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Full-Text Articles in Physical Sciences and Mathematics
Research Of Unsupervised Posture Modeling And Action Recognition Based On Spatial-Temporal Interesting Points, Chuan-Xu Wang, Yun Liu, Wanqing Li
Research Of Unsupervised Posture Modeling And Action Recognition Based On Spatial-Temporal Interesting Points, Chuan-Xu Wang, Yun Liu, Wanqing Li
Associate Professor Wanqing Li
Posture modeling is critical for action description and recognition,a posture modeling and action recognition method is proposed in this paper.Spatial Temporal Interesting Points (STIPs) are extracted from learning samples,in fact,one posture consists of a set of STIPs;a unsupervised clustering method is adopted to classify salient postures from these posture samples,then a GMM model is established for each clustering result;transitional probability among salient postures are calculated,and a Visible state Markov Model(VMM) is learnt to describe various actions.Bi-gram method is put forward for action recognition,Extensive experiments are conducted and the results prove its robustness and validity.
Modelling Awareness Of Agents Using Policies, Amir Talaei-Khoei, Pradeep Ray, Nandan Parameswaran, Ghassan Beydoun
Modelling Awareness Of Agents Using Policies, Amir Talaei-Khoei, Pradeep Ray, Nandan Parameswaran, Ghassan Beydoun
Associate Professor Ghassan Beydoun
In addition to cooperation, research in disaster management exposes the need for policy awareness to recognize relevant information in enhancing cooperation. Intelligent software agents have previously been employed for problem solving in disaster situations but without incorporating how the agents can create or model awareness. This paper presents an awareness based modelling method, called MAAP, to maintain awareness of software agents of a given set of policies. The paper presents preliminary results indicating that the use of policies as a source of awareness, as facilitated by MAAP, is a potentially effective method to enhance cooperation.
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Dr Jun Yan
Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …
Modeling And Solving Semiring Constraint Satisfaction Problems By Transformation To Weighted Semiring Max-Sat, Louise Leenen, Anbulagan Anbulagan, Thomas Meyer, Aditya K. Ghose
Modeling And Solving Semiring Constraint Satisfaction Problems By Transformation To Weighted Semiring Max-Sat, Louise Leenen, Anbulagan Anbulagan, Thomas Meyer, Aditya K. Ghose
Professor Aditya K. Ghose
We present a variant of the Weighted Maximum Satisfiability Problem(Weighted Max-SAT), which is a modeling of the Semiring Con- straint Satisfaction framework. We show how to encode a Semiring Con- straint Satisfaction Problem (SCSP) into an instance of a propositional Weighted Max-SAT, and call the encoding Weighted Semiring Max-SAT (WS-Max-SAT). The clauses in our encoding are highly structured and we exploit this feature to develop two algorithms for solving WS-Max- SAT: an incomplete algorithm based on the well-known GSAT algorithm for Max-SAT, and a branch-and-bound algorithm which is complete. Our preliminary experiments show that the translation of SCSP into WS- …
Contextual Effects In Modeling For Small Domain Estimation, Mohammad-Reza Namazi-Rad, David G. Steel
Contextual Effects In Modeling For Small Domain Estimation, Mohammad-Reza Namazi-Rad, David G. Steel
Professor David Steel
Many different Small Area Estimation (SAE) methods have been proposed to overcome the challenge of findingreliable estimates for small domains. Often, the required data for various research purposes are available at differentlevels of aggregation. Based on the available data, individual-level or aggregated-level models are used in SAE.However, parameter estimates obtained from individual and aggregated level analysis may be different, in practice.This may happen due to some substantial contextual or area-level effects in the covariates which may be misspecifiedin individual-level analysis. If small area models are going to be interpretable in practice, possible contextualeffects should be included. Ignoring these effects leads …
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Dr Jun Shen
Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …
Evaluating Participatory Modeling: Developing A Framework For Cross-Case Analysis. Socio-Economics And The Environment In Discussion (Seed), Natalie A. Jones, Pascal Perez, Thomas G. Measham, Gail J. Kelly, Patrick D'Aquino, Katherine Daniell, Anne Dray, Nils Ferrand
Evaluating Participatory Modeling: Developing A Framework For Cross-Case Analysis. Socio-Economics And The Environment In Discussion (Seed), Natalie A. Jones, Pascal Perez, Thomas G. Measham, Gail J. Kelly, Patrick D'Aquino, Katherine Daniell, Anne Dray, Nils Ferrand
Professor Pascal Perez
Participatory modeling is increasingly recognised as an effective way to assist collective decision-making processes in the domain of natural resource management. This paper introduces a framework for evaluating projects that have adopted a participatory modeling approach. This framework – known as the ‘Protocol of Canberra’ – was developed through a collaboration between French and Australian researchers engaged in participatory modeling and evaluation research. The framework seeks to assess the extent to which different participatory modeling practices reinforce or divert from the theoretical assumptions they are built upon. The paper discusses the application of the framework in three case-studies, two from …
3d Geometric And Haptic Modeling Of Hand-Woven Textile Artifacts, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy
3d Geometric And Haptic Modeling Of Hand-Woven Textile Artifacts, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy
Associate Professor Golshah Naghdy
Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require time-consuming objective measurement of textile propel1ies in mechanicaVphysjcal model constrUction. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure textile artifact, and deploys computational intelligence to estimate textile mechanical and physical properties. The approach is designed primarily for display of textile artifacts in museums. The haptic model is …
Modeling Dynamic Controls On Ice Streams: A Bayesian Statistical Approach, L Mark Berliner, Kenneth Jezek, Noel Cressie, Yongku Kim, Calvin Lam, Cornelis Van Der Veen
Modeling Dynamic Controls On Ice Streams: A Bayesian Statistical Approach, L Mark Berliner, Kenneth Jezek, Noel Cressie, Yongku Kim, Calvin Lam, Cornelis Van Der Veen
Professor Noel Cressie
Our main goal is to exemplify the study of ice-stream dynamics via Bayesian statistical analysis incorporating physical, though imperfectly known, models using data that are both incomplete and noisy. The physical-statistical models we propose account for these uncertainties in a coherent, hierarchical manner. The initial modeling assumption estimates basal shear stress as equal to driving stress, but subsequently includes a random corrector process to account for model error. The resulting stochastic equation is incorporated into a simple model for surface velocities. Use of Bayes' theorem allows us to make inferences on all unknowns given basal elevation, surface elevation and surface …
Dynamical Random-Set Modeling Of Concentrated Precipitation In North America, Noel Cressie, Renato Assuncao, Scott H. Holan, Michael Levine, Orietta Nicolis, Jun Zhang, Jian Zou
Dynamical Random-Set Modeling Of Concentrated Precipitation In North America, Noel Cressie, Renato Assuncao, Scott H. Holan, Michael Levine, Orietta Nicolis, Jun Zhang, Jian Zou
Professor Noel Cressie
In order to study climate at scales where policy decisions can be made, regional climate models (RCMs) have been developed with much finer resolution (~50 km) than the ~500 km resolution of atmosphere-ocean general circulation models (AOGCMs). The North American Regional Climate Change Assessment Program (NARCCAP) is an international program that provides 50-km resolution climate output for the United States, Canada, and northern Mexico. In Phase I, there are six RCMs, from which we choose one to illustrate our methodology. The RCMs are updated every 3 hours and contain a number of variables, including temperature, precipitation, wind speed, wind direction, …
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Professor Noel Cressie
Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple …
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Professor Brian Cullis
A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed …
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Professor Brian Cullis
A statistical approach for the analysis of multienvironment trials (METs) is presented, in which selection of best performing lines, best parents, and best combination of parents can be determined. The genetic effect of a line is partitioned into additive, dominance and residual nonadditive effects. The dominance effects are estimated through the incorporation of the dominance relationship matrix, which is presented under varying levels of inbreeding. A computationally efficient way of fitting dominance effects is presented which partitions dominance effects into between family dominance and within family dominance line effects. The overall approach is applicable to inbred lines, hybrid lines and …
Joint Modeling Of Spatial Variability And Within-Row Interplot Competition To Increase The Efficiency Of Plant Improvement, J. Stringer, Brian Cullis, R Thompson
Joint Modeling Of Spatial Variability And Within-Row Interplot Competition To Increase The Efficiency Of Plant Improvement, J. Stringer, Brian Cullis, R Thompson
Professor Brian Cullis
Trials in the early stages of selection are often subject to variation arising from spatial variability and interplot competition, which can seriously bias the assessment of varietal performance and reduce genetic progress. An approach to jointly model both sources of bias is presented. It models genotypic and residual competition and also global and extraneous spatial variation. Variety effects were considered random and residual maximum likelihood was used for parameter estimation. Competition at the residual level was examined using two special simultaneous autoregressive models. An equal-roots second-order autoregressive (EAR(2)) model is proposed for trials where competition is dominant. An equal-roots third-order …
A Gaussian-Rayleigh Mixture Modeling Approach For Through-The-Wall Radar Image Segmentation, Cher Hau Seng, Abdesselam Bouzerdoum, Moeness Amin, F Ahmad
A Gaussian-Rayleigh Mixture Modeling Approach For Through-The-Wall Radar Image Segmentation, Cher Hau Seng, Abdesselam Bouzerdoum, Moeness Amin, F Ahmad
Professor Salim Bouzerdoum
In this paper, we propose a Gausssian-Rayleigh mixture modeling approach to segment indoor radar images in urban sensing applications. The performance of the proposed method is evaluated on real 2D polarimetric data. Experimental results show that the proposed method enhances image quality by distinguishing between target and clutter regions. The proposed method is also compared to an existing Neyman-Pearson (NP) target detector that has been recently devised for through-the-wall radar imaging. Performance evaluation of both methods shows that the proposed method outperforms the NP detector in enhancing the input images.
Synthesis, Modeling, And Characterization Of Conducting Polymers, Geoffrey M. Spinks, Lianbin Zhao, Weihua Li, Yanzhe Wu, Dezhi Zhou, Gordon G. Wallace
Synthesis, Modeling, And Characterization Of Conducting Polymers, Geoffrey M. Spinks, Lianbin Zhao, Weihua Li, Yanzhe Wu, Dezhi Zhou, Gordon G. Wallace
Professor Weihua Li
This paper presents synthesis and characterization of polypyrrole based conducting polymers in terms of electronic and mechanical disciplines. Using the electrochemical polymerization approach, conducting polymer samples with different dimensions (length, width, and thickness) was fabricated. For each sample, both sinusoidal and step excitations were used to study its mechanical and electrical properties. An equivalent electric circuit based on constant phase element (CPE) is proposed to model such responses. Electrochemical impedance spectroscopy (EIS) method was used to identify the relationship between the dimensions of conducting polymers and model elements parameters.
Synthesis, Modeling, And Characterization Of Conducting Polymers, Geoffrey M. Spinks, Lianbin Zhao, Weihua Li, Yanzhe Wu, Dezhi Zhou, Gordon G. Wallace
Synthesis, Modeling, And Characterization Of Conducting Polymers, Geoffrey M. Spinks, Lianbin Zhao, Weihua Li, Yanzhe Wu, Dezhi Zhou, Gordon G. Wallace
Professor Weihua Li
This paper presents synthesis and characterization of polypyrrole based conducting polymers in terms of electronic and mechanical disciplines. Using the electrochemical polymerization approach, conducting polymer samples with different dimensions (length, width, and thickness) was fabricated. For each sample, both sinusoidal and step excitations were used to study its mechanical and electrical properties. An equivalent electric circuit based on constant phase element (CPE) is proposed to model such responses. Electrochemical impedance spectroscopy (EIS) method was used to identify the relationship between the dimensions of conducting polymers and model elements parameters.
A Statistical Model To Forecast Short-Term Atlantic Hurricane Intensity, Kevin Law, Jay Hobgood
A Statistical Model To Forecast Short-Term Atlantic Hurricane Intensity, Kevin Law, Jay Hobgood
Kevin Law
An alternative 24-h statistical hurricane intensity model is presented and verified for 13 hurricanes during the 2004–05 seasons. The model uses a new method involving a discriminant function analysis (DFA) to select from a collection of multiple regression equations. These equations were developed to predict the future 24-h wind speed increase and the 24-h pressure drop that were constructed from a dataset of 103 hurricanes from 1988 to 2003 that utilized 25 predictors of rapid intensification. The accuracy of the 24-h wind speed increase models was tested and compared with the official National Hurricane Center (NHC) 24-h intensity forecasts, which …
Evaluating Subdivisions For Identifying Extraneous Flow In Separate Sanitary Sewer Systems, Adam Lanning, Eric Wade Peterson
Evaluating Subdivisions For Identifying Extraneous Flow In Separate Sanitary Sewer Systems, Adam Lanning, Eric Wade Peterson
Eric Wade Peterson
No abstract provided.