is unreliable as those depend on uncertainty behind inspectors and their experience. Also, the non-destructive testing methods are found to be expensive. Therefore, recent researches have noticed that dynamic modal parameters or vibration measurement-based structural health monitoring methods are economical and may also provide more realistic predictions to damage state of civil infrastructure. Therefore this paper proposes a simple technique to locate the damage region of railway truss bridges based on measured modal parameters. The technique is discussed with a case study. Initially paper describes the details of considered railway bridge. Then observations of visual inspection, material testing and in situ load testing are discussed under separate sections. Development of validated finite element model of the considered bridge is comprehensively discussed. Hence, variations of modal parameters versus position of the damage are plotted. These plots are considered as the main reference for locating the damage of the railway bridge in future periodical inspection by comparing the measured corresponding modal parameters. Finally the procedure of periodical vibration measurement and damage locating technique are clearly illustrated.
Artificial Neural Network Based Model of Photovoltaic Cell

Directory of Open Access Journals (Sweden)

Messaouda Azzouzi

2017-03-01

Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results.
Fractal Image Compression Based on High Entropy Values Technique

Directory of Open Access Journals (Sweden)

Douaa Younis Abbaas

2018-04-01

Full Text Available There are many attempts tried to improve the encoding stage of FIC because it consumed time. These attempts worked by reducing size of the search pool for pair range-domain matching but most of them led to get a bad quality, or a lower compression ratio of reconstructed image. This paper aims to present a method to improve performance of the full search algorithm by combining FIC (lossy compression and another lossless technique (in this case entropy coding is used. The entropy technique will reduce size of the domain pool (i. e., number of domain blocks based on the entropy value of each range block and domain block and then comparing the results of full search algorithm and proposed algorithm based on entropy technique to see each of which give best results (such as reduced the encoding time with acceptable values in both compression quali-ty parameters which are C. R (Compression Ratio and PSNR (Image Quality. The experimental results of the proposed algorithm proven that using the proposed entropy technique reduces the encoding time while keeping compression rates and reconstruction image quality good as soon as possible.
A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

Science.gov (United States)

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748
Techniques for discrimination-free predictive models (Chapter 12)

NARCIS (Netherlands)

Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.; Custers, B.H.M.; Calders, T.G.K.; Schermer, B.W.; Zarsky, T.Z.

2013-01-01

In this chapter, we give an overview of the techniques developed ourselves for constructing discrimination-free classifiers. In discrimination-free classification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be
Air quality modelling using chemometric techniques | Azid | Journal ...

African Journals Online (AJOL)

This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management. Keywords: air pollutant index; chemometric; ANN; ...
Comparison of two structured illumination techniques based on different 3D illumination patterns

Science.gov (United States)

Shabani, H.; Patwary, N.; Doblas, A.; Saavedra, G.; Preza, C.

2017-02-01

Manipulating the excitation pattern in optical microscopy has led to several super-resolution techniques. Among different patterns, the lateral sinusoidal excitation was used for the first demonstration of structured illumination microscopy (SIM), which provides the fastest SIM acquisition system (based on the number of raw images required) compared to the multi-spot illumination approach. Moreover, 3D patterns that include lateral and axial variations in the illumination have attracted more attention recently as they address resolution enhancement in three dimensions. A threewave (3W) interference technique based on coherent illumination has already been shown to provide super-resolution and optical sectioning in 3D-SIM. In this paper, we investigate a novel tunable technique that creates a 3D pattern from a set of multiple incoherently illuminated parallel slits that act as light sources for a Fresnel biprism. This setup is able to modulate the illumination pattern in the object space both axially and laterally with adjustable modulation frequencies. The 3D forward model for the new system is developed here to consider the effect of the axial modulation due to the 3D patterned illumination. The performance of 3D-SIM based on 3W interference and the tunable system are investigated in simulation and compared based on two different criteria. First, restored images obtained for both 3D-SIM systems using a generalized Wiener filter are compared to determine the effect of the illumination pattern on the reconstruction. Second, the effective frequency response of both systems is studied to determine the axial and lateral resolution enhancement that is obtained in each case.
Artificial intelligence-based modeling and control of fluidized bed combustion

Energy Technology Data Exchange (ETDEWEB)

Ikonen, E.; Leppaekoski, K. (Univ. of Oulu, Dept. of Process and Environmental Engineering (Finland)). email: enso.ikonen@oulu.fi

2009-07-01

AI-inspired techniques have a lot to offer when developing methods for advanced identification, monitoring, control and optimization of industrial processes, such as power plants. Advanced control methods have been extensively examined in the research of the Power Plant Automation group at the Systems Engineering Laboratory, e.g., in fuel inventory modelling, combustion power control, modelling and control of flue gas oxygen, drum control, modelling and control of superheaters, or in optimization of flue-gas emissions. Most engineering approaches to artificial intelligence (AI) are characterized by two fundamental properties: the ability to learn from various sources and the ability to deal with plant complexity. Learning systems that are able to operate in uncertain environments based on incomplete information are commonly referred to as being intelligent. A number of other approaches exist, characterized by these properties, but not easily categorized as AI-systems. Advanced control methods (adaptive, predictive, multivariable, robust, etc.) are based on the availability of a model of the process to be controlled. Hence identification of processes becomes a key issue, leading to the use of adaptation and learning techniques. A typical learning control system concerns a selection of learning techniques applied for updating a process model, which in turn is used for the controller design. When design of learning control systems is complemented with concerns for dealing with uncertainties or vaguenesses in models, measurements, or even objectives, particularly close connections exist between advanced process control and methods of artificial intelligence and machine learning. Needs for advanced techniques are typically characterized by the desire to properly handle plant non-linearities, the multivariable nature of the dynamic problems, and the necessity to adapt to changing plant conditions. In the field of fluidized bed combustion (FBC) control, the many promising
Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study.

Science.gov (United States)

Shukla, Nagesh; Keast, John E; Ceglarek, Darek

2014-10-01

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
System-Level Modelling and Simulation of MEMS-Based Sensors

DEFF Research Database (Denmark)

Virk, Kashif M.; Madsen, Jan; Shafique, Mohammad

2005-01-01

The growing complexity of MEMS devices and their increased used in embedded systems (e.g., wireless integrated sensor networks) demands a disciplined aproach for MEMS design as well as the development of techniques for system-level modeling of these devices so that a seamless integration with the......The growing complexity of MEMS devices and their increased used in embedded systems (e.g., wireless integrated sensor networks) demands a disciplined aproach for MEMS design as well as the development of techniques for system-level modeling of these devices so that a seamless integration...... with the existing embedded system design methodologies is possible. In this paper, we present a MEMS design methodology that uses VHDL-AMS based system-level model of a MEMS device as a starting point and combines the top-down and bottom-up design approaches for design, verification, and optimization...
Test-Driven, Model-Based Systems Engineering

DEFF Research Database (Denmark)

Munck, Allan

Hearing systems have evolved over many years from simple mechanical devices (horns) to electronic units consisting of microphones, amplifiers, analog filters, loudspeakers, batteries, etc. Digital signal processors replaced analog filters to provide better performance end new features. Central....... This thesis concerns methods for identifying, selecting and implementing tools for various aspects of model-based systems engineering. A comprehensive method was proposed that include several novel steps such as techniques for analyzing the gap between requirements and tool capabilities. The method...... was verified with good results in two case studies for selection of a traceability tool (single-tool scenario) and a set of modeling tools (multi-tool scenarios). Models must be subjected to testing to allow engineers to predict functionality and performance of systems. Test-first strategies are known...

A Cyber-Attack Detection Model Based on Multivariate Analyses

Science.gov (United States)

Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.
Modeling and design techniques for RF power amplifiers

CERN Document Server

Raghavan, Arvind; Laskar, Joy

2008-01-01

The book covers RF power amplifier design, from device and modeling considerations to advanced circuit design architectures and techniques. It focuses on recent developments and advanced topics in this area, including numerous practical designs to back the theoretical considerations. It presents the challenges in designing power amplifiers in silicon and helps the reader improve the efficiency of linear power amplifiers, and design more accurate compact device models, with faster extraction routines, to create cost effective and reliable circuits.
Auto-correlation based intelligent technique for complex waveform presentation and measurement

International Nuclear Information System (INIS)

Rana, K P S; Singh, R; Sayann, K S

2009-01-01

Waveform acquisition and presentation forms the heart of many measurement systems. Particularly, data acquisition and presentation of repeating complex signals like sine sweep and frequency-modulated signals introduces the challenge of waveform time period estimation and live waveform presentation. This paper presents an intelligent technique, for waveform period estimation of both the complex and simple waveforms, based on the normalized auto-correlation method. The proposed technique is demonstrated using LabVIEW based intensive simulations on several simple and complex waveforms. Implementation of the technique is successfully demonstrated using LabVIEW based virtual instrumentation. Sine sweep vibration waveforms are successfully presented and measured for electrodynamic shaker system generated vibrations. The proposed method is also suitable for digital storage oscilloscope (DSO) triggering, for complex signals acquisition and presentation. This intelligence can be embodied into the DSO, making it an intelligent measurement system, catering wide varieties of the waveforms. The proposed technique, simulation results, robustness study and implementation results are presented in this paper.
Application of separable parameter space techniques to multi-tracer PET compartment modeling

International Nuclear Information System (INIS)

Zhang, Jeff L; Michael Morey, A; Kadrmas, Dan J

2016-01-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models. (paper)
Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species
Remediation of textile effluents by membrane based treatment techniques: a state of the art review.

Science.gov (United States)

Dasgupta, Jhilly; Sikder, Jaya; Chakraborty, Sudip; Curcio, Stefano; Drioli, Enrico

2015-01-01

The textile industries hold an important position in the global industrial arena because of their undeniable contributions to basic human needs satisfaction and to the world economy. These industries are however major consumers of water, dyes and other toxic chemicals. The effluents generated from each processing step comprise substantial quantities of unutilized resources. The effluents if discharged without prior treatment become potential sources of pollution due to their several deleterious effects on the environment. The treatment of heterogeneous textile effluents therefore demands the application of environmentally benign technology with appreciable quality water reclamation potential. These features can be observed in various innovative membrane based techniques. The present review paper thus elucidates the contributions of membrane technology towards textile effluent treatment and unexhausted raw materials recovery. The reuse possibilities of water recovered through membrane based techniques, such as ultrafiltration and nanofiltration in primary dye houses or auxiliary rinse vats have also been explored. Advantages and bottlenecks, such as membrane fouling associated with each of these techniques have also been highlighted. Additionally, several pragmatic models simulating transport mechanism across membranes have been documented. Finally, various accounts dealing with techno-economic evaluation of these membrane based textile wastewater treatment processes have been provided. Copyright © 2014 Elsevier Ltd. All rights reserved.
Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

International Nuclear Information System (INIS)

Bell, L R; Pogson, E M; Metcalfe, P; Holloway, L; Dowling, J A

2017-01-01

Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes. (paper)
Craniospinal radiotherapy in children: Electron- or photon-based technique of spinal irradiation

International Nuclear Information System (INIS)

Chojnacka, M.; Skowronska-Gardas, A.; Pedziwiatr, K.; Morawska-Kaczynska, M.; Zygmuntowicz-Pietka, A.; Semaniak, A.

2010-01-01

Background: The prone position and electron-based technique for craniospinal irradiation (CSI) have been standard in our department for many years. But this immobilization is difficult for the anaesthesiologist to gain airway access. The increasing number of children treated under anaesthesia led us to reconsider our technique. Aim: The purpose of this study is to report our new photon-based technique for CSI which could be applied in both the supine and the prone position and to compare this technique with our electron-based technique. Materials and methods: Between November 2007 and May 2008, 11 children with brain tumours were treated in the prone position with CSI. For 9 patients two treatment plans were created: the first one using photons and the second one using electron beams for spinal irradiation. We prepared seven 3D-conformal photon plans and four forward planned segmented field plans. We compared 20 treatment plans in terms of target dose homogeneity and sparing of organs at risk. Results: In segmented field plans better dose homogeneity in the thecal sac volume was achieved than in electron-based plans. Regarding doses in organs at risk, in photon-based plans we obtained a lower dose in the thyroid but a higher one in the heart and liver. Conclusions: Our technique can be applied in both the supine and prone position and it seems to be more feasible and precise than the electron technique. However, more homogeneous target coverage and higher precision of dose delivery for photons are obtained at the cost of slightly higher doses to the heart and liver. (authors)
Graph based techniques for tag cloud generation

DEFF Research Database (Denmark)

Leginus, Martin; Dolog, Peter; Lage, Ricardo Gomes

2013-01-01

Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings...... or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set....
Multidisciplinary Optimization of Tilt Rotor Blades Using Comprehensive Composite Modeling Technique

Science.gov (United States)

Chattopadhyay, Aditi; McCarthy, Thomas R.; Rajadas, John N.

1997-01-01

An optimization procedure is developed for addressing the design of composite tilt rotor blades. A comprehensive technique, based on a higher-order laminate theory, is developed for the analysis of the thick composite load-carrying sections, modeled as box beams, in the blade. The theory, which is based on a refined displacement field, is a three-dimensional model which approximates the elasticity solution so that the beam cross-sectional properties are not reduced to one-dimensional beam parameters. Both inplane and out-of-plane warping are included automatically in the formulation. The model can accurately capture the transverse shear stresses through the thickness of each wall while satisfying stress free boundary conditions on the inner and outer surfaces of the beam. The aerodynamic loads on the blade are calculated using the classical blade element momentum theory. Analytical expressions for the lift and drag are obtained based on the blade planform with corrections for the high lift capability of rotor blades. The aerodynamic analysis is coupled with the structural model to formulate the complete coupled equations of motion for aeroelastic analyses. Finally, a multidisciplinary optimization procedure is developed to improve the aerodynamic, structural and aeroelastic performance of the tilt rotor aircraft. The objective functions include the figure of merit in hover and the high speed cruise propulsive efficiency. Structural, aerodynamic and aeroelastic stability criteria are imposed as constraints on the problem. The Kreisselmeier-Steinhauser function is used to formulate the multiobjective function problem. The search direction is determined by the Broyden-Fletcher-Goldfarb-Shanno algorithm. The optimum results are compared with the baseline values and show significant improvements in the overall performance of the tilt rotor blade.
Backstepping Based Formation Control of Quadrotors with the State Transformation Technique

Directory of Open Access Journals (Sweden)

Keun Uk Lee

2017-11-01

Full Text Available In this paper, a backstepping-based formation control of quadrotors with the state transformation technique is proposed. First, the dynamics of a quadrotor is derived by using the Newton–Euler formulation. Next, a backstepping-based formation control for quadrotors using a state transformation technique is presented. In the position control, which is the basis of formation control, it is possible to derive the reference attitude angles employing a state transformation technique without the small angle assumption or the simplified dynamics usually used. Stability analysis based on the Lyapunov theorem shows that the proposed formation controller can provide a quadrotor formation error system that is asymptotically stabilized. Finally, we verify the performance of the proposed formation control method through comparison simulations.
Refractive index sensor based on optical fiber end face using pulse reference-based compensation technique

Science.gov (United States)

Bian, Qiang; Song, Zhangqi; Zhang, Xueliang; Yu, Yang; Chen, Yuzhong

2018-03-01

We proposed a refractive index sensor based on optical fiber end face using pulse reference-based compensation technique. With good compensation effect of this compensation technique, the power fluctuation of light source, the change of optic components transmission loss and coupler splitting ratio can be compensated, which largely reduces the background noise. The refractive index resolutions can achieve 3.8 × 10-6 RIU and1.6 × 10-6 RIU in different refractive index regions.
Nudging technique for scale bridging in air quality/climate atmospheric composition modelling

Directory of Open Access Journals (Sweden)

A. Maurizi

2012-04-01

Full Text Available The interaction between air quality and climate involves dynamical scales that cover a very wide range. Bridging these scales in numerical simulations is fundamental in studies devoted to megacity/hot-spot impacts on larger scales. A technique based on nudging is proposed as a bridging method that can couple different models at different scales.

Here, nudging is used to force low resolution chemical composition models with a run of a high resolution model on a critical area. A one-year numerical experiment focused on the Po Valley hot spot is performed using the BOLCHEM model to asses the method.

The results show that the model response is stable to perturbation induced by the nudging and that, taking the high resolution run as a reference, performances of the nudged run increase with respect to the non-forced run. The effect outside the forcing area depends on transport and is significant in a relevant number of events although it becomes weak on seasonal or yearly basis.
Validation of transport models using additive flux minimization technique

Energy Technology Data Exchange (ETDEWEB)

Pankin, A. Y.; Kruger, S. E. [Tech-X Corporation, 5621 Arapahoe Ave., Boulder, Colorado 80303 (United States); Groebner, R. J. [General Atomics, San Diego, California 92121 (United States); Hakim, A. [Princeton Plasma Physics Laboratory, Princeton, New Jersey 08543-0451 (United States); Kritz, A. H.; Rafiq, T. [Department of Physics, Lehigh University, Bethlehem, Pennsylvania 18015 (United States)

2013-10-15

A new additive flux minimization technique is proposed for carrying out the verification and validation (V and V) of anomalous transport models. In this approach, the plasma profiles are computed in time dependent predictive simulations in which an additional effective diffusivity is varied. The goal is to obtain an optimal match between the computed and experimental profile. This new technique has several advantages over traditional V and V methods for transport models in tokamaks and takes advantage of uncertainty quantification methods developed by the applied math community. As a demonstration of its efficiency, the technique is applied to the hypothesis that the paleoclassical density transport dominates in the plasma edge region in DIII-D tokamak discharges. A simplified version of the paleoclassical model that utilizes the Spitzer resistivity for the parallel neoclassical resistivity and neglects the trapped particle effects is tested in this paper. It is shown that a contribution to density transport, in addition to the paleoclassical density transport, is needed in order to describe the experimental profiles. It is found that more additional diffusivity is needed at the top of the H-mode pedestal, and almost no additional diffusivity is needed at the pedestal bottom. The implementation of this V and V technique uses the FACETS::Core transport solver and the DAKOTA toolkit for design optimization and uncertainty quantification. The FACETS::Core solver is used for advancing the plasma density profiles. The DAKOTA toolkit is used for the optimization of plasma profiles and the computation of the additional diffusivity that is required for the predicted density profile to match the experimental profile.
Validation of transport models using additive flux minimization technique

International Nuclear Information System (INIS)

Pankin, A. Y.; Kruger, S. E.; Groebner, R. J.; Hakim, A.; Kritz, A. H.; Rafiq, T.

2013-01-01

A new additive flux minimization technique is proposed for carrying out the verification and validation (V and V) of anomalous transport models. In this approach, the plasma profiles are computed in time dependent predictive simulations in which an additional effective diffusivity is varied. The goal is to obtain an optimal match between the computed and experimental profile. This new technique has several advantages over traditional V and V methods for transport models in tokamaks and takes advantage of uncertainty quantification methods developed by the applied math community. As a demonstration of its efficiency, the technique is applied to the hypothesis that the paleoclassical density transport dominates in the plasma edge region in DIII-D tokamak discharges. A simplified version of the paleoclassical model that utilizes the Spitzer resistivity for the parallel neoclassical resistivity and neglects the trapped particle effects is tested in this paper. It is shown that a contribution to density transport, in addition to the paleoclassical density transport, is needed in order to describe the experimental profiles. It is found that more additional diffusivity is needed at the top of the H-mode pedestal, and almost no additional diffusivity is needed at the pedestal bottom. The implementation of this V and V technique uses the FACETS::Core transport solver and the DAKOTA toolkit for design optimization and uncertainty quantification. The FACETS::Core solver is used for advancing the plasma density profiles. The DAKOTA toolkit is used for the optimization of plasma profiles and the computation of the additional diffusivity that is required for the predicted density profile to match the experimental profile
Validation techniques of agent based modelling for geospatial simulations

OpenAIRE

Darvishi, M.; Ahmadi, G.

2014-01-01

One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent...
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

Science.gov (United States)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.
Diagnosis and Model Based Identification of a Coupling Misalignment

Directory of Open Access Journals (Sweden)

P. Pennacchi

2005-01-01

Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.
Suppression of Spiral Waves by Voltage Clamp Techniques in a Conductance-Based Cardiac Tissue Model

International Nuclear Information System (INIS)

Lian-Chun, Yu; Guo-Yong, Zhang; Yong, Chen; Jun, Ma

2008-01-01

A new control method is proposed to control the spatio-temporal dynamics in excitable media, which is described by the Morris–Lecar cells model. It is confirmed that successful suppression of spiral waves can be obtained by spatially clamping the membrane voltage of the excitable cells. The low voltage clamping induces breakup of spiral waves and the fragments are soon absorbed by low voltage obstacles, whereas the high voltage clamping generates travel waves that annihilate spiral waves through collision with them. However, each method has its shortcomings. Furthermore, a two-step method that combines both low and high voltage clamp techniques is then presented as a possible way of out this predicament. (cross-disciplinary physics and related areas of science and technology)
Towards model-based testing of electronic funds transfer systems

NARCIS (Netherlands)

Asaadi, H.R.; Khosravi, R.; Mousavi, M.R.; Noroozi, N.; Arbab, F.; Sirjani, M.

2012-01-01

We report on our first experience with applying model-based testing techniques to an operational Electronic Funds Transfer (EFT) switch. The goal is to test the conformance of the EFT switch to the standard flows described by the ISO 8583 standard. To this end, we first make a formalization of the

Towards model-based testing of electronic funds transfer systems

NARCIS (Netherlands)

Asaadi, H.R.; Khosravi, R.; Mousavi, M.R.; Noroozi, N.

2010-01-01

We report on our first experience with applying model-based testing techniques to an operational Electronic Funds Transfer (EFT) switch. The goal is to test the conformance of the EFT switch to the standard flows described by the ISO 8583 standard. To this end, we first make a formalization of the
A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

Directory of Open Access Journals (Sweden)

Mansoor Ahmed Siddiqui

2017-01-01

Full Text Available Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions.
“In vitro” Implantation Technique Based on 3D Printed Prosthetic Prototypes

Science.gov (United States)

Tarnita, D.; Boborelu, C.; Geonea, I.; Malciu, R.; Grigorie, L.; Tarnita, D. N.

2018-06-01

In this paper, Rapid Prototyping ZCorp 310 system, based on high-performance composite powder and on resin-high strength infiltration system and three-dimensional printing as a manufacturing method are used to obtain physical prototypes of orthopaedic implants and prototypes of complex functional prosthetic systems directly from the 3D CAD data. These prototypes are useful for in vitro experimental tests and measurements to optimize and obtain final physical prototypes. Using a new elbow prosthesis model prototype obtained by 3D printing, the surgical technique of implantation is established. Surgical implantation was performed on male corpse elbow joint.
Top-Down and Bottom-Up Approach for Model-Based Testing of Product Lines

Directory of Open Access Journals (Sweden)

Stephan Weißleder

2013-03-01

Full Text Available Systems tend to become more and more complex. This has a direct impact on system engineering processes. Two of the most important phases in these processes are requirements engineering and quality assurance. Two significant complexity drivers located in these phases are the growing number of product variants that have to be integrated into the requirements engineering and the ever growing effort for manual test design. There are modeling techniques to deal with both complexity drivers like, e.g., feature modeling and model-based test design. Their combination, however, has been seldom the focus of investigation. In this paper, we present two approaches to combine feature modeling and model-based testing as an efficient quality assurance technique for product lines. We present the corresponding difficulties and approaches to overcome them. All explanations are supported by an example of an online shop product line.
improvement of digital image watermarking techniques based on FPGA implementation

International Nuclear Information System (INIS)

EL-Hadedy, M.E

2006-01-01

digital watermarking provides the ownership of a piece of digital data by marking the considered data invisibly or visibly. this can be used to protect several types of multimedia objects such as audio, text, image and video. this thesis demonstrates the different types of watermarking techniques such as (discrete cosine transform (DCT) and discrete wavelet transform (DWT) and their characteristics. then, it classifies these techniques declaring their advantages and disadvantages. an improved technique with distinguished features, such as peak signal to noise ratio ( PSNR) and similarity ratio (SR) has been introduced. the modified technique has been compared with the other techniques by measuring heir robustness against differ attacks. finally, field programmable gate arrays (FPGA) based implementation and comparison, for the proposed watermarking technique have been presented and discussed
Monte Carlo Techniques for the Comprehensive Modeling of Isotopic Inventories in Future Nuclear Systems and Fuel Cycles. Final Report

International Nuclear Information System (INIS)

Paul P.H. Wilson

2005-01-01

The development of Monte Carlo techniques for isotopic inventory analysis has been explored in order to facilitate the modeling of systems with flowing streams of material through varying neutron irradiation environments. This represents a novel application of Monte Carlo methods to a field that has traditionally relied on deterministic solutions to systems of first-order differential equations. The Monte Carlo techniques were based largely on the known modeling techniques of Monte Carlo radiation transport, but with important differences, particularly in the area of variance reduction and efficiency measurement. The software that was developed to implement and test these methods now provides a basis for validating approximate modeling techniques that are available to deterministic methodologies. The Monte Carlo methods have been shown to be effective in reproducing the solutions of simple problems that are possible using both stochastic and deterministic methods. The Monte Carlo methods are also effective for tracking flows of materials through complex systems including the ability to model removal of individual elements or isotopes in the system. Computational performance is best for flows that have characteristic times that are large fractions of the system lifetime. As the characteristic times become short, leading to thousands or millions of passes through the system, the computational performance drops significantly. Further research is underway to determine modeling techniques to improve performance within this range of problems. This report describes the technical development of Monte Carlo techniques for isotopic inventory analysis. The primary motivation for this solution methodology is the ability to model systems of flowing material being exposed to varying and stochastically varying radiation environments. The methodology was developed in three stages: analog methods which model each atom with true reaction probabilities (Section 2), non-analog methods
Comparison of small-group training with self-directed internet-based training in inhaler techniques.

Science.gov (United States)

Toumas, Mariam; Basheti, Iman A; Bosnic-Anticevich, Sinthia Z

2009-08-28

To compare the effectiveness of small-group training in correct inhaler technique with self-directed Internet-based training. Pharmacy students were randomly allocated to 1 of 2 groups: small-group training (n = 123) or self-directed Internet-based training (n = 113). Prior to intervention delivery, all participants were given a placebo Turbuhaler and product information leaflet and received inhaler technique training based on their group. Technique was assessed following training and predictors of correct inhaler technique were examined. There was a significant improvement in the number of participants demonstrating correct technique in both groups (small group training, 12% to 63%; p training, 9% to 59%; p groups in the percent change (n = 234, p > 0.05). Increased student confidence following the intervention was a predictor for correct inhaler technique. Self-directed Internet-based training is as effective as small-group training in improving students' inhaler technique.
Recent developments of the projected shell model based on many-body techniques

Directory of Open Access Journals (Sweden)

Sun Yang

2015-01-01

Full Text Available Recent developments of the projected shell model (PSM are summarized. Firstly, by using the Pfaffian algorithm, the multi-quasiparticle configuration space is expanded to include 6-quasiparticle states. The yrast band of 166Hf at very high spins is studied as an example, where the observed third back-bending in the moment of inertia is well reproduced and explained. Secondly, an angular-momentum projected generate coordinate method is developed based on PSM. The evolution of the low-lying states, including the second 0+ state, of the soft Gd, Dy, and Er isotopes to the well-deformed ones is calculated, and compared with experimental data.
Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

Energy Technology Data Exchange (ETDEWEB)

Deutsch, Jared L., E-mail: jdeutsch@ualberta.ca [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Palmer, Kevin [Teck Resources Limited (Canada); Deutsch, Clayton V.; Szymanski, Jozef [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Etsell, Thomas H. [University of Alberta, Department of Chemical and Materials Engineering (Canada)

2016-06-15

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.
A Synthesis of Star Calibration Techniques for Ground-Based Narrowband Electron-Multiplying Charge-Coupled Device Imagers Used in Auroral Photometry

Science.gov (United States)

Grubbs, Guy II; Michell, Robert; Samara, Marilia; Hampton, Don; Jahn, Jorg-Micha

2016-01-01

A technique is presented for the periodic and systematic calibration of ground-based optical imagers. It is important to have a common system of units (Rayleighs or photon flux) for cross comparison as well as self-comparison over time. With the advancement in technology, the sensitivity of these imagers has improved so that stars can be used for more precise calibration. Background subtraction, flat fielding, star mapping, and other common techniques are combined in deriving a calibration technique appropriate for a variety of ground-based imager installations. Spectral (4278, 5577, and 8446 A ) ground-based imager data with multiple fields of view (19, 47, and 180 deg) are processed and calibrated using the techniques developed. The calibration techniques applied result in intensity measurements in agreement between different imagers using identical spectral filtering, and the intensity at each wavelength observed is within the expected range of auroral measurements. The application of these star calibration techniques, which convert raw imager counts into units of photon flux, makes it possible to do quantitative photometry. The computed photon fluxes, in units of Rayleighs, can be used for the absolute photometry between instruments or as input parameters for auroral electron transport models.
Bi Input-extended Kalman filter based estimation technique for speed-sensorless control of induction motors

International Nuclear Information System (INIS)

Barut, Murat

2010-01-01

This study offers a novel extended Kalman filter (EKF) based estimation technique for the solution of the on-line estimation problem related to uncertainties in the stator and rotor resistances inherent to the speed-sensorless high efficiency control of induction motors (IMs) in the wide speed range as well as extending the limited number of states and parameter estimations possible with a conventional single EKF algorithm. For this aim, the introduced estimation technique in this work utilizes a single EKF algorithm with the consecutive execution of two inputs derived from the two individual extended IM models based on the stator resistance and rotor resistance estimation, differently from the other approaches in past studies, which require two separate EKF algorithms operating in a switching or braided manner; thus, it has superiority over the previous EKF schemes in this regard. The proposed EKF based estimation technique performing the on-line estimations of the stator currents, the rotor flux, the rotor angular velocity, and the load torque involving the viscous friction term together with the rotor and stator resistance is also used in the combination with the speed-sensorless direct vector control of IM and tested with simulations under the challenging 12 scenarios generated instantaneously via step and/or linear variations of the velocity reference, the load torque, the stator resistance, and the rotor resistance in the range of high and zero speed, assuming that the measured stator phase currents and voltages are available. Even under those variations, the performance of the speed-sensorless direct vector control system established on the novel EKF based estimation technique is observed to be quite good.
Bi Input-extended Kalman filter based estimation technique for speed-sensorless control of induction motors

Energy Technology Data Exchange (ETDEWEB)

Barut, Murat, E-mail: muratbarut27@yahoo.co [Nigde University, Department of Electrical and Electronics Engineering, 51245 Nigde (Turkey)

2010-10-15

This study offers a novel extended Kalman filter (EKF) based estimation technique for the solution of the on-line estimation problem related to uncertainties in the stator and rotor resistances inherent to the speed-sensorless high efficiency control of induction motors (IMs) in the wide speed range as well as extending the limited number of states and parameter estimations possible with a conventional single EKF algorithm. For this aim, the introduced estimation technique in this work utilizes a single EKF algorithm with the consecutive execution of two inputs derived from the two individual extended IM models based on the stator resistance and rotor resistance estimation, differently from the other approaches in past studies, which require two separate EKF algorithms operating in a switching or braided manner; thus, it has superiority over the previous EKF schemes in this regard. The proposed EKF based estimation technique performing the on-line estimations of the stator currents, the rotor flux, the rotor angular velocity, and the load torque involving the viscous friction term together with the rotor and stator resistance is also used in the combination with the speed-sensorless direct vector control of IM and tested with simulations under the challenging 12 scenarios generated instantaneously via step and/or linear variations of the velocity reference, the load torque, the stator resistance, and the rotor resistance in the range of high and zero speed, assuming that the measured stator phase currents and voltages are available. Even under those variations, the performance of the speed-sensorless direct vector control system established on the novel EKF based estimation technique is observed to be quite good.
A Technique for Real-Time Ionospheric Ranging Error Correction Based On Radar Dual-Frequency Detection

Science.gov (United States)

Lyu, Jiang-Tao; Zhou, Chen

2017-12-01

Ionospheric refraction is one of the principal error sources for limiting the accuracy of radar systems for space target detection. High-accuracy measurement of the ionospheric electron density along the propagation path of radar wave is the most important procedure for the ionospheric refraction correction. Traditionally, the ionospheric model and the ionospheric detection instruments, like ionosonde or GPS receivers, are employed for obtaining the electron density. However, both methods are not capable of satisfying the requirements of correction accuracy for the advanced space target radar system. In this study, we propose a novel technique for ionospheric refraction correction based on radar dual-frequency detection. Radar target range measurements at two adjacent frequencies are utilized for calculating the electron density integral exactly along the propagation path of the radar wave, which can generate accurate ionospheric range correction. The implementation of radar dual-frequency detection is validated by a P band radar located in midlatitude China. The experimental results present that the accuracy of this novel technique is more accurate than the traditional ionospheric model correction. The technique proposed in this study is very promising for the high-accuracy radar detection and tracking of objects in geospace.
Research in Model-Based Change Detection and Site Model Updating

National Research Council Canada - National Science Library

Nevatia, R

1998-01-01

.... Some of these techniques also are applicable to automatic site modeling and some of our change detection techniques may apply to detection of larger mobile objects, such as airplanes. We have implemented an interactive modeling system that works in conjunction with our automatic system to minimize the need for tedious interaction.
Next generation initiation techniques

Science.gov (United States)

Warner, Tom; Derber, John; Zupanski, Milija; Cohn, Steve; Verlinde, Hans

1993-01-01

Four-dimensional data assimilation strategies can generally be classified as either current or next generation, depending upon whether they are used operationally or not. Current-generation data-assimilation techniques are those that are presently used routinely in operational-forecasting or research applications. They can be classified into the following categories: intermittent assimilation, Newtonian relaxation, and physical initialization. It should be noted that these techniques are the subject of continued research, and their improvement will parallel the development of next generation techniques described by the other speakers. Next generation assimilation techniques are those that are under development but are not yet used operationally. Most of these procedures are derived from control theory or variational methods and primarily represent continuous assimilation approaches, in which the data and model dynamics are 'fitted' to each other in an optimal way. Another 'next generation' category is the initialization of convective-scale models. Intermittent assimilation systems use an objective analysis to combine all observations within a time window that is centered on the analysis time. Continuous first-generation assimilation systems are usually based on the Newtonian-relaxation or 'nudging' techniques. Physical initialization procedures generally involve the use of standard or nonstandard data to force some physical process in the model during an assimilation period. Under the topic of next-generation assimilation techniques, variational approaches are currently being actively developed. Variational approaches seek to minimize a cost or penalty function which measures a model's fit to observations, background fields and other imposed constraints. Alternatively, the Kalman filter technique, which is also under investigation as a data assimilation procedure for numerical weather prediction, can yield acceptable initial conditions for mesoscale models. The
Risk-based maintenance-Techniques and applications

International Nuclear Information System (INIS)

Arunraj, N.S.; Maiti, J.

2007-01-01

Plant and equipment, however well designed, will not remain safe or reliable if it is not maintained. The general objective of the maintenance process is to make use of the knowledge of failures and accidents to achieve the possible safety with the lowest possible cost. The concept of risk-based maintenance was developed to inspect the high-risk components usually with greater frequency and thoroughness and to maintain in a greater manner, to achieve tolerable risk criteria. Risk-based maintenance methodology provides a tool for maintenance planning and decision making to reduce the probability of failure of equipment and the consequences of failure. In this paper, the risk analysis and risk-based maintenance methodologies were identified and classified into suitable classes. The factors affecting the quality of risk analysis were identified and analyzed. The applications, input data and output data were studied to understand their functioning and efficiency. The review showed that there is no unique way to perform risk analysis and risk-based maintenance. The use of suitable techniques and methodologies, careful investigation during the risk analysis phase, and its detailed and structured results are necessary to make proper risk-based maintenance decisions
Feathering effect detection and artifact agglomeration index-based video deinterlacing technique

Science.gov (United States)

Martins, André Luis; Rodrigues, Evandro Luis Linhari; de Paiva, Maria Stela Veludo

2018-03-01

Several video deinterlacing techniques have been developed, and each one presents a better performance in certain conditions. Occasionally, even the most modern deinterlacing techniques create frames with worse quality than primitive deinterlacing processes. This paper validates that the final image quality can be improved by combining different types of deinterlacing techniques. The proposed strategy is able to select between two types of deinterlaced frames and, if necessary, make the local correction of the defects. This decision is based on an artifact agglomeration index obtained from a feathering effect detection map. Starting from a deinterlaced frame produced by the "interfield average" method, the defective areas are identified, and, if deemed appropriate, these areas are replaced by pixels generated through the "edge-based line average" method. Test results have proven that the proposed technique is able to produce video frames with higher quality than applying a single deinterlacing technique through getting what is good from intra- and interfield methods.
Agent-based modeling: a new approach for theory building in social psychology.

Science.gov (United States)

Smith, Eliot R; Conrey, Frederica R

2007-02-01

Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach.
Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models.

Science.gov (United States)

Niroomandi, S; Alfaro, I; Cueto, E; Chinesta, F

2012-01-01

Model reduction techniques have shown to constitute a valuable tool for real-time simulation in surgical environments and other fields. However, some limitations, imposed by real-time constraints, have not yet been overcome. One of such limitations is the severe limitation in time (established in 500Hz of frequency for the resolution) that precludes the employ of Newton-like schemes for solving non-linear models as the ones usually employed for modeling biological tissues. In this work we present a technique able to deal with geometrically non-linear models, based on the employ of model reduction techniques, together with an efficient non-linear solver. Examples of the performance of the technique over some examples will be given. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
An overview of the recent advances in delay-time-based maintenance modelling

International Nuclear Information System (INIS)

Wang, Wenbin

2012-01-01

Industrial plant maintenance is an area which has enormous potential to be improved. It is also an area attracted significant attention from mathematical modellers because of the random phenomenon of plant failures. This paper reviews the recent advances in delay-time-based maintenance modelling, which is one of the mathematical techniques for optimising inspection planning and related problems. The delay-time is a concept that divides a plant failure process into two stages: from new until the point of an identifiable defect, and then from this point to failure. The first stage is called the normal working stage and the second stage is called the failure delay-time stage. If the distributions of the two stages can be quantified, the relationship between the number of failures and the inspection interval can be readily established. This can then be used for optimizing the inspection interval and other related decision variables. In this review, we pay particular attention to new methodological developments and industrial applications of the delay-time-based models over the last few decades. The use of the delay-time concept and modeling techniques in other areas rather than in maintenance is also reviewed. Future research directions are also highlighted. - Highlights: ► Reviewed the recent advances in delay-time-based maintenance models and applications. ► Compared the delay-time-based models with other models. ► Focused on methodologies and applications. ► Pointed out future research directions.

Agent-based modeling in ecological economics.

Science.gov (United States)

Heckbert, Scott; Baynes, Tim; Reeson, Andrew

2010-01-01

Interconnected social and environmental systems are the domain of ecological economics, and models can be used to explore feedbacks and adaptations inherent in these systems. Agent-based modeling (ABM) represents autonomous entities, each with dynamic behavior and heterogeneous characteristics. Agents interact with each other and their environment, resulting in emergent outcomes at the macroscale that can be used to quantitatively analyze complex systems. ABM is contributing to research questions in ecological economics in the areas of natural resource management and land-use change, urban systems modeling, market dynamics, changes in consumer attitudes, innovation, and diffusion of technology and management practices, commons dilemmas and self-governance, and psychological aspects to human decision making and behavior change. Frontiers for ABM research in ecological economics involve advancing the empirical calibration and validation of models through mixed methods, including surveys, interviews, participatory modeling, and, notably, experimental economics to test specific decision-making hypotheses. Linking ABM with other modeling techniques at the level of emergent properties will further advance efforts to understand dynamics of social-environmental systems.
Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

Science.gov (United States)

Amicarelli, A.; Gariazzo, C.; Finardi, S.; Pelliccioni, A.; Silibello, C.

2008-05-01

Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.
Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

Energy Technology Data Exchange (ETDEWEB)

Amicarelli, A; Pelliccioni, A [ISPESL - Dipartimento Insediamenti Produttivi e Interazione con l' Ambiente, Via Fontana Candida, 1 00040 Monteporzio Catone (RM) Italy (Italy); Finardi, S; Silibello, C [ARIANET, via Gilino 9, 20128 Milano (Italy); Gariazzo, C

2008-05-01

Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM{sub 10} concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode.
Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

International Nuclear Information System (INIS)

Amicarelli, A; Pelliccioni, A; Finardi, S; Silibello, C; Gariazzo, C

2008-01-01

Data assimilation techniques are methods to limit the growth of errors in a dynamical model by allowing observations distributed in space and time to force (nudge) model solutions. They have become common for meteorological model applications in recent years, especially to enhance weather forecast and to support air-quality studies. In order to investigate the influence of different data assimilation techniques on the meteorological fields produced by RAMS model, and to evaluate their effects on the ozone and PM 10 concentrations predicted by FARM model, several numeric experiments were conducted over the urban area of Rome, Italy, during a summer episode
Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR Technique in EIT: A Simulation Study

Directory of Open Access Journals (Sweden)

Tushar Kanti Bera

2011-06-01

Full Text Available A Block Matrix based Multiple Regularization (BMMR technique is proposed for improving conductivity image quality in EIT. The response matrix (JTJ has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domain with circular inhomogeneity and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR and modified Levenberg-Marquardt Regularization (LMR methods. It is observed that the BMMR technique reduces the projection error and solution error and improves the conductivity reconstruction in EIT. Result show that the BMMR method also improves the image contrast and inhomogeneity conductivity profile and hence the reconstructed image quality is enhanced. ;doi:10.5617/jeb.170 J Electr Bioimp, vol. 2, pp. 33-47, 2011
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Science.gov (United States)

Dorigo, W. A.; Zurita-Milla, R.; de Wit, A. J. W.; Brazile, J.; Singh, R.; Schaepman, M. E.

2007-05-01

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by
An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.

Science.gov (United States)

Binder, Harald; Porzelius, Christine; Schumacher, Martin

2011-03-01

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Physics-Based Fragment Acceleration Modeling for Pressurized Tank Burst Risk Assessments

Science.gov (United States)

Manning, Ted A.; Lawrence, Scott L.

2014-01-01

As part of comprehensive efforts to develop physics-based risk assessment techniques for space systems at NASA, coupled computational fluid and rigid body dynamic simulations were carried out to investigate the flow mechanisms that accelerate tank fragments in bursting pressurized vessels. Simulations of several configurations were compared to analyses based on the industry-standard Baker explosion model, and were used to formulate an improved version of the model. The standard model, which neglects an external fluid, was found to agree best with simulation results only in configurations where the internal-to-external pressure ratio is very high and fragment curvature is small. The improved model introduces terms that accommodate an external fluid and better account for variations based on circumferential fragment count. Physics-based analysis was critical in increasing the model's range of applicability. The improved tank burst model can be used to produce more accurate risk assessments of space vehicle failure modes that involve high-speed debris, such as exploding propellant tanks and bursting rocket engines.
A systematic and critical review of model-based economic evaluations of pharmacotherapeutics in patients with bipolar disorder.

Science.gov (United States)

Mohiuddin, Syed

2014-08-01

Bipolar disorder (BD) is a chronic and relapsing mental illness with a considerable health-related and economic burden. The primary goal of pharmacotherapeutics for BD is to improve patients' well-being. The use of decision-analytic models is key in assessing the added value of the pharmacotherapeutics aimed at treating the illness, but concerns have been expressed about the appropriateness of different modelling techniques and about the transparency in the reporting of economic evaluations. This paper aimed to identify and critically appraise published model-based economic evaluations of pharmacotherapeutics in BD patients. A systematic review combining common terms for BD and economic evaluation was conducted in MEDLINE, EMBASE, PSYCINFO and ECONLIT. Studies identified were summarised and critically appraised in terms of the use of modelling technique, model structure and data sources. Considering the prognosis and management of BD, the possible benefits and limitations of each modelling technique are discussed. Fourteen studies were identified using model-based economic evaluations of pharmacotherapeutics in BD patients. Of these 14 studies, nine used Markov, three used discrete-event simulation (DES) and two used decision-tree models. Most of the studies (n = 11) did not include the rationale for the choice of modelling technique undertaken. Half of the studies did not include the risk of mortality. Surprisingly, no study considered the risk of having a mixed bipolar episode. This review identified various modelling issues that could potentially reduce the comparability of one pharmacotherapeutic intervention with another. Better use and reporting of the modelling techniques in the future studies are essential. DES modelling appears to be a flexible and comprehensive technique for evaluating the comparability of BD treatment options because of its greater flexibility of depicting the disease progression over time. However, depending on the research question
Modeling Fatigue Damage Onset and Progression in Composites Using an Element-Based Virtual Crack Closure Technique Combined With the Floating Node Method

Science.gov (United States)

De Carvalho, Nelson V.; Krueger, Ronald

2016-01-01

A new methodology is proposed to model the onset and propagation of matrix cracks and delaminations in carbon-epoxy composites subject to fatigue loading. An extended interface element, based on the Floating Node Method, is developed to represent delaminations and matrix cracks explicitly in a mesh independent fashion. Crack propagation is determined using an element-based Virtual Crack Closure Technique approach to determine mixed-mode energy release rates, and the Paris-Law relationship to obtain crack growth rate. Crack onset is determined using a stressbased onset criterion coupled with a stress vs. cycle curve and Palmgren-Miner rule to account for fatigue damage accumulation. The approach is implemented in Abaqus/Standard® via the user subroutine functionality. Verification exercises are performed to assess the accuracy and correct implementation of the approach. Finally, it was demonstrated that this approach captured the differences in failure morphology in fatigue for two laminates of identical stiffness, but with layups containing ?deg plies that were either stacked in a single group, or distributed through the laminate thickness.
An Image Registration Based Technique for Noninvasive Vascular Elastography

OpenAIRE

Valizadeh, Sina; Makkiabadi, Bahador; Mirbagheri, Alireza; Soozande, Mehdi; Manwar, Rayyan; Mozaffarzadeh, Moein; Nasiriavanaki, Mohammadreza

2018-01-01

Non-invasive vascular elastography is an emerging technique in vascular tissue imaging. During the past decades, several techniques have been suggested to estimate the tissue elasticity by measuring the displacement of the Carotid vessel wall. Cross correlation-based methods are the most prevalent approaches to measure the strain exerted in the wall vessel by the blood pressure. In the case of a low pressure, the displacement is too small to be apparent in ultrasound imaging, especially in th...
A hybrid SEA/modal technique for modeling structural-acoustic interior noise in rotorcraft.

Science.gov (United States)

Jayachandran, V; Bonilha, M W

2003-03-01

This paper describes a hybrid technique that combines Statistical Energy Analysis (SEA) predictions for structural vibration with acoustic modal summation techniques to predict interior noise levels in rotorcraft. The method was applied for predicting the sound field inside a mock-up of the interior panel system of the Sikorsky S-92 helicopter. The vibration amplitudes of the frame and panel systems were predicted using a detailed SEA model and these were used as inputs to the model of the interior acoustic space. The spatial distribution of the vibration field on individual panels, and their coupling to the acoustic space were modeled using stochastic techniques. Leakage and nonresonant transmission components were accounted for using space-averaged values obtained from a SEA model of the complete structural-acoustic system. Since the cabin geometry was quite simple, the modeling of the interior acoustic space was performed using a standard modal summation technique. Sound pressure levels predicted by this approach at specific microphone locations were compared with measured data. Agreement within 3 dB in one-third octave bands above 40 Hz was observed. A large discrepancy in the one-third octave band in which the first acoustic mode is resonant (31.5 Hz) was observed. Reasons for such a discrepancy are discussed in the paper. The developed technique provides a method for modeling helicopter cabin interior noise in the frequency mid-range where neither FEA nor SEA is individually effective or accurate.
Polymer-based blood vessel models with micro-temperature sensors in EVE

Science.gov (United States)

Mizoshiri, Mizue; Ito, Yasuaki; Hayakawa, Takeshi; Maruyama, Hisataka; Sakurai, Junpei; Ikeda, Seiichi; Arai, Fumihito; Hata, Seiichi

2017-04-01

Cu-based micro-temperature sensors were directly fabricated on poly(dimethylsiloxane) (PDMS) blood vessel models in EVE using a combined process of spray coating and femtosecond laser reduction of CuO nanoparticles. CuO nanoparticle solution coated on a PDMS blood vessel model are thermally reduced and sintered by focused femtosecond laser pulses in atmosphere to write the sensors. After removing the non-irradiated CuO nanoparticles, Cu-based microtemperature sensors are formed. The sensors are thermistor-type ones whose temperature dependences of the resistance are used for measuring temperature inside the blood vessel model. This fabrication technique is useful for direct-writing of Cu-based microsensors and actuators on arbitrary nonplanar substrates.
Effect of temperature on acid-base equilibria in separation techniques. A review.

Science.gov (United States)

Gagliardi, Leonardo G; Tascon, Marcos; Castells, Cecilia B

2015-08-19

Studies on the theoretical principles of acid-base equilibria are reviewed and the influence of temperature on secondary chemical equilibria within the context of separation techniques, in water and also in aqueous-organic solvent mixtures, is discussed. In order to define the relationships between the retention in liquid chromatography or the migration velocity in capillary electrophoresis and temperature, the main properties of acid-base equilibria have to be taken into account for both, the analytes and the conjugate pairs chosen to control the solution pH. The focus of this review is based on liquid-liquid extraction (LLE), liquid chromatography (LC) and capillary electrophoresis (CE), with emphasis on the use of temperature as a useful variable to modify selectivity on a predictable basis. Simpliﬁed models were evaluated to achieve practical optimizations involving pH and temperature (in LLE and CE) as well as solvent composition in reversed-phase LC. Copyright © 2015 Elsevier B.V. All rights reserved.
An Overview on Base Real-Time Hard Shadow Techniques in Virtual Environments

Directory of Open Access Journals (Sweden)

Mohd Shahrizal Sunar

2012-03-01

Full Text Available Shadows are elegant to create a realistic scene in virtual environments variety type of shadow techniques encourage us to prepare an overview on all base shadow techniques. Non real-time and real-time techniques are big subdivision of shadow generation. In non real-time techniques ray tracing, ray casting and radiosity are well known and are described deeply. Radiosity is implemented to create very realistic shadow on non real-time scene. Although traditional radiosity algorithm is difficult to implement, we have proposed a simple one. The proposed pseudo code is easier to understand and implement. Ray tracing is used to prevent of collision of movement objects. Projection shadow, shadow volume and shadow mapping are used to create real-time shadow in virtual environments. We have used projection shadow for some objects are static and have shadow on flat surface. Shadow volume is used to create accurate shadow with sharp outline. Shadow mapping that is the base of most recently techniques is reconstructed. The reconstruct algorithm gives some new idea to propose another algorithm based on shadow mapping.
Comparative assessment of PIV-based pressure evaluation techniques applied to a transonic base flow

NARCIS (Netherlands)

Blinde, P; Michaelis, D; van Oudheusden, B.W.; Weiss, P.E.; de Kat, R.; Laskari, A.; Jeon, Y.J.; David, L; Schanz, D; Huhn, F.; Gesemann, S; Novara, M.; McPhaden, C.; Neeteson, N.; Rival, D.; Schneiders, J.F.G.; Schrijer, F.F.J.

2016-01-01

A test case for PIV-based pressure evaluation techniques has been developed by constructing a simulated experiment from a ZDES simulation for an axisymmetric base flow at Mach 0.7. The test case comprises sequences of four subsequent particle images (representing multi-pulse data) as well as
A study on quantification of unavailability of DPPS with fault tolerant techniques considering fault tolerant techniques' characteristics

International Nuclear Information System (INIS)

Kim, B. G.; Kang, H. G.; Kim, H. E.; Seung, P. H.; Kang, H. G.; Lee, S. J.

2012-01-01

With the improvement of digital technologies, digital I and C systems have included more various fault tolerant techniques than conventional analog I and C systems have, in order to increase fault detection and to help the system safely perform the required functions in spite of the presence of faults. So, in the reliability evaluation of digital systems, the fault tolerant techniques (FTTs) and their fault coverage must be considered. To consider the effects of FTTs in a digital system, there have been several studies on the reliability of digital model. Therefore, this research based on literature survey attempts to develop a model to evaluate the plant reliability of the digital plant protection system (DPPS) with fault tolerant techniques considering detection and process characteristics and human errors. Sensitivity analysis is performed to ascertain important variables from the fault management coverage and unavailability based on the proposed model
An analysis of supersonic flows with low-Reynolds number compressible two-equation turbulence models using LU finite volume implicit numerical techniques

Science.gov (United States)

Lee, J.

1994-01-01

A generalized flow solver using an implicit Lower-upper (LU) diagonal decomposition based numerical technique has been coupled with three low-Reynolds number kappa-epsilon models for analysis of problems with engineering applications. The feasibility of using the LU technique to obtain efficient solutions to supersonic problems using the kappa-epsilon model has been demonstrated. The flow solver is then used to explore limitations and convergence characteristics of several popular two equation turbulence models. Several changes to the LU solver have been made to improve the efficiency of turbulent flow predictions. In general, the low-Reynolds number kappa-epsilon models are easier to implement than the models with wall-functions, but require much finer near-wall grid to accurately resolve the physics. The three kappa-epsilon models use different approaches to characterize the near wall regions of the flow. Therefore, the limitations imposed by the near wall characteristics have been carefully resolved. The convergence characteristics of a particular model using a given numerical technique are also an important, but most often overlooked, aspect of turbulence model predictions. It is found that some convergence characteristics could be sacrificed for more accurate near-wall prediction. However, even this gain in accuracy is not sufficient to model the effects of an external pressure gradient imposed by a shock-wave/ boundary-layer interaction. Additional work on turbulence models, especially for compressibility, is required since the solutions obtained with base line turbulence are in only reasonable agreement with the experimental data for the viscous interaction problems.
DCT-based cyber defense techniques

Science.gov (United States)

Amsalem, Yaron; Puzanov, Anton; Bedinerman, Anton; Kutcher, Maxim; Hadar, Ofer

2015-09-01

With the increasing popularity of video streaming services and multimedia sharing via social networks, there is a need to protect the multimedia from malicious use. An attacker may use steganography and watermarking techniques to embed malicious content, in order to attack the end user. Most of the attack algorithms are robust to basic image processing techniques such as filtering, compression, noise addition, etc. Hence, in this article two novel, real-time, defense techniques are proposed: Smart threshold and anomaly correction. Both techniques operate at the DCT domain, and are applicable for JPEG images and H.264 I-Frames. The defense performance was evaluated against a highly robust attack, and the perceptual quality degradation was measured by the well-known PSNR and SSIM quality assessment metrics. A set of defense techniques is suggested for improving the defense efficiency. For the most aggressive attack configuration, the combination of all the defense techniques results in 80% protection against cyber-attacks with PSNR of 25.74 db.
Key Techniques for the Development of Web-Based PDM System

Institute of Scientific and Technical Information of China (English)

WANG Li-juan; ZHANG Xu; NING Ru-xin

2006-01-01

Some key techniques for the development of web-based product data management (PDM) system are introduced. The four-tiered B/S architecture of a PDM system-BITPDM is introduced first, followed by its design and implementation, including virtual data vault, flexible coding system, document management,product structure and configuration management, workflow/process and product maturity management. BITPDM can facilitate the activities from new product introduction phase to manufacturing, and manage the product data and their dynamic changing history. Based on Microsoft. NET, XML, web service and SOAP techniques, BITPDM realizes the integration and efficient management of product information.

Theoretical Bound of CRLB for Energy Efficient Technique of RSS-Based Factor Graph Geolocation

Science.gov (United States)

Kahar Aziz, Muhammad Reza; Heriansyah; Saputra, EfaMaydhona; Musa, Ardiansyah

2018-03-01

To support the increase of wireless geolocation development as the key of the technology in the future, this paper proposes theoretical bound derivation, i.e., Cramer Rao lower bound (CRLB) for energy efficient of received signal strength (RSS)-based factor graph wireless geolocation technique. The theoretical bound derivation is crucially important to evaluate whether the energy efficient technique of RSS-based factor graph wireless geolocation is effective as well as to open the opportunity to further innovation of the technique. The CRLB is derived in this paper by using the Fisher information matrix (FIM) of the main formula of the RSS-based factor graph geolocation technique, which is lied on the Jacobian matrix. The simulation result shows that the derived CRLB has the highest accuracy as a bound shown by its lowest root mean squared error (RMSE) curve compared to the RMSE curve of the RSS-based factor graph geolocation technique. Hence, the derived CRLB becomes the lower bound for the efficient technique of RSS-based factor graph wireless geolocation.
Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System

Directory of Open Access Journals (Sweden)

S. M. Niaz Arifin

2015-05-01

Full Text Available A landscape epidemiology modeling framework is presented which integrates the simulation outputs from an established spatial agent-based model (ABM of malaria with a geographic information system (GIS. For a study area in Kenya, five landscape scenarios are constructed with varying coverage levels of two mosquito-control interventions. For each scenario, maps are presented to show the average distributions of three output indices obtained from the results of 750 simulation runs. Hot spot analysis is performed to detect statistically significant hot spots and cold spots. Additional spatial analysis is conducted using ordinary kriging with circular semivariograms for all scenarios. The integration of epidemiological simulation-based results with spatial analyses techniques within a single modeling framework can be a valuable tool for conducting a variety of disease control activities such as exploring new biological insights, monitoring epidemiological landscape changes, and guiding resource allocation for further investigation.
A neuro-fuzzy computing technique for modeling hydrological time series

Science.gov (United States)

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2004-05-01

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.
Towards a Business Process Modeling Technique for Agile Development of Case Management Systems

Directory of Open Access Journals (Sweden)

Ilia Bider

2017-12-01

Full Text Available A modern organization needs to adapt its behavior to changes in the business environment by changing its Business Processes (BP and corresponding Business Process Support (BPS systems. One way of achieving such adaptability is via separation of the system code from the process description/model by applying the concept of executable process models. Furthermore, to ease introduction of changes, such process model should separate different perspectives, for example, control-flow, human resources, and data perspectives, from each other. In addition, for developing a completely new process, it should be possible to start with a reduced process model to get a BPS system quickly running, and then continue to develop it in an agile manner. This article consists of two parts, the first sets requirements on modeling techniques that could be used in the tools that supports agile development of BPs and BPS systems. The second part suggests a business process modeling technique that allows to start modeling with the data/information perspective which would be appropriate for processes supported by Case or Adaptive Case Management (CM/ACM systems. In a model produced by this technique, called data-centric business process model, a process instance/case is defined as sequence of states in a specially designed instance database, while the process model is defined as a set of rules that set restrictions on allowed states and transitions between them. The article details the background for the project of developing the data-centric process modeling technique, presents the outline of the structure of the model, and gives formal definitions for a substantial part of the model.
Natural Model based Design in Context: an Effective Method for Environmental Problems

Directory of Open Access Journals (Sweden)

Eric D. Kameni

2017-10-01

Full Text Available Analyzing complex problem domains is not easy. Simulation tools support decision makers to find the best policies. Model-based system development is an approach where a model of the application domain is the central driving force when designing simulation tools. State-of-the-art techniques however still require both expert knowledge of the application domain and the implementation techniques as provided by ICT (such as multilevel agent technology. Domain experts, however, usually do not master ICT sufficiently. Modeling is more insightful for the domain expert when its goal is to formalize the language being used in that domain as a semi-natural language. At the meta level, this language describes the main concepts of the type of application domain. The model then is a concretization of this meta model. The main focus of this article is (1 to propose a natural-language-based approach to modeling application domains, (2 to show how these models can be transformed systematically into computational models, and (3 to propose the tool TiC (Tool in Context that supports the domain expert when developing a model and generating a simulation tool. Our research methodology is based on Design Science. We verify our approach by describing the various transformation steps in detail, and by demonstrating the way of working via a sample session applying a real problem of Laf Forest Reserve deforestation in North Cameroon.
GIS-Based bivariate statistical techniques for groundwater potential ...

Indian Academy of Sciences (India)

24

This study shows the potency of two GIS-based data driven bivariate techniques namely ... In the view of these weaknesses , there is a strong requirement for reassessment of .... Font color: Text 1, Not Expanded by / Condensed by , ...... West Bengal (India) using remote sensing, geographical information system and multi-.
Clustering economies based on multiple criteria decision making techniques

Directory of Open Access Journals (Sweden)

Mansour Momeni

2011-10-01

Full Text Available One of the primary concerns on many countries is to determine different important factors affecting economic growth. In this paper, we study some factors such as unemployment rate, inflation ratio, population growth, average annual income, etc to cluster different countries. The proposed model of this paper uses analytical hierarchy process (AHP to prioritize the criteria and then uses a K-mean technique to cluster 59 countries based on the ranked criteria into four groups. The first group includes countries with high standards such as Germany and Japan. In the second cluster, there are some developing countries with relatively good economic growth such as Saudi Arabia and Iran. The third cluster belongs to countries with faster rates of growth compared with the countries located in the second group such as China, India and Mexico. Finally, the fourth cluster includes countries with relatively very low rates of growth such as Jordan, Mali, Niger, etc.
Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

Directory of Open Access Journals (Sweden)

Donald Adjeroh

2018-03-01

Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.
Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

Science.gov (United States)

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.
The Effectiveness of Song Technique in Teaching Paper Based TOEFL (PBT’S Listening Comprehension Section

Directory of Open Access Journals (Sweden)

Heri Kuswoyo

2013-07-01

Full Text Available Among three sections that follow the Paper-Based TOEFL (PBT, many test takers find listening comprehension section is the most difficult. Thus, in this research the researcher aims to explore how students learn PBT’s listening comprehension section effectively through song technique. This sounds like a more interesting and engaging way to learn language because music is a very powerful motivational tool for learning language. To reach the goal of this study, the researcher applied the grammar approach. It is an appropriate approach since the main idea of grammar-based listening exercises is to analyze the language by its components and reconstruct an incomplete text. Besides, the researcher employed an English song as the media the researcher uses the top- down model for the Listening Process. In this research, the writer tries to share his experience in teaching listening in English department of Teknokrat College by implementing song technique.
Towards representing human behavior and decision making in Earth system models - an overview of techniques and approaches

Science.gov (United States)

Müller-Hansen, Finn; Schlüter, Maja; Mäs, Michael; Donges, Jonathan F.; Kolb, Jakob J.; Thonicke, Kirsten; Heitzig, Jobst

2017-11-01

Today, humans have a critical impact on the Earth system and vice versa, which can generate complex feedback processes between social and ecological dynamics. Integrating human behavior into formal Earth system models (ESMs), however, requires crucial modeling assumptions about actors and their goals, behavioral options, and decision rules, as well as modeling decisions regarding human social interactions and the aggregation of individuals' behavior. Here, we review existing modeling approaches and techniques from various disciplines and schools of thought dealing with human behavior at different levels of decision making. We demonstrate modelers' often vast degrees of freedom but also seek to make modelers aware of the often crucial consequences of seemingly innocent modeling assumptions. After discussing which socioeconomic units are potentially important for ESMs, we compare models of individual decision making that correspond to alternative behavioral theories and that make diverse modeling assumptions about individuals' preferences, beliefs, decision rules, and foresight. We review approaches to model social interaction, covering game theoretic frameworks, models of social influence, and network models. Finally, we discuss approaches to studying how the behavior of individuals, groups, and organizations can aggregate to complex collective phenomena, discussing agent-based, statistical, and representative-agent modeling and economic macro-dynamics. We illustrate the main ingredients of modeling techniques with examples from land-use dynamics as one of the main drivers of environmental change bridging local to global scales.
Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

International Nuclear Information System (INIS)

Han, G.; Lin, B.; Xu, Z.

2017-01-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.
Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

Science.gov (United States)

Han, G.; Lin, B.; Xu, Z.

2017-03-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.
Adaptive, Small-Rotation-Based, Corotational Technique for Analysis of 2D Nonlinear Elastic Frames

Directory of Open Access Journals (Sweden)

Jaroon Rungamornrat

2014-01-01

Full Text Available This paper presents an efficient and accurate numerical technique for analysis of two-dimensional frames accounted for both geometric nonlinearity and nonlinear elastic material behavior. An adaptive remeshing scheme is utilized to optimally discretize a structure into a set of elements where the total displacement can be decomposed into the rigid body movement and one possessing small rotations. This, therefore, allows the force-deformation relationship for the latter part to be established based on small-rotation-based kinematics. Nonlinear elastic material model is integrated into such relation via the prescribed nonlinear moment-curvature relationship. The global force-displacement relation for each element can be derived subsequently using corotational formulations. A final system of nonlinear algebraic equations along with its associated gradient matrix for the whole structure is obtained by a standard assembly procedure and then solved numerically by Newton-Raphson algorithm. A selected set of results is then reported to demonstrate and discuss the computational performance including the accuracy and convergence of the proposed technique.
A critical survey of agent-based wholesale electricity market models

International Nuclear Information System (INIS)

Weidlich, Anke; Veit, Daniel

2008-01-01

The complexity of electricity markets calls for rich and flexible modeling techniques that help to understand market dynamics and to derive advice for the design of appropriate regulatory frameworks. Agent-Based Computational Economics (ACE) is a fairly young research paradigm that offers methods for realistic electricity market modeling. A growing number of researchers have developed agent-based models for simulating electricity markets. The diversity of approaches makes it difficult to overview the field of ACE electricity research; this literature survey should guide the way through and describe the state-of-the-art of this research area. In a conclusive summary, shortcomings of existing approaches and open issues that should be addressed by ACE electricity researchers are critically discussed. (author)
Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.
Application of integrated modeling technique for data services ...

African Journals Online (AJOL)

This paper, therefore, describes the application of the integrated simulation technique for deriving the optimum resources required for data services in an asynchronous transfer mode (ATM) based private wide area network (WAN) to guarantee specific QoS requirement. The simulation tool drastically cuts the simulation ...
Low level waste management: a compilation of models and monitoring techniques. Volume 1

Energy Technology Data Exchange (ETDEWEB)

Mosier, J.E.; Fowler, J.R.; Barton, C.J. (comps.)

1980-04-01

In support of the National Low-Level Waste (LLW) Management Research and Development Program being carried out at Oak Ridge National Laboratory, Science Applications, Inc., conducted a survey of models and monitoring techniques associated with the transport of radionuclides and other chemical species from LLW burial sites. As a result of this survey, approximately 350 models were identified. For each model the purpose and a brief description are presented. To the extent possible, a point of contact and reference material are identified. The models are organized into six technical categories: atmospheric transport, dosimetry, food chain, groundwater transport, soil transport, and surface water transport. About 4% of the models identified covered other aspects of LLW management and are placed in a miscellaneous category. A preliminary assessment of all these models was performed to determine their ability to analyze the transport of other chemical species. The models that appeared to be applicable are identified. A brief survey of the state-of-the-art techniques employed to monitor LLW burial sites is also presented, along with a very brief discussion of up-to-date burial techniques.
Low level waste management: a compilation of models and monitoring techniques. Volume 1

International Nuclear Information System (INIS)

Mosier, J.E.; Fowler, J.R.; Barton, C.J.

1980-04-01

In support of the National Low-Level Waste (LLW) Management Research and Development Program being carried out at Oak Ridge National Laboratory, Science Applications, Inc., conducted a survey of models and monitoring techniques associated with the transport of radionuclides and other chemical species from LLW burial sites. As a result of this survey, approximately 350 models were identified. For each model the purpose and a brief description are presented. To the extent possible, a point of contact and reference material are identified. The models are organized into six technical categories: atmospheric transport, dosimetry, food chain, groundwater transport, soil transport, and surface water transport. About 4% of the models identified covered other aspects of LLW management and are placed in a miscellaneous category. A preliminary assessment of all these models was performed to determine their ability to analyze the transport of other chemical species. The models that appeared to be applicable are identified. A brief survey of the state-of-the-art techniques employed to monitor LLW burial sites is also presented, along with a very brief discussion of up-to-date burial techniques
Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

Science.gov (United States)

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Pro...

Model-Based GN and C Simulation and Flight Software Development for Orion Missions beyond LEO

Science.gov (United States)

Odegard, Ryan; Milenkovic, Zoran; Henry, Joel; Buttacoli, Michael

2014-01-01

For Orion missions beyond low Earth orbit (LEO), the Guidance, Navigation, and Control (GN&C) system is being developed using a model-based approach for simulation and flight software. Lessons learned from the development of GN&C algorithms and flight software for the Orion Exploration Flight Test One (EFT-1) vehicle have been applied to the development of further capabilities for Orion GN&C beyond EFT-1. Continuing the use of a Model-Based Development (MBD) approach with the Matlab®/Simulink® tool suite, the process for GN&C development and analysis has been largely improved. Furthermore, a model-based simulation environment in Simulink, rather than an external C-based simulation, greatly eases the process for development of flight algorithms. The benefits seen by employing lessons learned from EFT-1 are described, as well as the approach for implementing additional MBD techniques. Also detailed are the key enablers for improvements to the MBD process, including enhanced configuration management techniques for model-based software systems, automated code and artifact generation, and automated testing and integration.
Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts.

Science.gov (United States)

Wagner, Maximilian E H; Gellrich, Nils-Claudius; Friese, Karl-Ingo; Becker, Matthias; Wolter, Franz-Erich; Lichtenstein, Juergen T; Stoetzer, Marcus; Rana, Majeed; Essig, Harald

2016-01-01

Objective determination of the orbital volume is important in the diagnostic process and in evaluating the efficacy of medical and/or surgical treatment of orbital diseases. Tools designed to measure orbital volume with computed tomography (CT) often cannot be used with cone beam CT (CBCT) because of inferior tissue representation, although CBCT has the benefit of greater availability and lower patient radiation exposure. Therefore, a model-based segmentation technique is presented as a new method for measuring orbital volume and compared to alternative techniques. Both eyes from thirty subjects with no known orbital pathology who had undergone CBCT as a part of routine care were evaluated (n = 60 eyes). Orbital volume was measured with manual, atlas-based, and model-based segmentation methods. Volume measurements, volume determination time, and usability were compared between the three methods. Differences in means were tested for statistical significance using two-tailed Student's t tests. Neither atlas-based (26.63 ± 3.15 mm(3)) nor model-based (26.87 ± 2.99 mm(3)) measurements were significantly different from manual volume measurements (26.65 ± 4.0 mm(3)). However, the time required to determine orbital volume was significantly longer for manual measurements (10.24 ± 1.21 min) than for atlas-based (6.96 ± 2.62 min, p < 0.001) or model-based (5.73 ± 1.12 min, p < 0.001) measurements. All three orbital volume measurement methods examined can accurately measure orbital volume, although atlas-based and model-based methods seem to be more user-friendly and less time-consuming. The new model-based technique achieves fully automated segmentation results, whereas all atlas-based segmentations at least required manipulations to the anterior closing. Additionally, model-based segmentation can provide reliable orbital volume measurements when CT image quality is poor.
Human exposure assessment in the near field of GSM base-station antennas using a hybrid finite element/method of moments technique.

Science.gov (United States)

Meyer, Frans J C; Davidson, David B; Jakobus, Ulrich; Stuchly, Maria A

2003-02-01

A hybrid finite-element method (FEM)/method of moments (MoM) technique is employed for specific absorption rate (SAR) calculations in a human phantom in the near field of a typical group special mobile (GSM) base-station antenna. The MoM is used to model the metallic surfaces and wires of the base-station antenna, and the FEM is used to model the heterogeneous human phantom. The advantages of each of these frequency domain techniques are, thus, exploited, leading to a highly efficient and robust numerical method for addressing this type of bioelectromagnetic problem. The basic mathematical formulation of the hybrid technique is presented. This is followed by a discussion of important implementation details-in particular, the linear algebra routines for sparse, complex FEM matrices combined with dense MoM matrices. The implementation is validated by comparing results to MoM (surface equivalence principle implementation) and finite-difference time-domain (FDTD) solutions of human exposure problems. A comparison of the computational efficiency of the different techniques is presented. The FEM/MoM implementation is then used for whole-body and critical-organ SAR calculations in a phantom at different positions in the near field of a base-station antenna. This problem cannot, in general, be solved using the MoM or FDTD due to computational limitations. This paper shows that the specific hybrid FEM/MoM implementation is an efficient numerical tool for accurate assessment of human exposure in the near field of base-station antennas.
Endoscopic skull base training using 3D printed models with pre-existing pathology.

Science.gov (United States)

Narayanan, Vairavan; Narayanan, Prepageran; Rajagopalan, Raman; Karuppiah, Ravindran; Rahman, Zainal Ariff Abdul; Wormald, Peter-John; Van Hasselt, Charles Andrew; Waran, Vicknes

2015-03-01

Endoscopic base of skull surgery has been growing in acceptance in the recent past due to improvements in visualisation and micro instrumentation as well as the surgical maturing of early endoscopic skull base practitioners. Unfortunately, these demanding procedures have a steep learning curve. A physical simulation that is able to reproduce the complex anatomy of the anterior skull base provides very useful means of learning the necessary skills in a safe and effective environment. This paper aims to assess the ease of learning endoscopic skull base exposure and drilling techniques using an anatomically accurate physical model with a pre-existing pathology (i.e., basilar invagination) created from actual patient data. Five models of a patient with platy-basia and basilar invagination were created from the original MRI and CT imaging data of a patient. The models were used as part of a training workshop for ENT surgeons with varying degrees of experience in endoscopic base of skull surgery, from trainees to experienced consultants. The surgeons were given a list of key steps to achieve in exposing and drilling the skull base using the simulation model. They were then asked to list the level of difficulty of learning these steps using the model. The participants found the models suitable for learning registration, navigation and skull base drilling techniques. All participants also found the deep structures to be accurately represented spatially as confirmed by the navigation system. These models allow structured simulation to be conducted in a workshop environment where surgeons and trainees can practice to perform complex procedures in a controlled fashion under the supervision of experts.
Tilting-Twisting-Rolling: a pen-based technique for compass geometric construction

Institute of Scientific and Technical Information of China (English)

Fei LYU; Feng TIAN; Guozhong DAI; Hongan WANG

2017-01-01

This paper presents a new pen-based technique,Tilting-Twisting-Rolling,to support compass geometric construction.By leveraging the 3D orientation information and 3D rotation information of a pen,this technique allows smooth pen action to complete multi-step geometric construction without switching task states.Results from a user study show this Tilting-Twisting-Rolling technique can improve user performance and user experience in compass geometric construction.
D Modelling and Interactive Web-Based Visualization of Cultural Heritage Objects

Science.gov (United States)

Koeva, M. N.

2016-06-01

Nowadays, there are rapid developments in the fields of photogrammetry, laser scanning, computer vision and robotics, together aiming to provide highly accurate 3D data that is useful for various applications. In recent years, various LiDAR and image-based techniques have been investigated for 3D modelling because of their opportunities for fast and accurate model generation. For cultural heritage preservation and the representation of objects that are important for tourism and their interactive visualization, 3D models are highly effective and intuitive for present-day users who have stringent requirements and high expectations. Depending on the complexity of the objects for the specific case, various technological methods can be applied. The selected objects in this particular research are located in Bulgaria - a country with thousands of years of history and cultural heritage dating back to ancient civilizations. This motivates the preservation, visualisation and recreation of undoubtedly valuable historical and architectural objects and places, which has always been a serious challenge for specialists in the field of cultural heritage. In the present research, comparative analyses regarding principles and technological processes needed for 3D modelling and visualization are presented. The recent problems, efforts and developments in interactive representation of precious objects and places in Bulgaria are presented. Three technologies based on real projects are described: (1) image-based modelling using a non-metric hand-held camera; (2) 3D visualization based on spherical panoramic images; (3) and 3D geometric and photorealistic modelling based on architectural CAD drawings. Their suitability for web-based visualization are demonstrated and compared. Moreover the possibilities for integration with additional information such as interactive maps, satellite imagery, sound, video and specific information for the objects are described. This comparative study
Video Multiple Watermarking Technique Based on Image Interlacing Using DWT

Directory of Open Access Journals (Sweden)

Mohamed M. Ibrahim

2014-01-01

Full Text Available Digital watermarking is one of the important techniques to secure digital media files in the domains of data authentication and copyright protection. In the nonblind watermarking systems, the need of the original host file in the watermark recovery operation makes an overhead over the system resources, doubles memory capacity, and doubles communications bandwidth. In this paper, a robust video multiple watermarking technique is proposed to solve this problem. This technique is based on image interlacing. In this technique, three-level discrete wavelet transform (DWT is used as a watermark embedding/extracting domain, Arnold transform is used as a watermark encryption/decryption method, and different types of media (gray image, color image, and video are used as watermarks. The robustness of this technique is tested by applying different types of attacks such as: geometric, noising, format-compression, and image-processing attacks. The simulation results show the effectiveness and good performance of the proposed technique in saving system resources, memory capacity, and communications bandwidth.
Video multiple watermarking technique based on image interlacing using DWT.

Science.gov (United States)

Ibrahim, Mohamed M; Abdel Kader, Neamat S; Zorkany, M

2014-01-01

Digital watermarking is one of the important techniques to secure digital media files in the domains of data authentication and copyright protection. In the nonblind watermarking systems, the need of the original host file in the watermark recovery operation makes an overhead over the system resources, doubles memory capacity, and doubles communications bandwidth. In this paper, a robust video multiple watermarking technique is proposed to solve this problem. This technique is based on image interlacing. In this technique, three-level discrete wavelet transform (DWT) is used as a watermark embedding/extracting domain, Arnold transform is used as a watermark encryption/decryption method, and different types of media (gray image, color image, and video) are used as watermarks. The robustness of this technique is tested by applying different types of attacks such as: geometric, noising, format-compression, and image-processing attacks. The simulation results show the effectiveness and good performance of the proposed technique in saving system resources, memory capacity, and communications bandwidth.
Constrained convex minimization via model-based excessive gap

OpenAIRE

Tran Dinh, Quoc; Cevher, Volkan

2014-01-01

We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal feasibility gap of their iterates separately. Through a dual smoothing and prox-function selection strategy, our framework subsumes the augmented Lagrangian, and alternating methods as special cases, where our rates apply.
Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques

Directory of Open Access Journals (Sweden)

Temitope Mapayi

2015-01-01

Full Text Available Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.
Modelling of Evaporator in Waste Heat Recovery System using Finite Volume Method and Fuzzy Technique

Directory of Open Access Journals (Sweden)

Jahedul Islam Chowdhury

2015-12-01

Full Text Available The evaporator is an important component in the Organic Rankine Cycle (ORC-based Waste Heat Recovery (WHR system since the effective heat transfer of this device reflects on the efficiency of the system. When the WHR system operates under supercritical conditions, the heat transfer mechanism in the evaporator is unpredictable due to the change of thermo-physical properties of the fluid with temperature. Although the conventional finite volume model can successfully capture those changes in the evaporator of the WHR process, the computation time for this method is high. To reduce the computation time, this paper develops a new fuzzy based evaporator model and compares its performance with the finite volume method. The results show that the fuzzy technique can be applied to predict the output of the supercritical evaporator in the waste heat recovery system and can significantly reduce the required computation time. The proposed model, therefore, has the potential to be used in real time control applications.
Low Power LDPC Code Decoder Architecture Based on Intermediate Message Compression Technique

Science.gov (United States)

Shimizu, Kazunori; Togawa, Nozomu; Ikenaga, Takeshi; Goto, Satoshi

Reducing the power dissipation for LDPC code decoder is a major challenging task to apply it to the practical digital communication systems. In this paper, we propose a low power LDPC code decoder architecture based on an intermediate message-compression technique which features as follows: (i) An intermediate message compression technique enables the decoder to reduce the required memory capacity and write power dissipation. (ii) A clock gated shift register based intermediate message memory architecture enables the decoder to decompress the compressed messages in a single clock cycle while reducing the read power dissipation. The combination of the above two techniques enables the decoder to reduce the power dissipation while keeping the decoding throughput. The simulation results show that the proposed architecture improves the power efficiency up to 52% and 18% compared to that of the decoder based on the overlapped schedule and the rapid convergence schedule without the proposed techniques respectively.
A population-based Bayesian approach to the minimal model of glucose and insulin homeostasis

DEFF Research Database (Denmark)

Andersen, Kim Emil; Højbjerre, Malene

2005-01-01

-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minmal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait...... to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem...
Photovoltaic System Modeling with Fuzzy Logic Based Maximum Power Point Tracking Algorithm

Directory of Open Access Journals (Sweden)

Hasan Mahamudul

2013-01-01

Full Text Available This paper represents a novel modeling technique of PV module with a fuzzy logic based MPPT algorithm and boost converter in Simulink environment. The prime contributions of this work are simplification of PV modeling technique and implementation of fuzzy based MPPT system to track maximum power efficiently. The main highlighted points of this paper are to demonstrate the precise control of the duty cycle with respect to various atmospheric conditions, illustration of PV characteristic curves, and operation analysis of the converter. The proposed system has been applied for three different PV modules SOLKAR 36 W, BP MSX 60 W, and KC85T 87 W. Finally the resultant data has been compared with the theoretical prediction and company specified value to ensure the validity of the system.
Fault Tree Analysis with Temporal Gates and Model Checking Technique for Qualitative System Safety Analysis

International Nuclear Information System (INIS)

Koh, Kwang Yong; Seong, Poong Hyun

2010-01-01

Fault tree analysis (FTA) has suffered from several drawbacks such that it uses only static gates and hence can not capture dynamic behaviors of the complex system precisely, and it is in lack of rigorous semantics, and reasoning process which is to check whether basic events really cause top events is done manually and hence very labor-intensive and time-consuming for the complex systems while it has been one of the most widely used safety analysis technique in nuclear industry. Although several attempts have been made to overcome this problem, they can not still do absolute or actual time modeling because they adapt relative time concept and can capture only sequential behaviors of the system. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA
Impact of advanced microstructural characterization techniques on modeling and analysis of radiation damage

International Nuclear Information System (INIS)

Garner, F.A.; Odette, G.R.

1980-01-01

The evolution of radiation-induced alterations of dimensional and mechanical properties has been shown to be a direct and often predictable consequence of radiation-induced microstructural changes. Recent advances in understanding of the nature and role of each microstructural component in determining the property of interest has led to a reappraisal of the type and priority of data needed for further model development. This paper presents an overview of the types of modeling and analysis activities in progress, the insights that prompted these activities, and specific examples of successful and ongoing efforts. A review is presented of some problem areas that in the authors' opinion are not yet receiving sufficient attention and which may benefit from the application of advanced techniques of microstructural characterization. Guidelines based on experience gained in previous studies are also provided for acquisition of data in a form most applicable to modeling needs
Feasibility of CBCT-based dose calculation: Comparative analysis of HU adjustment techniques

International Nuclear Information System (INIS)

Fotina, Irina; Hopfgartner, Johannes; Stock, Markus; Steininger, Thomas; Lütgendorf-Caucig, Carola; Georg, Dietmar

2012-01-01

Background and purpose: The aim of this work was to compare the accuracy of different HU adjustments for CBCT-based dose calculation. Methods and materials: Dose calculation was performed on CBCT images of 30 patients. In the first two approaches phantom-based (Pha-CC) and population-based (Pop-CC) conversion curves were used. The third method (WAB) represents override of the structures with standard densities for water, air and bone. In ROI mapping approach all structures were overridden with average HUs from planning CT. All techniques were benchmarked to the Pop-CC and CT-based plans by DVH comparison and γ-index analysis. Results: For prostate plans, WAB and ROI mapping compared to Pop-CC showed differences in PTV D median below 2%. The WAB and Pha-CC methods underestimated the bladder dose in IMRT plans. In lung cases PTV coverage was underestimated by Pha-CC method by 2.3% and slightly overestimated by the WAB and ROI techniques. The use of the Pha-CC method for head–neck IMRT plans resulted in difference in PTV coverage up to 5%. Dose calculation with WAB and ROI techniques showed better agreement with pCT than conversion curve-based approaches. Conclusions: Density override techniques provide an accurate alternative to the conversion curve-based methods for dose calculation on CBCT images.
Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

Science.gov (United States)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Evaluation of the maladaptation of the test base in acrylic resin regarding the technique of preparation, place of measurement and storage time

Directory of Open Access Journals (Sweden)

Natália Pereira de OLIVEIRA

2018-02-01

Full Text Available Abstract Introduction The most used material for the preparation of the baseplates is the acrylic resin, but it can present distortions. Objective To evaluate preparation technique, region and storage time that presents less maladaptation of the base when made with self-cured acrylic resin. Material and method Models were created in gypsum type III simulating edentulous maxilla, as divided into 3 groups (n = 10: GC (control group thermopolymerizable acrylic resin; G1 - manual adaptation technique and G2 - drip technique. For the measurements, silicone by condensation of light consistency that was interposed between base and model was used. With a hydraulic press, 50 kg pressure was applied leading the base of the model. The obtained mold was measured in the palate, canine and molar regions with a digital caliper at the following times: immediately after the base polymerization, at 24, 48, 72, 96 hours and one week. The results were submitted to statistical analysis. Result G1 presented maladaptation of 0.43 mm ± 0.10, while G2 obtained 0.39 mm ± 0.11. The lowest maladaptation occurred in the CG. The palate region presented greater maladaptation (0.52 ± 0.07 and the canine region, the lowest (CD = 0.27 mm ± 0.07 and CE = 0.27 ± 0.09; There was no statistically significant difference for storage times. Conclusion G2 presented lower values than G1, with no statistically significant difference. The palate region presented greater maladaptation, followed by molars and canines. The bases continued to maladaptation the model after the immediate polymerization, with no statistically significant difference.
An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations

KAUST Repository

Iskandarani, Mohamed

2016-04-22

We give an overview of four different ensemble-based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model\\'s output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions\\' coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil-gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the different proxies. The examples illustrate how regression-based techniques can outperform projection-based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.

Ionospheric scintillation forecasting model based on NN-PSO technique

Science.gov (United States)

Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

2017-09-01

The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.
Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

Science.gov (United States)

Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

2017-08-15

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.
Sabots, Obturator and Gas-In-Launch Tube Techniques for Heat Flux Models in Ballistic Ranges

Science.gov (United States)

Bogdanoff, David W.; Wilder, Michael C.

2013-01-01

For thermal protection system (heat shield) design for space vehicle entry into earth and other planetary atmospheres, it is essential to know the augmentation of the heat flux due to vehicle surface roughness. At the NASA Ames Hypervelocity Free Flight Aerodynamic Facility (HFFAF) ballistic range, a campaign of heat flux studies on rough models, using infrared camera techniques, has been initiated. Several phenomena can interfere with obtaining good heat flux data when using this measuring technique. These include leakage of the hot drive gas in the gun barrel through joints in the sabot (model carrier) to create spurious thermal imprints on the model forebody, deposition of sabot material on the model forebody, thereby changing the thermal properties of the model surface and unknown in-barrel heating of the model. This report presents developments in launch techniques to greatly reduce or eliminate these problems. The techniques include the use of obturator cups behind the launch package, enclosed versus open front sabot designs and the use of hydrogen gas in the launch tube. Attention also had to be paid to the problem of the obturator drafting behind the model and impacting the model. Of the techniques presented, the obturator cups and hydrogen in the launch tube were successful when properly implemented
Modelling the effects of the sterile insect technique applied to Eldana saccharina Walker in sugarcane

Directory of Open Access Journals (Sweden)

L Potgieter

2012-12-01

Full Text Available A mathematical model is formulated for the population dynamics of an Eldana saccharina Walker infestation of sugarcane under the influence of partially sterile released insects. The model describes the population growth of and interaction between normal and sterile E.saccharina moths in a temporally variable, but spatially homogeneous environment. The model consists of a deterministic system of difference equations subject to strictly positive initial data. The primary objective of this model is to determine suitable parameters in terms of which the above population growth and interaction may be quantified and according to which E.saccharina infestation levels and the associated sugarcane damage may be measured. Although many models have been formulated in the past describing the sterile insect technique, few of these models describe the technique for Lepidopteran species with more than one life stage and where F1-sterility is relevant. In addition, none of these models consider the technique when fully sterile females and partially sterile males are being released. The model formulated is also the first to describe the technique applied specifically to E.saccharina, and to consider the economic viability of applying the technique to this species. Pertinent decision support is provided to farm managers in terms of the best timing for releases, release ratios and release frequencies.
Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

International Nuclear Information System (INIS)

Laghari, J.A.; Mokhlis, H.; Karimi, M.; Bakar, A.H.A.; Mohamad, Hasmaini

2014-01-01

Highlights: • Unintentional and intentional islanding, their causes, and solutions are presented. • Remote, passive, active and hybrid islanding detection techniques are discussed. • The limitation of these techniques in accurately detect islanding are discussed. • Computational intelligence techniques ability in detecting islanding is discussed. • Review of ANN, fuzzy logic control, ANFIS, Decision tree techniques is provided. - Abstract: Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system
The application of neural networks with artificial intelligence technique in the modeling of industrial processes

International Nuclear Information System (INIS)

Saini, K. K.; Saini, Sanju

2008-01-01

Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.
Use of hydrological modelling and isotope techniques in Guvenc basin

International Nuclear Information System (INIS)

Altinbilek, D.

1991-07-01

The study covers the work performed under Project No. 335-RC-TUR-5145 entitled ''Use of Hydrologic Modelling and Isotope Techniques in Guvenc Basin'' and is an initial part of a program for estimating runoff from Central Anatolia Watersheds. The study presented herein consists of mainly three parts: 1) the acquisition of a library of rainfall excess, direct runoff and isotope data for Guvenc basin; 2) the modification of SCS model to be applied to Guvenc basin first and then to other basins of Central Anatolia for predicting the surface runoff from gaged and ungaged watersheds; and 3) the use of environmental isotope technique in order to define the basin components of streamflow of Guvenc basin. 31 refs, figs and tabs
Image-based modeling of tumor shrinkage in head and neck radiation therapy

International Nuclear Information System (INIS)

Chao Ming; Xie Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth/shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the ''ground truth'' with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy.
The Aplication of Problem Based Learning Model on Heat and Temperature

OpenAIRE

Simamora, Pintor; Rotua Estomihi Pardede, Victorya

2016-01-01

This study aims to determine the effect of Problem Based Learning model to student learning outcomes on subject of Heat and Temperature. This research is quasi-experimental. Techniques that used to gain a sample is random-cluster-sampling technique that was chosen two classes as experimental and control classes. Instruments in the form of essays tests and observation sheets to measure affectivepsychomotor of students. Pretest data on both classes showed that both classes have the same ability...
Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

Science.gov (United States)

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to
RF Sub-sampling Receiver Architecture based on Milieu Adapting Techniques

DEFF Research Database (Denmark)

Behjou, Nastaran; Larsen, Torben; Jensen, Ole Kiel

2012-01-01

A novel sub-sampling based architecture is proposed which has the ability of reducing the problem of image distortion and improving the signal to noise ratio significantly. The technique is based on sensing the environment and adapting the sampling rate of the receiver to the best possible...
Bolt-loosening identification of bolt connections by vision image-based technique

Science.gov (United States)

Nguyen, Tuan-Cuong; Huynh, Thanh-Canh; Ryu, Joo-Young; Park, Jae-Hyung; Kim, Jeong-Tae

2016-04-01

In this study, an algorithm using image processing techniques is proposed to identify bolt-loosening in bolted connections of steel structures. Its basic concept is to identify rotation angles of nuts from a pictured image, and is mainly consisted of the following 3 steps: (1) taking a picture for a bolt joint, (2) segmenting the images for each nut by image processing techniques, and (3) identifying rotation angle of each nut and detecting bolt-loosening. By using the concept, an algorithm is designed for continuous monitoring and inspection of the bolt connections. As a key imageprocessing technique, Hough transform is used to identify rotation angles of nuts, and then bolt-loosening is detected by comparing the angles before and after bolt-loosening. Then the applicability of the proposed algorithm is evaluated by experimental tests for two lab-scaled models. A bolted joint model which consists of a splice plate and 8 sets of bolts and nuts with 2×4 array is used to simulate inspection of bridge connections, and a model which is consisted of a ring flange and 32 sets of bolt and nut is used to simulate continuous monitoring of bolted connections in wind turbine towers.
MEMS-Based Power Generation Techniques for Implantable Biosensing Applications

Directory of Open Access Journals (Sweden)

Jonathan Lueke

2011-01-01

Full Text Available Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.
MEMS-based power generation techniques for implantable biosensing applications.

Science.gov (United States)

Lueke, Jonathan; Moussa, Walied A

2011-01-01

Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS)-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.
Spatial generalised linear mixed models based on distances.

Science.gov (United States)

Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

2016-10-01

Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.
NMR and modelling techniques in structural and conformation analysis

Energy Technology Data Exchange (ETDEWEB)

Abraham, R J [Liverpool Univ. (United Kingdom)

1994-12-31

The use of Lanthanide Induced Shifts (L.I.S.) and modelling techniques in conformational analysis is presented. The use of Co{sup III} porphyrins as shift reagents is discussed, with examples of their use in the conformational analysis of some heterocyclic amines. (author) 13 refs., 9 figs.
Near-real-time regional troposphere models for the GNSS precise point positioning technique

International Nuclear Information System (INIS)

Hadas, T; Kaplon, J; Bosy, J; Sierny, J; Wilgan, K

2013-01-01

The GNSS precise point positioning (PPP) technique requires high quality product (orbits and clocks) application, since their error directly affects the quality of positioning. For real-time purposes it is possible to utilize ultra-rapid precise orbits and clocks which are disseminated through the Internet. In order to eliminate as many unknown parameters as possible, one may introduce external information on zenith troposphere delay (ZTD). It is desirable that the a priori model is accurate and reliable, especially for real-time application. One of the open problems in GNSS positioning is troposphere delay modelling on the basis of ground meteorological observations. Institute of Geodesy and Geoinformatics of Wroclaw University of Environmental and Life Sciences (IGG WUELS) has developed two independent regional troposphere models for the territory of Poland. The first one is estimated in near-real-time regime using GNSS data from a Polish ground-based augmentation system named ASG-EUPOS established by Polish Head Office of Geodesy and Cartography (GUGiK) in 2008. The second one is based on meteorological parameters (temperature, pressure and humidity) gathered from various meteorological networks operating over the area of Poland and surrounding countries. This paper describes the methodology of both model calculation and verification. It also presents results of applying various ZTD models into kinematic PPP in the post-processing mode using Bernese GPS Software. Positioning results were used to assess the quality of the developed models during changing weather conditions. Finally, the impact of model application to simulated real-time PPP on precision, accuracy and convergence time is discussed. (paper)
An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations

KAUST Repository

Iskandarani, Mohamed; Wang, Shitao; Srinivasan, Ashwanth; Carlisle Thacker, W.; Winokur, Justin; Knio, Omar

2016-01-01

We give an overview of four different ensemble-based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model's output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions' coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil-gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the different proxies. The examples illustrate how regression-based techniques can outperform projection-based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.
Structural reliability analysis based on the cokriging technique

International Nuclear Information System (INIS)

Zhao Wei; Wang Wei; Dai Hongzhe; Xue Guofeng

2010-01-01

Approximation methods are widely used in structural reliability analysis because they are simple to create and provide explicit functional relationships between the responses and variables in stead of the implicit limit state function. Recently, the kriging method which is a semi-parameter interpolation technique that can be used for deterministic optimization and structural reliability has gained popularity. However, to fully exploit the kriging method, especially in high-dimensional problems, a large number of sample points should be generated to fill the design space and this can be very expensive and even impractical in practical engineering analysis. Therefore, in this paper, a new method-the cokriging method, which is an extension of kriging, is proposed to calculate the structural reliability. cokriging approximation incorporates secondary information such as the values of the gradients of the function being approximated. This paper explores the use of the cokriging method for structural reliability problems by comparing it with the Kriging method based on some numerical examples. The results indicate that the cokriging procedure described in this work can generate approximation models to improve on the accuracy and efficiency for structural reliability problems and is a viable alternative to the kriging.
Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme

International Nuclear Information System (INIS)

Shayeghi, H.; Ghasemi, A.

2013-01-01

Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches

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