Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography

International Nuclear Information System (INIS)

Javaherian, Ashkan; Moeller, Knut; Soleimani, Manuchehr

2015-01-01

This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT. (paper)

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Study on a pattern classification method of soil quality based on simplified learning sample dataset

Science.gov (United States)

Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

2011-01-01

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

Finite-sample instrumental variables inference using an asymptotically pivotal statistic

NARCIS (Netherlands)

Bekker, P; Kleibergen, F

2003-01-01

We consider the K-statistic, Kleibergen's (2002, Econometrica 70, 1781-1803) adaptation of the Anderson-Rubin (AR) statistic in instrumental variables regression. Whereas Kleibergen (2002) especially analyzes the asymptotic behavior of the statistic, we focus on finite-sample properties in, a

Compressive Sampling of EEG Signals with Finite Rate of Innovation

Directory of Open Access Journals (Sweden)

Poh Kok-Kiong

2010-01-01

Full Text Available Analyses of electroencephalographic signals and subsequent diagnoses can only be done effectively on long term recordings that preserve the signals' morphologies. Currently, electroencephalographic signals are obtained at Nyquist rate or higher, thus introducing redundancies. Existing compression methods remove these redundancies, thereby achieving compression. We propose an alternative compression scheme based on a sampling theory developed for signals with a finite rate of innovation (FRI which compresses electroencephalographic signals during acquisition. We model the signals as FRI signals and then sample them at their rate of innovation. The signals are thus effectively represented by a small set of Fourier coefficients corresponding to the signals' rate of innovation. Using the FRI theory, original signals can be reconstructed using this set of coefficients. Seventy-two hours of electroencephalographic recording are tested and results based on metrices used in compression literature and morphological similarities of electroencephalographic signals are presented. The proposed method achieves results comparable to that of wavelet compression methods, achieving low reconstruction errors while preserving the morphologiies of the signals. More importantly, it introduces a new framework to acquire electroencephalographic signals at their rate of innovation, thus entailing a less costly low-rate sampling device that does not waste precious computational resources.

How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?

DEFF Research Database (Denmark)

Veraart, Almut

and present a new estimator for the asymptotic ‘variance’ of the centered realised variance in the presence of jumps. Next, we compare the finite sample performance of the various estimators by means of detailed Monte Carlo studies where we study the impact of the jump activity, the jump size of the jumps......This paper studies the impact of jumps on volatility estimation and inference based on various realised variation measures such as realised variance, realised multipower variation and truncated realised multipower variation. We review the asymptotic theory of those realised variation measures...... in the price and the presence of additional independent or dependent jumps in the volatility on the finite sample performance of the various estimators. We find that the finite sample performance of realised variance, and in particular of the log–transformed realised variance, is generally good, whereas...

Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study.

Science.gov (United States)

Tîrnăucă, Cristina; Montaña, José L; Ontañón, Santiago; González, Avelino J; Pardo, Luis M

2016-06-24

Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

A proof of the Woodward-Lawson sampling method for a finite linear array

Science.gov (United States)

Somers, Gary A.

1993-01-01

An extension of the continuous aperture Woodward-Lawson sampling theorem has been developed for a finite linear array of equidistant identical elements with arbitrary excitations. It is shown that by sampling the array factor at a finite number of specified points in the far field, the exact array factor over all space can be efficiently reconstructed in closed form. The specified sample points lie in real space and hence are measurable provided that the interelement spacing is greater than approximately one half of a wavelength. This paper provides insight as to why the length parameter used in the sampling formulas for discrete arrays is larger than the physical span of the lattice points in contrast with the continuous aperture case where the length parameter is precisely the physical aperture length.

Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning

OpenAIRE

Pallone, Stephen N.; Frazier, Peter I.; Henderson, Shane G.

2017-01-01

We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries have been shown to be a robust and efficient way to learn an individual's preferences. We take a parametric approach and model the user's preferences through a linear classifier...

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

Science.gov (United States)

Dral, Pavlo O.; Owens, Alec; Yurchenko, Sergei N.; Thiel, Walter

2017-06-01

We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). We introduce structure-based sampling to automatically assign nuclear configurations from a pre-defined grid to the training and prediction sets, respectively. Accurate high-level ab initio energies are required only for the points in the training set, while the energies for the remaining points are provided by the ML model with negligible computational cost. The proposed sampling procedure is shown to be superior to random sampling and also eliminates the need for training several ML models. Self-correcting machine learning has been implemented such that each additional layer corrects errors from the previous layer. The performance of our approach is demonstrated in a case study on a published high-level ab initio PES of methyl chloride with 44 819 points. The ML model is trained on sets of different sizes and then used to predict the energies for tens of thousands of nuclear configurations within seconds. The resulting datasets are utilized in variational calculations of the vibrational energy levels of CH3Cl. By using both structure-based sampling and self-correction, the size of the training set can be kept small (e.g., 10% of the points) without any significant loss of accuracy. In ab initio rovibrational spectroscopy, it is thus possible to reduce the number of computationally costly electronic structure calculations through structure-based sampling and self-correcting KRR-based machine learning by up to 90%.

3D visualization and finite element mesh formation from wood anatomy samples, Part I – Theoretical approach

Directory of Open Access Journals (Sweden)

Petr Koňas

2009-01-01

Full Text Available The work summarizes created algorithms for formation of finite element (FE mesh which is derived from bitmap pattern. Process of registration, segmentation and meshing is described in detail. C++ library of STL from Insight Toolkit (ITK Project together with Visualization Toolkit (VTK were used for base processing of images. Several methods for appropriate mesh output are discussed. Multiplatform application WOOD3D for the task under GNU GPL license was assembled. Several methods of segmentation and mainly different ways of contouring were included. Tetrahedral and rectilinear types of mesh were programmed. Improving of mesh quality in some simple ways is mentioned. Testing and verification of final program on wood anatomy samples of spruce and walnut was realized. Methods of microscopic anatomy samples preparation are depicted. Final utilization of formed mesh in the simple structural analysis was performed.The article discusses main problems in image analysis due to incompatible colour spaces, samples preparation, thresholding and final conversion into finite element mesh. Assembling of mentioned tasks together and evaluation of the application are main original results of the presented work. In presented program two thresholding filters were used. By utilization of ITK two following filters were included. Otsu filter based and binary filter based were used. The most problematic task occurred in a production of wood anatomy samples in the unique light conditions with minimal or zero co­lour space shift and the following appropriate definition of thresholds (corresponding thresholding parameters and connected methods (prefiltering + registration which influence the continuity and mainly separation of wood anatomy structure. Solution in samples staining is suggested with the following quick image analysis realization. Next original result of the work is complex fully automated application which offers three types of finite element mesh

Learning outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

OpenAIRE

Piyaluk Wongsri; Prasart Nuangchalerm

2010-01-01

Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade students who were organized between socioscientific issues-based learning and conventional learning activities. Approach: The samples used in research we...

Maximum likelihood estimation of finite mixture model for economic data

Science.gov (United States)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

A fast learning method for large scale and multi-class samples of SVM

Science.gov (United States)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

High Accuracy Evaluation of the Finite Fourier Transform Using Sampled Data

Science.gov (United States)

Morelli, Eugene A.

1997-01-01

Many system identification and signal processing procedures can be done advantageously in the frequency domain. A required preliminary step for this approach is the transformation of sampled time domain data into the frequency domain. The analytical tool used for this transformation is the finite Fourier transform. Inaccuracy in the transformation can degrade system identification and signal processing results. This work presents a method for evaluating the finite Fourier transform using cubic interpolation of sampled time domain data for high accuracy, and the chirp Zeta-transform for arbitrary frequency resolution. The accuracy of the technique is demonstrated in example cases where the transformation can be evaluated analytically. Arbitrary frequency resolution is shown to be important for capturing details of the data in the frequency domain. The technique is demonstrated using flight test data from a longitudinal maneuver of the F-18 High Alpha Research Vehicle.

Interactive Web-based e-learning for Studying Flexible Manipulator Systems

Directory of Open Access Journals (Sweden)

Abul K. M. Azad

2008-03-01

Full Text Available Abstract— This paper presents a web-based e-leaning facility for simulation, modeling, and control of flexible manipulator systems. The simulation and modeling part includes finite difference and finite element simulations along with neural network and genetic algorithm based modeling strategies for flexible manipulator systems. The controller part constitutes a number of open-loop and closed-loop designs. Closed loop control designs include the classical, adaptive, and neuro-model based strategies. Matlab software package and its associated toolboxes are used to implement these. The Matlab web server is used as the gateway between the facility and web-access. ASP.NET technology and SQL database are utilized to develop web applications for access control, user account and password maintenance, administrative management, and facility utilization monitoring. The reported facility provides a flexible but effective approach of web-based interactive e-learning facility of an engineering system. This can be extended to incorporate additional engineering systems within the e-learning framework.

Output Information Based Fault-Tolerant Iterative Learning Control for Dual-Rate Sampling Process with Disturbances and Output Delay

Directory of Open Access Journals (Sweden)

Hongfeng Tao

2018-01-01

Full Text Available For a class of single-input single-output (SISO dual-rate sampling processes with disturbances and output delay, this paper presents a robust fault-tolerant iterative learning control algorithm based on output information. Firstly, the dual-rate sampling process with output delay is transformed into discrete system in state-space model form with slow sampling rate without time delay by using lifting technology; then output information based fault-tolerant iterative learning control scheme is designed and the control process is turned into an equivalent two-dimensional (2D repetitive process. Moreover, based on the repetitive process stability theory, the sufficient conditions for the stability of system and the design method of robust controller are given in terms of linear matrix inequalities (LMIs technique. Finally, the flow control simulations of two flow tanks in series demonstrate the feasibility and effectiveness of the proposed method.

Finite mixture models for the computation of isotope ratios in mixed isotopic samples

Science.gov (United States)

Koffler, Daniel; Laaha, Gregor; Leisch, Friedrich; Kappel, Stefanie; Prohaska, Thomas

2013-04-01

parameters of the algorithm, i.e. the maximum count of ratios, the minimum relative group-size of data points belonging to each ratio has to be defined. Computation of the models can be done with statistical software. In this study Leisch and Grün's flexmix package [2] for the statistical open-source software R was applied. A code example is available in the electronic supplementary material of Kappel et al. [1]. In order to demonstrate the usefulness of finite mixture models in fields dealing with the computation of multiple isotope ratios in mixed samples, a transparent example based on simulated data is presented and problems regarding small group-sizes are illustrated. In addition, the application of finite mixture models to isotope ratio data measured in uranium oxide particles is shown. The results indicate that finite mixture models perform well in computing isotope ratios relative to traditional estimation procedures and can be recommended for more objective and straightforward calculation of isotope ratios in geochemistry than it is current practice. [1] S. Kappel, S. Boulyga, L. Dorta, D. Günther, B. Hattendorf, D. Koffler, G. Laaha, F. Leisch and T. Prohaska: Evaluation Strategies for Isotope Ratio Measurements of Single Particles by LA-MC-ICPMS, Analytical and Bioanalytical Chemistry, 2013, accepted for publication on 2012-12-18 (doi: 10.1007/s00216-012-6674-3) [2] B. Grün and F. Leisch: Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis, 51(11), 5247-5252, 2007. (doi:10.1016/j.csda.2006.08.014)

Vibronic Boson Sampling: Generalized Gaussian Boson Sampling for Molecular Vibronic Spectra at Finite Temperature.

Science.gov (United States)

Huh, Joonsuk; Yung, Man-Hong

2017-08-07

Molecular vibroic spectroscopy, where the transitions involve non-trivial Bosonic correlation due to the Duschinsky Rotation, is strongly believed to be in a similar complexity class as Boson Sampling. At finite temperature, the problem is represented as a Boson Sampling experiment with correlated Gaussian input states. This molecular problem with temperature effect is intimately related to the various versions of Boson Sampling sharing the similar computational complexity. Here we provide a full description to this relation in the context of Gaussian Boson Sampling. We find a hierarchical structure, which illustrates the relationship among various Boson Sampling schemes. Specifically, we show that every instance of Gaussian Boson Sampling with an initial correlation can be simulated by an instance of Gaussian Boson Sampling without initial correlation, with only a polynomial overhead. Since every Gaussian state is associated with a thermal state, our result implies that every sampling problem in molecular vibronic transitions, at any temperature, can be simulated by Gaussian Boson Sampling associated with a product of vacuum modes. We refer such a generalized Gaussian Boson Sampling motivated by the molecular sampling problem as Vibronic Boson Sampling.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Science.gov (United States)

Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M; Krasnogor, Natalio

2012-01-01

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Finite Discrete Gabor Analysis

DEFF Research Database (Denmark)

Søndergaard, Peter Lempel

2007-01-01

frequency bands at certain times. Gabor theory can be formulated for both functions on the real line and for discrete signals of finite length. The two theories are largely the same because many aspects come from the same underlying theory of locally compact Abelian groups. The two types of Gabor systems...... can also be related by sampling and periodization. This thesis extends on this theory by showing new results for window construction. It also provides a discussion of the problems associated to discrete Gabor bases. The sampling and periodization connection is handy because it allows Gabor systems...... on the real line to be well approximated by finite and discrete Gabor frames. This method of approximation is especially attractive because efficient numerical methods exists for doing computations with finite, discrete Gabor systems. This thesis presents new algorithms for the efficient computation of finite...

The generalization ability of online SVM classification based on Markov sampling.

Science.gov (United States)

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Directory of Open Access Journals (Sweden)

Enrico Glaab

Full Text Available Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Active-Learning versus Teacher-Centered Instruction for Learning Acids and Bases

Science.gov (United States)

Sesen, Burcin Acar; Tarhan, Leman

2011-01-01

Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of "acids and bases". Sample: The sample of this…

Finite Action-Set Learning Automata for Economic Dispatch Considering Electric Vehicles and Renewable Energy Sources

Directory of Open Access Journals (Sweden)

Junpeng Zhu

2014-07-01

Full Text Available The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.

Less is more: Sampling chemical space with active learning

Science.gov (United States)

Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

2018-06-01

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

Finite element analysis of trabecular bone structures : a comparison of image-based meshing techniques

NARCIS (Netherlands)

Ulrich, D.; Rietbergen, van B.; Weinans, H.; Rüegsegger, P.

1998-01-01

In this study, we investigate if finite element (FE) analyses of human trabecular bone architecture based on 168 microm images can provide relevant information about the bone mechanical characteristics. Three human trabecular bone samples, one taken from the femoral head, one from the iliac crest,

Practical iterative learning control with frequency domain design and sampled data implementation

CERN Document Server

Wang, Danwei; Zhang, Bin

2014-01-01

This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much h...

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

Science.gov (United States)

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

Directory of Open Access Journals (Sweden)

Cuicui Zhang

2014-12-01

Full Text Available Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1 how to define diverse base classifiers from the small data; (2 how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

2014-01-01

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Distribution-Preserving Stratified Sampling for Learning Problems.

Science.gov (United States)

Cervellera, Cristiano; Maccio, Danilo

2017-06-09

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

Classification and authentication of unknown water samples using machine learning algorithms.

Science.gov (United States)

Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

2011-07-01

This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive local learning in sampling based motion planning for protein folding.

Science.gov (United States)

Ekenna, Chinwe; Thomas, Shawna; Amato, Nancy M

2016-08-01

Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52-114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

Science.gov (United States)

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Support vector machine incremental learning triggered by wrongly predicted samples

Science.gov (United States)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

Directory of Open Access Journals (Sweden)

Hayati .

2013-06-01

Full Text Available The objective in this research: (1 Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2 Determine the level of motivation to learn in affects physics student learning outcomes. (3 Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all students in class XI SMA Negeri 1 T.P Sunggal Semester I 2012/2013. The sample of this research was consisted of two classes with a sample of 70 peoples who are determined by purposive sampling, the IPA XI-2 as a class experiment using a model-based multimedia learning Training Inquiry as many as 35 peoples and XI IPA-3 as a control class using learning model Inquiry Training 35 peoples. Hypotheses were analyzed using the GLM at significant level of 0.05 using SPSS 17.0 for Windows. Based on data analysis and hypothesis testing conducted found that: (1 Training Inquiry-based multimedia learning model in improving student learning outcomes rather than learning model physics Inquiry Training. (2 The results of studying physics students who have high motivation to learn better than students who have a low learning motivation. (3 From this research there was an interaction between learning model inquiry-based multimedia training and motivation to study on learning outcomes of students.

Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

DEFF Research Database (Denmark)

Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan

2017-01-01

We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approxi...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.......We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

Science.gov (United States)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

Science.gov (United States)

Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

2016-01-22

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Iterative learning control with sampled-data feedback for robot manipulators

Directory of Open Access Journals (Sweden)

Delchev Kamen

2014-09-01

Full Text Available This paper deals with the improvement of the stability of sampled-data (SD feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more, while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

Percolation through voids around overlapping spheres: A dynamically based finite-size scaling analysis

Science.gov (United States)

Priour, D. J.

2014-01-01

The percolation threshold for flow or conduction through voids surrounding randomly placed spheres is calculated. With large-scale Monte Carlo simulations, we give a rigorous continuum treatment to the geometry of the impenetrable spheres and the spaces between them. To properly exploit finite-size scaling, we examine multiple systems of differing sizes, with suitable averaging over disorder, and extrapolate to the thermodynamic limit. An order parameter based on the statistical sampling of stochastically driven dynamical excursions and amenable to finite-size scaling analysis is defined, calculated for various system sizes, and used to determine the critical volume fraction ϕc=0.0317±0.0004 and the correlation length exponent ν =0.92±0.05.

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

PENGGUNAAN MODEL PROBLEM BASED LEARNING BERBANTUAN E-LEARNING TERHADAP KEMANDIRIAN BELAJAR MAHASISWA

Directory of Open Access Journals (Sweden)

Jusep Saputra

2017-11-01

Full Text Available Self-regulated learning of learners can be achieved, if in the process of learning mathematics provides an open opportunity for students to learn independently. This research is a mixed method type embedded design, which aims to do studies focused on the use of the Problem Based Learning (PBL model assisted e-learning to student self-regulated learning. Sample selection is done on the purposive sampling and was taken 2 class contracting courses of school math III. Class A numbered 50 members, 24 the superior group and 26 the low group, given the treatment with PBL models assisted e-learning and class B numbered 50, 27 the superior group and 23 the low group, with expository. Instruments used in this research is self-regulated learning questionnaire with Likert scale. Based on data analysis we concluded that (1 Self-regulated learning of superior and low student who obtains aided PBL models assisted e-learning is better than self-regulated learning of superior and low superior students who obtain expository.

LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

Science.gov (United States)

Nakamura, Munehiro; Kajiwara, Yusuke; Otsuka, Atsushi; Kimura, Haruhiko

2013-10-02

Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets. Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses. The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

Science.gov (United States)

Wang, Leimin; Shen, Yi; Zhang, Guodong

Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

Optimization of thermal systems based on finite-time thermodynamics and thermoeconomics

Energy Technology Data Exchange (ETDEWEB)

Durmayaz, A. [Istanbul Technical University (Turkey). Department of Mechanical Engineering; Sogut, O.S. [Istanbul Technical University, Maslak (Turkey). Department of Naval Architecture and Ocean Engineering; Sahin, B. [Yildiz Technical University, Besiktas, Istanbul (Turkey). Department of Naval Architecture; Yavuz, H. [Istanbul Technical University, Maslak (Turkey). Institute of Energy

2004-07-01

The irreversibilities originating from finite-time and finite-size constraints are important in the real thermal system optimization. Since classical thermodynamic analysis based on thermodynamic equilibrium do not consider these constraints directly, it is necessary to consider the energy transfer between the system and its surroundings in the rate form. Finite-time thermodynamics provides a fundamental starting point for the optimization of real thermal systems including the fundamental concepts of heat transfer and fluid mechanics to classical thermodynamics. In this study, optimization studies of thermal systems, that consider various objective functions, based on finite-time thermodynamics and thermoeconomics are reviewed. (author)

How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?

DEFF Research Database (Denmark)

Veraart, Almut

2011-01-01

and present a new estimator for the asymptotic "variance" of the centered realised variance in the presence of jumps. Next, we compare the finite sample performance of the various estimators by means of detailed Monte Carlo studies. Here we study the impact of the jump activity, of the jump size of the jumps......This paper studies the impact of jumps on volatility estimation and inference based on various realised variation measures such as realised variance, realised multipower variation and truncated realised multipower variation. We review the asymptotic theory of those realised variation measures...... in the price and of the presence of additional independent or dependent jumps in the volatility. We find that the finite sample performance of realised variance and, in particular, of log--transformed realised variance is generally good, whereas the jump--robust statistics tend to struggle in the presence...

Statistical learning and the challenge of syntax: Beyond finite state automata

Science.gov (United States)

Elman, Jeff

2003-10-01

Over the past decade, it has been clear that even very young infants are sensitive to the statistical structure of language input presented to them, and use the distributional regularities to induce simple grammars. But can such statistically-driven learning also explain the acquisition of more complex grammar, particularly when the grammar includes recursion? Recent claims (e.g., Hauser, Chomsky, and Fitch, 2002) have suggested that the answer is no, and that at least recursion must be an innate capacity of the human language acquisition device. In this talk evidence will be presented that indicates that, in fact, statistically-driven learning (embodied in recurrent neural networks) can indeed enable the learning of complex grammatical patterns, including those that involve recursion. When the results are generalized to idealized machines, it is found that the networks are at least equivalent to Push Down Automata. Perhaps more interestingly, with limited and finite resources (such as are presumed to exist in the human brain) these systems demonstrate patterns of performance that resemble those in humans.

Features and characteristics of problem based learning

Directory of Open Access Journals (Sweden)

Eser Ceker

2016-12-01

Full Text Available Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look for the latest technology supported tools of Problem Based Learning. This research showed that the most researched characteristics of PBL are; teacher and student assessments on Problem Based Learning, Variety of disciplines in which Problem Based Learning strategies were tried and success evaluated, Using Problem Based Learning alone or with other strategies (Hybrid or Mix methods, Comparing Problem Based Learning with other strategies, and new trends and tendencies in Problem Based Learning related research. Our research may help us to identify the latest trends and tendencies referred to in the published studies related to “problem based learning” areas. In this research, Science Direct and Ulakbim were used as our main database resources. The sample of this study consists of 150 articles.

Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm.

Science.gov (United States)

Sengur, Abdulkadir; Akbulut, Yaman; Guo, Yanhui; Bajaj, Varun

2017-12-01

Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features. In it, Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The CNN architecture is trained with the reinforcement sample learning strategy. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.80% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

A three-dimensional cell-based smoothed finite element method for elasto-plasticity

International Nuclear Information System (INIS)

Lee, Kye Hyung; Im, Se Yong; Lim, Jae Hyuk; Sohn, Dong Woo

2015-01-01

This work is concerned with a three-dimensional cell-based smoothed finite element method for application to elastic-plastic analysis. The formulation of smoothed finite elements is extended to cover elastic-plastic deformations beyond the classical linear theory of elasticity, which has been the major application domain of smoothed finite elements. The finite strain deformations are treated with the aid of the formulation based on the hyperelastic constitutive equation. The volumetric locking originating from the nearly incompressible behavior of elastic-plastic deformations is remedied by relaxing the volumetric strain through the mean value. The comparison with the conventional finite elements demonstrates the effectiveness and accuracy of the present approach.

A three-dimensional cell-based smoothed finite element method for elasto-plasticity

Energy Technology Data Exchange (ETDEWEB)

Lee, Kye Hyung; Im, Se Yong [KAIST, Daejeon (Korea, Republic of); Lim, Jae Hyuk [KARI, Daejeon (Korea, Republic of); Sohn, Dong Woo [Korea Maritime and Ocean University, Busan (Korea, Republic of)

2015-02-15

This work is concerned with a three-dimensional cell-based smoothed finite element method for application to elastic-plastic analysis. The formulation of smoothed finite elements is extended to cover elastic-plastic deformations beyond the classical linear theory of elasticity, which has been the major application domain of smoothed finite elements. The finite strain deformations are treated with the aid of the formulation based on the hyperelastic constitutive equation. The volumetric locking originating from the nearly incompressible behavior of elastic-plastic deformations is remedied by relaxing the volumetric strain through the mean value. The comparison with the conventional finite elements demonstrates the effectiveness and accuracy of the present approach.

The effect of discovery learning and problem-based learning on middle school students’ self-regulated learning

Science.gov (United States)

Miatun, A.; Muntazhimah

2018-01-01

The aim of this research was to determine the effect of learning models on mathematics achievement viewed from student’s self-regulated learning. The learning model compared were discovery learning and problem-based learning. The population was all students at the grade VIII of Junior High School in Boyolali regency. The samples were students of SMPN 4 Boyolali, SMPN 6 Boyolali, and SMPN 4 Mojosongo. The instruments used were mathematics achievement tests and self-regulated learning questionnaire. The data were analyzed using unbalanced two-ways Anova. The conclusion was as follows: (1) discovery learning gives better achievement than problem-based learning. (2) Achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. (3) For discovery learning, achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. For problem-based learning, students who have high and medium self-regulated learning have the same achievement. (4) For students who have high self-regulated learning, discovery learning gives better achievement than problem-based learning. Students who have medium and low self-regulated learning, both learning models give the same achievement.

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.

Precision of quantization of the hall conductivity in a finite-size sample: Power law

International Nuclear Information System (INIS)

Greshnov, A. A.; Kolesnikova, E. N.; Zegrya, G. G.

2006-01-01

A microscopic calculation of the conductivity in the integer quantum Hall effect (IQHE) mode is carried out. The precision of quantization is analyzed for finite-size samples. The precision of quantization shows a power-law dependence on the sample size. A new scaling parameter describing this dependence is introduced. It is also demonstrated that the precision of quantization linearly depends on the ratio between the amplitude of the disorder potential and the cyclotron energy. The data obtained are compared with the results of magnetotransport measurements in mesoscopic samples

PROJECT BASED LEARNING BERMUATAN ETNOMATEMATIKA DALAM PEMBELAJAR MATEMATIKA

Directory of Open Access Journals (Sweden)

I Wayan Eka Mahendra

2017-03-01

Full Text Available This study aims to determine differences simultaneously in motivation and mathematics learning outcomes between students taking project based learningmodel charged ethnomathematics and students who followed the conventional learning modelon the class VIII SMP Negeri 3 Abiansemalyear 2016/2017. It was a quasi experiment with a sample of 71 student obtain by using simple random sampling. The data were analyzed by one-way multivariate analysis (Manova.The results of this study indicate that there are differences in simultaneously in learning motivation and learning outcomes between students taking mathematics model project based learning charged ethnomathematics and students who followed the conventional learning model on the class VIII SMP Negeri 3 Abiansemal year 2016/2017. Besed on the research findings, junior high school teachers are suggested to improve their student learning outcome for mathematics. Teachers also need to use a learning models accurately and correctly.

Learning to reason from samples

NARCIS (Netherlands)

Ben-Zvi, Dani; Bakker, Arthur; Makar, Katie

2015-01-01

The goal of this article is to introduce the topic of learning to reason from samples, which is the focus of this special issue of Educational Studies in Mathematics on statistical reasoning. Samples are data sets, taken from some wider universe (e.g., a population or a process) using a particular

Taking account of sample finite dimensions in processing measurements of double differential cross sections of slow neutron scattering

International Nuclear Information System (INIS)

Lisichkin, Yu.V.; Dovbenko, A.G.; Efimenko, B.A.; Novikov, A.G.; Smirenkina, L.D.; Tikhonova, S.I.

1979-01-01

Described is a method of taking account of finite sample dimensions in processing measurement results of double differential cross sections (DDCS) of slow neutron scattering. A necessity of corrective approach to the account taken of the effect of sample finite dimensions is shown, and, in particular, the necessity to conduct preliminary processing of DDCS, the account being taken of attenuation coefficients of single scattered neutrons (SSN) for measurements on the sample with a container, and on the container. Correction for multiple scattering (MS) calculated on the base of the dynamic model should be obtained, the account being taken of resolution effects. To minimize the effect of the dynamic model used in calculations it is preferred to make absolute measurements of DDCS and to use the subraction method. The above method was realized in the set of programs for the BESM-5 computer. The FISC program computes the coefficients of SSN attenuation and correction for MS. The DDS program serves to compute a model DDCS averaged as per the resolution function of an instrument. The SCATL program is intended to prepare initial information necessary for the FISC program, and permits to compute the scattering law for all materials. Presented are the results of using the above method while processing experimental data on measuring DDCS of water by the DIN-1M spectrometer

Dynamic pricing and learning with finite inventories

NARCIS (Netherlands)

den Boer, A.V.; Zwart, Bert

2013-01-01

We study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season, perishes. The goal of the seller is to determine a pricing strategy

Dynamic pricing and learning with finite inventories

NARCIS (Netherlands)

den Boer, A.V.; Zwart, Bert

We study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season perishes. The goal of the seller is to determine a pricing strategy

Dynamic Pricing and Learning with Finite Inventories

NARCIS (Netherlands)

A.P. Zwart (Bert); A.V. den Boer (Arnoud)

2015-01-01

htmlabstractWe study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season perishes. The goal of the seller is to determine a

Dynamic pricing and learning with finite inventories

NARCIS (Netherlands)

Boer, den A.V.; Zwart, B.

2015-01-01

We study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season perishes. The goal of the seller is to determine a pricing strategy

Bare-Bones Teaching-Learning-Based Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2014-01-01

Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Active-learning versus teacher-centered instruction for learning acids and bases

Science.gov (United States)

Acar Sesen, Burcin; Tarhan, Leman

2011-07-01

Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of 'acids and bases'. Sample The sample of this study was 45 high-school students (average age 17 years) from two different classes, which were randomly assigned to the experimental (n = 21) and control groups (n = 25), in a high school in Turkey. Design and methods A pre-test consisting of 25 items was applied to both experimental and control groups before the treatment in order to identify student prerequisite knowledge about their proficiency for learning 'acids and bases'. A one-way analysis of variance (ANOVA) was conducted to compare the pre-test scores for groups and no significant difference was found between experimental (ME = 40.14) and control groups (MC = 41.92) in terms of mean scores (F 1,43 = 2.66, p > 0.05). The experimental group was taught using an active-learning curriculum developed by the authors and the control group was taught using traditional course content based on teacher-centered instruction. After the implementation, 'Acids and Bases Achievement Test' scores were collected for both groups. Results ANOVA results showed that students' 'Acids and Bases Achievement Test' post-test scores differed significantly in terms of groups (F 1,43 = 102.53; p acid and base theories'; 'metal and non-metal oxides'; 'acid and base strengths'; 'neutralization'; 'pH and pOH'; 'hydrolysis'; 'acid-base equilibrium'; 'buffers'; 'indicators'; and 'titration'. Based on the achievement test and individual interview results, it was found that high-school students in the experimental group had fewer misconceptions and understood the concepts more meaningfully than students in control group. Conclusion The study revealed that active-learning implementation is more effective at

Unsupervised active learning based on hierarchical graph-theoretic clustering.

Science.gov (United States)

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

Effects of team-based learning on self-regulated online learning.

Science.gov (United States)

Whittaker, Alice A

2015-04-10

Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

The effect of multiple intelligence-based learning towards students’ concept mastery and interest in learning matter

Science.gov (United States)

Pratiwi, W. N.; Rochintaniawati, D.; Agustin, R. R.

2018-05-01

This research was focused on investigating the effect of multiple intelligence -based learning as a learning approach towards students’ concept mastery and interest in learning matter. The one-group pre-test - post-test design was used in this research towards a sample which was according to the suitable situation of the research sample, n = 13 students of the 7th grade in a private school in Bandar Seri Begawan. The students’ concept mastery was measured using achievement test and given at the pre-test and post-test, meanwhile the students’ interest level was measured using a Likert Scale for interest. Based on the analysis of the data, the result shows that the normalized gain was .61, which was considered as a medium improvement. in other words, students’ concept mastery in matter increased after being taught using multiple intelligence-based learning. The Likert scale of interest shows that most students have a high interest in learning matter after being taught by multiple intelligence-based learning. Therefore, it is concluded that multiple intelligence – based learning helped in improving students’ concept mastery and gain students’ interest in learning matter.

Design-based Sample and Probability Law-Assumed Sample: Their Role in Scientific Investigation.

Science.gov (United States)

Ojeda, Mario Miguel; Sahai, Hardeo

2002-01-01

Discusses some key statistical concepts in probabilistic and non-probabilistic sampling to provide an overview for understanding the inference process. Suggests a statistical model constituting the basis of statistical inference and provides a brief review of the finite population descriptive inference and a quota sampling inferential theory.…

A Web-Based Learning Support System for Inquiry-Based Learning

Science.gov (United States)

Kim, Dong Won; Yao, Jingtao

The emergence of the Internet and Web technology makes it possible to implement the ideals of inquiry-based learning, in which students seek truth, information, or knowledge by questioning. Web-based learning support systems can provide a good framework for inquiry-based learning. This article presents a study on a Web-based learning support system called Online Treasure Hunt. The Web-based learning support system mainly consists of a teaching support subsystem, a learning support subsystem, and a treasure hunt game. The teaching support subsystem allows instructors to design their own inquiry-based learning environments. The learning support subsystem supports students' inquiry activities. The treasure hunt game enables students to investigate new knowledge, develop ideas, and review their findings. Online Treasure Hunt complies with a treasure hunt model. The treasure hunt model formalizes a general treasure hunt game to contain the learning strategies of inquiry-based learning. This Web-based learning support system empowered with the online-learning game and founded on the sound learning strategies furnishes students with the interactive and collaborative student-centered learning environment.

Keefektifan setting TPS dalam pendekatan discovery learning dan problem-based learning pada pembelajaran materi lingkaran SMP

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Rahmi Hidayati

2017-05-01

The purpose of this study was to describe the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning in terms of student achievement, mathematical communication skills, and interpersonal skills of the student.  This study was a quasi-experimental study using the pretest-posttest nonequivalent group design. The research population comprised all Year VIII students of SMP Negeri 1 Yogyakarta. The research sample was randomly selected from eight classes, two classes were elected. The instrument used in this study is the learning achievement test, a test of mathematical communication skills, and interpersonal skills student questionnaires. To test the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the one sample t-test was carried out. Then, to investigate the difference in effectiveness between the setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the Multivariate Analysis of Variance (MANOVA was carried out. The research findings indicate that the setting TPS discovery approach to learning and problem-based approach to learning (PBL is effective in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. No difference in effectiveness between setting TPS discovery approach to learning and problem-based learning (PBL in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. Keywords: TPS setting in discovery learning approach, in problem-based learning, academic achievement, mathematical communication skills, and interpersonal skills of the student

Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces

Science.gov (United States)

Neukart, Florian; Von Dollen, David; Seidel, Christian; Compostella, Gabriele

2017-12-01

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed n sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.

Static Object Detection Based on a Dual Background Model and a Finite-State Machine

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Heras Evangelio Rubén

2011-01-01

Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

Finite element analysis theory and application with ANSYS

CERN Document Server

Moaveni, Saeed

2015-01-01

For courses in Finite Element Analysis, offered in departments of Mechanical or Civil and Environmental Engineering. While many good textbooks cover the theory of finite element modeling, Finite Element Analysis: Theory and Application with ANSYS is the only text available that incorporates ANSYS as an integral part of its content. Moaveni presents the theory of finite element analysis, explores its application as a design/modeling tool, and explains in detail how to use ANSYS intelligently and effectively. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students. It will help: *Present the Theory of Finite Element Analysis: The presentation of theoretical aspects of finite element analysis is carefully designed not to overwhelm students. *Explain How to Use ANSYS Effectively: ANSYS is incorporated as an integral part of the content throughout the book. *Explore How to Use FEA as a Design/Modeling Tool: Open-ended design problems help stude...

Bayesian analysis of finite population sampling in multivariate co-exchangeable structures with separable covariance matric

OpenAIRE

Shaw, Simon C.; Goldstein, Michael

2017-01-01

We explore the effect of finite population sampling in design problems with many variables cross-classified in many ways. In particular, we investigate designs where we wish to sample individuals belonging to different groups for which the underlying covariance matrices are separable between groups and variables. We exploit the generalised conditional independence structure of the model to show how the analysis of the full model can be reduced to an interpretable series of lower dimensional p...

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

Directory of Open Access Journals (Sweden)

Anne Hsu

Full Text Available A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

Science.gov (United States)

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

Improving self-regulated learning junior high school students through computer-based learning

Science.gov (United States)

Nurjanah; Dahlan, J. A.

2018-05-01

This study is back grounded by the importance of self-regulated learning as an affective aspect that determines the success of students in learning mathematics. The purpose of this research is to see how the improvement of junior high school students' self-regulated learning through computer based learning is reviewed in whole and school level. This research used a quasi-experimental research method. This is because individual sample subjects are not randomly selected. The research design used is Pretest-and-Posttest Control Group Design. Subjects in this study were students of grade VIII junior high school in Bandung taken from high school (A) and middle school (B). The results of this study showed that the increase of the students' self-regulated learning who obtain learning with computer-based learning is higher than students who obtain conventional learning. School-level factors have a significant effect on increasing of the students' self-regulated learning.

Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming

Directory of Open Access Journals (Sweden)

Atta Ullah

2014-01-01

Full Text Available In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint. An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample. In many real life situations, a linear cost function of a sample size nh is not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant. In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming. A solution procedure is proposed using extended lexicographic goal programming approach. A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.

Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

Science.gov (United States)

Zhang, Chao; Tao, Dacheng

2012-12-01

Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

Gear hot forging process robust design based on finite element method

International Nuclear Information System (INIS)

Xuewen, Chen; Won, Jung Dong

2008-01-01

During the hot forging process, the shaping property and forging quality will fluctuate because of die wear, manufacturing tolerance, dimensional variation caused by temperature and the different friction conditions, etc. In order to control this variation in performance and to optimize the process parameters, a robust design method is proposed in this paper, based on the finite element method for the hot forging process. During the robust design process, the Taguchi method is the basic robust theory. The finite element analysis is incorporated in order to simulate the hot forging process. In addition, in order to calculate the objective function value, an orthogonal design method is selected to arrange experiments and collect sample points. The ANOVA method is employed to analyze the relationships of the design parameters and design objectives and to find the best parameters. Finally, a case study for the gear hot forging process is conducted. With the objective to reduce the forging force and its variation, the robust design mathematical model is established. The optimal design parameters obtained from this study indicate that the forging force has been reduced and its variation has been controlled

Dynamic Pricing and Learning with Finite Inventories

OpenAIRE

Zwart, Bert; Boer, Arnoud

2015-01-01

We study a dynamic pricing problem with finite inventory and parametric uncertainty on the demand distribution. Products are sold during selling seasons of finite length, and inventory that is unsold at the end of a selling season, perishes. The goal of the seller is to determine a pricing strategy that maximizes the expected revenue. Inference on the unknown parameters is made by maximum likelihood estimation. We propose a pricing strategy for this problem, and show that the Regret - which i...

Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

4/2010, č. 3 (2010), s. 236-250 ISSN 1802-4696 R&D Projects: GA ČR GD402/09/H045; GA ČR GA402/09/0965 Grant - others:GA UK(CZ) 118310 Institutional research plan: CEZ:AV0Z10750506 Keywords : rescaled range analysis * detrended fluctuation analysis * Hurst exponent * long-range dependence Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2010/E/kristoufek-rescaled range analysis and detrended fluctuation analysis finite sample properties and confidence intervals.pdf

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

Directory of Open Access Journals (Sweden)

Juan Carlos Davila

2017-06-01

Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

Science.gov (United States)

Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

2017-06-07

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Finite sample performance of the E-M algorithm for ranks data modelling

Directory of Open Access Journals (Sweden)

Angela D'Elia

2007-10-01

Full Text Available We check the finite sample performance of the maximum likelihood estimators of the parameters of a mixture distribution recently introduced for modelling ranks/preference data. The estimates are derived by the E-M algorithm and the performance is evaluated both from an univariate and bivariate points of view. While the results are generally acceptable as far as it concerns the bias, the Monte Carlo experiment shows a different behaviour of the estimators efficiency for the two parameters of the mixture, mainly depending upon their location in the admissible parametric space. Some operative suggestions conclude the paer.

Sampling algorithms for validation of supervised learning models for Ising-like systems

Science.gov (United States)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

Finite element model updating of concrete structures based on imprecise probability

Science.gov (United States)

Biswal, S.; Ramaswamy, A.

2017-09-01

Imprecise probability based methods are developed in this study for the parameter estimation, in finite element model updating for concrete structures, when the measurements are imprecisely defined. Bayesian analysis using Metropolis Hastings algorithm for parameter estimation is generalized to incorporate the imprecision present in the prior distribution, in the likelihood function, and in the measured responses. Three different cases are considered (i) imprecision is present in the prior distribution and in the measurements only, (ii) imprecision is present in the parameters of the finite element model and in the measurement only, and (iii) imprecision is present in the prior distribution, in the parameters of the finite element model, and in the measurements. Procedures are also developed for integrating the imprecision in the parameters of the finite element model, in the finite element software Abaqus. The proposed methods are then verified against reinforced concrete beams and prestressed concrete beams tested in our laboratory as part of this study.

Artificial emotional model based on finite state machine

Institute of Scientific and Technical Information of China (English)

MENG Qing-mei; WU Wei-guo

2008-01-01

According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

International Nuclear Information System (INIS)

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

2008-01-01

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

Science.gov (United States)

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-06-01

This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.

Higher Order Thinking Skills as Effect of Problem Based Learning in the 21st Century Learning

Directory of Open Access Journals (Sweden)

Leni Widiawati

2018-03-01

Full Text Available This study aims to determine the responses of learners to learning using a scientific approach in Problem Based Learning integrated with the inculcation of critical thinking, communicative, collaboration; and creative (4C skills in 21st century learning. The design of this study is true experiment by using posttest only control design. The sample of the research is vocational school students selected by using cluster random sampling technique in Surakarta, Indonesia. The techniques of collecting data are using tests whose validity, reliability, level of difficulty, and the discrimination index have been tested. The data obtained are then tested using t test. The result of the research shows that higher order thinking skills of experimental class students learning using scientific approach in Problem Based Learning which is integrated with the inculcation of 4C skills are higher than those of the control class that are learning using scientific approach in Think-Pair-Share which is integrated with the inculcation of 4C skills.

Learning second language vocabulary: neural dissociation of situation-based learning and text-based learning.

Science.gov (United States)

Jeong, Hyeonjeong; Sugiura, Motoaki; Sassa, Yuko; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2010-04-01

Second language (L2) acquisition necessitates learning and retrieving new words in different modes. In this study, we attempted to investigate the cortical representation of an L2 vocabulary acquired in different learning modes and in cross-modal transfer between learning and retrieval. Healthy participants learned new L2 words either by written translations (text-based learning) or in real-life situations (situation-based learning). Brain activity was then measured during subsequent retrieval of these words. The right supramarginal gyrus and left middle frontal gyrus were involved in situation-based learning and text-based learning, respectively, whereas the left inferior frontal gyrus was activated when learners used L2 knowledge in a mode different from the learning mode. Our findings indicate that the brain regions that mediate L2 memory differ according to how L2 words are learned and used. Copyright 2009 Elsevier Inc. All rights reserved.

Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method

DEFF Research Database (Denmark)

Goo, Seongyeol; Wang, Semyung; Kook, Junghwan

2017-01-01

This paper presents an alternative topology optimization method for bounded acoustic problems that uses the hybrid finite element-wave based method (FE-WBM). The conventional method for the topology optimization of bounded acoustic problems is based on the finite element method (FEM), which...

An efficicient data structure for three-dimensional vertex based finite volume method

Science.gov (United States)

Akkurt, Semih; Sahin, Mehmet

2017-11-01

A vertex based three-dimensional finite volume algorithm has been developed using an edge based data structure.The mesh data structure of the given algorithm is similar to ones that exist in the literature. However, the data structures are redesigned and simplied in order to fit requirements of the vertex based finite volume method. In order to increase the cache efficiency, the data access patterns for the vertex based finite volume method are investigated and these datas are packed/allocated in a way that they are close to each other in the memory. The present data structure is not limited with tetrahedrons, arbitrary polyhedrons are also supported in the mesh without putting any additional effort. Furthermore, the present data structure also supports adaptive refinement and coarsening. For the implicit and parallel implementation of the FVM algorithm, PETSc and MPI libraries are employed. The performance and accuracy of the present algorithm are tested for the classical benchmark problems by comparing the CPU time for the open source algorithms.

The Effectiveness of Problem Based Learning (PBL on Intermediate Financial Accounting Subject

Directory of Open Access Journals (Sweden)

Nunuk Suryanti

2016-12-01

Full Text Available This research aims to know the effectiveness of Problem Based Learning (PBL Model comparing to Drill Model on Intermediate Financial Accounting subject. The research was a quasi-experimental research. Population was four classes of Accounting Education students in the year of 2014/2015 at Faculty of Educational Science and Teaching of Riau Islamic University (UIR. Sample was taken by using purposive sampling. Then, it used Problem Based Learning (PBL at experimental class and Drill Model at controlled class. Data was collected by using interview, observation, and tests (pre-test and post-test. Moreover, data were analyzed by using independent sample test. Findings show that there is no any difference of learning outcomes between students who taught by Problem Based Learning (PBL Model and Drill Model on Intermediate Financial Accounting.

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

Energy Technology Data Exchange (ETDEWEB)

Lee, Youngrok [Iowa State Univ., Ames, IA (United States)

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates of nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.

Effects of case-based learning on communication skills, problem-solving ability, and learning motivation in nursing students.

Science.gov (United States)

Yoo, Moon-Sook; Park, Hyung-Ran

2015-06-01

The purpose of this study was to explore the effects of case-based learning on communication skills, problem-solving ability, and learning motivation in sophomore nursing students. In this prospective, quasi-experimental study, we compared the pretest and post-test scores of an experimental group and a nonequivalent, nonsynchronized control group. Both groups were selected using convenience sampling, and consisted of students enrolled in a health communication course in the fall semesters of 2011 (control group) and 2012 (experimental group) at a nursing college in Suwon, South Korea. The two courses covered the same material, but in 2011 the course was lecture-based, while in 2012, lectures were replaced by case-based learning comprising five authentic cases of patient-nurse communication. At post-test, the case-based learning group showed significantly greater communication skills, problem-solving ability, and learning motivation than the lecture-based learning group. This finding suggests that case-based learning is an effective learning and teaching method. © 2014 Wiley Publishing Asia Pty Ltd.

Node-based finite element method for large-scale adaptive fluid analysis in parallel environments

International Nuclear Information System (INIS)

Toshimitsu, Fujisawa; Genki, Yagawa

2003-01-01

In this paper, a FEM-based (finite element method) mesh free method with a probabilistic node generation technique is presented. In the proposed method, all computational procedures, from the mesh generation to the solution of a system of equations, can be performed fluently in parallel in terms of nodes. Local finite element mesh is generated robustly around each node, even for harsh boundary shapes such as cracks. The algorithm and the data structure of finite element calculation are based on nodes, and parallel computing is realized by dividing a system of equations by the row of the global coefficient matrix. In addition, the node-based finite element method is accompanied by a probabilistic node generation technique, which generates good-natured points for nodes of finite element mesh. Furthermore, the probabilistic node generation technique can be performed in parallel environments. As a numerical example of the proposed method, we perform a compressible flow simulation containing strong shocks. Numerical simulations with frequent mesh refinement, which are required for such kind of analysis, can effectively be performed on parallel processors by using the proposed method. (authors)

Node-based finite element method for large-scale adaptive fluid analysis in parallel environments

Energy Technology Data Exchange (ETDEWEB)

Toshimitsu, Fujisawa [Tokyo Univ., Collaborative Research Center of Frontier Simulation Software for Industrial Science, Institute of Industrial Science (Japan); Genki, Yagawa [Tokyo Univ., Department of Quantum Engineering and Systems Science (Japan)

2003-07-01

In this paper, a FEM-based (finite element method) mesh free method with a probabilistic node generation technique is presented. In the proposed method, all computational procedures, from the mesh generation to the solution of a system of equations, can be performed fluently in parallel in terms of nodes. Local finite element mesh is generated robustly around each node, even for harsh boundary shapes such as cracks. The algorithm and the data structure of finite element calculation are based on nodes, and parallel computing is realized by dividing a system of equations by the row of the global coefficient matrix. In addition, the node-based finite element method is accompanied by a probabilistic node generation technique, which generates good-natured points for nodes of finite element mesh. Furthermore, the probabilistic node generation technique can be performed in parallel environments. As a numerical example of the proposed method, we perform a compressible flow simulation containing strong shocks. Numerical simulations with frequent mesh refinement, which are required for such kind of analysis, can effectively be performed on parallel processors by using the proposed method. (authors)

A point-value enhanced finite volume method based on approximate delta functions

Science.gov (United States)

Xuan, Li-Jun; Majdalani, Joseph

2018-02-01

We revisit the concept of an approximate delta function (ADF), introduced by Huynh (2011) [1], in the form of a finite-order polynomial that holds identical integral properties to the Dirac delta function when used in conjunction with a finite-order polynomial integrand over a finite domain. We show that the use of generic ADF polynomials can be effective at recovering and generalizing several high-order methods, including Taylor-based and nodal-based Discontinuous Galerkin methods, as well as the Correction Procedure via Reconstruction. Based on the ADF concept, we then proceed to formulate a Point-value enhanced Finite Volume (PFV) method, which stores and updates the cell-averaged values inside each element as well as the unknown quantities and, if needed, their derivatives on nodal points. The sharing of nodal information with surrounding elements saves the number of degrees of freedom compared to other compact methods at the same order. To ensure conservation, cell-averaged values are updated using an identical approach to that adopted in the finite volume method. Here, the updating of nodal values and their derivatives is achieved through an ADF concept that leverages all of the elements within the domain of integration that share the same nodal point. The resulting scheme is shown to be very stable at successively increasing orders. Both accuracy and stability of the PFV method are verified using a Fourier analysis and through applications to the linear wave and nonlinear Burgers' equations in one-dimensional space.

A Kohn–Sham equation solver based on hexahedral finite elements

International Nuclear Information System (INIS)

Fang Jun; Gao Xingyu; Zhou Aihui

2012-01-01

We design a Kohn–Sham equation solver based on hexahedral finite element discretizations. The solver integrates three schemes proposed in this paper. The first scheme arranges one a priori locally-refined hexahedral mesh with appropriate multiresolution. The second one is a modified mass-lumping procedure which accelerates the diagonalization in the self-consistent field iteration. The third one is a finite element recovery method which enhances the eigenpair approximations with small extra work. We carry out numerical tests on each scheme to investigate the validity and efficiency, and then apply them to calculate the ground state total energies of nanosystems C 60 , C 120 , and C 275 H 172 . It is shown that our solver appears to be computationally attractive for finite element applications in electronic structure study.

Extension to linear dynamics for hybrid stress finite element formulation based on additional displacements

Science.gov (United States)

Sumihara, K.

Based upon legitimate variational principles, one microscopic-macroscopic finite element formulation for linear dynamics is presented by Hybrid Stress Finite Element Method. The microscopic application of Geometric Perturbation introduced by Pian and the introduction of infinitesimal limit core element (Baby Element) have been consistently combined according to the flexible and inherent interpretation of the legitimate variational principles initially originated by Pian and Tong. The conceptual development based upon Hybrid Finite Element Method is extended to linear dynamics with the introduction of physically meaningful higher modes.

The Effect of Animation in Multimedia Computer-Based Learning and Learning Style to the Learning Results

Directory of Open Access Journals (Sweden)

Muhammad RUSLI

2017-10-01

Full Text Available The effectiveness of a learning depends on four main elements, they are content, desired learning outcome, instructional method and the delivery media. The integration of those four elements can be manifested into a learning modul which is called multimedia learning or learning by using multimedia. In learning context by using computer-based multimedia, there are two main things that need to be noticed so that the learning process can run effectively: how the content is presented, and what the learner’s chosen way in accepting and processing the information into a meaningful knowledge. First it is related with the way to visualize the content and how people learn. The second one is related with the learning style of the learner. This research aims to investigate the effect of the type of visualization—static vs animated—on a multimedia computer-based learning, and learning styles—visual vs verbal, towards the students’ capability in applying the concepts, procedures, principles of Java programming. Visualization type act as independent variables, and learning styles of the students act as a moderator variable. Moreover, the instructional strategies followed the Component Display Theory of Merril, and the format of presentation of multimedia followed the Seven Principles of Multimedia Learning of Mayer and Moreno. Learning with the multimedia computer-based learning has been done in the classroom. The subject of this research was the student of STMIK-STIKOM Bali in odd semester 2016-2017 which followed the course of Java programming. The Design experiments used multivariate analysis of variance, MANOVA 2 x 2, with a large sample of 138 students in 4 classes. Based on the results of the analysis, it can be concluded that the animation in multimedia interactive learning gave a positive effect in improving students’ learning outcomes, particularly in the applying the concepts, procedures, and principles of Java programming. The

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

OpenAIRE

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and di...

Programming the finite element method

CERN Document Server

Smith, I M; Margetts, L

2013-01-01

Many students, engineers, scientists and researchers have benefited from the practical, programming-oriented style of the previous editions of Programming the Finite Element Method, learning how to develop computer programs to solve specific engineering problems using the finite element method. This new fifth edition offers timely revisions that include programs and subroutine libraries fully updated to Fortran 2003, which are freely available online, and provides updated material on advances in parallel computing, thermal stress analysis, plasticity return algorithms, convection boundary c

Bounds on the sample complexity for private learning and private data release

Energy Technology Data Exchange (ETDEWEB)

Kasiviswanathan, Shiva [Los Alamos National Laboratory; Beime, Amos [BEN-GURION UNIV.; Nissim, Kobbi [BEN-GURION UNIV.

2009-01-01

Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is natural to ask what can be learned while preserving individual privacy. [Kasiviswanathan, Lee, Nissim, Raskhodnikova, and Smith; FOCS 2008] initiated such a discussion. They formalized the notion of private learning, as a combination of PAC learning and differential privacy, and investigated what concept classes can be learned privately. Somewhat surprisingly, they showed that, ignoring time complexity, every PAC learning task could be performed privately with polynomially many samples, and in many natural cases this could even be done in polynomial time. While these results seem to equate non-private and private learning, there is still a significant gap: the sample complexity of (non-private) PAC learning is crisply characterized in terms of the VC-dimension of the concept class, whereas this relationship is lost in the constructions of private learners, which exhibit, generally, a higher sample complexity. Looking into this gap, we examine several private learning tasks and give tight bounds on their sample complexity. In particular, we show strong separations between sample complexities of proper and improper private learners (such separation does not exist for non-private learners), and between sample complexities of efficient and inefficient proper private learners. Our results show that VC-dimension is not the right measure for characterizing the sample complexity of proper private learning. We also examine the task of private data release (as initiated by [Blum, Ligett, and Roth; STOC 2008]), and give new lower bounds on the sample complexity. Our results show that the logarithmic dependence on size of the instance space is essential for private data release.

Biquartic Finite Volume Element Metho d Based on Lobatto-Guass Structure

Institute of Scientific and Technical Information of China (English)

Gao Yan-ni; Chen Yan-li

2015-01-01

In this paper, a biquartic finite volume element method based on Lobatto-Guass structure is presented for variable coefficient elliptic equation on rectangular partition. Not only the optimal H1 and L2 error estimates but also some super-convergent properties are available and could be proved for this method. The numer-ical results obtained by this finite volume element scheme confirm the validity of the theoretical analysis and the effectiveness of this method.

Sample-efficient Strategies for Learning in the Presence of Noise

DEFF Research Database (Denmark)

Cesa-Bianchi, N.; Dichterman, E.; Fischer, Paul

1999-01-01

In this paper, we prove various results about PAC learning in the presence of malicious noise. Our main interest is the sample size behavior of learning algorithms. We prove the first nontrivial sample complexity lower bound in this model by showing that order of &egr;/&Dgr;2 + d/&Dgr; (up...... to logarithmic factors) examples are necessary for PAC learning any target class of {#123;0,1}#125;-valued functions of VC dimension d, where &egr; is the desired accuracy and &eegr; = &egr;/(1 + &egr;) - &Dgr; the malicious noise rate (it is well known that any nontrivial target class cannot be PAC learned...... with accuracy &egr; and malicious noise rate &eegr; &egr;/(1 + &egr;), this irrespective to sample complexity). We also show that this result cannot be significantly improved in general by presenting efficient learning algorithms for the class of all subsets of d elements and the class of unions of at most d...

Scene recognition based on integrating active learning with dictionary learning

Science.gov (United States)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

Science.gov (United States)

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don’t include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results. PMID:28931066

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

Science.gov (United States)

Chen, Chuan; Li, Lixiang; Peng, Haipeng; Yang, Yixian

2017-01-01

Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs) with both discrete delay and distributed delay (mixed delays). By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays.

Directory of Open Access Journals (Sweden)

Chuan Chen

Full Text Available Finite time synchronization, which means synchronization can be achieved in a settling time, is desirable in some practical applications. However, most of the published results on finite time synchronization don't include delays or only include discrete delays. In view of the fact that distributed delays inevitably exist in neural networks, this paper aims to investigate the finite time synchronization of memristor-based Cohen-Grossberg neural networks (MCGNNs with both discrete delay and distributed delay (mixed delays. By means of a simple feedback controller and novel finite time synchronization analysis methods, several new criteria are derived to ensure the finite time synchronization of MCGNNs with mixed delays. The obtained criteria are very concise and easy to verify. Numerical simulations are presented to demonstrate the effectiveness of our theoretical results.

Comparison the Application of PBL (Project Based Learning and PBL (Problem Based Learning Learning Model on Online Marketing Subjects

Directory of Open Access Journals (Sweden)

Agnes Dini Mardani

2017-09-01

Full Text Available Purpose of this study are (1 the application of learning PjBL with PBL to improve study results students, (2 assessing the domain affective, cognitive, and psychomotor, (3 the difference study results use the PjBL with PBL to improve study results students. The research is research quantitative and including research apparent experiment (quasi eksperiment by taking sample class two classes X PM 1 as a class experiment and class X PM 2 as a class control. Research instruments used for data collection namely: (1 tests to pretes and postest used to determine the cognitive assessment, (2 sheets observation affective, (3 sheets of the process for the psychomotor. The trial research instruments use the validity and reabilitas. Analysis techniques data using: (1 test a prerequisite analysis consisting of normality test and the homogeneity (2 T test unpaired which ended with the help of computer programs spss. Based on the result of this research can be concluded that: (1 the application of PjBL (Project Based Learning and PBL (Problem Based Learning should be conducted well in accordance syntax learning, (2 assessing the cognitive students have a difference and class experiment having an average higher than class control, (3 assessing the results affective students have a difference and on the application of PjBL is better than PBL.

Problem Based Learning

DEFF Research Database (Denmark)

de Graaff, Erik; Guerra, Aida

, the key principles remain the same everywhere. Graaff & Kolmos (2003) identify the main PBL principles as follows: 1. Problem orientation 2. Project organization through teams or group work 3. Participant-directed 4. Experiental learning 5. Activity-based learning 6. Interdisciplinary learning and 7...... model and in general problem based and project based learning. We apply the principle of teach as you preach. The poster aims to outline the visitors’ workshop programme showing the results of some recent evaluations.......Problem-Based Learning (PBL) is an innovative method to organize the learning process in such a way that the students actively engage in finding answers by themselves. During the past 40 years PBL has evolved and diversified resulting in a multitude in variations in models and practices. However...

Development of a three-dimensional neutron transport code DFEM based on the double finite element method

International Nuclear Information System (INIS)

Fujimura, Toichiro

1996-01-01

A three-dimensional neutron transport code DFEM has been developed by the double finite element method to analyze reactor cores with complex geometry as large fast reactors. Solution algorithm is based on the double finite element method in which the space and angle finite elements are employed. A reactor core system can be divided into some triangular and/or quadrangular prism elements, and the spatial distribution of neutron flux in each element is approximated with linear basis functions. As for the angular variables, various basis functions are applied, and their characteristics were clarified by comparison. In order to enhance the accuracy, a general method is derived to remedy the truncation errors at reflective boundaries, which are inherent in the conventional FEM. An adaptive acceleration method and the source extrapolation method were applied to accelerate the convergence of the iterations. The code structure is outlined and explanations are given on how to prepare input data. A sample input list is shown for reference. The eigenvalue and flux distribution for real scale fast reactors and the NEA benchmark problems were presented and discussed in comparison with the results of other transport codes. (author)

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

Science.gov (United States)

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

Science.gov (United States)

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

Finite element simulation of the T-shaped ECAP processing of round samples

Science.gov (United States)

Shaban Ghazani, Mehdi; Fardi-Ilkhchy, Ali; Binesh, Behzad

2018-05-01

Grain refinement is the only mechanism that increases the yield strength and toughness of the materials simultaneously. Severe plastic deformation is one of the promising methods to refine the microstructure of materials. Among different severe plastic deformation processes, the T-shaped equal channel angular pressing (T-ECAP) is a relatively new technique. In the present study, finite element analysis was conducted to evaluate the deformation behavior of metals during T-ECAP process. The study was focused mainly on flow characteristics, plastic strain distribution and its homogeneity, damage development, and pressing force which are among the most important factors governing the sound and successful processing of nanostructured materials by severe plastic deformation techniques. The results showed that plastic strain is localized in the bottom side of sample and uniform deformation cannot be possible using T-ECAP processing. Friction coefficient between sample and die channel wall has a little effect on strain distributions in mirror plane and transverse plane of deformed sample. Also, damage analysis showed that superficial cracks may be initiated from bottom side of sample and their propagation will be limited due to the compressive state of stress. It was demonstrated that the V shaped deformation zone are existed in T-ECAP process and the pressing load needed for execution of deformation process is increased with friction.

Isogeometric finite element data structures based on Bézier extraction of T-splines

NARCIS (Netherlands)

Scott, M.A.; Borden, M.J.; Verhoosel, C.V.; Sederberg, T.W.; Hughes, T.J.R.

2011-01-01

We develop finite element data structures for T-splines based on Bézier extraction generalizing our previous work for NURBS. As in traditional finite element analysis, the extracted Bézier elements are defined in terms of a fixed set of polynomial basis functions, the so-called Bernstein basis. The

Active learning for clinical text classification: is it better than random sampling?

Science.gov (United States)

Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

Nonlinear Finite Strain Consolidation Analysis with Secondary Consolidation Behavior

Directory of Open Access Journals (Sweden)

Jieqing Huang

2014-01-01

Full Text Available This paper aims to analyze nonlinear finite strain consolidation with secondary consolidation behavior. On the basis of some assumptions about the secondary consolidation behavior, the continuity equation of pore water in Gibson’s consolidation theory is modified. Taking the nonlinear compressibility and nonlinear permeability of soils into consideration, the governing equation for finite strain consolidation analysis is derived. Based on the experimental data of Hangzhou soft clay samples, the new governing equation is solved with the finite element method. Afterwards, the calculation results of this new method and other two methods are compared. It can be found that Gibson’s method may underestimate the excess pore water pressure during primary consolidation. The new method which takes the secondary consolidation behavior, the nonlinear compressibility, and nonlinear permeability of soils into consideration can precisely estimate the settlement rate and the final settlement of Hangzhou soft clay sample.

An Image-Based Finite Element Approach for Simulating Viscoelastic Response of Asphalt Mixture

Directory of Open Access Journals (Sweden)

Wenke Huang

2016-01-01

Full Text Available This paper presents an image-based micromechanical modeling approach to predict the viscoelastic behavior of asphalt mixture. An improved image analysis technique based on the OTSU thresholding operation was employed to reduce the beam hardening effect in X-ray CT images. We developed a voxel-based 3D digital reconstruction model of asphalt mixture with the CT images after being processed. In this 3D model, the aggregate phase and air void were considered as elastic materials while the asphalt mastic phase was considered as linear viscoelastic material. The viscoelastic constitutive model of asphalt mastic was implemented in a finite element code using the ABAQUS user material subroutine (UMAT. An experimental procedure for determining the parameters of the viscoelastic constitutive model at a given temperature was proposed. To examine the capability of the model and the accuracy of the parameter, comparisons between the numerical predictions and the observed laboratory results of bending and compression tests were conducted. Finally, the verified digital sample of asphalt mixture was used to predict the asphalt mixture viscoelastic behavior under dynamic loading and creep-recovery loading. Simulation results showed that the presented image-based digital sample may be appropriate for predicting the mechanical behavior of asphalt mixture when all the mechanical properties for different phases became available.

Evaluation of finite difference and FFT-based solutions of the transport of intensity equation.

Science.gov (United States)

Zhang, Hongbo; Zhou, Wen-Jing; Liu, Ying; Leber, Donald; Banerjee, Partha; Basunia, Mahmudunnabi; Poon, Ting-Chung

2018-01-01

A finite difference method is proposed for solving the transport of intensity equation. Simulation results show that although slower than fast Fourier transform (FFT)-based methods, finite difference methods are able to reconstruct the phase with better accuracy due to relaxed assumptions for solving the transport of intensity equation relative to FFT methods. Finite difference methods are also more flexible than FFT methods in dealing with different boundary conditions.

Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

Directory of Open Access Journals (Sweden)

YiNan Zhang

2017-01-01

Full Text Available Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

Strengths-based Learning

DEFF Research Database (Denmark)

Ledertoug, Mette Marie

-being. The Ph.D.-project in Strength-based learning took place in a Danish school with 750 pupils age 6-16 and a similar school was functioning as a control group. The presentation will focus on both the aware-explore-apply processes and the practical implications for the schools involved, and on measurable......Strength-based learning - Children͛s Character Strengths as Means to their Learning Potential͛ is a Ph.D.-project aiming to create a strength-based mindset in school settings and at the same time introducing strength-based interventions as specific tools to improve both learning and well...

Brain based learning with contextual approach to mathematics achievement

Directory of Open Access Journals (Sweden)

V Kartikaningtyas

2017-12-01

Full Text Available The aim of this study was to know the effect of Brain Based Learning (BBL with a contextual approach to mathematics achievement. BBL-contextual is the learning model that designed to develop and optimize the brain ability for getting a new concept and solving the real life problem. This study method was a quasi-experiment. The population was the junior high school students. The sample chosen by using stratified cluster random sampling. The sample was 109 students. The data collected through a mathematics achievement test that was given after the treatment. The data analyzed by using one way ANOVA. The results of the study showed that BBL-contextual is better than direct learning on mathematics achievement. It means BBL-contextual could be an effective and innovative model.

A Comparative Study of Paper-based and Computer-based Contextualization in Vocabulary Learning of EFL Students

Directory of Open Access Journals (Sweden)

Mousa Ahmadian

2015-04-01

Full Text Available Vocabulary acquisition is one of the largest and most important tasks in language classes. New technologies, such as computers, have helped a lot in this way. The importance of the issue led the researchers to do the present study which concerns the comparison of contextualized vocabulary learning on paper and through Computer Assisted Language Learning (CALL. To this end, 52 Pre-university EFL learners were randomly assigned in two groups: a paper-based group (PB and a computer-based (CB group each with 26 learners. The PB group received PB contextualization of vocabulary items, while the CB group received CB contextualization of the vocabulary items thorough PowerPoint (PP software. One pretest, posttest, along with an immediate and a delayed posttest were given to the learners. Paired samples t-test of pretest and posttest and independent samples t-test of the delayed and immediate posttest were executed by SPSS software. The results revealed that computer-based contextualization had more effects on vocabulary learning of Iranian EFL learners than paper-based contextualization of the words. Keywords: Computer-based contextualization, Paper-based contextualization, Vocabulary learning, CALL

MEMECAHKAN MASALAH GEOGRAFI MELALUI PROBLEM BASED LEARNING

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Sujiono Sujiono

2018-01-01

Full Text Available This study aims to determine the effect of Problem Based Learning model on geography problem-solving sklills. This research model is quasi experiment with non-equivalent control group design. The subjects of the study were the students of XI IPS SMA Negeri 1 Pulau Laut Timur, academic year 2016/2017. The assessment instrument is an essay test based on an indicator of problem solving skills, ie (1 identifying problems; (2 formulate the problem; (3 finding alternative solutions; (4 choose alternative solutions; and (5 make conclusions. Data analysis using independent sample t-test model with 5% significance level. The results showed that there is an influence of PBL model on geography problem-solving sklills. The geography problem-solving skills of experimental class with PBL model is higher than control class with conventional model. Suggestion given, that is to make a plan of learning well and doing learning PBL on outdoor study.   Keywords Problem Based Learning, problem-solving skills, geography   http://dx.doi.org/10.17977/um022v2i22017p072

Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

International Nuclear Information System (INIS)

Roche-Lima, Abiel; Thulasiram, Ruppa K

2012-01-01

Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

The Influence of Problem Based Learning Model toward Students’ Activities and Learning Outcomes on Financial Management Subject

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Han Tantri Hardini

2016-12-01

Full Text Available This research aims to know the influence of problem based learning model toward students’ activities and achievement on Financial Management subject for undergraduate program students of Accounting Education. It was a quantitative research that used true experimental design. Samples of this study were undergraduate program students of Accounting Education in the year of 2014. Class A were control class and class B were experimental class. Data were analyzed by using t-test in order to determine the differences of learning outcomes between control class and experimental class. Then, questionnaires were distributed to gather students’ activities information in their students’ learning model. Findings show that there is an influence of Problem Based Learning model toward students’ activities and learning outcomes on Financial Management subject for undergraduate program students of Accounting Education since t-count ≥ t-table. It is 6.120 ≥ 1.9904. Students’ learning activities with Problem Based Learning model are better than students who are taught by conventional learning model.

Groebner Finite Path Algebras

OpenAIRE

Leamer, Micah J.

2004-01-01

Let K be a field and Q a finite directed multi-graph. In this paper I classify all path algebras KQ and admissible orders with the property that all of their finitely generated ideals have finite Groebner bases. MS

A finite element perspective on nonlinear FFT-based micromechanical simulations

NARCIS (Netherlands)

Zeman, J.; de Geus, T.W.J.; Vondrejc, J.; Peerlings, R.H.J.; Geers, M.G.D.

2017-01-01

Fourier solvers have become efficient tools to establish structure-property relations in heterogeneous materials. Introduced as an alternative to the Finite Element (FE) method, they are based on fixed-point solutions of the Lippmann-Schwinger type integral equation. Their computational efficiency

Coupled thermomechanical behavior of graphene using the spring-based finite element approach

Energy Technology Data Exchange (ETDEWEB)

Georgantzinos, S. K., E-mail: sgeor@mech.upatras.gr; Anifantis, N. K., E-mail: nanif@mech.upatras.gr [Machine Design Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26500 Patras (Greece); Giannopoulos, G. I., E-mail: ggiannopoulos@teiwest.gr [Materials Science Laboratory, Department of Mechanical Engineering, Technological Educational Institute of Western Greece, 1 Megalou Alexandrou Street, 26334 Patras (Greece)

2016-07-07

The prediction of the thermomechanical behavior of graphene using a new coupled thermomechanical spring-based finite element approach is the aim of this work. Graphene sheets are modeled in nanoscale according to their atomistic structure. Based on molecular theory, the potential energy is defined as a function of temperature, describing the interatomic interactions in different temperature environments. The force field is approached by suitable straight spring finite elements. Springs simulate the interatomic interactions and interconnect nodes located at the atomic positions. Their stiffness matrix is expressed as a function of temperature. By using appropriate boundary conditions, various different graphene configurations are analyzed and their thermo-mechanical response is approached using conventional finite element procedures. A complete parametric study with respect to the geometric characteristics of graphene is performed, and the temperature dependency of the elastic material properties is finally predicted. Comparisons with available published works found in the literature demonstrate the accuracy of the proposed method.

Monte Carlo Simulation Of The Portfolio-Balance Model Of Exchange Rates: Finite Sample Properties Of The GMM Estimator

OpenAIRE

Hong-Ghi Min

2011-01-01

Using Monte Carlo simulation of the Portfolio-balance model of the exchange rates, we report finite sample properties of the GMM estimator for testing over-identifying restrictions in the simultaneous equations model. F-form of Sargans statistic performs better than its chi-squared form while Hansens GMM statistic has the smallest bias.

The effectiveness of snow cube throwing learning model based on exploration

Science.gov (United States)

Sari, Nenden Mutiara

2017-08-01

This study aimed to know the effectiveness of Snow Cube Throwing (SCT) and Cooperative Model in Exploration-Based Math Learning in terms of the time required to complete the teaching materials and student engagement. This study was quasi-experimental research was conducted at SMPN 5 Cimahi, Indonesia. All student in grade VIII SMPN 5 Cimahi which consists of 382 students is used as population. The sample consists of two classes which had been chosen randomly with purposive sampling. First experiment class consists of 38 students and the second experiment class consists of 38 students. Observation sheet was used to observe the time required to complete the teaching materials and record the number of students involved in each meeting. The data obtained was analyzed by independent sample-t test and used the chart. The results of this study: SCT learning model based on exploration are more effective than cooperative learning models based on exploration in terms of the time required to complete teaching materials based on exploration and student engagement.

Generalized SMO algorithm for SVM-based multitask learning.

Science.gov (United States)

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

Science.gov (United States)

Martínez-Martínez, F; Rupérez-Moreno, M J; Martínez-Sober, M; Solves-Llorens, J A; Lorente, D; Serrano-López, A J; Martínez-Sanchis, S; Monserrat, C; Martín-Guerrero, J D

2017-11-01

This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (<0.2 s). Copyright © 2017 Elsevier Ltd. All rights reserved.

Finite element and finite difference methods in electromagnetic scattering

CERN Document Server

Morgan, MA

2013-01-01

This second volume in the Progress in Electromagnetic Research series examines recent advances in computational electromagnetics, with emphasis on scattering, as brought about by new formulations and algorithms which use finite element or finite difference techniques. Containing contributions by some of the world's leading experts, the papers thoroughly review and analyze this rapidly evolving area of computational electromagnetics. Covering topics ranging from the new finite-element based formulation for representing time-harmonic vector fields in 3-D inhomogeneous media using two coupled sca

Active learning and adaptive sampling for non-parametric inference

NARCIS (Netherlands)

Castro, R.M.

2007-01-01

This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can

Language, reading, and math learning profiles in an epidemiological sample of school age children.

Science.gov (United States)

Archibald, Lisa M D; Oram Cardy, Janis; Joanisse, Marc F; Ansari, Daniel

2013-01-01

Dyscalculia, dyslexia, and specific language impairment (SLI) are relatively specific developmental learning disabilities in math, reading, and oral language, respectively, that occur in the context of average intellectual capacity and adequate environmental opportunities. Past research has been dominated by studies focused on single impairments despite the widespread recognition that overlapping and comorbid deficits are common. The present study took an epidemiological approach to study the learning profiles of a large school age sample in language, reading, and math. Both general learning profiles reflecting good or poor performance across measures and specific learning profiles involving either weak language, weak reading, weak math, or weak math and reading were observed. These latter four profiles characterized 70% of children with some evidence of a learning disability. Low scores in phonological short-term memory characterized clusters with a language-based weakness whereas low or variable phonological awareness was associated with the reading (but not language-based) weaknesses. The low math only group did not show these phonological deficits. These findings may suggest different etiologies for language-based deficits in language, reading, and math, reading-related impairments in reading and math, and isolated math disabilities.

Language, reading, and math learning profiles in an epidemiological sample of school age children.

Directory of Open Access Journals (Sweden)

Lisa M D Archibald

Full Text Available Dyscalculia, dyslexia, and specific language impairment (SLI are relatively specific developmental learning disabilities in math, reading, and oral language, respectively, that occur in the context of average intellectual capacity and adequate environmental opportunities. Past research has been dominated by studies focused on single impairments despite the widespread recognition that overlapping and comorbid deficits are common. The present study took an epidemiological approach to study the learning profiles of a large school age sample in language, reading, and math. Both general learning profiles reflecting good or poor performance across measures and specific learning profiles involving either weak language, weak reading, weak math, or weak math and reading were observed. These latter four profiles characterized 70% of children with some evidence of a learning disability. Low scores in phonological short-term memory characterized clusters with a language-based weakness whereas low or variable phonological awareness was associated with the reading (but not language-based weaknesses. The low math only group did not show these phonological deficits. These findings may suggest different etiologies for language-based deficits in language, reading, and math, reading-related impairments in reading and math, and isolated math disabilities.

Geodetic Finite-Fault-based Earthquake Early Warning Performance for Great Earthquakes Worldwide

Science.gov (United States)

Ruhl, C. J.; Melgar, D.; Grapenthin, R.; Allen, R. M.

2017-12-01

GNSS-based earthquake early warning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. The Geodetic Alarm System (G-larmS) is a GNSS-based finite-fault algorithm developed as part of the ShakeAlert EEW system in the western US. Performance evaluations using synthetic earthquakes offshore Cascadia showed that G-larmS satisfactorily recovers magnitude and fault length, providing useful alerts 30-40 s after origin time and timely warnings of ground motion for onshore urban areas. An end-to-end test of the ShakeAlert system demonstrated the need for GNSS data to accurately estimate ground motions in real-time. We replay real data from several subduction-zone earthquakes worldwide to demonstrate the value of GNSS-based EEW for the largest, most damaging events. We compare predicted ground acceleration (PGA) from first-alert-solutions with those recorded in major urban areas. In addition, where applicable, we compare observed tsunami heights to those predicted from the G-larmS solutions. We show that finite-fault inversion based on GNSS-data is essential to achieving the goals of EEW.

GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

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Christopher Ouma Onyango

2010-09-01

Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

PENGARUH MODEL PROJECT BASED LEARNING TERHADAP KEMAMPUAN BERPIKIR KREATIF MATEMATIKA SISWA

Directory of Open Access Journals (Sweden)

Hesti Noviyana

2017-09-01

Full Text Available Abstract: The problems in this study relate to the learning model of Project Based Learning and students' creative thinking ability in mathematics. The purpose of the research to know the influence of the model of Project Based Learning on the ability to think creatively mathematics students VIII grade even semester SMP Negeri 3 Bandar Lampung lesson 2016/2017 . The research used experimental method with the population that is all students of class VIII with the amount of 347, while the sample is taken 2 class that is class VIII A as experiment class which amounted to 31, class VIII C as control class which amounted 30. The sample was taken using Cluster Random Sampling technique. To know the ability of creative thinking mathematics students authors perform tests in the form of essays as many as 5 questions that have been tested the validity and reliability. Hypothesis testing in this study using t test. From the results of hypothesis testing using t-test obtained t value = 14.27. From the distribution table t at the significant level of 5% is known t = 2.00 means t> t, so it can be concluded "There is Influence of Model Based Project Based on the Ability of Creative Thinking Mathematics Students".Keywords: Project Based Learning, creative thinking ability of mathematics

A novel local learning based approach with application to breast cancer diagnosis

Science.gov (United States)

Xu, Songhua; Tourassi, Georgia

2012-03-01

In this paper, we introduce a new local learning based approach and apply it for the well-studied problem of breast cancer diagnosis using BIRADS-based mammographic features. To learn from our clinical dataset the latent relationship between these features and the breast biopsy result, our method first dynamically partitions the whole sample population into multiple sub-population groups through stochastically searching the sample population clustering space. Each encountered clustering scheme in our online searching process is then used to create a certain sample population partition plan. For every resultant sub-population group identified according to a partition plan, our method then trains a dedicated local learner to capture the underlying data relationship. In our study, we adopt the linear logistic regression model as our local learning method's base learner. Such a choice is made both due to the well-understood linear nature of the problem, which is compellingly revealed by a rich body of prior studies, and the computational efficiency of linear logistic regression--the latter feature allows our local learning method to more effectively perform its search in the sample population clustering space. Using a database of 850 biopsy-proven cases, we compared the performance of our method with a large collection of publicly available state-of-the-art machine learning methods and successfully demonstrated its performance advantage with statistical significance.

Evaluation of bacterial motility from non-Gaussianity of finite-sample trajectories using the large deviation principle

International Nuclear Information System (INIS)

Hanasaki, Itsuo; Kawano, Satoyuki

2013-01-01

Motility of bacteria is usually recognized in the trajectory data and compared with Brownian motion, but the diffusion coefficient is insufficient to evaluate it. In this paper, we propose a method based on the large deviation principle. We show that it can be used to evaluate the non-Gaussian characteristics of model Escherichia coli motions and to distinguish combinations of the mean running duration and running speed that lead to the same diffusion coefficient. Our proposed method does not require chemical stimuli to induce the chemotaxis in a specific direction, and it is applicable to various types of self-propelling motions for which no a priori information of, for example, threshold parameters for run and tumble or head/tail direction is available. We also address the issue of the finite-sample effect on the large deviation quantities, but we propose to make use of it to characterize the nature of motility. (paper)

The Effectiveness of Problem Based Learning (PBL) on Intermediate Financial Accounting Subject

OpenAIRE

Nunuk Suryanti

2016-01-01

This research aims to know the effectiveness of Problem Based Learning (PBL) Model comparing to Drill Model on Intermediate Financial Accounting subject. The research was a quasi-experimental research. Population was four classes of Accounting Education students in the year of 2014/2015 at Faculty of Educational Science and Teaching of Riau Islamic University (UIR). Sample was taken by using purposive sampling. Then, it used Problem Based Learning (PBL) at experimental class and Drill Model a...

Integrating a logarithmic-strain based hyper-elastic formulation into a three-field mixed finite element formulation to deal with incompressibility in finite-strain elasto-plasticity

International Nuclear Information System (INIS)

Dina Al Akhrass; Bruchon, Julien; Drapier, Sylvain; Fayolle, Sebastien

2014-01-01

This paper deals with the treatment of incompressibility in solid mechanics in finite-strain elasto-plasticity. A finite-strain model proposed by Miehe, Apel and Lambrecht, which is based on a logarithmic strain measure and its work-conjugate stress tensor is chosen. Its main interest is that it allows for the adoption of standard constitutive models established in a small-strain framework. This model is extended to take into account the plastic incompressibility constraint intrinsically. In that purpose, an extension of this model to a three-field mixed finite element formulation is proposed, involving displacements, a strain variable and pressure as nodal variables with respect to standard finite element. Numerical examples of finite-strain problems are presented to assess the performance of the formulation. To conclude, an industrial case for which the classical under-integrated elements fail is considered. (authors)

Model Predictive Control based on Finite Impulse Response Models

DEFF Research Database (Denmark)

Prasath, Guru; Jørgensen, John Bagterp

2008-01-01

We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

The Determining Finite Automata Process

Directory of Open Access Journals (Sweden)

M. S. Vinogradova

2017-01-01

Full Text Available The theory of formal languages widely uses finite state automata both in implementation of automata-based approach to programming, and in synthesis of logical control algorithms.To ensure unambiguous operation of the algorithms, the synthesized finite state automata must be deterministic. Within the approach to the synthesis of the mobile robot controls, for example, based on the theory of formal languages, there are problems concerning the construction of various finite automata, but such finite automata, as a rule, will not be deterministic. The algorithm of determinization can be applied to the finite automata, as specified, in various ways. The basic ideas of the algorithm of determinization can be most simply explained using the representations of a finite automaton in the form of a weighted directed graph.The paper deals with finite automata represented as weighted directed graphs, and discusses in detail the procedure for determining the finite automata represented in this way. Gives a detailed description of the algorithm for determining finite automata. A large number of examples illustrate a capability of the determinization algorithm.

A finite element-based algorithm for rubbing induced vibration prediction in rotors

Science.gov (United States)

Behzad, Mehdi; Alvandi, Mehdi; Mba, David; Jamali, Jalil

2013-10-01

In this paper, an algorithm is developed for more realistic investigation of rotor-to-stator rubbing vibration, based on finite element theory with unilateral contact and friction conditions. To model the rotor, cross sections are assumed to be radially rigid. A finite element discretization based on traditional beam theories which sufficiently accounts for axial and transversal flexibility of the rotor is used. A general finite element discretization model considering inertial and viscoelastic characteristics of the stator is used for modeling the stator. Therefore, for contact analysis, only the boundary of the stator is discretized. The contact problem is defined as the contact between the circular rigid cross section of the rotor and “nodes” of the stator only. Next, Gap function and contact conditions are described for the contact problem. Two finite element models of the rotor and the stator are coupled via the Lagrange multipliers method in order to obtain the constrained equation of motion. A case study of the partial rubbing is simulated using the algorithm. The synchronous and subsynchronous responses of the partial rubbing are obtained for different rotational speeds. In addition, a sensitivity analysis is carried out with respect to the initial clearance, the stator stiffness, the damping parameter, and the coefficient of friction. There is a good agreement between the result of this research and the experimental result in the literature.

Analysis of Learning Tools in the study of Developmental of Interactive Multimedia Based Physic Learning Charged in Problem Solving

Science.gov (United States)

Manurung, Sondang; Demonta Pangabean, Deo

2017-05-01

The main purpose of this study is to produce needs analysis, literature review, and learning tools in the study of developmental of interactive multimedia based physic learning charged in problem solving to improve thinking ability of physic prospective student. The first-year result of the study is: result of the draft based on a needs analysis of the facts on the ground, the conditions of existing learning and literature studies. Following the design of devices and instruments performed as well the development of media. Result of the second study is physics learning device -based interactive multimedia charged problem solving in the form of textbooks and scientific publications. Previous learning models tested in a limited sample, then in the evaluation and repair. Besides, the product of research has an economic value on the grounds: (1) a virtual laboratory to offer this research provides a solution purchases physics laboratory equipment is expensive; (2) address the shortage of teachers of physics in remote areas as a learning tool can be accessed offline and online; (3). reducing material or consumables as tutorials can be done online; Targeted research is the first year: i.e story board learning physics that have been scanned in a web form CD (compact disk) and the interactive multimedia of gas Kinetic Theory concept. This draft is based on a needs analysis of the facts on the ground, the existing learning conditions, and literature studies. Previous learning models tested in a limited sample, then in the evaluation and repair.

Wavelet-based spectral finite element dynamic analysis for an axially moving Timoshenko beam

Science.gov (United States)

Mokhtari, Ali; Mirdamadi, Hamid Reza; Ghayour, Mostafa

2017-08-01

In this article, wavelet-based spectral finite element (WSFE) model is formulated for time domain and wave domain dynamic analysis of an axially moving Timoshenko beam subjected to axial pretension. The formulation is similar to conventional FFT-based spectral finite element (SFE) model except that Daubechies wavelet basis functions are used for temporal discretization of the governing partial differential equations into a set of ordinary differential equations. The localized nature of Daubechies wavelet basis functions helps to rule out problems of SFE model due to periodicity assumption, especially during inverse Fourier transformation and back to time domain. The high accuracy of WSFE model is then evaluated by comparing its results with those of conventional finite element and SFE results. The effects of moving beam speed and axial tensile force on vibration and wave characteristics, and static and dynamic stabilities of moving beam are investigated.

Windowed active sampling for reliable neural learning

NARCIS (Netherlands)

Barakova, E.I; Spaanenburg, L

The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these

Out-of-Sample Generalizations for Supervised Manifold Learning for Classification.

Science.gov (United States)

Vural, Elif; Guillemot, Christine

2016-03-01

Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

PREFERENCES ON INTERNET BASED LEARNING ENVIRONMENTS IN STUDENT-CENTERED EDUCATION

Directory of Open Access Journals (Sweden)

Zuhal CUBUKCU

2008-10-01

Full Text Available Nowadays, educational systems are being questionned to find effective solutions to problems that are being encountered, and discussions are centered around the ways of restructuring systems so as to overcome difficulties. As the consequences of the traditional teaching approach, we can indicate that the taught material is not long-lasting but easily forgotten, that students do not sufficiently acquire the knowledge and skills that are aimed at developing, and that students lack transferring their knowledge to real life. In our current situation, individuals prefer to use educational resources where and when they want, based on their individual skills and abilities. Throughout the world, because the internet infrastructure has developed quite rapidly, it has been offered as an alternative way for a rich learning and teaching environment. This study aims at determining teacher candidates’ preferences regarding internet-based learning environments in student-centered education by involving the teacher candidates enrolled at Osmangazi University, Faculty of Education, Primary School Teaching, Mathematics Teaching and Computer and Educational Technologies Education programmes. This study is a descriptive study. The data collection scale consists of the “Constructivist Internet-based Education of Science Scale (CILES-S”. The sample group of teacher candidates in the study showed differences with respect to their preferences regarding internet-based learning in student-centered education. The candidates scored higher in the internet-based learning environments of Cognitive Development and Critical Judgement. The lowest average scores of the sample group were observed in the internet-based learning environment of Episthemologic awareness.

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

Science.gov (United States)

Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

2011-07-01

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Convergence of a residual based artificial viscosity finite element method

KAUST Repository

Nazarov, Murtazo

2013-02-01

We present a residual based artificial viscosity finite element method to solve conservation laws. The Galerkin approximation is stabilized by only residual based artificial viscosity, without any least-squares, SUPG, or streamline diffusion terms. We prove convergence of the method, applied to a scalar conservation law in two space dimensions, toward an unique entropy solution for implicit time stepping schemes. © 2012 Elsevier B.V. All rights reserved.

3D visualization and finite element mesh formation from wood anatomy samples, Part II – Algorithm approach

Directory of Open Access Journals (Sweden)

Petr Koňas

2009-01-01

Full Text Available Paper presents new original application WOOD3D in form of program code assembling. The work extends the previous article “Part I – Theoretical approach” in detail description of implemented C++ classes of utilized projects Visualization Toolkit (VTK, Insight Toolkit (ITK and MIMX. Code is written in CMake style and it is available as multiplatform application. Currently GNU Linux (32/64b and MS Windows (32/64b platforms were released. Article discusses various filter classes for image filtering. Mainly Otsu and Binary threshold filters are classified for anatomy wood samples thresholding. Registration of images series is emphasized for difference of colour spaces compensation is included. Resulted work flow of image analysis is new methodological approach for images processing through the composition, visualization, filtering, registration and finite element mesh formation. Application generates script in ANSYS parametric design language (APDL which is fully compatible with ANSYS finite element solver and designer environment. The script includes the whole definition of unstructured finite element mesh formed by individual elements and nodes. Due to simple notation, the same script can be used for generation of geometrical entities in element positions. Such formed volumetric entities are prepared for further geometry approximation (e.g. by boolean or more advanced methods. Hexahedral and tetrahedral types of mesh elements are formed on user request with specified mesh options. Hexahedral meshes are formed both with uniform element size and with anisotropic character. Modified octree method for hexahedral mesh with anisotropic character was declared in application. Multicore CPUs in the application are supported for fast image analysis realization. Visualization of image series and consequent 3D image are realized in VTK format sufficiently known and public format, visualized in GPL application Paraview. Future work based on mesh

Mobile Inquiry Based Learning

NARCIS (Netherlands)

Specht, Marcus

2012-01-01

Specht, M. (2012, 8 November). Mobile Inquiry Based Learning. Presentation given at the Workshop "Mobile inquiry-based learning" at the Mobile Learning Day 2012 at the Fernuniversität Hagen, Hagen, Germany.

Differentiating case-based learning from problem-based learning after a twoday introductory workshop on case-based learning

Directory of Open Access Journals (Sweden)

Aqil Mohammad Daher

2017-12-01

Full Text Available Background Considerable overlap exists between case-based learning (CBL and problem-based learning (PBL and differentiating between the two can be difficult for a lot of the academicians. Aims This study gauged the ability of members of medical school, familiar with a problem-based learning (PBL curriculum, to differentiate between case-based learning (CBL and PBL after a two-day workshop on CBL. Methods A questionnaire was distributed to all participants, attending the introductory course on CBL. It was designed to document the basic characteristics of the respondents, their preference for either CBL or PBL, their ability to recognize differences between CBL and PBL, and their overall perception of the course. Results Of the total workshop participants, 80.5 per cent returned the completed questionnaire. The mean age of the respondents was 44.12±12.31 years and women made up a slight majority. Majority favoured CBL over PBL and felt it was more clinical, emphasizes on self-directed learning, provides more opportunities for learning, permits in-depth exploration of cases, has structured environment and encourages the use of all learning resources. On the respondents’ ability to discriminate CBL from PBL, a weighted score of 39.9 per cent indicated a failure on the part of the respondents to correctly identify differences between CBL and PBL. Less than half opined that CBL was a worthwhile progression from PBL and about third would recommend CBL over PBL. Conclusion It seems that majority of the respondents failed to adequately differentiate between CBL and PBL and didn’t favour CBL over PBL.

Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

KAUST Repository

Busbait, Monther I.

2014-01-01

We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

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darsih darsih darsih

2016-04-01

Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

A finite element perspective on non-linear FFT-based micromechanical simulations

NARCIS (Netherlands)

Zeman, J.; de Geus, T.W.J.; Vondřejc, J.; Peerlings, R.H.J.; Geers, M.G.D.

2016-01-01

Fourier solvers have become efficient tools to establish structure-property relations in heterogeneous materials. Introduced as an alternative to the Finite Element (FE) method, they are based on fixed-point solutions of the Lippmann-Schwinger type integral equation. Their computational efficiency

Students' Motivation toward Computer-Based Language Learning

Science.gov (United States)

Genc, Gulten; Aydin, Selami

2011-01-01

The present article examined some factors affecting the motivation level of the preparatory school students in using a web-based computer-assisted language-learning course. The sample group of the study consisted of 126 English-as-a-foreign-language learners at a preparatory school of a state university. After performing statistical analyses…

Reference Function Based Spatiotemporal Fuzzy Logic Control Design Using Support Vector Regression Learning

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Xian-Xia Zhang

2013-01-01

Full Text Available This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF, which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

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...

[Verification of Learning Effects by Team-based Learning].

Science.gov (United States)

Ono, Shin-Ichi; Ito, Yoshihisa; Ishige, Kumiko; Inokuchi, Norio; Kosuge, Yasuhiro; Asami, Satoru; Izumisawa, Megumi; Kobayashi, Hiroko; Hayashi, Hiroyuki; Suzuki, Takashi; Kishikawa, Yukinaga; Hata, Harumi; Kose, Eiji; Tabata, Kei-Ichi

2017-11-01

 It has been recommended that active learning methods, such as team-based learning (TBL) and problem-based learning (PBL), be introduced into university classes by the Central Council for Education. As such, for the past 3 years, we have implemented TBL in a medical therapeutics course for 4-year students. Based upon our experience, TBL is characterized as follows: TBL needs fewer teachers than PBL to conduct a TBL module. TBL enables both students and teachers to recognize and confirm the learning results from preparation and reviewing. TBL grows students' responsibility for themselves and their teams, and likely facilitates learning activities through peer assessment.

The Effectiveness of Self-Regulated Learning Scaffolds on Academic Performance in Computer-Based Learning Environments: A Meta-Analysis

Science.gov (United States)

Zheng, Lanqin

2016-01-01

This meta-analysis examined research on the effects of self-regulated learning scaffolds on academic performance in computer-based learning environments from 2004 to 2015. A total of 29 articles met inclusion criteria and were included in the final analysis with a total sample size of 2,648 students. Moderator analyses were performed using a…

A complementarity-based approach to phase in finite-dimensional quantum systems

International Nuclear Information System (INIS)

Klimov, A B; Sanchez-Soto, L L; Guise, H de

2005-01-01

We develop a comprehensive theory of phase for finite-dimensional quantum systems. The only physical requirement we impose is that phase is complementary to amplitude. To implement this complementarity we use the notion of mutually unbiased bases, which exist for dimensions that are powers of a prime. For a d-dimensional system (qudit) we explicitly construct d+1 classes of maximally commuting operators, each one consisting of d-1 operators. One of these classes consists of diagonal operators that represent amplitudes (or inversions). By finite Fourier transformation, it is mapped onto ladder operators that can be appropriately interpreted as phase variables. We discuss examples of qubits and qutrits, and show how these results generalize previous approaches

Finite element analysis-based design of a fluid-flow control nano-valve

International Nuclear Information System (INIS)

Grujicic, M.; Cao, G.; Pandurangan, B.; Roy, W.N.

2005-01-01

A finite element method-based procedure is developed for the design of molecularly functionalized nano-size devices. The procedure is aimed at the single-walled carbon nano-tubes (SWCNTs) used in the construction of such nano-devices and utilizes spatially varying nodal forces to represent electrostatic interactions between the charged groups of the functionalizing molecules. The procedure is next applied to the design of a fluid-flow control nano-valve. The results obtained suggest that the finite element-based procedure yields the results, which are very similar to their molecular modeling counterparts for small-size nano-valves, for which both types of analyses are feasible. The procedure is finally applied to optimize the design of a larger-size nano-valve, for which the molecular modeling approach is not practical

Achievement of learning outcome after implemented physical modules based on problem based learning

Science.gov (United States)

Isna, R.; Masykuri, M.; Sukarmin

2018-03-01

Implementation of Problem BasedLearning (PBL) modules can grow the students' thinking skills to solve the problems in daily life and equip the students into higher education levels. The purpose of this research is to know the achievement of learning outcome after implementation physics module based on PBL in Newton,s Law of Gravity. This research method use the experimental method with posttest only group design. To know the achievement of student learning outcomes was analyzed using t test through application of SPSS 18. Based on research result, it is found that the average of student learning outcomes after appliying physics module based on PBL has reached the minimal exhaustiveness criteria. In addition, students' scientific attitudes also improved at each meeting. Presentation activities which contained at learning sync are also able to practice speaking skills and broaden their knowledge. Looking at some shortcomings during the study, it is suggested the issues raised into learning should be a problem close to the life of students so that, the students are more active and enthusiastic in following the learning of physics.

Finite Element Method Based Modeling of Resistance Spot-Welded Mild Steel

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Miloud Zaoui

Full Text Available Abstract This paper deals with Finite Element refined and simplified models of a mild steel spot-welded specimen, developed and validated based on quasi-static cross-tensile experimental tests. The first model was constructed with a fine discretization of the metal sheet and the spot weld was defined as a special geometric zone of the specimen. This model provided, in combination with experimental tests, the input data for the development of the second model, which was constructed with respect to the mesh size used in the complete car finite element model. This simplified model was developed with coarse shell elements and a spring-type beam element was used to model the spot weld behavior. The global accuracy of the two models was checked by comparing simulated and experimental load-displacement curves and by studying the specimen deformed shapes and the plastic deformation growth in the metal sheets. The obtained results show that both fine and coarse finite element models permit a good prediction of the experimental tests.

Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

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Nikola Simidjievski

Full Text Available Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting, significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

Finite element analysis of rotating beams physics based interpolation

CERN Document Server

Ganguli, Ranjan

2017-01-01

This book addresses the solution of rotating beam free-vibration problems using the finite element method. It provides an introduction to the governing equation of a rotating beam, before outlining the solution procedures using Rayleigh-Ritz, Galerkin and finite element methods. The possibility of improving the convergence of finite element methods through a judicious selection of interpolation functions, which are closer to the problem physics, is also addressed. The book offers a valuable guide for students and researchers working on rotating beam problems – important engineering structures used in helicopter rotors, wind turbines, gas turbines, steam turbines and propellers – and their applications. It can also be used as a textbook for specialized graduate and professional courses on advanced applications of finite element analysis.

Intelligent Web-Based Learning System with Personalized Learning Path Guidance

Science.gov (United States)

Chen, C. M.

2008-01-01

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths…

The Effectiveness of Problem Based Learning Integrated With Islamic Values Based on ICT on Higher Order Thinking Skill and Students’ Character

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Chairul Anwar

2017-02-01

Full Text Available The focus of this research is to known the influence of Problem Based Learning (PBL model application, that intergrated with Islamic values based on ICT, toward the ability of higher-order thinkingskill and the strenghtening of students’ characters. This research is quasy experiment type with group design pretest-postest. The research was conducted in SMA.Sampling by means of random sampling, to determine the control class and experimentalclass.Data analysis technique used is the t-test, based on the value of significance, as well as test-effect size. The research data shows that the model of problem based learning integrates Islamic values based on ICThas positive influence towards the increasing of higher-order thinking skill and the strenghtening of students’ characters compared to the students that use conventional method.The result of effect size test on experimental class in on medium category. It means that the learning which use problem based learning (PBL model, integrated with Islamic values based on ICT, can be said effective on increasing higher order thinking skillof students.

Virtual Learning Environments and Learning Forms -experiments in ICT-based learning

DEFF Research Database (Denmark)

Helbo, Jan; Knudsen, Morten

2004-01-01

This paper report the main results of a three year experiment in ICT-based distance learning. The results are based on a full scale experiment in the education, Master of Industrial Information Technology (MII) and is one of many projects deeply rooted in the project Virtual Learning Environments...... and Learning forms (ViLL). The experiment was to transfer a well functioning on-campus engineering program based on project organized collaborative learning to a technology supported distance education program. After three years the experiments indicate that adjustments are required in this transformation....... The main problem is that we do not find the same self regulatoring learning effect in the group work among the off-campus students as is the case for on-campus students. Based on feedback from evaluation questionnaires and discussions with the students didactic adjustments have been made. The revised...

Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Web-Based Instruction, Learning Effectiveness and Learning Behavior: The Impact of Relatedness

Science.gov (United States)

Shieh, Chich-Jen; Liao, Ying; Hu, Ridong

2013-01-01

This study aims to discuss the effects of Web-based Instruction and Learning Behavior on Learning Effectiveness. Web-based Instruction contains the dimensions of Active Learning, Simulation-based Learning, Interactive Learning, and Accumulative Learning; and, Learning Behavior covers Learning Approach, Learning Habit, and Learning Attitude. The…

Inference and learning in sparse systems with multiple states

International Nuclear Information System (INIS)

Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.

2011-01-01

We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

Science.gov (United States)

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

2016-05-01

Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

Employee’s Learning in the Organization - A Study of Knowledge Based Industries

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Kuruppuge Ravindra Hewa

2018-03-01

Full Text Available This paper focuses on reviewing the learning behavior of individual employees in the firm over other influencing factors in knowledge-based industries in Sri Lanka. Using a stratified random sampling technique, a sample of 143 employees from jobs in Database Administration & Development, Systems & Network Administration, Web Development & Programming and Software Engineering was selected as respondents for the survey from 13 knowledge based industrial firms in Sri Lanka. After a descriptive analysis of the characteristics of respondents, the causal relationships among predictor and outcome variables were tested using the partial least squares regression method. The results indicated that the use of digital methods, digital tools, organizational identification and knowledge sharing are positively influenced by employee’s learning in the firm. Yet, the employee’s turnover intention has negatively influenced employee learning.

Original science-based music and student learning

Science.gov (United States)

Smolinski, Keith

American middle school student science scores have been stagnating for several years, demonstrating a need for better learning strategies to aid teachers in instruction and students in content learning. It has also been suggested by researchers that music can be used to aid students in their learning and memory. Employing the theoretical framework of brain-based learning, the purpose of this study was to examine the impact of original, science-based music on student content learning and student perceptions of the music and its impact on learning. Students in the treatment group at a public middle school learned songs with lyrics related to the content of a 4-week cells unit in science; whereas an equally sized control group was taught the same material using existing methods. The content retention and learning experiences of the students in this study were examined using a concurrent triangulation, mixed-methods study. Independent sample t test and ANOVA analyses were employed to determine that the science posttest scores of students in the treatment group (N = 93) were significantly higher than the posttest scores of students in the control group (N = 93), and that the relative gains of the boys in the treatment group exceeded those of the girls. The qualitative analysis of 10 individual interviews and 3 focus group interviews followed Patton's method of a priori coding, cross checking, and thematic analysis to examine the perceptions of the treatment group. These results confirmed that the majority of the students thought the music served as an effective learning tool and enhanced recall. This study promoted social change because students and teachers gained insight into how music can be used in science classrooms to aid in the learning of science content. Researchers could also utilize the findings for continued investigation of the interdisciplinary use of music in educational settings.

Controlling chaos in permanent magnet synchronous motor based on finite-time stability theory

International Nuclear Information System (INIS)

Du-Qu, Wei; Bo, Zhang

2009-01-01

This paper reports that the performance of permanent magnet synchronous motor (PMSM) degrades due to chaos when its systemic parameters fall into a certain area. To control the undesirable chaos in PMSM, a nonlinear controller, which is simple and easy to be constructed, is presented to achieve finite-time chaos control based on the finite-time stability theory. Computer simulation results show that the proposed controller is very effective. The obtained results may help to maintain the industrial servo driven system's security operation. (general)

On Chudnovsky-Based Arithmetic Algorithms in Finite Fields

OpenAIRE

Atighehchi, Kevin; Ballet, Stéphane; Bonnecaze, Alexis; Rolland, Robert

2015-01-01

Thanks to a new construction of the so-called Chudnovsky-Chudnovsky multiplication algorithm, we design efficient algorithms for both the exponentiation and the multiplication in finite fields. They are tailored to hardware implementation and they allow computations to be parallelized while maintaining a low number of bilinear multiplications. We give an example with the finite field ${\\mathbb F}_{16^{13}}$.

A finite volume method for cylindrical heat conduction problems based on local analytical solution

KAUST Repository

Li, Wang

2012-10-01

A new finite volume method for cylindrical heat conduction problems based on local analytical solution is proposed in this paper with detailed derivation. The calculation results of this new method are compared with the traditional second-order finite volume method. The newly proposed method is more accurate than conventional ones, even though the discretized expression of this proposed method is slightly more complex than the second-order central finite volume method, making it cost more calculation time on the same grids. Numerical result shows that the total CPU time of the new method is significantly less than conventional methods for achieving the same level of accuracy. © 2012 Elsevier Ltd. All rights reserved.

A finite volume method for cylindrical heat conduction problems based on local analytical solution

KAUST Repository

Li, Wang; Yu, Bo; Wang, Xinran; Wang, Peng; Sun, Shuyu

2012-01-01

A new finite volume method for cylindrical heat conduction problems based on local analytical solution is proposed in this paper with detailed derivation. The calculation results of this new method are compared with the traditional second-order finite volume method. The newly proposed method is more accurate than conventional ones, even though the discretized expression of this proposed method is slightly more complex than the second-order central finite volume method, making it cost more calculation time on the same grids. Numerical result shows that the total CPU time of the new method is significantly less than conventional methods for achieving the same level of accuracy. © 2012 Elsevier Ltd. All rights reserved.

Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

Science.gov (United States)

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.

Web-Based Learning Environment Based on Students’ Needs

Science.gov (United States)

Hamzah, N.; Ariffin, A.; Hamid, H.

2017-08-01

Traditional learning needs to be improved since it does not involve active learning among students. Therefore, in the twenty-first century, the development of internet technology in the learning environment has become the main needs of each student. One of the learning environments to meet the needs of the teaching and learning process is a web-based learning environment. This study aims to identify the characteristics of a web-based learning environment that supports students’ learning needs. The study involved 542 students from fifteen faculties in a public higher education institution in Malaysia. A quantitative method was used to collect the data via a questionnaire survey by randomly. The findings indicate that the characteristics of a web-based learning environment that support students’ needs in the process of learning are online discussion forum, lecture notes, assignments, portfolio, and chat. In conclusion, the students overwhelmingly agreed that online discussion forum is the highest requirement because the tool can provide a space for students and teachers to share knowledge and experiences related to teaching and learning.

Mutual learning in a tree parity machine and its application to cryptography

International Nuclear Information System (INIS)

Rosen-Zvi, Michal; Klein, Einat; Kanter, Ido; Kinzel, Wolfgang

2002-01-01

Mutual learning of a pair of tree parity machines with continuous and discrete weight vectors is studied analytically. The analysis is based on a mapping procedure that maps the mutual learning in tree parity machines onto mutual learning in noisy perceptrons. The stationary solution of the mutual learning in the case of continuous tree parity machines depends on the learning rate where a phase transition from partial to full synchronization is observed. In the discrete case the learning process is based on a finite increment and a full synchronized state is achieved in a finite number of steps. The synchronization of discrete parity machines is introduced in order to construct an ephemeral key-exchange protocol. The dynamic learning of a third tree parity machine (an attacker) that tries to imitate one of the two machines while the two still update their weight vectors is also analyzed. In particular, the synchronization times of the naive attacker and the flipping attacker recently introduced in Ref. 9 are analyzed. All analytical results are found to be in good agreement with simulation results

Consideration of a Learning Programming Process based on Software Design for Beginners

OpenAIRE

大村, 基将; 紅林, 秀治

2016-01-01

We considered a learning programming process based on software design for technology education. Lessons of computer program-aided measurement and control are for beginners to learn programming. These lessons are efficient to learn the step of programming, but the main of the lessons are works of typing the sample programming and debugging. Therefore, these lessons have a fundamental lack of the concept of design. Then we considered learning processes of programming and applied the process of ...

Learn with SAT to Minimize Büchi Automata

Directory of Open Access Journals (Sweden)

Stephan Barth

2012-10-01

Full Text Available We describe a minimization procedure for nondeterministic Büchi automata (NBA. For an automaton A another automaton A_min with the minimal number of states is learned with the help of a SAT-solver. This is done by successively computing automata A' that approximate A in the sense that they accept a given finite set of positive examples and reject a given finite set of negative examples. In the course of the procedure these example sets are successively increased. Thus, our method can be seen as an instance of a generic learning algorithm based on a "minimally adequate teacher'' in the sense of Angluin. We use a SAT solver to find an NBA for given sets of positive and negative examples. We use complementation via construction of deterministic parity automata to check candidates computed in this manner for equivalence with A. Failure of equivalence yields new positive or negative examples. Our method proved successful on complete samplings of small automata and of quite some examples of bigger automata. We successfully ran the minimization on over ten thousand automata with mostly up to ten states, including the complements of all possible automata with two states and alphabet size three and discuss results and runtimes; single examples had over 100 states.

EFFECT OF PROBLEM BASED LEARNING IN COMPARISION WITH LECTURE BASED LEARNING IN FORENSIC MEDICINE

Directory of Open Access Journals (Sweden)

Padmakumar

2015-09-01

Full Text Available BACKGROUND: Problem based learning (PBL is an approach to learning and instruction in which students tackle problems in small groups under the supervision of a teacher. This style of learning assumed to foster increased retention of knowledge, improve student’s gene ral problem solving skills, enhance integration of basic science concepts in to clinical problems, foster the development of self - directed learning skills and strengthen student’s intrinsic motivation. AIM: The study was conducted to compare the effect of Problem based learning in comparison with lecture based learning. SETTING: A cross - sectional study was conducted among 2nd year MBBS students of Jubilee Mission Medical College and Research Institute, Thrissur during the period of December 2014 to March 20 15. METHODOLOGY: The batch is divided into two groups (A & B, 45 in each group. By using PBL method, blunt force injuries were taught to Group - A and sharp weapon injuries to group - B. By using lecture based learning (LBL method blunt force injuries were t aught to Group - B and sharp weapon injuries to group - A. At the end of the session a test in the form of MCQ was conducted on the students to evaluate their learning outcome. OBSERVATION AND RESU LTS: In session I, the average test score of LBL group was 8.16 and PBL group was 12. The difference was statistically significant. In session - II also 45 students has participated each in LBL and PBL classes. The average of test score of LBL group was 7.267 and PBL was 11.289, which was highly significant statistical ly . CONCLUSION: Study has proven that problem based learning is an effective teaching learning method when compared to conventional lecture based learning.

Finite Element Based Design and Optimization for Piezoelectric Accelerometers

DEFF Research Database (Denmark)

Liu, Bin; Kriegbaum, B.; Yao, Q.

1998-01-01

A systematic Finite Element design and optimisation procedure is implemented for the development of piezoelectric accelerometers. Most of the specifications of accelerometers can be obtained using the Finite Element simulations. The deviations between the simulated and calibrated sensitivities...

Project- Based Learning and Problem-Based Learning: Are They Effective to Improve Student's Thinking Skills?

OpenAIRE

Anazifa, R. D; Djukri, D

2017-01-01

The study aims at finding (1) the effect of project-based learning and problem-based learning on student's creativity and critical thinking and (2) the difference effect of project-based learning and problem-based learning on student's creativity and critical thinking. This study is quasi experiment using non-equivalent control-group design. Research population of this study was all classes in eleventh grade of mathematics and natural science program of SMA N 1 Temanggung. The participants we...

Three-dimensional parallel edge-based finite element modeling of electromagnetic data with field redatuming

DEFF Research Database (Denmark)

Cai, Hongzhu; Čuma, Martin; Zhdanov, Michael

2015-01-01

This paper presents a parallelized version of the edge-based finite element method with a novel post-processing approach for numerical modeling of an electromagnetic field in complex media. The method uses an unstructured tetrahedral mesh which can reduce the number of degrees of freedom signific......This paper presents a parallelized version of the edge-based finite element method with a novel post-processing approach for numerical modeling of an electromagnetic field in complex media. The method uses an unstructured tetrahedral mesh which can reduce the number of degrees of freedom...... significantly. The linear system of finite element equations is solved using parallel direct solvers which are robust for ill-conditioned systems and efficient for multiple source electromagnetic (EM) modeling. We also introduce a novel approach to compute the scalar components of the electric field from...... the tangential components along each edge based on field redatuming. The method can produce a more accurate result as compared to conventional approach. We have applied the developed algorithm to compute the EM response for a typical 3D anisotropic geoelectrical model of the off-shore HC reservoir with complex...

Local Projection-Based Stabilized Mixed Finite Element Methods for Kirchhoff Plate Bending Problems

Directory of Open Access Journals (Sweden)

Xuehai Huang

2013-01-01

Full Text Available Based on stress-deflection variational formulation, we propose a family of local projection-based stabilized mixed finite element methods for Kirchhoff plate bending problems. According to the error equations, we obtain the error estimates of the approximation to stress tensor in energy norm. And by duality argument, error estimates of the approximation to deflection in H1-norm are achieved. Then we design an a posteriori error estimator which is closely related to the equilibrium equation, constitutive equation, and nonconformity of the finite element spaces. With the help of Zienkiewicz-Guzmán-Neilan element spaces, we prove the reliability of the a posteriori error estimator. And the efficiency of the a posteriori error estimator is proved by standard bubble function argument.

Finite element approximations of the stokes flow problem based upon various variational principles

International Nuclear Information System (INIS)

Franca, L.P.; Hughers, T.J.R.; Stenberg, R.

1989-05-01

Finite element methods are constructed by adding to the usual Galerkin method terms that are mesh-dependent least-squares forms of the Euler-Lagrange equations. The methods are consistent and possess additional stability compared to the Galerkin method. Finite element interpolations, which are unstable in the Galerkin approach, are now convergent. The methodology is applied to the velocity-pressure formulation, a.k.a., Herrmann's formulation, to the stress-velocity formulation, a.k.a., Hellinger-Reissner's formulation and to a new formulation based on augmented stress, pressure and velocity [pt

Multi-rate sensor fusion-based adaptive discrete finite-time synergetic control for flexible-joint mechanical systems

International Nuclear Information System (INIS)

Xue Guang-Yue; Ren Xue-Mei; Xia Yuan-Qing

2013-01-01

This paper proposes an adaptive discrete finite-time synergetic control (ADFTSC) scheme based on a multi-rate sensor fusion estimator for flexible-joint mechanical systems in the presence of unmeasured states and dynamic uncertainties. Multi-rate sensors are employed to observe the system states which cannot be directly obtained by encoders due to the existence of joint flexibilities. By using an extended Kalman filter (EKF), the finite-time synergetic controller is designed based on a sensor fusion estimator which estimates states and parameters of the mechanical system with multi-rate measurements. The proposed controller can guarantee the finite-time convergence of tracking errors by the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach. (general)

The SAMPLE experience: The development of a rich media online mathematics learning environment

OpenAIRE

Chang, Jen

2006-01-01

This report documents the development of Sample Architecture for Mathematically Productive Learning Experiences (SAMPLE), a rich media, online, mathematics learning environment created to meet the needs of middle school educators. It explores some of the current pedagogical challenges in mathematics education, and their amplified impacts when coupled with under-prepared teachers, a decidedly wide-spread phenomenon. The SAMPLE publishing experience is discussed in terms of its instructional de...

Factors Influencing Learning Satisfaction of Migrant Workers in Korea with E-learning-Based Occupational Safety and Health Education

Science.gov (United States)

Lee, Young Joo; Lee, Dongjoo

2015-01-01

Background E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, “learning content” (β = 0.344, p e-learning” (β = 0.095, p E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs. PMID:26929830

How to Enhance Interdisciplinary Competence--Interdisciplinary Problem-Based Learning versus Interdisciplinary Project-Based Learning

Science.gov (United States)

Brassler, Mirjam; Dettmers, Jan

2017-01-01

Interdisciplinary competence is important in academia for both employability and sustainable development. However, to date, there are no specific interdisciplinary education models and, naturally, no empirical studies to assess them. Since problem-based learning (PBL) and project-based learning (PjBL) are learning approaches that emphasize…

Problem-based learning and radiology

International Nuclear Information System (INIS)

Thurley, P.; Dennick, R.

2008-01-01

The Royal College of Radiologists recently published documents setting out guidelines to improve the teaching of radiology to medical students. These included recommendations that clinicians who teach radiology should be aware of newer educational techniques, such as problem-based learning, and should be involved in the development of curricula and assessment in medical schools. This review aims to introduce the educational theories behind problem-based learning and describe how a problem-based learning tutorial is run. The relevance of problem-based learning to radiology and the potential advantages and disadvantages are discussed

Finite element simulations with ANSYS workbench 16

CERN Document Server

Lee , Huei-Huang

2015-01-01

Finite Element Simulations with ANSYS Workbench 16 is a comprehensive and easy to understand workbook. It utilizes step-by-step instructions to help guide readers to learn finite element simulations. Twenty seven real world case studies are used throughout the book. Many of these cases are industrial or research projects the reader builds from scratch. All the files readers may need if they have trouble are available for download on the publishers website. Companion videos that demonstrate exactly how to preform each tutorial are available to readers by redeeming the access code that comes in the book. Relevant background knowledge is reviewed whenever necessary. To be efficient, the review is conceptual rather than mathematical. Key concepts are inserted whenever appropriate and summarized at the end of each chapter. Additional exercises or extension research problems are provided as homework at the end of each chapter. A learning approach emphasizing hands-on experiences spreads through this entire book. A...

Sampling Memories: Using Hip-Hop Aesthetics to Learn from Urban Schooling Experiences

Science.gov (United States)

Petchauer, Emery

2012-01-01

This article theorizes and charts the implementation of a learning activity designed from the hip-hop aesthetic of sampling. The purpose of this learning activity was to enable recent urban school graduates to reflect upon their previous schooling experiences as a platform for future learning in higher education. This article illustrates what…

A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional Raw Moments

Directory of Open Access Journals (Sweden)

Manzoor Khan

2014-01-01

Full Text Available This paper presents new classes of estimators in estimating the finite population mean under double sampling in the presence of nonresponse when using information on fractional raw moments. The expressions for mean square error of the proposed classes of estimators are derived up to the first degree of approximation. It is shown that a proposed class of estimators performs better than the usual mean estimator, ratio type estimators, and Singh and Kumar (2009 estimator. An empirical study is carried out to demonstrate the performance of a proposed class of estimators.

Teaching Problem Based Learning as Blended Learning

DEFF Research Database (Denmark)

Kolbæk, Ditte; Nortvig, Anne-Mette

2018-01-01

Problem-based and project organized learning (PBL) was originally developed for collaboration between physically present students, but political decisions at many universities require that collaboration, dialogues, and other PBL activities take place online as well. With a theoretical point...... of departure in Dewey and a methodological point of departure in netnography, this study focuses on an online module at Aalborg University where teaching is based on PBL. With the research question ‘How can teachers design for PBL online,’ this study explores the teacher’s role in a six weeks’ blended learning...... program, and we present suggestions for designs for blended learning PBL based on case studies from two PBL courses...

On the Stability of the Finite Difference based Lattice Boltzmann Method

KAUST Repository

El-Amin, Mohamed; Sun, Shuyu; Salama, Amgad

2013-01-01

This paper is devoted to determining the stability conditions for the finite difference based lattice Boltzmann method (FDLBM). In the current scheme, the 9-bit two-dimensional (D2Q9) model is used and the collision term of the Bhatnagar- Gross-Krook (BGK) is treated implicitly. The implicitness of the numerical scheme is removed by introducing a new distribution function different from that being used. Therefore, a new explicit finite-difference lattice Boltzmann method is obtained. Stability analysis of the resulted explicit scheme is done using Fourier expansion. Then, stability conditions in terms of time and spatial steps, relaxation time and explicitly-implicitly parameter are determined by calculating the eigenvalues of the given difference system. The determined conditions give the ranges of the parameters that have stable solutions.

On the Stability of the Finite Difference based Lattice Boltzmann Method

KAUST Repository

El-Amin, Mohamed

2013-06-01

This paper is devoted to determining the stability conditions for the finite difference based lattice Boltzmann method (FDLBM). In the current scheme, the 9-bit two-dimensional (D2Q9) model is used and the collision term of the Bhatnagar- Gross-Krook (BGK) is treated implicitly. The implicitness of the numerical scheme is removed by introducing a new distribution function different from that being used. Therefore, a new explicit finite-difference lattice Boltzmann method is obtained. Stability analysis of the resulted explicit scheme is done using Fourier expansion. Then, stability conditions in terms of time and spatial steps, relaxation time and explicitly-implicitly parameter are determined by calculating the eigenvalues of the given difference system. The determined conditions give the ranges of the parameters that have stable solutions.

Parallel iterative procedures for approximate solutions of wave propagation by finite element and finite difference methods

Energy Technology Data Exchange (ETDEWEB)

Kim, S. [Purdue Univ., West Lafayette, IN (United States)

1994-12-31

Parallel iterative procedures based on domain decomposition techniques are defined and analyzed for the numerical solution of wave propagation by finite element and finite difference methods. For finite element methods, in a Lagrangian framework, an efficient way for choosing the algorithm parameter as well as the algorithm convergence are indicated. Some heuristic arguments for finding the algorithm parameter for finite difference schemes are addressed. Numerical results are presented to indicate the effectiveness of the methods.

A Learning Object Approach To Evidence based learning

Directory of Open Access Journals (Sweden)

Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

Science.gov (United States)

Fung, Tak; Keenan, Kevin

2014-01-01

The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%), a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L.), occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

Directory of Open Access Journals (Sweden)

Tak Fung

Full Text Available The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%, a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L., occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

Integrating Problem-Based Learning and Simulation: Effects on Student Motivation and Life Skills.

Science.gov (United States)

Roh, Young Sook; Kim, Sang Suk

2015-07-01

Previous research has suggested that a teaching strategy integrating problem-based learning and simulation may be superior to traditional lecture. The purpose of this study was to assess learner motivation and life skills before and after taking a course involving problem-based learning and simulation. The design used repeated measures with a convenience sample of 83 second-year nursing students who completed the integrated course. Data from a self-administered questionnaire measuring learner motivation and life skills were collected at pretest, post-problem-based learning, and post-simulation time points. Repeated-measures analysis of variance determined that the mean scores for total learner motivation (F=6.62, P=.003), communication (F=8.27, Plearning (F=4.45, P=.016) differed significantly between time points. Post hoc tests using the Bonferroni correction revealed that total learner motivation and total life skills significantly increased both from pretest to postsimulation and from post-problem-based learning test to postsimulation test. Subscales of learner motivation and life skills, intrinsic goal orientation, self-efficacy for learning and performance, problem-solving skills, and self-directed learning skills significantly increased both from pretest to postsimulation test and from post-problem-based learning test to post-simulation test. The results demonstrate that an integrating problem-based learning and simulation course elicits significant improvement in learner motivation and life skills. Simulation plus problem-based learning is more effective than problem-based learning alone at increasing intrinsic goal orientation, task value, self-efficacy for learning and performance, problem solving, and self-directed learning.

Deep Learning based Super-Resolution for Improved Action Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Guerrero, Sergio Escalera; Rasti, Pejman

2015-01-01

with results of a state-of- the-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate...

Students Learn How Nonprofits Utilize Volunteers through Inquiry-Based Learning

Science.gov (United States)

Bolton, Elizabeth B.; Brennan, M. A.; Terry, Bryan D.

2009-01-01

This article highlights how undergraduate students implemented inquiry-based learning strategies to learn how nonprofit organizations utilize volunteers. In inquiry-based learning, students begin with a problem or question with some degree of focus or structure provided by the professor. The student inquiry showcased in this article was based on a…

A self-learning rule base for command following in dynamical systems

Science.gov (United States)

Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

FACET CLASSIFICATIONS OF E-LEARNING TOOLS

Directory of Open Access Journals (Sweden)

Olena Yu. Balalaieva

2013-12-01

Full Text Available The article deals with the classification of e-learning tools based on the facet method, which suggests the separation of the parallel set of objects into independent classification groups; at the same time it is not assumed rigid classification structure and pre-built finite groups classification groups are formed by a combination of values taken from the relevant facets. An attempt to systematize the existing classification of e-learning tools from the standpoint of classification theory is made for the first time. Modern Ukrainian and foreign facet classifications of e-learning tools are described; their positive and negative features compared to classifications based on a hierarchical method are analyzed. The original author's facet classification of e-learning tools is proposed.

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

Science.gov (United States)

Ou, Meiying; Li, Shihua; Wang, Chaoli

2013-12-01

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.

Pupils' Activities in a Multimaterial Learning Environment in Craft subject A Pilot Study using an Experience Sampling Method based on a Mobile Application in Classroom Settings

Directory of Open Access Journals (Sweden)

Juha Jaatinen

2017-12-01

Full Text Available This study investigates holistic craft processes in craft education with an instrument for data-collection and self-assessment. Teaching in a study context is based on co-teaching and a design process, highlighted by the Finnish Basic Education Core Curriculum 2014. The school architecture and web-based learning environment is combined. Division for textiles and technical work is no longer supported in this multimaterial learning environment. The aim of the study is to 1 make pupils’ holistic craft processes visible in everyday classroom practices with information collected by a mobile-application and 2 point out the curriculum topics that are covered during everyday classroom practices as defined by the teachers. The data is collected using an Experience Sampling Method with a gamified learning analytics instrument. Teachers’ classroom activities were used as the backbone for the thematic mapping of the craft curriculum. Preliminary measurements were carried out in a Finnish primary school in grades 5–6 (age 10–12, n = 125 during a four-week period in October-November 2016. The list of classroom activities was updated after the four weeks’ experiment and was tested in March-May 2017 with all the pupils of the pilot school (N = 353. The key findings were that a for pupils the self-assessment was easy as a technical process but there were several factors in the everyday classroom settings that made the process challenging and b it was relatively difficult for teachers to describe the classroom activities in terms of the new curriculum; however, after four weeks they could not only described the activities in more details but had also developed new activities that supported the ideas of the new curriculum better.Keywords: multi-material craft, learning environment, holistic craft process, experience sampling method

Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

Directory of Open Access Journals (Sweden)

Fei Chen

2013-01-01

Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

Problem-based learning

NARCIS (Netherlands)

Loyens, Sofie; Kirschner, Paul A.; Paas, Fred

2010-01-01

Loyens, S. M. M., Kirschner, P. A., & Paas, F. (2011). Problem-based learning. In S. Graham (Editor-in-Chief), A. Bus, S. Major, & L. Swanson (Associate Editors), APA educational psychology handbook: Vol. 3. Application to learning and teaching (pp. 403-425). Washington, DC: American Psychological

Phase transitions in finite systems

Energy Technology Data Exchange (ETDEWEB)

Chomaz, Ph. [Grand Accelerateur National d' Ions Lourds (GANIL), DSM-CEA / IN2P3-CNRS, 14 - Caen (France); Gulminelli, F. [Caen Univ., 14 (France). Lab. de Physique Corpusculaire

2002-07-01

In this series of lectures we will first review the general theory of phase transition in the framework of information theory and briefly address some of the well known mean field solutions of three dimensional problems. The theory of phase transitions in finite systems will then be discussed, with a special emphasis to the conceptual problems linked to a thermodynamical description for small, short-lived, open systems as metal clusters and data samples coming from nuclear collisions. The concept of negative heat capacity developed in the early seventies in the context of self-gravitating systems will be reinterpreted in the general framework of convexity anomalies of thermo-statistical potentials. The connection with the distribution of the order parameter will lead us to a definition of first order phase transitions in finite systems based on topology anomalies of the event distribution in the space of observations. Finally a careful study of the thermodynamical limit will provide a bridge with the standard theory of phase transitions and show that in a wide class of physical situations the different statistical ensembles are irreducibly inequivalent. (authors)

Phase transitions in finite systems

International Nuclear Information System (INIS)

Chomaz, Ph.; Gulminelli, F.

2002-01-01

In this series of lectures we will first review the general theory of phase transition in the framework of information theory and briefly address some of the well known mean field solutions of three dimensional problems. The theory of phase transitions in finite systems will then be discussed, with a special emphasis to the conceptual problems linked to a thermodynamical description for small, short-lived, open systems as metal clusters and data samples coming from nuclear collisions. The concept of negative heat capacity developed in the early seventies in the context of self-gravitating systems will be reinterpreted in the general framework of convexity anomalies of thermo-statistical potentials. The connection with the distribution of the order parameter will lead us to a definition of first order phase transitions in finite systems based on topology anomalies of the event distribution in the space of observations. Finally a careful study of the thermodynamical limit will provide a bridge with the standard theory of phase transitions and show that in a wide class of physical situations the different statistical ensembles are irreducibly inequivalent. (authors)

STEM-based science learning implementation to identify student’s personal intelligences profiles

Science.gov (United States)

Wiguna, B. J. P. K.; Suwarma, I. R.; Liliawati, W.

2018-05-01

Science and technology are rapidly developing needs to be balanced with the human resources that have the qualified ability. Not only cognitive ability, but also have the soft skills that support 21st century skills. Science, Technology, Engineering, and Mathematics (STEM) Education is a solution to improve the quality of learning and prepare students may be able to trained 21st century skills. This study aims to analyse the implementation of STEM-based science learning on Newton’s law of motion by identifying the personal intelligences profile junior high school students. The method used in this research is pre experiment with the design of the study one group pre-test post-test. Samples in this study were 26 junior high school students taken using Convenience Sampling. Students personal intelligences profile after learning STEM-based science uses two instruments, self-assessment and peer assessment. Intrapersonal intelligence profile based self-assessment and peer assessment are respectively 69.38; and 64.08. As for interpersonal intelligence for self-assessment instrument is 73 and the peer assessment is 60.23.

An investigation of the effects of relevant samples and a comparison of verification versus discovery based lab design

Science.gov (United States)

Rieben, James C., Jr.

This study focuses on the effects of relevance and lab design on student learning within the chemistry laboratory environment. A general chemistry conductivity of solutions experiment and an upper level organic chemistry cellulose regeneration experiment were employed. In the conductivity experiment, the two main variables studied were the effect of relevant (or "real world") samples on student learning and a verification-based lab design versus a discovery-based lab design. With the cellulose regeneration experiment, the effect of a discovery-based lab design vs. a verification-based lab design was the sole focus. Evaluation surveys consisting of six questions were used at three different times to assess student knowledge of experimental concepts. In the general chemistry laboratory portion of this study, four experimental variants were employed to investigate the effect of relevance and lab design on student learning. These variants consisted of a traditional (or verification) lab design, a traditional lab design using "real world" samples, a new lab design employing real world samples/situations using unknown samples, and the new lab design using real world samples/situations that were known to the student. Data used in this analysis were collected during the Fall 08, Winter 09, and Fall 09 terms. For the second part of this study a cellulose regeneration experiment was employed to investigate the effects of lab design. A demonstration creating regenerated cellulose "rayon" was modified and converted to an efficient and low-waste experiment. In the first variant students tested their products and verified a list of physical properties. In the second variant, students filled in a blank physical property chart with their own experimental results for the physical properties. Results from the conductivity experiment show significant student learning of the effects of concentration on conductivity and how to use conductivity to differentiate solution types with the

Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

Directory of Open Access Journals (Sweden)

Mingchen Yao

2015-01-01

Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Directory of Open Access Journals (Sweden)

ZALL, R.

2016-05-01

Full Text Available Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE. SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.

Discrete phase space based on finite fields

International Nuclear Information System (INIS)

Gibbons, Kathleen S.; Hoffman, Matthew J.; Wootters, William K.

2004-01-01

The original Wigner function provides a way of representing in phase space the quantum states of systems with continuous degrees of freedom. Wigner functions have also been developed for discrete quantum systems, one popular version being defined on a 2Nx2N discrete phase space for a system with N orthogonal states. Here we investigate an alternative class of discrete Wigner functions, in which the field of real numbers that labels the axes of continuous phase space is replaced by a finite field having N elements. There exists such a field if and only if N is a power of a prime; so our formulation can be applied directly only to systems for which the state-space dimension takes such a value. Though this condition may seem limiting, we note that any quantum computer based on qubits meets the condition and can thus be accommodated within our scheme. The geometry of our NxN phase space also leads naturally to a method of constructing a complete set of N+1 mutually unbiased bases for the state space

Learning Object Retrieval and Aggregation Based on Learning Styles

Science.gov (United States)

Ramirez-Arellano, Aldo; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel

2017-01-01

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step…

Merging Problem-Based Learning with Simulation-Based Learning in the Medical Undergraduate Curriculum: The PAIRED Framework for Enhancing Lifelong Learning

Science.gov (United States)

Koh, Jansen

2016-01-01

Lifelong learning is an essential trait that is expected of every physician. The CanMeds 2005 Physician Competency Framework emphasizes lifelong learning as a key competency that physicians must achieve in becoming better physicians. However, many physicians are not competent at engaging in lifelong learning. The current medical education system is deficient in preparing medical students to develop and carry out their own lifelong learning curriculum upon graduation. Despite understanding how physicians learn at work, medical students are not trained to learn while working. Similarly, although barriers to lifelong learning are known, medical students are not adequately skilled in overcoming these barriers. Learning to learn is just as important, if not more, as acquiring the skills and knowledge required of a physician. The medical undergraduate curriculum lacks a specific learning strategy to prepare medical students in becoming an adept lifelong learner. In this article, we propose a learning strategy for lifelong learning at the undergraduate level. In developing this novel strategy, we paid particular attention to two parameters. First, this strategy should be grounded on literature describing a physician’s lifelong learning process. Second, the framework for implementing this strategy must be based on existing undergraduate learning strategies to obviate the need for additional resources, learner burden, and faculty time. In this paper, we propose a Problem, Analysis, Independent Research Reporting, Experimentation Debriefing (PAIRED) framework that follows the learning process of a physician and serves to synergize the components of problem-based learning and simulation-based learning in specifically targeting the barriers to lifelong learning. PMID:27446767

Problem based learning: the effect of real time data on the website to student independence

Science.gov (United States)

Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

2018-05-01

Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

Web-Based Learning Support System

Science.gov (United States)

Fan, Lisa

Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student's needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

Quadrature demodulation based circuit implementation of pulse stream for ultrasonic signal FRI sparse sampling

International Nuclear Information System (INIS)

Shoupeng, Song; Zhou, Jiang

2017-01-01

Converting ultrasonic signal to ultrasonic pulse stream is the key step of finite rate of innovation (FRI) sparse sampling. At present, ultrasonic pulse-stream-forming techniques are mainly based on digital algorithms. No hardware circuit that can achieve it has been reported. This paper proposes a new quadrature demodulation (QD) based circuit implementation method for forming an ultrasonic pulse stream. Elaborating on FRI sparse sampling theory, the process of ultrasonic signal is explained, followed by a discussion and analysis of ultrasonic pulse-stream-forming methods. In contrast to ultrasonic signal envelope extracting techniques, a quadrature demodulation method (QDM) is proposed. Simulation experiments were performed to determine its performance at various signal-to-noise ratios (SNRs). The circuit was then designed, with mixing module, oscillator, low pass filter (LPF), and root of square sum module. Finally, application experiments were carried out on pipeline sample ultrasonic flaw testing. The experimental results indicate that the QDM can accurately convert ultrasonic signal to ultrasonic pulse stream, and reverse the original signal information, such as pulse width, amplitude, and time of arrival. This technique lays the foundation for ultrasonic signal FRI sparse sampling directly with hardware circuitry. (paper)

How to Incorporate Technology with Inquiry-Based Learning to Enhance the Understanding of Chemical Composition; How to Analyze Unknown Samples

Directory of Open Access Journals (Sweden)

Suzanne Lunsford

2017-02-01

Full Text Available The use of technology in teaching offers numerous amounts of possibilities and can be challenging for physics, chemistry and geology content courses. When incorporating technology into a science content lab it is better to be driven by pedagogy than by technology in an inquiry-based lab setting. Students need to be introduced to real-world technology in the beginning of first year chemistry or physics course to ensure real-world technology concepts while assisting with content such as periodic trends on the periodic table. This article will describe the use of technology with Raman Spectroscopy and Energy Dispersive XRay Spectroscopy (EDS and Fourier Transform Infrared Spectroscopy (FTIR to research chemical compositions in the real world of unknown samples. Such unknown samples utilized in this lab were clamshell (parts of clams that look like shark teeth versus shark teeth. The data will be shared to show how the students (pre-service teachers and in-service teachers solved the problem using technology while learning important content that will assist in the next level of chemistry, physics and even geology.

Cross-situational statistically based word learning intervention for late-talking toddlers.

Science.gov (United States)

Alt, Mary; Meyers, Christina; Oglivie, Trianna; Nicholas, Katrina; Arizmendi, Genesis

2014-01-01

To explore the efficacy of a word learning intervention for late-talking toddlers that is based on principles of cross-situational statistical learning. Four late-talking toddlers were individually provided with 7-10 weeks of bi-weekly word learning intervention that incorporated principles of cross-situational statistical learning. Treatment was input-based meaning that, aside from initial probes, children were not asked to produce any language during the sessions. Pre-intervention data included parent-reported measures of productive vocabulary and language samples. Data collected during intervention included production on probes, spontaneous production during treatment, and parent report of words used spontaneously at home. Data were analyzed for number of target words learned relative to control words, effect sizes, and pre-post treatment vocabulary measures. All children learned more target words than control words and, on average, showed a large treatment effect size. Children made pre-post vocabulary gains, increasing their percentile scores on the MCDI, and demonstrated a rate of word learning that was faster than rates found in the literature. Cross-situational statistically based word learning intervention has the potential to improve vocabulary learning in late-talking toddlers. Limitations on interpretation are also discussed. Readers will describe what cross-situational learning is and how it might apply to treatment. They will identify how including lexical and contextual variability in a word learning intervention for toddlers affected treatment outcomes. They will also recognize evidence of improved rate of vocabulary learning following treatment. Copyright © 2014 Elsevier Inc. All rights reserved.

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.

Science.gov (United States)

Liang, Liang; Liu, Minliang; Martin, Caitlin; Sun, Wei

2018-01-01

Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis. © 2018 The Author(s).

Virtual Learning Environments and Learning Forms -experiments in ICT-based learning

DEFF Research Database (Denmark)

Helbo, Jan; Knudsen, Morten

2004-01-01

This paper report the main results of a three year experiment in ICT-based distance learning. The results are based on a full scale experiment in the education, Master of Industrial Information Technology (MII) and is one of many projects deeply rooted in the project Virtual Learning Environments...... didactic model has until now been a positive experience........ The main problem is that we do not find the same self regulatoring learning effect in the group work among the off-campus students as is the case for on-campus students. Based on feedback from evaluation questionnaires and discussions with the students didactic adjustments have been made. The revised...

Virk: An Active Learning-based System for Bootstrapping Knowledge Base Development in the Neurosciences

Directory of Open Access Journals (Sweden)

Kyle H. Ambert

2013-12-01

Full Text Available The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning, builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an active learning system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1-2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in active learning.

Finite fields and applications

CERN Document Server

Mullen, Gary L

2007-01-01

This book provides a brief and accessible introduction to the theory of finite fields and to some of their many fascinating and practical applications. The first chapter is devoted to the theory of finite fields. After covering their construction and elementary properties, the authors discuss the trace and norm functions, bases for finite fields, and properties of polynomials over finite fields. Each of the remaining chapters details applications. Chapter 2 deals with combinatorial topics such as the construction of sets of orthogonal latin squares, affine and projective planes, block designs, and Hadamard matrices. Chapters 3 and 4 provide a number of constructions and basic properties of error-correcting codes and cryptographic systems using finite fields. Each chapter includes a set of exercises of varying levels of difficulty which help to further explain and motivate the material. Appendix A provides a brief review of the basic number theory and abstract algebra used in the text, as well as exercises rel...

Inquiry-Based Learning in China: Lesson Learned for School Science Practices

Science.gov (United States)

Nuangchalerm, Prasart

2014-01-01

Inquiry-based learning is widely considered for science education in this era. This study aims to explore inquiry-based learning in teacher preparation program and the findings will help us to understanding what inquiry-based classroom is and how inquiry-based learning are. Data were collected by qualitative methods; classroom observation,…

Innovation in preregistration midwifery education: Web based interactive storytelling learning.

OpenAIRE

Scamell, M.; Hanley, T.

2017-01-01

BACKGROUND: through a critical description of the implementation of a web based interactive storytelling learning activity introduced into an undergraduate, preregistration midwifery education programme, this paper will explore how low-cost, low-fidelity online storytelling, designed using Moodle, can be used to enhance students' understanding of compassion and empathy in practice.\\ud \\ud SAMPLE: cross sectional sample of first year undergraduate Midwifery students (n111)\\ud \\ud METHOD: drawi...

Stochastic Finite Elements in Reliability-Based Structural Optimization

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Engelund, S.

Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect to optimi......Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect...

Implementation of student’s worksheet based on project based learning (pjbl to foster student’s creativity

Directory of Open Access Journals (Sweden)

Sahtoni Sahtoni

2017-12-01

Full Text Available This study aimed to foster student creativity through the use of student worksheet based on Project Base Learning (PjBL on dynamic electrical material in making alternative power sources. The research method is using a pre-experimental design with One-Shot case study type. The study population was students of class IX MTs Al-Islah in Pesawaran Lampung. A sample of one class consisting  of 30 people was obtained by purposive sumpling. Data analysis was done by using descriptive concerning creativity, product, and response of students to see effectiveness of learning. The results showed that the application of student worksheet based on project based learning is overall effective to foster creativity of student. Based on the average of student’s creativity, the result is 80% which categorized as “creative.” Based on the average of student’s product, the result is 76.2% which categorized as “valuable.” The student’s response is positive as much as 92% which categorized as “very agree.”

PENINGKATAN SELF-REGULATED LEARNING SKILLS MAHASISWA PADA MATA KULIAH AKUNTANSI PENGANTAR MELALUI PROBLEM-BASED LEARNING

Directory of Open Access Journals (Sweden)

Andian Ari Istiningrum

2017-02-01

Full Text Available Abstrak: Penelitian ini bertujuan untuk mengetahui: (i peningkatan self-regulated learning skills (SRL melalui implementasi problem-based learning (PBL dan (ii peningkatan kemampuan dosen pelaksana dalam mengimplementasikan PBL. Penelitian ini merupakan lesson study terbagi atas dua siklus dimana masing-masing siklus terdiri atas tahap plan, do, dan see.Subjek penelitian adalah mahasiswa Akuntansi Universitas Negeri Yogyakarta semester pertama yang mengambil mata kuliah Akuntansi Pengantar sebanyak 35 mahasiswa. Data mengenai SRL dikumpulkan dengan angket yang diisi mahasiswa, sedangkan data mengenai implementasi PBL oleh dosen pelaksana dikumpulkan dengan lembar observasi yang diisi oleh mahasiswa dan anggota timlesson study. Data dianalisis secara deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa (i PBL mampu meningkatkan SRL mahasiswa walaupun tingkat ketercapaiannya masih belum optimal, dan (ii kemampuan dosen pelaksana dalam melaksanakan PBL meningkat dengan tingkat ketercapaian yang optimal. IMPROVING STUDENTS’ SELF-REGULATED LEARNING SKILLS IN THE INTRODUCTION TO ACCOUNTING COURSE THROUGH PROBLEM-BASED LEARNING Abstract: This study aims to reveal (i the improvement of self-regulated learning skills (SRL through problem-based learning (PBL, and (ii the improvement of lecturers’ performance in implementing PBL. To achieve these purposes, a lesson study with two cycles was conducted. Each cycle consisted of plan phase, do phase, and see phase. The study was conducted to the 1 semester Accounting Students at Yogyakarta State University who attended the Introduction to Accounting course. There were 35 students as the research subjects. The sampling technique used to collect data regarding SRL was questionnaires which were filled out by the students; while the data regarding the lecturer’s performance was collected by observation sheets that were filled out by students and members of lesson study group. The study

Modeling hemodynamics in intracranial aneurysms: Comparing accuracy of CFD solvers based on finite element and finite volume schemes.

Science.gov (United States)

Botti, Lorenzo; Paliwal, Nikhil; Conti, Pierangelo; Antiga, Luca; Meng, Hui

2018-06-01

Image-based computational fluid dynamics (CFD) has shown potential to aid in the clinical management of intracranial aneurysms (IAs) but its adoption in the clinical practice has been missing, partially due to lack of accuracy assessment and sensitivity analysis. To numerically solve the flow-governing equations CFD solvers generally rely on two spatial discretization schemes: Finite Volume (FV) and Finite Element (FE). Since increasingly accurate numerical solutions are obtained by different means, accuracies and computational costs of FV and FE formulations cannot be compared directly. To this end, in this study we benchmark two representative CFD solvers in simulating flow in a patient-specific IA model: (1) ANSYS Fluent, a commercial FV-based solver and (2) VMTKLab multidGetto, a discontinuous Galerkin (dG) FE-based solver. The FV solver's accuracy is improved by increasing the spatial mesh resolution (134k, 1.1m, 8.6m and 68.5m tetrahedral element meshes). The dGFE solver accuracy is increased by increasing the degree of polynomials (first, second, third and fourth degree) on the base 134k tetrahedral element mesh. Solutions from best FV and dGFE approximations are used as baseline for error quantification. On average, velocity errors for second-best approximations are approximately 1cm/s for a [0,125]cm/s velocity magnitude field. Results show that high-order dGFE provide better accuracy per degree of freedom but worse accuracy per Jacobian non-zero entry as compared to FV. Cross-comparison of velocity errors demonstrates asymptotic convergence of both solvers to the same numerical solution. Nevertheless, the discrepancy between under-resolved velocity fields suggests that mesh independence is reached following different paths. This article is protected by copyright. All rights reserved.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

Science.gov (United States)

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

Science.gov (United States)

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

Directory of Open Access Journals (Sweden)

Rebeca Cerezo

2017-08-01

Full Text Available Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs. Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

A hierarchical model for structure learning based on the physiological characteristics of neurons

Institute of Scientific and Technical Information of China (English)

WEI Hui

2007-01-01

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and globalized optimizations,all based on a mathematical approach.This makes those models to be deficient in robustness,efficiency of learning,capacity,anti-jamming between training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has demonstrated the feasibility and advantages of structure learning and representation.This model can serve as a fundamental element of cognitive systems such as perception and associative memory.Key-words structure learning,representation,associative memory,computational neuroscience

The finite-dimensional Freeman thesis.

Science.gov (United States)

Rudolph, Lee

2008-06-01

I suggest a modification--and mathematization--of Freeman's thesis on the relations among "perception", "the finite brain", and "the world", based on my recent proposal that the theory of finite topological spaces is both an adequate and a natural mathematical foundation for human psychology.

Mathematics Literacy on Problem Based Learning with Indonesian Realistic Mathematics Education Approach Assisted E-Learning Edmodo

Science.gov (United States)

Wardono; Waluya, S. B.; Mariani, Scolastika; Candra D, S.

2016-02-01

This study aims to find out that there are differences in mathematical literacy ability in content Change and Relationship class VII Junior High School 19, Semarang by Problem Based Learning (PBL) model with an Indonesian Realistic Mathematics Education (called Pendidikan Matematika Realistik Indonesia or PMRI in Indonesia) approach assisted Elearning Edmodo, PBL with a PMRI approach, and expository; to know whether the group of students with learning PBL models with PMRI approach and assisted E-learning Edmodo can improve mathematics literacy; to know that the quality of learning PBL models with a PMRI approach assisted E-learning Edmodo has a good category; to describe the difficulties of students in working the problems of mathematical literacy ability oriented PISA. This research is a mixed methods study. The population was seventh grade students of Junior High School 19, Semarang Indonesia. Sample selection is done by random sampling so that the selected experimental class 1, class 2 and the control experiment. Data collected by the methods of documentation, tests and interviews. From the results of this study showed average mathematics literacy ability of students in the group PBL models with a PMRI approach assisted E-learning Edmodo better than average mathematics literacy ability of students in the group PBL models with a PMRI approach and better than average mathematics literacy ability of students in the expository models; Mathematics literacy ability in the class using the PBL model with a PMRI approach assisted E-learning Edmodo have increased and the improvement of mathematics literacy ability is higher than the improvement of mathematics literacy ability of class that uses the model of PBL learning with PMRI approach and is higher than the improvement of mathematics literacy ability of class that uses the expository models; The quality of learning using PBL models with a PMRI approach assisted E-learning Edmodo have very good category.

Dimensions of problem based learning

DEFF Research Database (Denmark)

Nielsen, Jørgen Lerche; Andreasen, Lars Birch

2013-01-01

The article contributes to the literature on problem based learning and problem-oriented project work, building on and reflecting the experiences of the authors through decades of work with problem-oriented project pedagogy. The article explores different dimensions of problem based learning such...... and Learning (MIL). We discuss changes in the roles of the teachers as supervisors within this learning environment, and we explore the involvement of students as active participants and co-designers of how course and project activities unfold....

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

eLearning techniques supporting problem based learning in clinical simulation.

Science.gov (United States)

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Mesh Partitioning Algorithm Based on Parallel Finite Element Analysis and Its Actualization

Directory of Open Access Journals (Sweden)

Lei Zhang

2013-01-01

Full Text Available In parallel computing based on finite element analysis, domain decomposition is a key technique for its preprocessing. Generally, a domain decomposition of a mesh can be realized through partitioning of a graph which is converted from a finite element mesh. This paper discusses the method for graph partitioning and the way to actualize mesh partitioning. Relevant softwares are introduced, and the data structure and key functions of Metis and ParMetis are introduced. The writing, compiling, and testing of the mesh partitioning interface program based on these key functions are performed. The results indicate some objective law and characteristics to guide the users who use the graph partitioning algorithm and software to write PFEM program, and ideal partitioning effects can be achieved by actualizing mesh partitioning through the program. The interface program can also be used directly by the engineering researchers as a module of the PFEM software. So that it can reduce the application of the threshold of graph partitioning algorithm, improve the calculation efficiency, and promote the application of graph theory and parallel computing.

A method for predicting errors when interacting with finite state systems. How implicit learning shapes the user's knowledge of a system

International Nuclear Information System (INIS)

Javaux, Denis

2002-01-01

This paper describes a method for predicting the errors that may appear when human operators or users interact with systems behaving as finite state systems. The method is a generalization of a method used for predicting errors when interacting with autopilot modes on modern, highly computerized airliners [Proc 17th Digital Avionics Sys Conf (DASC) (1998); Proc 10th Int Symp Aviat Psychol (1999)]. A cognitive model based on spreading activation networks is used for predicting the user's model of the system and its impact on the production of errors. The model strongly posits the importance of implicit learning in user-system interaction and its possible detrimental influence on users' knowledge of the system. An experiment conducted with Airbus Industrie and a major European airline on pilots' knowledge of autopilot behavior on the A340-200/300 confirms the model predictions, and in particular the impact of the frequencies with which specific state transitions and contexts are experienced

Sampling theory, a renaissance compressive sensing and other developments

CERN Document Server

2015-01-01

Reconstructing or approximating objects from seemingly incomplete information is a frequent challenge in mathematics, science, and engineering. A multitude of tools designed to recover hidden information are based on Shannon’s classical sampling theorem, a central pillar of Sampling Theory. The growing need to efficiently obtain precise and tailored digital representations of complex objects and phenomena requires the maturation of available tools in Sampling Theory as well as the development of complementary, novel mathematical theories. Today, research themes such as Compressed Sensing and Frame Theory re-energize the broad area of Sampling Theory. This volume illustrates the renaissance that the area of Sampling Theory is currently experiencing. It touches upon trendsetting areas such as Compressed Sensing, Finite Frames, Parametric Partial Differential Equations, Quantization, Finite Rate of Innovation, System Theory, as well as sampling in Geometry and Algebraic Topology.

EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

Directory of Open Access Journals (Sweden)

Teguh Febri Sudarma

2013-06-01

Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

Simulation and case-based learning

DEFF Research Database (Denmark)

Ørngreen, Rikke; Guralnick, David

2008-01-01

Abstract- This paper has its origin in the authors' reflection on years of practical experiences combined with literature readings in our preparation for a workshop on learn-by-doing simulation and case-based learning to be held at the ICELW 2008 conference (the International Conference on E-Learning...... in the Workplace). The purpose of this paper is to describe the two online learning methodologies and to raise questions for future discussion. In the workshop, the organizers and participants work with and discuss differences and similarities within the two pedagogical methodologies, focusing on how...... they are applied in workplace related and e-learning contexts. In addition to the organizers, a small number of invited presenters will attend, giving demonstrations of their work within learn-by-doing simulation and cases-based learning, but still leaving ample of time for discussion among all participants....

Design for game based learning platforms

DEFF Research Database (Denmark)

Sørensen, Birgitte Holm; Meyer, Bente

2010-01-01

This paper focuses on the challenges related to the design of game based learning platforms for formal learning contexts that are inspired by the pupil's leisure time related use of web 2.0. The paper is based on the project Serious Games on a Global Market Place (2007-2011) founded by the Danish...... of web 2.0 and integrates theories of learning, didactics, games, play, communication, multimodality and different pedagogical approaches. In relation to the introduced model the teacher role is discussed.......This paper focuses on the challenges related to the design of game based learning platforms for formal learning contexts that are inspired by the pupil's leisure time related use of web 2.0. The paper is based on the project Serious Games on a Global Market Place (2007-2011) founded by the Danish...... Council for Strategic Research, in which an online game-based platform for English as a foreign language in primary school is studied. The paper presents a model for designing for game based learning platforms. This design is based on cultural and ethnographic based research on children's leisure time use...

Promoting of Thematic-Based Integrated Science Learning on the Junior High School

Science.gov (United States)

Pursitasari, Indarini Dwi; Nuryanti, Siti; Rede, Amran

2015-01-01

This study was conducted to explain the effect of thematic based integrated science learning to the student's critical thinking skills and character. One group pretest-posttest design is involving thirty students in one of the junior high school in the Palu city. A sample was taken using purposive sampling. Data of critical thinking skills…

The Effectiveness of the Game-Based Learning System for the Improvement of American Sign Language Using Kinect

Science.gov (United States)

Kamnardsiri, Teerawat; Hongsit, Ler-on; Khuwuthyakorn, Pattaraporn; Wongta, Noppon

2017-01-01

This paper investigated students' achievement for learning American Sign Language (ASL), using two different methods. There were two groups of samples. The first experimental group (Group A) was the game-based learning for ASL, using Kinect. The second control learning group (Group B) was the traditional face-to-face learning method, generally…

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

The Impact of Cultural Dimensions on Islamic Students’ Attitude Towards Problem-Based Learning

Directory of Open Access Journals (Sweden)

Esti Zaduqisti

2016-06-01

Full Text Available The current study aims to examine the impact of cultural dimensions (i.e., collectivism, power distance, uncertainty avoidance, and masculinity on students’ attitude towards problem-based learning. The design of the current study was a correlational survey, wherein participants were recruited by means of a convenient sampling. Inspection of a multiple regression analysis (N = 549 revealed that collectivism and masculinity positively corresponded with the attitudes. In particular, we found that that the higher the level of collectivism and masculinity, the more students supported the implementation of problem-based learning. In contrast, uncertainty avoidance was negatively related to the attitude in such a way that the higher this cultural dimension, the less students supported problem-based learning. Power distance was the only predictor that did not significantly predict students’ attitude towards problem-based learning. These findings overall suggest the importance of taking into account the characteristics of norms and values people hold within a country that might contribute to the success, feasibility, and  suitability of problem-based learning. Theoretical implications and study limitations of the current findings are discussed, as are practical strategies highlighting on how to deal with cultural potentials and pitfalls in an attempt to promote problem-based learning.

A Proposed Stochastic Finite Difference Approach Based on Homogenous Chaos Expansion

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O. H. Galal

2013-01-01

Full Text Available This paper proposes a stochastic finite difference approach, based on homogenous chaos expansion (SFDHC. The said approach can handle time dependent nonlinear as well as linear systems with deterministic or stochastic initial and boundary conditions. In this approach, included stochastic parameters are modeled as second-order stochastic processes and are expanded using Karhunen-Loève expansion, while the response function is approximated using homogenous chaos expansion. Galerkin projection is used in converting the original stochastic partial differential equation (PDE into a set of coupled deterministic partial differential equations and then solved using finite difference method. Two well-known equations were used for efficiency validation of the method proposed. First one being the linear diffusion equation with stochastic parameter and the second is the nonlinear Burger's equation with stochastic parameter and stochastic initial and boundary conditions. In both of these examples, the probability distribution function of the response manifested close conformity to the results obtained from Monte Carlo simulation with optimized computational cost.

Measuring the Differences between Traditional Learning and Game-Based Learning Using Electroencephalography (EEG) Physiologically Based Methodology

Science.gov (United States)

Chen, Ching-Huei

2017-01-01

Students' cognitive states can reflect a learning experience that results in engagement in an activity. In this study, we used electroencephalography (EEG) physiologically based methodology to evaluate students' levels of attention and relaxation, as well as their learning performance within a traditional and game-based learning context. While no…

Gameplay Engagement and Learning in Game-Based Learning: A Systematic Review

Science.gov (United States)

Abdul Jabbar, Azita Iliya; Felicia, Patrick

2015-01-01

In this review, we investigated game design features that promote engagement and learning in game-based learning (GBL) settings. The aim was to address the lack of empirical evidence on the impact of game design on learning outcomes, identify how the design of game-based activities may affect learning and engagement, and develop a set of general…

Measuring strategies for learning regulation in medical education: scale reliability and dimensionality in a Swedish sample.

Science.gov (United States)

Edelbring, Samuel

2012-08-15

The degree of learners' self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206). The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach's alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students' regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

PENINGKATAN AKTIVITAS MENGGUNAKAN LABORATORIUM KOMPUTER DAN HASIL BELAJAR SISWA SMK PROGRAM KEAHLIAN AKUNTANSI MELALUI PENERAPAN PROJECT BASED LEARNING

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David Firna Setiawan

2017-06-01

Full Text Available The existence of the computer lab is one thing that is essential to support the improvement of students’ competence. Some other researcherssaid that some of the characteristics of PBL and its influence on motivation and learning outcomes, however, these studies can not describe the improvement of the activity in utilizing a computer lab. This study aimed to analyze the differences in the use of computer labs and student learning outcomes at the accounting program to operate accounting computer before and after the implementation of project based learning (PBL. The population of this study was all students of XI class and XII class in accounting programof SMK Al Falah Winong. Sampling in this study is using proportionate stratified random sampling. Data collected through observation. Hypothesis testing is done through t test with two independent samples. The results showed that the application of PBL was able to increase the activity in computer lab and student learning outcomes in the competence particularity on operate computer accounting application.   Keywords: Project based learning, computer lab, Student learning outcomes

Culture-Based Contextual Learning to Increase Problem-Solving Ability of First Year University Student

Science.gov (United States)

Samo, Damianus Dao; Darhim; Kartasasmita, Bana G.

2018-01-01

The purpose of this study is to show the differences in problem-solving ability between first-year University students who received culture-based contextual learning and conventional learning. This research is a quantitative research using quasi-experimental research design. Samples were the First-year students of mathematics education department;…

DEVELOPMENT OF SCIENCE PROCESS SKILLS STUDENTS WITH PROJECT BASED LEARNING MODEL- BASED TRAINING IN LEARNING PHYSICS

Directory of Open Access Journals (Sweden)

Ratna Malawati

2016-06-01

Full Text Available This study aims to improve the physics Science Process Skills Students on cognitive and psychomotor aspects by using model based Project Based Learning training.The object of this study is the Project Based Learning model used in the learning process of Computationa Physics.The method used is classroom action research through two learning cycles, each cycle consisting of the stages of planning, implementation, observation and reflection. In the first cycle of treatment with their emphasis given training in the first phase up to third in the model Project Based Learning, while the second cycle is given additional treatment with emphasis discussion is collaboration in achieving the best results for each group of products. The results of data analysis showed increased ability to think Students on cognitive and Science Process Skills in the psychomotor.

Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine

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Ming-Hung Wang

2017-01-01

Full Text Available With the rapid development of the Internet, several emerging technologies are adopted to construct fancy, interactive, and user-friendly websites. Among these technologies, HTML5 is a popular one and is widely used in establishing modern sites. However, the security issues in the new web technologies are also raised and are worthy of investigation. For vulnerability investigation, many previous studies used fuzzing and focused on generation-based approaches to produce test cases for fuzzing; however, these methods require a significant amount of knowledge and mental efforts to develop test patterns for generating test cases. To decrease the entry barrier of conducting fuzzing, in this study, we propose a test pattern generation algorithm based on the concept of finite state machines. We apply graph analysis techniques to extract paths from finite state machines and use these paths to construct test patterns automatically. According to the proposal, fuzzing can be completed through inputting a regular expression corresponding to the test target. To evaluate the performance of our proposal, we conduct an experiment in identifying vulnerabilities of the input attributes in HTML5. According to the results, our approach is not only efficient but also effective for identifying weak validators in HTML5.

Probabilistic finite elements

Science.gov (United States)

Belytschko, Ted; Wing, Kam Liu

1987-01-01

In the Probabilistic Finite Element Method (PFEM), finite element methods have been efficiently combined with second-order perturbation techniques to provide an effective method for informing the designer of the range of response which is likely in a given problem. The designer must provide as input the statistical character of the input variables, such as yield strength, load magnitude, and Young's modulus, by specifying their mean values and their variances. The output then consists of the mean response and the variance in the response. Thus the designer is given a much broader picture of the predicted performance than with simply a single response curve. These methods are applicable to a wide class of problems, provided that the scale of randomness is not too large and the probabilistic density functions possess decaying tails. By incorporating the computational techniques we have developed in the past 3 years for efficiency, the probabilistic finite element methods are capable of handling large systems with many sources of uncertainties. Sample results for an elastic-plastic ten-bar structure and an elastic-plastic plane continuum with a circular hole subject to cyclic loadings with the yield stress on the random field are given.

Kirkwood-Buff integrals of finite systems: shape effects

Science.gov (United States)

Dawass, Noura; Krüger, Peter; Simon, Jean-Marc; Vlugt, Thijs J. H.

2018-06-01

The Kirkwood-Buff (KB) theory provides an important connection between microscopic density fluctuations in liquids and macroscopic properties. Recently, Krüger et al. derived equations for KB integrals for finite subvolumes embedded in a reservoir. Using molecular simulation of finite systems, KB integrals can be computed either from density fluctuations inside such subvolumes, or from integrals of radial distribution functions (RDFs). Here, based on the second approach, we establish a framework to compute KB integrals for subvolumes with arbitrary convex shapes. This requires a geometric function w(x) which depends on the shape of the subvolume, and the relative position inside the subvolume. We present a numerical method to compute w(x) based on Umbrella Sampling Monte Carlo (MC). We compute KB integrals of a liquid with a model RDF for subvolumes with different shapes. KB integrals approach the thermodynamic limit in the same way: for sufficiently large volumes, KB integrals are a linear function of area over volume, which is independent of the shape of the subvolume.

Diagnosis of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2015-01-01

We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

Diagnosis of constant faults in read-once contact networks over finite bases

KAUST Repository

Busbait, Monther I.

2015-03-01

We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

A spatial discretization of the MHD equations based on the finite volume - spectral method

International Nuclear Information System (INIS)

Miyoshi, Takahiro

2000-05-01

Based on the finite volume - spectral method, we present new discretization formulae for the spatial differential operators in the full system of the compressible MHD equations. In this approach, the cell-centered finite volume method is adopted in a bounded plane (poloidal plane), while the spectral method is applied to the differential with respect to the periodic direction perpendicular to the poloidal plane (toroidal direction). Here, an unstructured grid system composed of the arbitrary triangular elements is utilized for constructing the cell-centered finite volume method. In order to maintain the divergence free constraint of the magnetic field numerically, only the poloidal component of the rotation is defined at three edges of the triangular element. This poloidal component is evaluated under the assumption that the toroidal component of the operated vector times the radius, RA φ , is linearly distributed in the element. The present method will be applied to the nonlinear MHD dynamics in an realistic torus geometry without the numerical singularities. (author)

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

Science.gov (United States)

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

A sampling approach to constructing Lyapunov functions for nonlinear continuous–time systems

NARCIS (Netherlands)

Bobiti, R.V.; Lazar, M.

2016-01-01

The problem of constructing a Lyapunov function for continuous-time nonlinear dynamical systems is tackled in this paper via a sampling-based approach. The main idea of the sampling-based method is to verify a Lyapunov-type inequality for a finite number of points (known state vectors) in the

Stochastic Finite Elements in Reliability-Based Structural Optimization

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Engelund, S.

1995-01-01

Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect to optimi......Application of stochastic finite elements in structural optimization is considered. It is shown how stochastic fields modelling e.g. the modulus of elasticity can be discretized in stochastic variables and how a sensitivity analysis of the reliability of a structural system with respect...... to optimization variables can be performed. A computer implementation is described and an illustrative example is given....

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

Science.gov (United States)

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

Directory of Open Access Journals (Sweden)

Yi Zhang

2015-01-01

Full Text Available Maximum likelihood classifier (MLC and support vector machines (SVM are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Foundations of Game-Based Learning

Science.gov (United States)

Plass, Jan L.; Homer, Bruce D.; Kinzer, Charles K.

2015-01-01

In this article we argue that to study or apply games as learning environments, multiple perspectives have to be taken into account. We first define game-based learning and gamification, and then discuss theoretical models that describe learning with games, arguing that playfulness is orthogonal to learning theory. We then review design elements…

Porosity estimation by semi-supervised learning with sparsely available labeled samples

Science.gov (United States)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

Science.gov (United States)

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

Directory of Open Access Journals (Sweden)

Guofeng Wang

2014-11-01

Full Text Available Tool condition monitoring (TCM plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM, hidden Markov model (HMM and radius basis function (RBF are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

Characteristics of Problem-Based Learning

DEFF Research Database (Denmark)

Kolmos, Anette

2003-01-01

Problem BAsed LEarning (PBL) is widely regarded as a successful and innovative method for engineering education. The article highlights the Dutch approach of directing the learning process throuogh problem analysis and the Danish model of project-organised learning...

Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

Science.gov (United States)

Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

2016-09-01

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

Assessing performance and validating finite element simulations using probabilistic knowledge

Energy Technology Data Exchange (ETDEWEB)

Dolin, Ronald M.; Rodriguez, E. A. (Edward A.)

2002-01-01

Two probabilistic approaches for assessing performance are presented. The first approach assesses probability of failure by simultaneously modeling all likely events. The probability each event causes failure along with the event's likelihood of occurrence contribute to the overall probability of failure. The second assessment method is based on stochastic sampling using an influence diagram. Latin-hypercube sampling is used to stochastically assess events. The overall probability of failure is taken as the maximum probability of failure of all the events. The Likelihood of Occurrence simulation suggests failure does not occur while the Stochastic Sampling approach predicts failure. The Likelihood of Occurrence results are used to validate finite element predictions.

What students learn in problem-based learning: a process analysis

NARCIS (Netherlands)

E.H.J. Yew (Elaine); H.G. Schmidt (Henk)

2012-01-01

textabstractThis study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine

Effect of dislocation pile-up on size-dependent yield strength in finite single-crystal micro-samples

Energy Technology Data Exchange (ETDEWEB)

Pan, Bo; Shibutani, Yoji, E-mail: sibutani@mech.eng.osaka-u.ac.jp [Department of Mechanical Engineering, Osaka University, Suita 565-0871 (Japan); Zhang, Xu [State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi' an Jiaotong University, Xi' an 710049 (China); School of Mechanics and Engineering Science, Zhengzhou University, Zhengzhou 450001 (China); Shang, Fulin [State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi' an Jiaotong University, Xi' an 710049 (China)

2015-07-07

Recent research has explained that the steeply increasing yield strength in metals depends on decreasing sample size. In this work, we derive a statistical physical model of the yield strength of finite single-crystal micro-pillars that depends on single-ended dislocation pile-up inside the micro-pillars. We show that this size effect can be explained almost completely by considering the stochastic lengths of the dislocation source and the dislocation pile-up length in the single-crystal micro-pillars. The Hall–Petch-type relation holds even in a microscale single-crystal, which is characterized by its dislocation source lengths. Our quantitative conclusions suggest that the number of dislocation sources and pile-ups are significant factors for the size effect. They also indicate that starvation of dislocation sources is another reason for the size effect. Moreover, we investigated the explicit relationship between the stacking fault energy and the dislocation “pile-up” effect inside the sample: materials with low stacking fault energy exhibit an obvious dislocation pile-up effect. Our proposed physical model predicts a sample strength that agrees well with experimental data, and our model can give a more precise prediction than the current single arm source model, especially for materials with low stacking fault energy.

Practice and effectiveness of web-based problem-based learning approach in a large class-size system: A comparative study.

Science.gov (United States)

Ding, Yongxia; Zhang, Peili

2018-06-12

Problem-based learning (PBL) is an effective and highly efficient teaching approach that is extensively applied in education systems across a variety of countries. This study aimed to investigate the effectiveness of web-based PBL teaching pedagogies in large classes. The cluster sampling method was used to separate two college-level nursing student classes (graduating class of 2013) into two groups. The experimental group (n = 162) was taught using a web-based PBL teaching approach, while the control group (n = 166) was taught using conventional teaching methods. We subsequently assessed the satisfaction of the experimental group in relation to the web-based PBL teaching mode. This assessment was performed following comparison of teaching activity outcomes pertaining to exams and self-learning capacity between the two groups. When compared with the control group, the examination scores and self-learning capabilities were significantly higher in the experimental group (P web-based PBL teaching approach. In a large class-size teaching environment, the web-based PBL teaching approach appears to be more optimal than traditional teaching methods. These results demonstrate the effectiveness of web-based teaching technologies in problem-based learning. Copyright © 2018. Published by Elsevier Ltd.

Learning Object Metadata in a Web-Based Learning Environment

NARCIS (Netherlands)

Avgeriou, Paris; Koutoumanos, Anastasios; Retalis, Symeon; Papaspyrou, Nikolaos

2000-01-01

The plethora and variance of learning resources embedded in modern web-based learning environments require a mechanism to enable their structured administration. This goal can be achieved by defining metadata on them and constructing a system that manages the metadata in the context of the learning

Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Directory of Open Access Journals (Sweden)

Caihong Zhang

2014-01-01

Full Text Available This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results.

Strain-based finite elements for the analysis of cylinders with holes and normally intersecting cylinders

International Nuclear Information System (INIS)

Sabir, A.B.

1983-01-01

A finite element solution to the problems of stress distribution for cylindrical shells with circular and elliptical holes and also for normally intersecting thin elastic cylindrical shells is given. Quadrilateral and triangular curved finite elements are used in the analysis. The elements are of a new class, based on simple independent generalised strain functions insofar as this is allowed by the compatibility equations. The elements also satisfy exactly the requirements of strain-free-rigid body displacements and uses only the external 'geometrical' nodal degrees of freedom to avoid the difficulties associated with unnecessary internal degrees of freedom. We first develop strain based quadrilateral and triangular elements and apply them to the solution of the problem of stress concentrations in the neighbourhood of small and large circular and elliptical holes when the cylinders are subjected to a uniform axial tension. These results are compared with analytical solutions based on shallow shell approximations and show that the use of these strain based elements obviates the need for using an inordinately large number of elements. Normally intersecting cylinders are common configurations in structural components for nuclear reactor systems and design information for such configurations are generally lacking. The opportunity is taken in the present paper to provide a finite element solution to this problem. A method of substructing will be introduced to enable a solution to the large number of non banded set of simultaneous equations encountered. (orig./HP)

Generalized finite elements

International Nuclear Information System (INIS)

Wachspress, E.

2009-01-01

Triangles and rectangles are the ubiquitous elements in finite element studies. Only these elements admit polynomial basis functions. Rational functions provide a basis for elements having any number of straight and curved sides. Numerical complexities initially associated with rational bases precluded extensive use. Recent analysis has reduced these difficulties and programs have been written to illustrate effectiveness. Although incorporation in major finite element software requires considerable effort, there are advantages in some applications which warrant implementation. An outline of the basic theory and of recent innovations is presented here. (authors)

Team-based learning for midwifery education.

Science.gov (United States)

Moore-Davis, Tonia L; Schorn, Mavis N; Collins, Michelle R; Phillippi, Julia; Holley, Sharon

2015-01-01

Many US health care and education stakeholder groups, recognizing the need to prepare learners for collaborative practice in complex care environments, have called for innovative approaches in health care education. Team-based learning is an educational method that relies on in-depth student preparation prior to class, individual and team knowledge assessment, and use of small-group learning to apply knowledge to complex scenarios. Although team-based learning has been studied as an approach to health care education, its application to midwifery education is not well described. A master's-level, nurse-midwifery, didactic antepartum course was revised to a team-based learning format. Student grades, course evaluations, and aggregate American Midwifery Certification Board examination pass rates for 3 student cohorts participating in the team-based course were compared with 3 student cohorts receiving traditional, lecture-based instruction. Students had mixed responses to the team-based learning format. Student evaluations improved when faculty added recorded lectures as part of student preclass preparation. Statistical comparisons were limited by variations across cohorts; however, student grades and certification examination pass rates did not change substantially after the course revision. Although initial course revision was time-consuming for faculty, subsequent iterations of the course required less effort. Team-based learning provides students with more opportunity to interact during on-site classes and may spur application of knowledge into practice. However, it is difficult to assess the effect of the team-based learning approach with current measures. Further research is needed to determine the effects of team-based learning on communication and collaboration skills, as well as long-term performance in clinical practice. This article is part of a special series of articles that address midwifery innovations in clinical practice, education, interprofessional

PENGARUH STRATEGI PROJECT BASED LEARNING (PJBL TERHADAP KEMAMPUAN BERPIKIR KRITIS SISWA KELAS XI IPA PADA MATERI KOLOID

Directory of Open Access Journals (Sweden)

Nur Hikmah

2016-11-01

Full Text Available The 21st century education aims to develop the ability of intelligence of students in order to resolve the problems faced in real life. Project-Based Learning is one instructional strategies to develop the skills required in the 21st century. Through a given project, students are not only required to achieve the learning objectives that have been set, but students will be trained to face the world of work that requires their ability to access, mesintesis, communicating information, and work together to solve complex problems so as to improve the ability of students critical thinking. This research is a quasi-experimental research (quasy experiment with posttest only control group design. This research aims to determine the influence of Project Based Learning Strategy (PjBL to the critical thinking skills of students of class XI IPA at SMAN 1 Malua on colloidal material. The research population includes students of class XI IPA at SMAN 1 Malua, with random cluster sampling technique sampling. Data were analyzed using independent sample t-test in SPSS 20 for windows at the 0.05 level of significance. The result showed that the significance level of critical thinking skills of 0.001 which indicates that there are differences between students' critical thinking skills that learned using a strategy of Project Based Learning (PjBL with students that learned using conventional methods. Pendidikan abad 21 bertujuan untuk membangun kemampuan intelegensi siswa dalam pembelajaran agar mampu menyelesaikan permasalahan yang dihadapi. salah satu strategi pembelaj aran di abad 21 yang mengembangkan keterampilan siswa ialah Project Based Learning. Melalui proyek yang diberikan, siswa tidak hanya dituntut untuk mencapai tujuan pembelajaran yang telah ditetapkan, tetapi siswa akan lebih terlatih menghadapi dunia kerja yang membutuhkan kemampuan mereka dalam mengakses, mesintesis, mengomunikasikan infomasi, dan bekerja sama memecahkan masalah yang kompleks

Learning Markov Decision Processes for Model Checking

DEFF Research Database (Denmark)

Mao, Hua; Chen, Yingke; Jaeger, Manfred

2012-01-01

. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...

Characteristics of Israeli School Teachers in Computer-based Learning Environments

Directory of Open Access Journals (Sweden)

Noga Magen-Nagar

2013-01-01

Full Text Available The purpose of this research is to investigate whether there are differences in the level of computer literacy, the amount of implementation of ICT in teaching and learning-assessment processes and the attitudes of teachers from computerized schools in comparison to teachers in non-computerized schools. In addition, the research investigates the characteristics of Israeli school teachers in a 21st century computer-based learning environment. A quantitative research methodology was used. The research sample included 811 elementary school teachers from the Jewish sector of whom 402 teachers were from the computerized school sample and 409 were teachers from the non-computerized school sample. The research findings show that teachers from the computerized school sample are more familiar with ICT, tend to use ICT more and have a more positive attitude towards ICT than teachers in the non-computerized school sample. The main conclusion which can be drawn from this research is that positive attitudes of teachers towards ICT are not sufficient for the integration of technology to occur. Future emphasis on new teaching skills of collective Technological Pedagogical Content Knowledge is necessary to promote the implementation of optimal pedagogy in innovative environments.

Cloud-Based Mobile Learning

Directory of Open Access Journals (Sweden)

Alexandru BUTOI

2013-01-01

Full Text Available As the cloud technologies are largely studied and mobile technologies are evolving, new di-rections for development of mobile learning tools deployed on cloud are proposed.. M-Learning is treated as part of the ubiquitous learning paradigm and is a pervasive extension of E-Learning technologies. Development of such learning tools requires specific development strategies for an effective abstracting of pedagogical principles at the software design and implementation level. Current paper explores an interdisciplinary approach for designing and development of cloud based M-Learning tools by mapping a specific development strategy used for educational programs to software prototyping strategy. In order for such instruments to be user effective from the learning outcome point of view, the evaluation process must be rigorous as we propose a metric model for expressing the trainee’s overall learning experience with evaluated levels of interactivity, content presentation and graphical user interface usability.

Personalised Learning Object System Based on Self-Regulated Learning Theories

Directory of Open Access Journals (Sweden)

Ali Alharbi

2014-06-01

Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

Supporting Case-Based Learning in Information Security with Web-Based Technology

Science.gov (United States)

He, Wu; Yuan, Xiaohong; Yang, Li

2013-01-01

Case-based learning has been widely used in many disciplines. As an effective pedagogical method, case-based learning is also being used to support teaching and learning in the domain of information security. In this paper, we demonstrate case-based learning in information security by sharing our experiences in using a case study to teach security…

Dictionary learning for data recovery in positron emission tomography

International Nuclear Information System (INIS)

Valiollahzadeh, SeyyedMajid; Clark, John W Jr; Mawlawi, Osama

2015-01-01

Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients’ studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification. (paper)

Prevalence of learned grapheme-color pairings in a large online sample of synesthetes.

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Nathan Witthoft

Full Text Available In this paper we estimate the minimum prevalence of grapheme-color synesthetes with letter-color matches learned from an external stimulus, by analyzing a large sample of English-speaking grapheme-color synesthetes. We find that at least 6% (400/6588 participants of the total sample learned many of their matches from a widely available colored letter toy. Among those born in the decade after the toy began to be manufactured, the proportion of synesthetes with learned letter-color pairings approaches 15% for some 5-year periods. Among those born 5 years or more before it was manufactured, none have colors learned from the toy. Analysis of the letter-color matching data suggests the only difference between synesthetes with matches to the toy and those without is exposure to the stimulus. These data indicate learning of letter-color pairings from external contingencies can occur in a substantial fraction of synesthetes, and are consistent with the hypothesis that grapheme-color synesthesia is a kind of conditioned mental imagery.

Prediction of Path Deviation in Robot Based Incremental Sheet Metal Forming by Means of a New Solid-Shell Finite Element Technology and a Finite Elastoplastic Model with Combined Hardening

Science.gov (United States)

Kiliclar, Yalin; Laurischkat, Roman; Vladimirov, Ivaylo N.; Reese, Stefanie

2011-08-01

The presented project deals with a robot based incremental sheet metal forming process, which is called roboforming and has been developed at the Chair of Production Systems. It is characterized by flexible shaping using a freely programmable path-synchronous movement of two industrial robots. The final shape is produced by the incremental infeed of the forming tool in depth direction and its movement along the part contour in lateral direction. However, the resulting geometries formed in roboforming deviate several millimeters from the reference geometry. This results from the compliance of the involved machine structures and the springback effects of the workpiece. The project aims to predict these deviations caused by resiliences and to carry out a compensative path planning based on this prediction. Therefore a planning tool is implemented which compensates the robots's compliance and the springback effects of the sheet metal. The forming process is simulated by means of a finite element analysis using a material model developed at the Institute of Applied Mechanics (IFAM). It is based on the multiplicative split of the deformation gradient in the context of hyperelasticity and combines nonlinear kinematic and isotropic hardening. Low-order finite elements used to simulate thin sheet structures, such as used for the experiments, have the major problem of locking, a nonphysical stiffening effect. For an efficient finite element analysis a special solid-shell finite element formulation based on reduced integration with hourglass stabilization has been developed. To circumvent different locking effects, the enhanced assumed strain (EAS) and the assumed natural strain (ANS) concepts are included in this formulation. Having such powerful tools available we obtain more accurate geometries.

Managing the Gap between Curriculum Based and Problem Based Learning

DEFF Research Database (Denmark)

Bygholm, Ann; Buus, Lillian

2009-01-01

/or but rather both/and. In this paper we describe an approach to design and delivery of online courses in computer science which on the one hand is based on a specified curriculum and on the other hand gives room for different learning strategies, problem based learning being one of them. We discuss......Traditionally there has been a clear distinction between curriculum based and problem based approaches to accomplish learning. Preferred approaches depend of course on conviction, culture, traditions and also on the specific learning situation. We will argue that it is not a question of either...

The use of a mobile assistant learning system for health education based on project-based learning.

Science.gov (United States)

Wu, Ting-Ting

2014-10-01

With the development of mobile devices and wireless technology, mobile technology has gradually infiltrated nursing practice courses to facilitate instruction. Mobile devices save manpower and reduce errors while enhancing nursing students' professional knowledge and skills. To achieve teaching objectives and address the drawbacks of traditional education, this study presents a mobile assistant learning system to help nursing students prepare health education materials. The proposed system is based on a project-based learning strategy to assist nursing students with internalizing professional knowledge and developing critical thinking skills. Experimental results show that the proposed mobile system and project-based learning strategy can promote learning effectiveness and efficiency. Most nursing students and nursing educators showed positive attitudes toward this mobile learning system and looked forward to using it again in related courses in the future.

Video-Based Surgical Learning: Improving Trainee Education and Preparation for Surgery.

Science.gov (United States)

Mota, Paulo; Carvalho, Nuno; Carvalho-Dias, Emanuel; João Costa, Manuel; Correia-Pinto, Jorge; Lima, Estevão

2017-10-11

Since the end of the XIX century, teaching of surgery has remained practically unaltered until now. With the dawn of video-assisted laparoscopy, surgery has faced new technical and learning challenges. Due to technological advances, from Internet access to portable electronic devices, the use of online resources is part of the educational armamentarium. In this respect, videos have already proven to be effective and useful, however the best way to benefit from these tools is still not clearly defined. To assess the importance of video-based learning, using an electronic questionnaire applied to residents and specialists of different surgical fields. Importance of video-based learning was assessed in a sample of 141 subjects, using a questionnaire distributed by a GoogleDoc online form. We found that 98.6% of the respondents have already used videos to prepare for surgery. When comparing video sources by formation status, residents were found to use Youtube significantly more often than specialists (p learning is currently a hallmark of surgical preparation among residents and specialists working in Portugal. Based on these findings we believe that the creation of quality and scientifically accurate videos, and subsequent compilation in available video-libraries appears to be the future landscape for video-based learning. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

The Effectiveness of Problem-Based Learning in the Web-Based Environment for the Delivery of an Undergraduate Physics Course

Science.gov (United States)

Atan, Hanafi; Sulaiman, Fauziah; Idrus, Rozhan M.

2005-01-01

This paper reports the investigation of the effectiveness of Problem-Based Learning (PBL) within a web-based environment in the delivery of an undergraduate Physics course. The effectiveness was evaluated by comparing the performances and the perceptions of the sample students (n=67) using the web-based PBL and comparing the outcomes with those of…

A sliding point contact model for the finite element structures code EURDYN

International Nuclear Information System (INIS)

Smith, B.L.

1986-01-01

A method is developed by which sliding point contact between two moving deformable structures may be incorporated within a lumped mass finite element formulation based on displacements. The method relies on a simple mechanical interpretation of the contact constraint in terms of equivalent nodal forces and avoids the use of nodal connectivity via a master slave arrangement or pseudo contact element. The methodology has been iplemented into the EURDYN finite element program for the (2D axisymmetric) version coupled to the hydro code SEURBNUK. Sample calculations are presented illustrating the use of the model in various contact situations. Effects due to separation and impact of structures are also included. (author)

Understanding compressive deformation behavior of porous Ti using finite element analysis

Energy Technology Data Exchange (ETDEWEB)

Roy, Sandipan; Khutia, Niloy [Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur (India); Das, Debdulal [Department of Metallurgy and Materials Engineering, Indian Institute of Engineering Science and Technology, Shibpur (India); Das, Mitun, E-mail: mitun@cgcri.res.in [Bioceramics and Coating Division, CSIR-Central Glass and Ceramic Research Institute, Kolkata (India); Balla, Vamsi Krishna [Bioceramics and Coating Division, CSIR-Central Glass and Ceramic Research Institute, Kolkata (India); Bandyopadhyay, Amit [W. M. Keck Biomedical Materials Research Laboratory, School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164 (United States); Chowdhury, Amit Roy, E-mail: arcbesu@gmail.com [Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur (India)

2016-07-01

In the present study, porous commercially pure (CP) Ti samples with different volume fraction of porosities were fabricated using a commercial additive manufacturing technique namely laser engineered net shaping (LENS™). Mechanical behavior of solid and porous samples was evaluated at room temperature under quasi-static compressive loading. Fracture surfaces of the failed samples were analyzed to determine the failure modes. Finite Element (FE) analysis using representative volume element (RVE) model and micro-computed tomography (CT) based model have been performed to understand the deformation behavior of laser deposited solid and porous CP-Ti samples. In vitro cell culture on laser processed porous CP-Ti surfaces showed normal cell proliferation with time, and confirmed non-toxic nature of these samples. - Highlights: • Porous CP-Ti samples fabricated using additive manufacturing technique • Compressive deformation behavior of porous samples closely matches with micro-CT and RVE based analysis • In vitro studies showed better cell proliferation with time on porous CP-Ti surfaces.

Understanding compressive deformation behavior of porous Ti using finite element analysis

International Nuclear Information System (INIS)

Roy, Sandipan; Khutia, Niloy; Das, Debdulal; Das, Mitun; Balla, Vamsi Krishna; Bandyopadhyay, Amit; Chowdhury, Amit Roy

2016-01-01

In the present study, porous commercially pure (CP) Ti samples with different volume fraction of porosities were fabricated using a commercial additive manufacturing technique namely laser engineered net shaping (LENS™). Mechanical behavior of solid and porous samples was evaluated at room temperature under quasi-static compressive loading. Fracture surfaces of the failed samples were analyzed to determine the failure modes. Finite Element (FE) analysis using representative volume element (RVE) model and micro-computed tomography (CT) based model have been performed to understand the deformation behavior of laser deposited solid and porous CP-Ti samples. In vitro cell culture on laser processed porous CP-Ti surfaces showed normal cell proliferation with time, and confirmed non-toxic nature of these samples. - Highlights: • Porous CP-Ti samples fabricated using additive manufacturing technique • Compressive deformation behavior of porous samples closely matches with micro-CT and RVE based analysis • In vitro studies showed better cell proliferation with time on porous CP-Ti surfaces

Finite element design for the HPHT synthesis of diamond

Science.gov (United States)

Li, Rui; Ding, Mingming; Shi, Tongfei

2018-06-01

The finite element method is used to simulate the steady-state temperature field in diamond synthesis cell. The 2D and 3D models of the China-type cubic press with large deformation of the synthesis cell was established successfully, which has been verified by situ measurements of synthesis cell. The assembly design, component design and process design for the HPHT synthesis of diamond based on the finite element simulation were presented one by one. The temperature field in a high-pressure synthetic cavity for diamond production is optimized by adjusting the cavity assembly. A series of analysis about the influence of the pressure media parameters on the temperature field are examined through adjusting the model parameters. Furthermore, the formation mechanism of wasteland was studied in detail. It indicates that the wasteland is inevitably exists in the synthesis sample, the distribution of growth region of the diamond with hex-octahedral is move to the center of the synthesis sample from near the heater as the power increasing, and the growth conditions of high quality diamond is locating at the center of the synthesis sample. These works can offer suggestion and advice to the development and optimization of a diamond production process.

Adding Social Elements to Game-Based Learning

Directory of Open Access Journals (Sweden)

Chien-Hung Lai

2014-05-01

Full Text Available Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners’ motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenarios in games with proper courses. However, in the past game-based learning, students were gathered in regular places for several times of game-based learning. Students’ learning was limited by time and space. Therefore, for students’ game-based learning at any time and in any places, based on theories of design elements of online community game Aki Järvinen, this study treats Facebook as the platform of games. The development by online community game is easier, faster and cheaper than traditional video games. In 2006, Facebook allowed API program of the third party. Therefore, by Facebook, this study provides the platform for students to learn in social lives to explore students’ activities in online community games. Questionnaire survey is conducted to find out if the design of non-single user game is attractive for students to participate in game-based learning. In order to make sure that the questionnaires can be the criteria to investigate students’ intention to play games, by statistical program of social science; this study validates reliability and validity of items of questionnaire to effectively control the effect of online community games on students’ learning intention.

Learning Theory Foundations of Simulation-Based Mastery Learning.

Science.gov (United States)

McGaghie, William C; Harris, Ilene B

2018-06-01

Simulation-based mastery learning (SBML), like all education interventions, has learning theory foundations. Recognition and comprehension of SBML learning theory foundations are essential for thoughtful education program development, research, and scholarship. We begin with a description of SBML followed by a section on the importance of learning theory foundations to shape and direct SBML education and research. We then discuss three principal learning theory conceptual frameworks that are associated with SBML-behavioral, constructivist, social cognitive-and their contributions to SBML thought and practice. We then discuss how the three learning theory frameworks converge in the course of planning, conducting, and evaluating SBML education programs in the health professions. Convergence of these learning theory frameworks is illustrated by a description of an SBML education and research program in advanced cardiac life support. We conclude with a brief coda.

Model-Based Learning Environment Based on The Concept IPS School-Based Management

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Hamid Darmadi

2017-03-01

Full Text Available The results showed: (1 learning model IPS-oriented environment can grow and not you love the cultural values of the area as a basis for the development of national culture, (2 community participation, and the role of government in implementing learning model of IPS-based environment provides a positive impact for the improvement of management school resources, (3 learning model IPS-based environment effectively creating a way of life together peacefully, increase the intensity of togetherness and mutual respect (4 learning model IPS-based environment can improve student learning outcomes, (5 there are differences in the expression of attitudes and results learning among students who are located in the area of conflict with students who are outside the area of conflict (6 analysis of the scale of attitudes among school students da SMA result rewards high school students to the values of unity and nation, respect for diversity and peaceful coexistence, It is recommended that the Department of Education authority as an institution of Trustees and the development of social and cultural values in the province can apply IPS learning model based environments.

Seismic wavefield modeling based on time-domain symplectic and Fourier finite-difference method

Science.gov (United States)

Fang, Gang; Ba, Jing; Liu, Xin-xin; Zhu, Kun; Liu, Guo-Chang

2017-06-01

Seismic wavefield modeling is important for improving seismic data processing and interpretation. Calculations of wavefield propagation are sometimes not stable when forward modeling of seismic wave uses large time steps for long times. Based on the Hamiltonian expression of the acoustic wave equation, we propose a structure-preserving method for seismic wavefield modeling by applying the symplectic finite-difference method on time grids and the Fourier finite-difference method on space grids to solve the acoustic wave equation. The proposed method is called the symplectic Fourier finite-difference (symplectic FFD) method, and offers high computational accuracy and improves the computational stability. Using acoustic approximation, we extend the method to anisotropic media. We discuss the calculations in the symplectic FFD method for seismic wavefield modeling of isotropic and anisotropic media, and use the BP salt model and BP TTI model to test the proposed method. The numerical examples suggest that the proposed method can be used in seismic modeling of strongly variable velocities, offering high computational accuracy and low numerical dispersion. The symplectic FFD method overcomes the residual qSV wave of seismic modeling in anisotropic media and maintains the stability of the wavefield propagation for large time steps.

Measuring strategies for learning regulation in medical education: Scale reliability and dimensionality in a Swedish sample

Directory of Open Access Journals (Sweden)

Edelbring Samuel

2012-08-01

Full Text Available Abstract Background The degree of learners’ self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206. Methods The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Results Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach’s alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. Discussion The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students’ regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

Transforming Classrooms through Game-Based Learning: A Feasibility Study in a Developing Country

Science.gov (United States)

Vate-U-Lan, Poonsri

2015-01-01

This article reports an exploratory study which investigated attitudes towards the practice of game-based learning in teaching STEM (science, technology, engineering and mathematics) within a Thai educational context. This self-administered Internet-based survey yielded 169 responses from a snowball sampling technique. Three fifths of respondents…

Finite N=1 SUSY gauge field theories

International Nuclear Information System (INIS)

Kazakov, D.I.

1986-01-01

The authors give a detailed description of the method to construct finite N=1 SUSY gauge field theories in the framework of N=1 superfields within dimensional regularization. The finiteness of all Green functions is based on supersymmetry and gauge invariance and is achieved by a proper choice of matter content of the theory and Yukawa couplings in the form Y i =f i (ε)g, where g is the gauge coupling, and the function f i (ε) is regular at ε=0 and is calculated in perturbation theory. Necessary and sufficient conditions for finiteness are determined already in the one-loop approximation. The correspondence with an earlier proposed approach to construct finite theories based on aigenvalue solutions of renormalization-group equations is established

Brain-Based Learning and Standards-Based Elementary Science.

Science.gov (United States)

Konecki, Loretta R.; Schiller, Ellen

This paper explains how brain-based learning has become an area of interest to elementary school science teachers, focusing on the possible relationships between, and implications of, research on brain-based learning to the teaching of science education standards. After describing research on the brain, the paper looks at three implications from…

Finite-time and finite-size scalings in the evaluation of large-deviation functions: Numerical approach in continuous time.

Science.gov (United States)

Guevara Hidalgo, Esteban; Nemoto, Takahiro; Lecomte, Vivien

2017-06-01

Rare trajectories of stochastic systems are important to understand because of their potential impact. However, their properties are by definition difficult to sample directly. Population dynamics provides a numerical tool allowing their study, by means of simulating a large number of copies of the system, which are subjected to selection rules that favor the rare trajectories of interest. Such algorithms are plagued by finite simulation time and finite population size, effects that can render their use delicate. In this paper, we present a numerical approach which uses the finite-time and finite-size scalings of estimators of the large deviation functions associated to the distribution of rare trajectories. The method we propose allows one to extract the infinite-time and infinite-size limit of these estimators, which-as shown on the contact process-provides a significant improvement of the large deviation function estimators compared to the standard one.

Finite-time and finite-size scalings in the evaluation of large-deviation functions: Numerical approach in continuous time

Science.gov (United States)

Guevara Hidalgo, Esteban; Nemoto, Takahiro; Lecomte, Vivien

2017-06-01

Rare trajectories of stochastic systems are important to understand because of their potential impact. However, their properties are by definition difficult to sample directly. Population dynamics provides a numerical tool allowing their study, by means of simulating a large number of copies of the system, which are subjected to selection rules that favor the rare trajectories of interest. Such algorithms are plagued by finite simulation time and finite population size, effects that can render their use delicate. In this paper, we present a numerical approach which uses the finite-time and finite-size scalings of estimators of the large deviation functions associated to the distribution of rare trajectories. The method we propose allows one to extract the infinite-time and infinite-size limit of these estimators, which—as shown on the contact process—provides a significant improvement of the large deviation function estimators compared to the standard one.

Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis.

Science.gov (United States)

Schenkl, Sebastian; Muggenthaler, Holger; Hubig, Michael; Erdmann, Bodo; Weiser, Martin; Zachow, Stefan; Heinrich, Andreas; Güttler, Felix Victor; Teichgräber, Ulf; Mall, Gita

2017-05-01

Temperature-based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex but allow a higher accuracy of death time estimation, as in principle, all relevant cooling mechanisms can be taken into account.Here, a complete workflow for finite element-based cooling simulation is presented. The following steps are demonstrated on a CT phantom: Computer tomography (CT) scan Segmentation of the CT images for thermodynamically relevant features of individual geometries and compilation in a geometric computer-aided design (CAD) model Conversion of the segmentation result into a finite element (FE) simulation model Computation of the model cooling curve (MOD) Calculation of the cooling time (CTE) For the first time in FE-based cooling time estimation, the steps from the CT image over segmentation to FE model generation are performed semi-automatically. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using a CT phantom. Some of the phantoms' thermodynamic material parameters had to be determined via independent experiments.Moreover, the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.

'To Philosophize is to Learn How to Die?'

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Saitya Brata Das

2008-06-01

Full Text Available Philosophical thinking, as it is thinking of existence, is essentially finite thinking. This is to say that as thinking of existence, philosophical thinking is essentially also thinking of finitude. This ‘also' is not the accidental relationship between existence and finitude. Rather, to think existence in its finitude, insofar as existence is finite, is to think existence in its existentiality. Philosophy that gives itself the task of thinking the relationship between existence and finitude, must in the same gesture, be concerned with its own finitude: to philosophize is not only to think the finitude of existence, but the very finitude of thinking that thinks finite existence. To philosophize is not only to philosophize the finitude of existence as such, but also in so far as philosophising itself is a task which is essentially in itself finite. To assume as the task of thinking the finitude of existence is to think the very finitude of philosophical thinking: this is the profound relationship that exists between existence and philosophy, which is that philosophizing existence and an existential philosophy are essentially finite. This is perhaps what Socrates says of philosophizing: ‘to philosophize is to learn how to die.' "To philosophize is to learn how to die": this is to say, to philosophize is to learn that philosophy and existence are essentially finite. Philosophy and existence belong to finitude and gifts of finitude; therefore to philosophize is to learn how existence is this gift. To be able to learn how existence is this gift of finitude, to be able to assume this gift that makes existence essentially finite, which is to be able to assume existence at all, is to be able to die.' Learning to die' then comes to signify the ability of dying, which is in the same gesture, the ability of existing: existence, and dying at the end must be this ability, of existing and dying. Philosophizing must provide, then, the learning of this

The Impact of Problem-Based Learning on Iranian EFL Learners’ Speaking Proficiency

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Loghman Ansarian

2016-06-01

Full Text Available The study investigated the effect of problem-based learning through cognition-based tasks on speaking proficiency of Iranian intermediate EFL learners in comparison to the effect of objective-based tasks. To this end, a true experimental research design was employed. Ninety five (N=95 language learners studying at a language institute in the city of Esfahan, Iran were given an IELTS listening and speaking test as the proficiency test and 75 learners were selected. In the next phase of the study, a second IELTS speaking test was administered as the homogeneity test and the pre-test to seventy five (N=75 learners chosen from the population and forty-eight (N=48 homogeneous intermediate learners were selected for the study (i.e., 24 learners in control group and 24 in experimental one. The results of an independent-sample t-test gained from the study proved that not only does implementation of problem-based learning through cognition-based tasks significantly increased intermediate participants’ speaking proficiency, but also it had more positive effect in comparison to objective-based tasks on participants’ speaking proficiency. Therefore, it is suggested that problem-based learning ought to be taken into account by educational scholars, those in charge of syllabus, material producers, language teachers and language learners. Keywords: Cognition-Based Tasks, Objective-Based Tasks, EFL Learners, Speaking Proficiency, Problem-Based Learning

Transient finite element magnetic field calculation method in the anisotropic magnetic material based on the measured magnetization curves

International Nuclear Information System (INIS)

Jesenik, M.; Gorican, V.; Trlep, M.; Hamler, A.; Stumberger, B.

2006-01-01

A lot of magnetic materials are anisotropic. In the 3D finite element method calculation, anisotropy of the material is taken into account. Anisotropic magnetic material is described with magnetization curves for different magnetization directions. The 3D transient calculation of the rotational magnetic field in the sample of the round rotational single sheet tester with circular sample considering eddy currents is made and compared with the measurement to verify the correctness of the method and to analyze the magnetic field in the sample

Development of a finite-element-based design sensitivity analysis for buckling and postbuckling of composite plates

Directory of Open Access Journals (Sweden)

Guo Ruijiang

1995-01-01

Full Text Available A finite element based sensitivity analysis procedure is developed for buckling and postbuckling of composite plates. This procedure is based on the direct differentiation approach combined with the reference volume concept. Linear elastic material model and nonlinear geometric relations are used. The sensitivity analysis technique results in a set of linear algebraic equations which are easy to solve. The procedure developed provides the sensitivity derivatives directly from the current load and responses by solving the set of linear equations. Numerical results are presented and are compared with those obtained using finite difference technique. The results show good agreement except at points near critical buckling load where discontinuities occur. The procedure is very efficient computationally.

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.

Graph sampling

OpenAIRE

Zhang, L.-C.; Patone, M.

2017-01-01

We synthesise the existing theory of graph sampling. We propose a formal definition of sampling in finite graphs, and provide a classification of potential graph parameters. We develop a general approach of Horvitz–Thompson estimation to T-stage snowball sampling, and present various reformulations of some common network sampling methods in the literature in terms of the outlined graph sampling theory.

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

Science.gov (United States)

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Digital game-based learning in secondary education

NARCIS (Netherlands)

Huizenga, J.C.

2017-01-01

This PhD thesis presents research on digital game-based learning in secondary education. The main research question is: How do digital games contribute to learning, engagement and motivation to learn? The thesis contains seven chapters. Chapter one is an introduction to digital game-based learning

Learning-based diagnosis and repair

NARCIS (Netherlands)

Roos, Nico

2017-01-01

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

Music Learning Based on Computer Software

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Baihui Yan

2017-12-01

Full Text Available In order to better develop and improve students’ music learning, the authors proposed the method of music learning based on computer software. It is still a new field to use computer music software to assist teaching. Hereby, we conducted an in-depth analysis on the computer-enabled music learning and the music learning status in secondary schools, obtaining the specific analytical data. Survey data shows that students have many cognitive problems in the current music classroom, and yet teachers have not found a reasonable countermeasure to them. Against this background, the introduction of computer music software to music learning is a new trial that can not only cultivate the students’ initiatives of music learning, but also enhance their abilities to learn music. Therefore, it is concluded that the computer software based music learning is of great significance to improving the current music learning modes and means.

Mixed finite element-based fully conservative methods for simulating wormhole propagation

KAUST Repository

Kou, Jisheng; Sun, Shuyu; Wu, Yuanqing

2015-01-01

Wormhole propagation during reactive dissolution of carbonates plays a very important role in the product enhancement of oil and gas reservoir. Because of high velocity and nonuniform porosity, the Darcy–Forchheimer model is applicable for this problem instead of conventional Darcy framework. We develop a mixed finite element scheme for numerical simulation of this problem, in which mixed finite element methods are used not only for the Darcy–Forchheimer flow equations but also for the solute transport equation by introducing an auxiliary flux variable to guarantee full mass conservation. In theoretical analysis aspects, based on the cut-off operator of solute concentration, we construct an analytical function to control and handle the change of porosity with time; we treat the auxiliary flux variable as a function of velocity and establish its properties; we employ the coupled analysis approach to deal with the fully coupling relation of multivariables. From this, the stability analysis and a priori error estimates for velocity, pressure, concentration and porosity are established in different norms. Numerical results are also given to verify theoretical analysis and effectiveness of the proposed scheme.

Mixed finite element-based fully conservative methods for simulating wormhole propagation

KAUST Repository

Kou, Jisheng

2015-10-11

Wormhole propagation during reactive dissolution of carbonates plays a very important role in the product enhancement of oil and gas reservoir. Because of high velocity and nonuniform porosity, the Darcy–Forchheimer model is applicable for this problem instead of conventional Darcy framework. We develop a mixed finite element scheme for numerical simulation of this problem, in which mixed finite element methods are used not only for the Darcy–Forchheimer flow equations but also for the solute transport equation by introducing an auxiliary flux variable to guarantee full mass conservation. In theoretical analysis aspects, based on the cut-off operator of solute concentration, we construct an analytical function to control and handle the change of porosity with time; we treat the auxiliary flux variable as a function of velocity and establish its properties; we employ the coupled analysis approach to deal with the fully coupling relation of multivariables. From this, the stability analysis and a priori error estimates for velocity, pressure, concentration and porosity are established in different norms. Numerical results are also given to verify theoretical analysis and effectiveness of the proposed scheme.

Learning from Past Classification Errors: Exploring Methods for Improving the Performance of a Deep Learning-based Building Extraction Model through Quantitative Analysis of Commission Errors for Optimal Sample Selection

Science.gov (United States)

Swan, B.; Laverdiere, M.; Yang, L.

2017-12-01

In the past five years, deep Convolutional Neural Networks (CNN) have been increasingly favored for computer vision applications due to their high accuracy and ability to generalize well in very complex problems; however, details of how they function and in turn how they may be optimized are still imperfectly understood. In particular, their complex and highly nonlinear network architecture, including many hidden layers and self-learned parameters, as well as their mathematical implications, presents open questions about how to effectively select training data. Without knowledge of the exact ways the model processes and transforms its inputs, intuition alone may fail as a guide to selecting highly relevant training samples. Working in the context of improving a CNN-based building extraction model used for the LandScan USA gridded population dataset, we have approached this problem by developing a semi-supervised, highly-scalable approach to select training samples from a dataset of identified commission errors. Due to the large scope this project, tens of thousands of potential samples could be derived from identified commission errors. To efficiently trim those samples down to a manageable and effective set for creating additional training sample, we statistically summarized the spectral characteristics of areas with rates of commission errors at the image tile level and grouped these tiles using affinity propagation. Highly representative members of each commission error cluster were then used to select sites for training sample creation. The model will be incrementally re-trained with the new training data to allow for an assessment of how the addition of different types of samples affects the model performance, such as precision and recall rates. By using quantitative analysis and data clustering techniques to select highly relevant training samples, we hope to improve model performance in a manner that is resource efficient, both in terms of training process

Modelling robot's behaviour using finite automata

Science.gov (United States)

Janošek, Michal; Žáček, Jaroslav

2017-07-01

This paper proposes a model of a robot's behaviour described by finite automata. We split robot's knowledge into several knowledge bases which are used by the inference mechanism of the robot's expert system to make a logic deduction. Each knowledgebase is dedicated to the particular behaviour domain and the finite automaton helps us switching among these knowledge bases with the respect of actual situation. Our goal is to simplify and reduce complexity of one big knowledgebase splitting it into several pieces. The advantage of this model is that we can easily add new behaviour by adding new knowledgebase and add this behaviour into the finite automaton and define necessary states and transitions.

Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

DEFF Research Database (Denmark)

Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

2013-01-01

Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

A Learning Object Approach To Evidence based learning

OpenAIRE

Zabin Visram; Bruce Elson; Patricia Reynolds

2005-01-01

This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has mea...

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets.

Directory of Open Access Journals (Sweden)

Der-Chiang Li

Full Text Available It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP method merging in the D3C method (PPDP+D3C with those of the one-sided selection (OSS, the well-known SMOTEBoost (SB study, and the normal distribution-based oversampling (NDO approach, and the proposed data pre-processing (PPDP method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.

Toward Project-based Learning and Team Formation in Open Learning Environments

NARCIS (Netherlands)

Spoelstra, Howard; Van Rosmalen, Peter; Sloep, Peter

2014-01-01

Open Learning Environments, MOOCs, as well as Social Learning Networks, embody a new approach to learning. Although both emphasise interactive participation, somewhat surprisingly, they do not readily support bond creating and motivating collaborative learning opportunities. Providing project-based

A hybrid finite-volume and finite difference scheme for depth-integrated non-hydrostatic model

Science.gov (United States)

Yin, Jing; Sun, Jia-wen; Wang, Xing-gang; Yu, Yong-hai; Sun, Zhao-chen

2017-06-01

A depth-integrated, non-hydrostatic model with hybrid finite difference and finite volume numerical algorithm is proposed in this paper. By utilizing a fraction step method, the governing equations are decomposed into hydrostatic and non-hydrostatic parts. The first part is solved by using the finite volume conservative discretization method, whilst the latter is considered by solving discretized Poisson-type equations with the finite difference method. The second-order accuracy, both in time and space, of the finite volume scheme is achieved by using an explicit predictor-correction step and linear construction of variable state in cells. The fluxes across the cell faces are computed in a Godunov-based manner by using MUSTA scheme. Slope and flux limiting technique is used to equip the algorithm with total variation dimensioning property for shock capturing purpose. Wave breaking is treated as a shock by switching off the non-hydrostatic pressure in the steep wave front locally. The model deals with moving wet/dry front in a simple way. Numerical experiments are conducted to verify the proposed model.

Teachers' Attitudes toward Web-Based Professional Development, with Relation to Internet Self-Efficacy and Beliefs about Web-Based Learning

Science.gov (United States)

Kao, Chia-Pin; Tsai, Chin-Chung

2009-01-01

This study was conducted to explore the relationships between teachers' Internet self-efficacy, beliefs about web-based learning and attitudes toward web-based professional development. The sample of this study included 421 teachers, coming from 20 elementary schools in Taiwan. The three instruments used to assess teachers' Internet self-efficacy…

Motion Learning Based on Bayesian Program Learning

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Cheng Meng-Zhen

2017-01-01

Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.

Concept-Based Learning in Clinical Experiences: Bringing Theory to Clinical Education for Deep Learning.

Science.gov (United States)

Nielsen, Ann

2016-07-01

Concept-based learning is used increasingly in nursing education to support the organization, transfer, and retention of knowledge. Concept-based learning activities (CBLAs) have been used in clinical education to explore key aspects of the patient situation and principles of nursing care, without responsibility for total patient care. The nature of best practices in teaching and the resultant learning are not well understood. The purpose of this multiple-case study research was to explore and describe concept-based learning in the context of clinical education in inpatient settings. Four clinical groups (each a case) were observed while they used CBLAs in the clinical setting. Major findings include that concept-based learning fosters deep learning, connection of theory with practice, and clinical judgment. Strategies used to support learning, major teaching-learning foci, and preconditions for concept-based teaching and learning will be described. Concept-based learning is promising to support integration of theory with practice and clinical judgment through application experiences with patients. [J Nurs Educ. 2016;55(7):365-371.]. Copyright 2016, SLACK Incorporated.

From scientifically based research to evidence based learning

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Rosa Cera

2016-02-01

Full Text Available This essay is a reflection on the peculiarities of the scientifically based research and on the distinctive elements of the EBL (evidence based learning, methodology used in the study on the “Relationship between Metacognition, Self-efficacy and Self-regulation in Learning”. The EBL method, based on the standardization of data, explains how the students’ learning experience can be considered as a set of “data” and can be used to explain how and when the research results can be considered generalizable and transferable to other learning situations. The reflections present in this study have also allowed us to illustrate the impact that its results have had on the micro and macro level of reality. They helped to fill in the gaps concerning the learning/teaching processes, contributed to the enrichment of the scientific literature on this subject and allowed to establish standards through rigorous techniques such as systematic reviews and meta-analysis.

Constructivism Based Learning: Design and Practice

Directory of Open Access Journals (Sweden)

Lia Kurniawati

2016-06-01

Full Text Available Abstract One of many problems in the madrasahs is that learning processes less-involve students actively (teacher-centered, thus, it affects to the improvement of learning outcomes and quality of the graduates. The purposes of this study are , firstly, to analyze what type of constructivism learning models, which can be developed to overcome madrasahs’ problems. Secondly, how to design and implement a learning plan based on the developed constructivism models. This research was conducted at Private Islamic Elementary School  (Madrasah Ad-Diyanah Ciputat, South Tangerang. Research method used in this study is descriptive-qualitative research. The results showed that the active learning models based on constructivism are suitable to be developed in the Madarasah, which were the models of Problem Based Learning (PBM, Realistic Learning, Inquiry Learning and Thematic Learning and also how the development of the learning processes from the lesson plans to the learning implementation showed a paradigm shifting from teacher-centered to student-centered. Abstrak Salah satu permasalahan di madrasah-madrasah adalah proses pembelajaran yang kurang melibatkan siswa secara aktif (berpusat pada guru, sehingga hal ini mengakibatkan pada peningkatan hasil belajar dan kualitas lulusan. Tujuan dari penelitian ini adalah, pertama, untuk menganalisis jenis model pembelajaran konstruktivisme apa yang dapat dikembangkan untuk mengatasi permasalahan di madrasah. Ke dua, bagaimana merancang dan melaksanakan rencana pembelajaran berdasarkan model konstruktivisme yang dikembangkan. Penelitian ini dilaksanakan di Sekolah Dasar Swasta (madrasah Ad-Diayanah Ciputat, Tangerang Selatan. Metode penelitian yang digunakan adalah metode deskriptif-kualitatif. Hasil penelitian menunjukkan bahwa model pembelajaran aktif yang berbasis konstruktivisme sesuai untuk dikembangkan di madrasah, yakni model pembelajaran Problem Based Learning (PBL, Pembelajaran Realistis, Pembelajaran

Integrating Project-Based Service-Learning into an Advanced Environmental Chemistry Course

Science.gov (United States)

Draper, Alison J.

2004-02-01

In an advanced environmental chemistry course, the inclusion of semester-long scientific service projects successfully integrated the research process with course content. Each project involved a unique community-based environmental analysis in which students assessed an aspect of environmental health. The projects were due in small pieces at even intervals, and students worked independently or in pairs. Initially, students wrote a project proposal in which they chose and justified a project. Following a literature review of their topic, they drafted sampling and analysis plans using methods in the literature. Samples were collected and analyzed, and all students assembled scientific posters describing the results of their study. In the last week of the semester, the class traveled to a regional professional meeting to present the posters. In all, students found the experience valuable. They learned to be professional environmental chemists and learned the value of the discipline to community health. Students not only learned about their own project in depth, but they were inspired to learn textbook material, not for an exam, but because it helped them understand their own project. Finally, having a community to answer to at the end of the project motivated students to do careful work.

Support Required for Primary and Secondary Students with Communication Disorders and/or Other Learning Needs

Science.gov (United States)

McLeod, Sharynne; McKinnon, David H.

2010-01-01

Prioritization of school students with additional learning needs is a reality due to a finite resource base. Limited evidence exists regarding teachers' prioritization of primary and secondary school students with additional learning needs. The aim of the present article was to differentiate teachers' perceptions of the level of support required…

Charged hadrons in local finite-volume QED+QCD with C* boundary conditions

CERN Document Server

Lucini, Biagio; Ramos, Alberto; Tantalo, Nazario

2016-01-01

In order to calculate QED corrections to hadronic physical quantities by means of lattice simulations, a coherent description of electrically-charged states in finite volume is needed. In the usual periodic setup, Gauss's law and large gauge transformations forbid the propagation of electrically-charged states. A possible solution to this problem, which does not violate the axioms of local quantum field theory, has been proposed by Wiese and Polley, and is based on the use of C* boundary conditions. We present a thorough analysis of the properties and symmetries of QED in isolation and QED coupled to QCD, with C* boundary conditions. In particular we learn that a certain class of electrically-charged states can be constructed in this setup in a fully consistent fashion, without relying on gauge fixing. We argue that this class of states covers most of the interesting phenomenological applications in the framework of numerical simulations. We also calculate finite-volume corrections to the mass of stable charg...

Exploring creativity and critical thinking in traditional and innovative problem-based learning groups.

Science.gov (United States)

Chan, Zenobia C Y

2013-08-01

To explore students' attitude towards problem-based learning, creativity and critical thinking, and the relevance to nursing education and clinical practice. Critical thinking and creativity are crucial in nursing education. The teaching approach of problem-based learning can help to reduce the difficulties of nurturing problem-solving skills. However, there is little in the literature on how to improve the effectiveness of a problem-based learning lesson by designing appropriate and innovative activities such as composing songs, writing poems and using role plays. Exploratory qualitative study. A sample of 100 students participated in seven semi-structured focus groups, of which two were innovative groups and five were standard groups, adopting three activities in problem-based learning, namely composing songs, writing poems and performing role plays. The data were analysed using thematic analysis. There are three themes extracted from the conversations: 'students' perceptions of problem-based learning', 'students' perceptions of creative thinking' and 'students' perceptions of critical thinking'. Participants generally agreed that critical thinking is more important than creativity in problem-based learning and clinical practice. Participants in the innovative groups perceived a significantly closer relationship between critical thinking and nursing care, and between creativity and nursing care than the standard groups. Both standard and innovative groups agreed that problem-based learning could significantly increase their critical thinking and problem-solving skills. Further, by composing songs, writing poems and using role plays, the innovative groups had significantly increased their awareness of the relationship among critical thinking, creativity and nursing care. Nursing educators should include more types of creative activities than it often does in conventional problem-based learning classes. The results could help nurse educators design an appropriate

Development Of Entrepreneur Learning Model Based On Problem Based Learning To Increase Competency Independence And Creativity Students Of Industrial Engineering

Directory of Open Access Journals (Sweden)

Leola Dewiyani

2017-10-01

Full Text Available Currently it is undeniable that the competition to get a job is very tight and of course universities have an important role in printing human resources that can compete globally not least with the Department of Industrial Engineering Faculty of Engineering Muhammadiyah University of Jakarta FT UMJ. Problems that occur is based on the analysis obtained from the track record of graduates researchers found that 60 percent of students of Industrial Engineering FT UMJ work not in accordance with the level of education owned so financially their income is still below the standard. This study aims to improve the competence of students of Industrial Engineering Department FT UMJ in entrepreneurship courses especially through the development of Problem Based Learning based learning model. Specific targets of this research were conducted with the aim to identify and analyze the need to implement learning model based on Problem Based Learning Entrepreneurship and to design and develop the model of entrepreneurship based on Problem Based Learning to improve the competence independence and creativity of Industrial Engineering students of FT UMJ in Entrepreneurship course. To achieve the above objectives this research uses research and development R amp D method. The product produced in this research is the detail of learning model of entrepreneurial model based on Problem Based Learning entrepreneurship model based on Problem Based Learning and international journals

The effect of brain based learning with contextual approach viewed from adversity quotient

Science.gov (United States)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi, R.

2018-05-01

The aim of this research was to find out the effect of Brain Based Learning (BBL) with contextual approach viewed from adversity quotient (AQ) on mathematics achievement. BBL-contextual is the model to optimize the brain in the new concept learning and real life problem solving by making the good environment. Adversity Quotient is the ability to response and faces the problems. In addition, it is also about how to turn the difficulties into chances. This AQ classified into quitters, campers, and climbers. The research method used in this research was quasi experiment by using 2x3 factorial designs. The sample was chosen by using stratified cluster random sampling. The instruments were test and questionnaire for the data of AQ. The results showed that (1) BBL-contextual is better than direct learning on mathematics achievement, (2) there is no significant difference between each types of AQ on mathematics achievement, and (3) there is no interaction between learning model and AQ on mathematics achievement.

Finite-Element Software for Conceptual Design

DEFF Research Database (Denmark)

Lindemann, J.; Sandberg, G.; Damkilde, Lars

2010-01-01

and research. Forcepad is an effort to provide a conceptual design and teaching tool in a finite-element software package. Forcepad is a two-dimensional finite-element application based on the same conceptual model as image editing applications such as Adobe Photoshop or Microsoft Paint. Instead of using...